• Ah, the glorious return of the zine! Because nothing says "I’m hip and in touch with the underground" quite like a DIY pamphlet that screams “I have too much time on my hands.” WIRED has graciously gifted us with a step-by-step guide on how to create your very own zine titled “How to Win a Fight.”

    Print. Fold. Share. Download. Sounds easy, right? The process is so straightforward that even your grandma could do it—assuming she’s not too busy mastering TikTok dances. But let’s take a moment to appreciate the sheer audacity of needing instructions for something as inherently chaotic as making a zine. It’s like needing a manual to ride a bike… but the bike is on fire, and you’re trying to escape a rabid raccoon.

    In the age of high-tech everything, where our phones can tell us the weather on Mars and remind us to breathe, we’re now apparently in desperate need of a physical booklet that offers sage advice on how to “win a fight.” Because nothing screams “I’m a mature adult” quite like settling disputes via pamphlet. Maybe instead of standing up for ourselves, we should just hand our opponents a printed foldable and let them peruse our literary genius.

    And let’s not forget the nostalgia factor here! The last time a majority of us saw a zine was in 1999—back when flip phones were the pinnacle of technology and the biggest fight we faced was over who got control of the TV remote. Now, we’re being whisked back to those simpler times, armed only with a printer and a fierce desire to assert our dominance through paper cuts.

    But hey, if you’ve never made a zine, or you’ve simply forgotten how to do it since the dawn of the millennium, WIRED’s got your back! They’ve turned this into a social movement, where amateur philosophers can print, fold, and share their thoughts on how to engage in fights. Because why have a conversation when you can battle with paper instead?

    Let’s be honest: this is all about making “fighting” a trendy topic again. Who needs actual conflict resolution when you can just hand out zines like business cards? Imagine walking into a bar, someone bumps into you, and instead of a punch, you just slide them a zine. “Here’s how to win a fight, buddy. Chapter One: Don’t.”

    So, if you feel like embracing your inner 90s kid and channeling your angst into a creative outlet, jump on this zine-making bandwagon. Who knows? You might just win a fight—against boredom, at least.

    #ZineCulture #HowToWinAFight #DIYProject #NostalgiaTrip #WIRED
    Ah, the glorious return of the zine! Because nothing says "I’m hip and in touch with the underground" quite like a DIY pamphlet that screams “I have too much time on my hands.” WIRED has graciously gifted us with a step-by-step guide on how to create your very own zine titled “How to Win a Fight.” Print. Fold. Share. Download. Sounds easy, right? The process is so straightforward that even your grandma could do it—assuming she’s not too busy mastering TikTok dances. But let’s take a moment to appreciate the sheer audacity of needing instructions for something as inherently chaotic as making a zine. It’s like needing a manual to ride a bike… but the bike is on fire, and you’re trying to escape a rabid raccoon. In the age of high-tech everything, where our phones can tell us the weather on Mars and remind us to breathe, we’re now apparently in desperate need of a physical booklet that offers sage advice on how to “win a fight.” Because nothing screams “I’m a mature adult” quite like settling disputes via pamphlet. Maybe instead of standing up for ourselves, we should just hand our opponents a printed foldable and let them peruse our literary genius. And let’s not forget the nostalgia factor here! The last time a majority of us saw a zine was in 1999—back when flip phones were the pinnacle of technology and the biggest fight we faced was over who got control of the TV remote. Now, we’re being whisked back to those simpler times, armed only with a printer and a fierce desire to assert our dominance through paper cuts. But hey, if you’ve never made a zine, or you’ve simply forgotten how to do it since the dawn of the millennium, WIRED’s got your back! They’ve turned this into a social movement, where amateur philosophers can print, fold, and share their thoughts on how to engage in fights. Because why have a conversation when you can battle with paper instead? Let’s be honest: this is all about making “fighting” a trendy topic again. Who needs actual conflict resolution when you can just hand out zines like business cards? Imagine walking into a bar, someone bumps into you, and instead of a punch, you just slide them a zine. “Here’s how to win a fight, buddy. Chapter One: Don’t.” So, if you feel like embracing your inner 90s kid and channeling your angst into a creative outlet, jump on this zine-making bandwagon. Who knows? You might just win a fight—against boredom, at least. #ZineCulture #HowToWinAFight #DIYProject #NostalgiaTrip #WIRED
    Print. Fold. Share. Download WIRED's How to Win a Fight Zine Here
    Never made a zine? Haven’t made one since 1999? We made one, and so can you.
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  • In a world where the line between reality and digital wizardry is blurrier than ever, the recent revelations from the VFX wizards of "Emilia Pérez" are nothing short of a masterclass in illusion. Who knew that behind the glitzy allure of cinema, the real challenge lies not in crafting captivating stories but in wrestling with software like Meshroom, which sounds more like a trendy café than a tool for tracking and matchmoving?

    Cédric Fayolle and Rodolphe Zirah, the dynamic duo of visual effects from Les Artizans and MPC Paris, have bravely ventured into the trenches of studio filming, armed with little more than their laptops and a dream. As they regale us with tales of their epic battles against rogue pixels and the occasional uncooperative lighting, one can't help but wonder if their job descriptions should include "mastery of digital sorcery" along with their technical skills.

    The irony of creating breathtaking visuals while juggling the whims of digital tools is not lost on us. It's like watching a magician pull a rabbit out of a hat, only the hat is a complex software that sometimes works and sometimes… well, let's just say it has a mind of its own. Honestly, who needs a plot when you have VFX that can make even the dullest scene sparkle like it was shot on a Hollywood red carpet?

    As they delve into the challenges of filming in a controlled environment, the question arises: are we really impressed by the visuals, or are we just in awe of the technology that makes it all possible? Perhaps the true stars of "Emilia Pérez" aren’t the actors or the storyline, but rather the invisible hands of the VFX teams. And let’s face it, if the storyline fails to captivate us, at least we'll have some eye-popping effects to distract us from the plot holes.

    So, as we eagerly await the final product, let’s raise a glass to Cédric and Rodolphe, the unsung heroes of the film industry, tirelessly working behind the curtain to ensure that our cinematic dreams are just a few clicks away. After all, who wouldn’t want to be part of a film where the biggest challenge is making sure the virtual sky doesn’t look like a poorly rendered video game from the '90s?

    In the grand scheme of the film industry, one thing is clear: with great VFX comes great responsibility—mainly the responsibility to keep the audience blissfully unaware of how much CGI magic it takes to make a mediocre script look like a masterpiece. Cheers to that!

    #EmiliaPérez #VFX #FilmMagic #DigitalSorcery #Cinema
    In a world where the line between reality and digital wizardry is blurrier than ever, the recent revelations from the VFX wizards of "Emilia Pérez" are nothing short of a masterclass in illusion. Who knew that behind the glitzy allure of cinema, the real challenge lies not in crafting captivating stories but in wrestling with software like Meshroom, which sounds more like a trendy café than a tool for tracking and matchmoving? Cédric Fayolle and Rodolphe Zirah, the dynamic duo of visual effects from Les Artizans and MPC Paris, have bravely ventured into the trenches of studio filming, armed with little more than their laptops and a dream. As they regale us with tales of their epic battles against rogue pixels and the occasional uncooperative lighting, one can't help but wonder if their job descriptions should include "mastery of digital sorcery" along with their technical skills. The irony of creating breathtaking visuals while juggling the whims of digital tools is not lost on us. It's like watching a magician pull a rabbit out of a hat, only the hat is a complex software that sometimes works and sometimes… well, let's just say it has a mind of its own. Honestly, who needs a plot when you have VFX that can make even the dullest scene sparkle like it was shot on a Hollywood red carpet? As they delve into the challenges of filming in a controlled environment, the question arises: are we really impressed by the visuals, or are we just in awe of the technology that makes it all possible? Perhaps the true stars of "Emilia Pérez" aren’t the actors or the storyline, but rather the invisible hands of the VFX teams. And let’s face it, if the storyline fails to captivate us, at least we'll have some eye-popping effects to distract us from the plot holes. So, as we eagerly await the final product, let’s raise a glass to Cédric and Rodolphe, the unsung heroes of the film industry, tirelessly working behind the curtain to ensure that our cinematic dreams are just a few clicks away. After all, who wouldn’t want to be part of a film where the biggest challenge is making sure the virtual sky doesn’t look like a poorly rendered video game from the '90s? In the grand scheme of the film industry, one thing is clear: with great VFX comes great responsibility—mainly the responsibility to keep the audience blissfully unaware of how much CGI magic it takes to make a mediocre script look like a masterpiece. Cheers to that! #EmiliaPérez #VFX #FilmMagic #DigitalSorcery #Cinema
    Emilia Pérez : Les Artizans et MPC nous dévoilent les secrets des VFX !
    Nous vous proposons un retour en vidéo sur les effets visuels du film Emilia Pérez de Jacques Audiard, avec Cédric Fayolle (Superviseur VFX Général, Les Artizans) et Rodolphe Zirah (Superviseur VFX, MPC Paris). Le duo revient sur les défis d’un
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  • Blender, 3D modeling, Allan Brito, beginners, user interface, step-by-step guide, learning resources, digital art

    ## Introduction: The Blender Odyssey

    So, you’ve decided to leap into the mesmerizing world of 3D modeling, armed only with a dream and your trusty computer. Welcome to Blender 4.4! Now, before you pull out your hair and question your life choices, let’s talk about "Learn Blender 4.4: A Step-by-Step Guide for Beginners." This isn't just a book; it’s your lifebuoy in the choppy water...
    Blender, 3D modeling, Allan Brito, beginners, user interface, step-by-step guide, learning resources, digital art ## Introduction: The Blender Odyssey So, you’ve decided to leap into the mesmerizing world of 3D modeling, armed only with a dream and your trusty computer. Welcome to Blender 4.4! Now, before you pull out your hair and question your life choices, let’s talk about "Learn Blender 4.4: A Step-by-Step Guide for Beginners." This isn't just a book; it’s your lifebuoy in the choppy water...
    Learn Blender 4.4: A Step-by-Step Guide for Beginners (Book) [$]
    Blender, 3D modeling, Allan Brito, beginners, user interface, step-by-step guide, learning resources, digital art ## Introduction: The Blender Odyssey So, you’ve decided to leap into the mesmerizing world of 3D modeling, armed only with a dream and your trusty computer. Welcome to Blender 4.4! Now, before you pull out your hair and question your life choices, let’s talk about "Learn Blender...
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  • How to optimize your hybrid waterfall with CPM buckets

    In-app bidding has automated most waterfall optimization, yet developers still manage multiple hybrid waterfalls, each with dozens of manual instances. Naturally, this can be timely and overwhelming to maintain, keeping you from optimizing to perfection and focusing on other opportunities to boost revenue.Rather than analyzing each individual network and checking if instances are available at each price point, breaking down your waterfall into different CPM ranges allows you to visualize the waterfall and easily identify the gaps.Here are some tips on how to use CPM buckets to better optimize your waterfall’s performance.What are CPM buckets?CPM buckets show you exactly how much revenue and how many impressions you’re getting from each CPM price range, giving you a more granular idea of how different networks are competing in the waterfall. CPM buckets are a feature of real time pivot reports, available on ironSource LevelPlay.Identifying and closing the gapsTypically in a waterfall, you can only see each ad network’s average CPM. But this keeps you from seeing ad network distribution across all price points and understanding exactly where ad networks are bidding. Bottom line - you don’t know where in the waterfall you should add a new instance.By separating CPM into buckets,you understand exactly which networks are driving impressions and revenue and which CPMs aren’t being filledNow how do you do it? As a LevelPlay client, simply use ironSource’s real time pivot reports - choose the CPM bucket filter option and sort by “average bid price.” From here, you’ll see how your revenue spreads out among CPM ranges and you’ll start to notice gaps in your bar graph. Every gap in revenue - where revenue is much lower than the neighboring CPM group - indicates an opportunity to optimize your monetization strategy. The buckets can range from small increments like to larger increments like so it’s important to compare CPM buckets of the same incremental value.Pro tip: To best set up your waterfall, create one tab with the general waterfalland make sure to look at Revenue and eCPM in the “measures” dropdown. In the “show” section, choose CPM buckets and sort by average bid price. From here, you can mark down any gaps.But where do these gaps come from? Gaps in revenue are often due to friction in the waterfall, like not enough instances, instances that aren’t working, or a waterfall setup mistake. But gaps can also be adjusted and fixed.Once you’ve found a gap, you can look at the CPM buckets around it to better understand the context. Let’s say you see a strong instance generating significant revenue in the CPM bucket right below it, in the -80 group. This instance from this specific ad network has a lot of potential, so it’s worth trying to push it to a higher CPM bucket.In fact, when you look at higher CPM buckets, you don’t see this ad network anywhere else in the waterfall - what a missed opportunity! Try adding another instance of this network higher up in the waterfall. If you’re profiting well with a -80 CPM, imagine how much more revenue you could bring at a CPM.Pro tip: Focusing on higher areas in the waterfall makes a larger financial impact, leading to bigger increases in ARPDAU.Let’s say you decide to add 5 instances of that network to higher CPM buckets. You can use LevelPlay’s quick A/B test to understand if this adjustment boosts your revenue - not just for this gap, but for any and all that you find. Simply compare your existing waterfall against the new waterfall with these 5 higher instances - then implement the one that drives the highest instances.Božo Janković, Head of Ad Monetization at GameBiz Consulting, uses CPM buckets "to understand at which CPMs the bidding networks are filling. From there, I can pinpoint exactly where in the waterfall to add more traditional instances - which creates more competition, especially for the bidding networks, and creates an opportunity for revenue growth."Finding new insightsYou can dig even deeper into your data by filtering by ad source. Before CPM buckets, you were limited to seeing an average eCPM for each bidding network. Maybe you knew that one ad source had an average CPM of but the distribution of impression across the waterfall was a black box. Now, we know exactly which CPMs the bidders are filling. “I find ironSource CPM buckets feature very insightful and and use it daily. It’s an easy way to identify opportunities to optimize the waterfall and earn even more revenue."

    -Božo Janković, Head of Ad Monetization at GameBiz ConsultingUnderstanding your CPM distribution empowers you to not only identify your revenue sources, but also to promote revenue growth. Armed with the knowledge of which buckets some of their stronger bidding networking are performing in, some publishers actively add instances from traditional networks above those ranges. This creates better competition and also helps drive up the bids from the biddersThere’s no need for deep analysis - once you see the gaps, you can quickly understand who’s performing in the lower and higher buckets, and see exactly what’s missing. This way, you won’t miss out on any lost revenue.Learn more about CPM buckets, available exclusively to ironSource LevelPlay here.
    #how #optimize #your #hybrid #waterfall
    How to optimize your hybrid waterfall with CPM buckets
    In-app bidding has automated most waterfall optimization, yet developers still manage multiple hybrid waterfalls, each with dozens of manual instances. Naturally, this can be timely and overwhelming to maintain, keeping you from optimizing to perfection and focusing on other opportunities to boost revenue.Rather than analyzing each individual network and checking if instances are available at each price point, breaking down your waterfall into different CPM ranges allows you to visualize the waterfall and easily identify the gaps.Here are some tips on how to use CPM buckets to better optimize your waterfall’s performance.What are CPM buckets?CPM buckets show you exactly how much revenue and how many impressions you’re getting from each CPM price range, giving you a more granular idea of how different networks are competing in the waterfall. CPM buckets are a feature of real time pivot reports, available on ironSource LevelPlay.Identifying and closing the gapsTypically in a waterfall, you can only see each ad network’s average CPM. But this keeps you from seeing ad network distribution across all price points and understanding exactly where ad networks are bidding. Bottom line - you don’t know where in the waterfall you should add a new instance.By separating CPM into buckets,you understand exactly which networks are driving impressions and revenue and which CPMs aren’t being filledNow how do you do it? As a LevelPlay client, simply use ironSource’s real time pivot reports - choose the CPM bucket filter option and sort by “average bid price.” From here, you’ll see how your revenue spreads out among CPM ranges and you’ll start to notice gaps in your bar graph. Every gap in revenue - where revenue is much lower than the neighboring CPM group - indicates an opportunity to optimize your monetization strategy. The buckets can range from small increments like to larger increments like so it’s important to compare CPM buckets of the same incremental value.Pro tip: To best set up your waterfall, create one tab with the general waterfalland make sure to look at Revenue and eCPM in the “measures” dropdown. In the “show” section, choose CPM buckets and sort by average bid price. From here, you can mark down any gaps.But where do these gaps come from? Gaps in revenue are often due to friction in the waterfall, like not enough instances, instances that aren’t working, or a waterfall setup mistake. But gaps can also be adjusted and fixed.Once you’ve found a gap, you can look at the CPM buckets around it to better understand the context. Let’s say you see a strong instance generating significant revenue in the CPM bucket right below it, in the -80 group. This instance from this specific ad network has a lot of potential, so it’s worth trying to push it to a higher CPM bucket.In fact, when you look at higher CPM buckets, you don’t see this ad network anywhere else in the waterfall - what a missed opportunity! Try adding another instance of this network higher up in the waterfall. If you’re profiting well with a -80 CPM, imagine how much more revenue you could bring at a CPM.Pro tip: Focusing on higher areas in the waterfall makes a larger financial impact, leading to bigger increases in ARPDAU.Let’s say you decide to add 5 instances of that network to higher CPM buckets. You can use LevelPlay’s quick A/B test to understand if this adjustment boosts your revenue - not just for this gap, but for any and all that you find. Simply compare your existing waterfall against the new waterfall with these 5 higher instances - then implement the one that drives the highest instances.Božo Janković, Head of Ad Monetization at GameBiz Consulting, uses CPM buckets "to understand at which CPMs the bidding networks are filling. From there, I can pinpoint exactly where in the waterfall to add more traditional instances - which creates more competition, especially for the bidding networks, and creates an opportunity for revenue growth."Finding new insightsYou can dig even deeper into your data by filtering by ad source. Before CPM buckets, you were limited to seeing an average eCPM for each bidding network. Maybe you knew that one ad source had an average CPM of but the distribution of impression across the waterfall was a black box. Now, we know exactly which CPMs the bidders are filling. “I find ironSource CPM buckets feature very insightful and and use it daily. It’s an easy way to identify opportunities to optimize the waterfall and earn even more revenue." -Božo Janković, Head of Ad Monetization at GameBiz ConsultingUnderstanding your CPM distribution empowers you to not only identify your revenue sources, but also to promote revenue growth. Armed with the knowledge of which buckets some of their stronger bidding networking are performing in, some publishers actively add instances from traditional networks above those ranges. This creates better competition and also helps drive up the bids from the biddersThere’s no need for deep analysis - once you see the gaps, you can quickly understand who’s performing in the lower and higher buckets, and see exactly what’s missing. This way, you won’t miss out on any lost revenue.Learn more about CPM buckets, available exclusively to ironSource LevelPlay here. #how #optimize #your #hybrid #waterfall
    UNITY.COM
    How to optimize your hybrid waterfall with CPM buckets
    In-app bidding has automated most waterfall optimization, yet developers still manage multiple hybrid waterfalls, each with dozens of manual instances. Naturally, this can be timely and overwhelming to maintain, keeping you from optimizing to perfection and focusing on other opportunities to boost revenue.Rather than analyzing each individual network and checking if instances are available at each price point, breaking down your waterfall into different CPM ranges allows you to visualize the waterfall and easily identify the gaps.Here are some tips on how to use CPM buckets to better optimize your waterfall’s performance.What are CPM buckets?CPM buckets show you exactly how much revenue and how many impressions you’re getting from each CPM price range, giving you a more granular idea of how different networks are competing in the waterfall. CPM buckets are a feature of real time pivot reports, available on ironSource LevelPlay.Identifying and closing the gapsTypically in a waterfall, you can only see each ad network’s average CPM. But this keeps you from seeing ad network distribution across all price points and understanding exactly where ad networks are bidding. Bottom line - you don’t know where in the waterfall you should add a new instance.By separating CPM into buckets, (for example, seeing all the ad networks generating a CPM of $10-$20) you understand exactly which networks are driving impressions and revenue and which CPMs aren’t being filledNow how do you do it? As a LevelPlay client, simply use ironSource’s real time pivot reports - choose the CPM bucket filter option and sort by “average bid price.” From here, you’ll see how your revenue spreads out among CPM ranges and you’ll start to notice gaps in your bar graph. Every gap in revenue - where revenue is much lower than the neighboring CPM group - indicates an opportunity to optimize your monetization strategy. The buckets can range from small increments like $1 to larger increments like $10, so it’s important to compare CPM buckets of the same incremental value.Pro tip: To best set up your waterfall, create one tab with the general waterfall (filter app, OS, Ad unit, geo/geos from a specific group) and make sure to look at Revenue and eCPM in the “measures” dropdown. In the “show” section, choose CPM buckets and sort by average bid price. From here, you can mark down any gaps.But where do these gaps come from? Gaps in revenue are often due to friction in the waterfall, like not enough instances, instances that aren’t working, or a waterfall setup mistake. But gaps can also be adjusted and fixed.Once you’ve found a gap, you can look at the CPM buckets around it to better understand the context. Let’s say you see a strong instance generating significant revenue in the CPM bucket right below it, in the $70-80 group. This instance from this specific ad network has a lot of potential, so it’s worth trying to push it to a higher CPM bucket.In fact, when you look at higher CPM buckets, you don’t see this ad network anywhere else in the waterfall - what a missed opportunity! Try adding another instance of this network higher up in the waterfall. If you’re profiting well with a $70-80 CPM, imagine how much more revenue you could bring at a $150 CPM.Pro tip: Focusing on higher areas in the waterfall makes a larger financial impact, leading to bigger increases in ARPDAU.Let’s say you decide to add 5 instances of that network to higher CPM buckets. You can use LevelPlay’s quick A/B test to understand if this adjustment boosts your revenue - not just for this gap, but for any and all that you find. Simply compare your existing waterfall against the new waterfall with these 5 higher instances - then implement the one that drives the highest instances.Božo Janković, Head of Ad Monetization at GameBiz Consulting, uses CPM buckets "to understand at which CPMs the bidding networks are filling. From there, I can pinpoint exactly where in the waterfall to add more traditional instances - which creates more competition, especially for the bidding networks, and creates an opportunity for revenue growth."Finding new insightsYou can dig even deeper into your data by filtering by ad source. Before CPM buckets, you were limited to seeing an average eCPM for each bidding network. Maybe you knew that one ad source had an average CPM of $50, but the distribution of impression across the waterfall was a black box. Now, we know exactly which CPMs the bidders are filling. “I find ironSource CPM buckets feature very insightful and and use it daily. It’s an easy way to identify opportunities to optimize the waterfall and earn even more revenue." -Božo Janković, Head of Ad Monetization at GameBiz ConsultingUnderstanding your CPM distribution empowers you to not only identify your revenue sources, but also to promote revenue growth. Armed with the knowledge of which buckets some of their stronger bidding networking are performing in, some publishers actively add instances from traditional networks above those ranges. This creates better competition and also helps drive up the bids from the biddersThere’s no need for deep analysis - once you see the gaps, you can quickly understand who’s performing in the lower and higher buckets, and see exactly what’s missing. This way, you won’t miss out on any lost revenue.Learn more about CPM buckets, available exclusively to ironSource LevelPlay here.
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  • Patch Notes #9: Xbox debuts its first handhelds, Hong Kong authorities ban a video game, and big hopes for Big Walk

    We did it gang. We completed another week in the impossible survival sim that is real life. Give yourself a appreciative pat on the back and gaze wistfully towards whatever adventures or blissful respite the weekend might bring.This week I've mostly been recovering from my birthday celebrations, which entailed a bountiful Korean Barbecue that left me with a rampant case of the meat sweats and a pub crawl around one of Manchester's finest suburbs. There was no time for video games, but that's not always a bad thing. Distance makes the heart grow fonder, after all.I was welcomed back to the imaginary office with a news bludgeon to the face. The headlines this week have come thick and fast, bringing hardware announcements, more layoffs, and some notable sales milestones. As always, there's a lot to digest, so let's venture once more into the fray. The first Xbox handhelds have finally arrivedvia Game Developer // Microsoft finally stopped flirting with the idea of launching a handheld this week and unveiled not one, but two devices called the ROG Xbox Ally and ROG Xbox Ally X. The former is pitched towards casual players, while the latter aims to entice hardcore video game aficionados. Both devices were designed in collaboration with Asus and will presumably retail at price points that reflect their respective innards. We don't actually know yet, mind, because Microsoft didn't actually state how much they'll cost. You have the feel that's where the company really needs to stick the landing here.Related:Switch 2 tops 3.5 million sales to deliver Nintendo's biggest console launchvia Game Developer // Four days. That's all it took for the Switch 2 to shift over 3.5 million units worldwide to deliver Nintendo's biggest console launch ever. The original Switch needed a month to reach 2.74 million sales by contrast, while the PS5 needed two months to sell 4.5 million units worldwide. Xbox sales remain a mystery because Microsoft just doesn't talk about that sort of thing anymore, which is decidedly frustrating for those oddballswho actually enjoy sifting through financial documents in search of those juicy juicy numbers.Inside the ‘Dragon Age’ Debacle That Gutted EA’s BioWare Studiovia Bloomberg// How do you kill a franchise like Dragon Age and leave a studio with the pedigree of BioWare in turmoil? According to a new report from Bloomberg, the answer will likely resonate with developers across the industry: corporate meddling. Sources speaking to the publication explained how Dragon Age: The Veilguard, which failed to meet the expectations of parent company EA, was in constant disarray because the American publisher couldn't decide whether it should be a live-service or single player title. Indecision from leadership within EA and an eventual pivot away from the live-service model only caused more confusion, with BioWare being told to implement foundational changes within impossible timelines. It's a story that's all the more alarming because of how familiar it feels.Related:Sony is making layoffs at Days Gone developer Bend Studiovia Game Developer // Sony has continued its Tony Award-winning tun as the Grim Reaper by cutting even more jobs within PlayStation Studios. Days Gone developer Bend Studio was the latest casualty, with the first-party developer confirming a number of employees were laid off just months after the cancellation of a live-service project. Sony didn't confirm how many people lost their jobs, but Bloomberg reporter Jason Schreier heard that around 40 peoplewere let go. Embracer CEO Lars Wingefors to become executive chair and focus on M&Avia Game Developer // Somewhere, in a deep dark corner of the world, the monkey's paw has curled. Embracer CEO Lars Wingefors, who demonstrated his leadership nous by spending years embarking on a colossal merger and acquisition spree only to immediately start downsizing, has announced he'll be stepping down as CEO. The catch? Wingefors is currently proposed to be appointed executive chair of the board of Embracer. In his new role, he'll apparently focus on strategic initiatives, capital allocation, and mergers and acquisitions. And people wonder why satire is dead. Related:Hong Kong Outlaws a Video Game, Saying It Promotes 'Armed Revolution'via The New York Times// National security police in Hong Kong have banned a Taiwanese video game called Reversed Front: Bonfire for supposedly "advocating armed revolution." Authorities in the region warned that anybody who downloads or recommends the online strategy title will face serious legal charges. The game has been pulled from Apple's marketplace in Hong Kong but is still available for download elsewhere. It was never available in mainland China. Developer ESC Taiwan, part of an group of volunteers who are vocal detractors of China's Communist Party, thanked Hong Kong authorities for the free publicity in a social media post and said the ban shows how political censorship remains prominent in the territory. RuneScape developer accused of ‘catering to American conservatism’ by rolling back Pride Month eventsvia PinkNews // Runescape developers inside Jagex have reportedly been left reeling after the studio decided to pivot away from Pride Month content to focus more on "what players wanted." Jagex CEO broke the news to staff with a post on an internal message board, prompting a rush of complaints—with many workers explaining the content was either already complete or easy to implement. Though Jagex is based in the UK, it's parent company CVC Capital Partners operates multiple companies in the United States. It's a situation that left one employee who spoke to PinkNews questioning whether the studio has caved to "American conservatism." SAG-AFTRA suspends strike and instructs union members to return to workvia Game Developer // It has taken almost a year, but performer union SAG-AFTRA has finally suspended strike action and instructed members to return to work. The decision comes after protracted negotiations with major studios who employ performers under the Interactive Media Agreement. SAG-AFTRA had been striking to secure better working conditions and AI protections for its members, and feels it has now secured a deal that will install vital "AI guardrails."A Switch 2 exclusive Splatoon spinoff was just shadow-announced on Nintendo Todayvia Game Developer // Nintendo did something peculiar this week when it unveiled a Splatoon spinoff out of the blue. That in itself might not sound too strange, but for a short window the announcement was only accessible via the company's new Nintendo Today mobile app. It's a situation that left people without access to the app questioning whether the news was even real. Nintendo Today prevented users from capturing screenshots or footage, only adding to the sense of confusion. It led to this reporter branding the move a "shadow announcement," which in turn left some of our readers perplexed. Can you ever announce and announcement? What does that term even mean? Food for thought. A wonderful new Big Walk trailer melted this reporter's heartvia House House//  The mad lads behind Untitled Goose Game are back with a new jaunt called Big Walk. This one has been on my radar for a while, but the studio finally debuted a gameplay overview during Summer Game Fest and it looks extraordinary in its purity. It's about walking and talking—and therein lies the charm. Players are forced to cooperate to navigate a lush open world, solve puzzles, and embark upon hijinks. Proximity-based communication is the core mechanic in Big Walk—whether that takes the form of voice chat, written text, hand signals, blazing flares, or pictograms—and it looks like it'll lead to all sorts of weird and wonderful antics. It's a pitch that cuts through because it's so unashamedly different, and there's a lot to love about that. I'm looking forward to this one.
    #patch #notes #xbox #debuts #its
    Patch Notes #9: Xbox debuts its first handhelds, Hong Kong authorities ban a video game, and big hopes for Big Walk
    We did it gang. We completed another week in the impossible survival sim that is real life. Give yourself a appreciative pat on the back and gaze wistfully towards whatever adventures or blissful respite the weekend might bring.This week I've mostly been recovering from my birthday celebrations, which entailed a bountiful Korean Barbecue that left me with a rampant case of the meat sweats and a pub crawl around one of Manchester's finest suburbs. There was no time for video games, but that's not always a bad thing. Distance makes the heart grow fonder, after all.I was welcomed back to the imaginary office with a news bludgeon to the face. The headlines this week have come thick and fast, bringing hardware announcements, more layoffs, and some notable sales milestones. As always, there's a lot to digest, so let's venture once more into the fray. The first Xbox handhelds have finally arrivedvia Game Developer // Microsoft finally stopped flirting with the idea of launching a handheld this week and unveiled not one, but two devices called the ROG Xbox Ally and ROG Xbox Ally X. The former is pitched towards casual players, while the latter aims to entice hardcore video game aficionados. Both devices were designed in collaboration with Asus and will presumably retail at price points that reflect their respective innards. We don't actually know yet, mind, because Microsoft didn't actually state how much they'll cost. You have the feel that's where the company really needs to stick the landing here.Related:Switch 2 tops 3.5 million sales to deliver Nintendo's biggest console launchvia Game Developer // Four days. That's all it took for the Switch 2 to shift over 3.5 million units worldwide to deliver Nintendo's biggest console launch ever. The original Switch needed a month to reach 2.74 million sales by contrast, while the PS5 needed two months to sell 4.5 million units worldwide. Xbox sales remain a mystery because Microsoft just doesn't talk about that sort of thing anymore, which is decidedly frustrating for those oddballswho actually enjoy sifting through financial documents in search of those juicy juicy numbers.Inside the ‘Dragon Age’ Debacle That Gutted EA’s BioWare Studiovia Bloomberg// How do you kill a franchise like Dragon Age and leave a studio with the pedigree of BioWare in turmoil? According to a new report from Bloomberg, the answer will likely resonate with developers across the industry: corporate meddling. Sources speaking to the publication explained how Dragon Age: The Veilguard, which failed to meet the expectations of parent company EA, was in constant disarray because the American publisher couldn't decide whether it should be a live-service or single player title. Indecision from leadership within EA and an eventual pivot away from the live-service model only caused more confusion, with BioWare being told to implement foundational changes within impossible timelines. It's a story that's all the more alarming because of how familiar it feels.Related:Sony is making layoffs at Days Gone developer Bend Studiovia Game Developer // Sony has continued its Tony Award-winning tun as the Grim Reaper by cutting even more jobs within PlayStation Studios. Days Gone developer Bend Studio was the latest casualty, with the first-party developer confirming a number of employees were laid off just months after the cancellation of a live-service project. Sony didn't confirm how many people lost their jobs, but Bloomberg reporter Jason Schreier heard that around 40 peoplewere let go. Embracer CEO Lars Wingefors to become executive chair and focus on M&Avia Game Developer // Somewhere, in a deep dark corner of the world, the monkey's paw has curled. Embracer CEO Lars Wingefors, who demonstrated his leadership nous by spending years embarking on a colossal merger and acquisition spree only to immediately start downsizing, has announced he'll be stepping down as CEO. The catch? Wingefors is currently proposed to be appointed executive chair of the board of Embracer. In his new role, he'll apparently focus on strategic initiatives, capital allocation, and mergers and acquisitions. And people wonder why satire is dead. Related:Hong Kong Outlaws a Video Game, Saying It Promotes 'Armed Revolution'via The New York Times// National security police in Hong Kong have banned a Taiwanese video game called Reversed Front: Bonfire for supposedly "advocating armed revolution." Authorities in the region warned that anybody who downloads or recommends the online strategy title will face serious legal charges. The game has been pulled from Apple's marketplace in Hong Kong but is still available for download elsewhere. It was never available in mainland China. Developer ESC Taiwan, part of an group of volunteers who are vocal detractors of China's Communist Party, thanked Hong Kong authorities for the free publicity in a social media post and said the ban shows how political censorship remains prominent in the territory. RuneScape developer accused of ‘catering to American conservatism’ by rolling back Pride Month eventsvia PinkNews // Runescape developers inside Jagex have reportedly been left reeling after the studio decided to pivot away from Pride Month content to focus more on "what players wanted." Jagex CEO broke the news to staff with a post on an internal message board, prompting a rush of complaints—with many workers explaining the content was either already complete or easy to implement. Though Jagex is based in the UK, it's parent company CVC Capital Partners operates multiple companies in the United States. It's a situation that left one employee who spoke to PinkNews questioning whether the studio has caved to "American conservatism." SAG-AFTRA suspends strike and instructs union members to return to workvia Game Developer // It has taken almost a year, but performer union SAG-AFTRA has finally suspended strike action and instructed members to return to work. The decision comes after protracted negotiations with major studios who employ performers under the Interactive Media Agreement. SAG-AFTRA had been striking to secure better working conditions and AI protections for its members, and feels it has now secured a deal that will install vital "AI guardrails."A Switch 2 exclusive Splatoon spinoff was just shadow-announced on Nintendo Todayvia Game Developer // Nintendo did something peculiar this week when it unveiled a Splatoon spinoff out of the blue. That in itself might not sound too strange, but for a short window the announcement was only accessible via the company's new Nintendo Today mobile app. It's a situation that left people without access to the app questioning whether the news was even real. Nintendo Today prevented users from capturing screenshots or footage, only adding to the sense of confusion. It led to this reporter branding the move a "shadow announcement," which in turn left some of our readers perplexed. Can you ever announce and announcement? What does that term even mean? Food for thought. A wonderful new Big Walk trailer melted this reporter's heartvia House House//  The mad lads behind Untitled Goose Game are back with a new jaunt called Big Walk. This one has been on my radar for a while, but the studio finally debuted a gameplay overview during Summer Game Fest and it looks extraordinary in its purity. It's about walking and talking—and therein lies the charm. Players are forced to cooperate to navigate a lush open world, solve puzzles, and embark upon hijinks. Proximity-based communication is the core mechanic in Big Walk—whether that takes the form of voice chat, written text, hand signals, blazing flares, or pictograms—and it looks like it'll lead to all sorts of weird and wonderful antics. It's a pitch that cuts through because it's so unashamedly different, and there's a lot to love about that. I'm looking forward to this one. #patch #notes #xbox #debuts #its
    WWW.GAMEDEVELOPER.COM
    Patch Notes #9: Xbox debuts its first handhelds, Hong Kong authorities ban a video game, and big hopes for Big Walk
    We did it gang. We completed another week in the impossible survival sim that is real life. Give yourself a appreciative pat on the back and gaze wistfully towards whatever adventures or blissful respite the weekend might bring.This week I've mostly been recovering from my birthday celebrations, which entailed a bountiful Korean Barbecue that left me with a rampant case of the meat sweats and a pub crawl around one of Manchester's finest suburbs. There was no time for video games, but that's not always a bad thing. Distance makes the heart grow fonder, after all.I was welcomed back to the imaginary office with a news bludgeon to the face. The headlines this week have come thick and fast, bringing hardware announcements, more layoffs, and some notable sales milestones. As always, there's a lot to digest, so let's venture once more into the fray. The first Xbox handhelds have finally arrivedvia Game Developer // Microsoft finally stopped flirting with the idea of launching a handheld this week and unveiled not one, but two devices called the ROG Xbox Ally and ROG Xbox Ally X. The former is pitched towards casual players, while the latter aims to entice hardcore video game aficionados. Both devices were designed in collaboration with Asus and will presumably retail at price points that reflect their respective innards. We don't actually know yet, mind, because Microsoft didn't actually state how much they'll cost. You have the feel that's where the company really needs to stick the landing here.Related:Switch 2 tops 3.5 million sales to deliver Nintendo's biggest console launchvia Game Developer // Four days. That's all it took for the Switch 2 to shift over 3.5 million units worldwide to deliver Nintendo's biggest console launch ever. The original Switch needed a month to reach 2.74 million sales by contrast, while the PS5 needed two months to sell 4.5 million units worldwide. Xbox sales remain a mystery because Microsoft just doesn't talk about that sort of thing anymore, which is decidedly frustrating for those oddballs (read: this writer) who actually enjoy sifting through financial documents in search of those juicy juicy numbers.Inside the ‘Dragon Age’ Debacle That Gutted EA’s BioWare Studiovia Bloomberg (paywalled) // How do you kill a franchise like Dragon Age and leave a studio with the pedigree of BioWare in turmoil? According to a new report from Bloomberg, the answer will likely resonate with developers across the industry: corporate meddling. Sources speaking to the publication explained how Dragon Age: The Veilguard, which failed to meet the expectations of parent company EA, was in constant disarray because the American publisher couldn't decide whether it should be a live-service or single player title. Indecision from leadership within EA and an eventual pivot away from the live-service model only caused more confusion, with BioWare being told to implement foundational changes within impossible timelines. It's a story that's all the more alarming because of how familiar it feels.Related:Sony is making layoffs at Days Gone developer Bend Studiovia Game Developer // Sony has continued its Tony Award-winning tun as the Grim Reaper by cutting even more jobs within PlayStation Studios. Days Gone developer Bend Studio was the latest casualty, with the first-party developer confirming a number of employees were laid off just months after the cancellation of a live-service project. Sony didn't confirm how many people lost their jobs, but Bloomberg reporter Jason Schreier heard that around 40 people (roughly 30 percent of the studio's headcount) were let go. Embracer CEO Lars Wingefors to become executive chair and focus on M&Avia Game Developer // Somewhere, in a deep dark corner of the world, the monkey's paw has curled. Embracer CEO Lars Wingefors, who demonstrated his leadership nous by spending years embarking on a colossal merger and acquisition spree only to immediately start downsizing, has announced he'll be stepping down as CEO. The catch? Wingefors is currently proposed to be appointed executive chair of the board of Embracer. In his new role, he'll apparently focus on strategic initiatives, capital allocation, and mergers and acquisitions. And people wonder why satire is dead. Related:Hong Kong Outlaws a Video Game, Saying It Promotes 'Armed Revolution'via The New York Times (paywalled) // National security police in Hong Kong have banned a Taiwanese video game called Reversed Front: Bonfire for supposedly "advocating armed revolution." Authorities in the region warned that anybody who downloads or recommends the online strategy title will face serious legal charges. The game has been pulled from Apple's marketplace in Hong Kong but is still available for download elsewhere. It was never available in mainland China. Developer ESC Taiwan, part of an group of volunteers who are vocal detractors of China's Communist Party, thanked Hong Kong authorities for the free publicity in a social media post and said the ban shows how political censorship remains prominent in the territory. RuneScape developer accused of ‘catering to American conservatism’ by rolling back Pride Month eventsvia PinkNews // Runescape developers inside Jagex have reportedly been left reeling after the studio decided to pivot away from Pride Month content to focus more on "what players wanted." Jagex CEO broke the news to staff with a post on an internal message board, prompting a rush of complaints—with many workers explaining the content was either already complete or easy to implement. Though Jagex is based in the UK, it's parent company CVC Capital Partners operates multiple companies in the United States. It's a situation that left one employee who spoke to PinkNews questioning whether the studio has caved to "American conservatism." SAG-AFTRA suspends strike and instructs union members to return to workvia Game Developer // It has taken almost a year, but performer union SAG-AFTRA has finally suspended strike action and instructed members to return to work. The decision comes after protracted negotiations with major studios who employ performers under the Interactive Media Agreement. SAG-AFTRA had been striking to secure better working conditions and AI protections for its members, and feels it has now secured a deal that will install vital "AI guardrails."A Switch 2 exclusive Splatoon spinoff was just shadow-announced on Nintendo Todayvia Game Developer // Nintendo did something peculiar this week when it unveiled a Splatoon spinoff out of the blue. That in itself might not sound too strange, but for a short window the announcement was only accessible via the company's new Nintendo Today mobile app. It's a situation that left people without access to the app questioning whether the news was even real. Nintendo Today prevented users from capturing screenshots or footage, only adding to the sense of confusion. It led to this reporter branding the move a "shadow announcement," which in turn left some of our readers perplexed. Can you ever announce and announcement? What does that term even mean? Food for thought. A wonderful new Big Walk trailer melted this reporter's heartvia House House (YouTube) //  The mad lads behind Untitled Goose Game are back with a new jaunt called Big Walk. This one has been on my radar for a while, but the studio finally debuted a gameplay overview during Summer Game Fest and it looks extraordinary in its purity. It's about walking and talking—and therein lies the charm. Players are forced to cooperate to navigate a lush open world, solve puzzles, and embark upon hijinks. Proximity-based communication is the core mechanic in Big Walk—whether that takes the form of voice chat, written text, hand signals, blazing flares, or pictograms—and it looks like it'll lead to all sorts of weird and wonderful antics. It's a pitch that cuts through because it's so unashamedly different, and there's a lot to love about that. I'm looking forward to this one.
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  • Turning Points: Accept & Proceed

    12 June, 2025

    In our turning points series, design studios share some of the key moments that shaped their business. This week, we meet Accept & Proceed.

    Accept & Proceed is a London based brand and design studio that works with clients like NASA, Nike and LEGO.
    Founder David Johnston talks us through some of the decisions that defined his business.
    In 2006, Johnston took the leap to start his own business, armed with a good name and a willingness to bend the truth about his team…
    I’d gone through my career learning from big organisations, and one small organisation, and I felt like I wasn’t happy where I was. It was my dad who encouraged me to take a leap of faith and try and go it alone. With nothing more than a month’s wages in the bank and a lot of energy, I decided to go and set up an agency.
    That really just means giving yourself a name and starting to promote yourself in the world.
    Accept & Proceed founder David Johnston
    I think the name itself is a very important thing. I wanted something that was memorable but also layered in meaning. A name that starts with an “a” is very beneficial when you’re being listed in the index of books and things like that.
    But it became a bit of a compass for the way that we wanted to create work, around accepting the status quo for what it is, but with a continual commitment to proceed nonetheless.
    Because I didn’t have anyone to work with, in those early months I just made up email addresses of people that didn’t exist. That allowed me to cost projects up for multiple people. That’s obviously a degree of hustle I wouldn’t encourage in everyone, but it meant I was able to charge multiple day rates for projects where I was playing the role of four or five people.
    Self-initiated projects have long been part of the studio’s DNA and played a key role in building key client relationships.
    A&P by… was a brief to explore these letterforms without any commercial intent apart from the joy of creative expression. I started reaching out to illustrators and artists and photographers and designers that I really rated, and the things that started coming back were incredible.
    I was overwhelmed by the amount of energy and passion that people like Mr Bingo and Jason Evans were bringing to this.
    I think in so many ways, the answer to everything is community. I’ve gone on to work with a lot of the people that created these, and they also became friends. It was an early example of dissolving these illusionary boundaries around what an agency might be, but also expanding and amplifying your potential.
    The first of Accept & Proceed’s Light Calendars
    Then in 2006, I was trying to establish our portfolio and I wanted something to send out into the world that would also be an example of how Accept & Proceed thinks about design. I landed on these data visualisations that show the amount of light and darkness that would happen in London in the year ahead.
    I worked with a freelance designer called Stephen Heath on the first one – he is now our creative director.
    This kickstarted a 10-year exploration, and they became a rite of passage for new designers that came into the studio, to take that very similar data and express it in completely new ways. It culminated in an exhibition in London in 2016, showing ten years’ of prints.
    They were a labour of love, but they also meant that every single year we had a number of prints that we could send out to new potential contacts. Still when I go to the global headquarters of Nike in Beaverton in Portland, I’m amazed at how many of these sit in leaders’ offices there.
    When we first got a finance director, they couldn’t believe how much we’d invested as a business in things like this – we even had our own gallery for a while. It doesn’t make sense from a purely numbers mindset, but if you put things out there for authentic reasons, there are ripple effects over time.
    In 2017, the studio became a B-corp, the fourth creative agency in the UK to get this accreditation.
    Around 2016, I couldn’t help but look around – as we probably all have at varying points over the last 10 years – and wondered, what the fuck is going on?
    All these systems are not fit for purpose for the future – financial systems, food systems, relationship systems, energy systems. They’re not working. And I was like shit, are we part of the problem?
    Accept & Proceed’s work for the NASA Jet Propulsion Laboratory
    I’ve always thought of brand as a piece of technology that can fundamentally change our actions and the world around us. That comes with a huge responsibility.
    We probably paid four months’ wages of two people full-time just to get accredited, so it’s quite a high bar. But I like that the programme shackles you to this idea of improvement. You can’t rest on your laurels if you want to be re-accredited. It’s like the way design works as an iterative process – you have to keep getting better.
    In 2019, Johnston and his team started thinking seriously about the studio’s own brand, and created a punchy, nuanced new positioning.
    We got to a point where we’d proven we could help brands achieve their commercial aims. But we wanted to hold a position ourselves, not just be a conduit between a brand and its audience.
    It still amazes me that so few agencies actually stand for anything. We realised that all the things – vision, mission, principles – that we’ve been creating for brands for years, we hadn’t done for ourselves.
    It’s a bit like when you see a hairdresser with a really dodgy haircut. But it’s hard to cut your own hair.
    So we went through that process, which was really difficult, and we landed on “Design for the future” as our promise to the world.
    And if you’re going to have that as a promise, you better be able to describe the world you’re creating through your work, which we call “the together world.”
    Accept & Proceed’s work for Second Sea
    We stand at this most incredible moment in history where the latest technology and science is catching up with ancient wisdom, to know that we must become more entangled, more together, more whole.
    And we’ve assessed five global shifts that are happening in order to be able to take us towards a more together world through our work – interbeing, reciprocity, healing, resilience and liberation.
    The year before last, we lost three global rebrand projects based on our positioning. Every one of them said to me, “You’re right but we’re not ready.”
    But this year, I think the product market fit of what we’ve been saying for the last five years is really starting to mesh. We’re working with Arc’teryx on their 2030 landscape, evolving Nike’s move to zero, and working with LEGO on what their next 100 years might look like, which is mind-boggling work.
    I don’t think we could have won any of those opportunities had we not been talking for quite a long time about design for the future.
    In 2023, Johnston started a sunrise gathering on Hackney Marshes, which became a very significant part of his life.
    I had the flu and I had a vision in my dreamy fluey state of a particular spot on Hackney Marshes where people were gathering and watching the sunrise. I happened to tell my friend, the poet Thomas Sharp this, and he said, “That’s a premonition. You have to make it happen.”
    The first year there were five of us – this year there were 300 people for the spring equinox in March.
    I don’t fully know what these gatherings will lead to. Will Accept & Proceed start to introduce the seasons to the way we operate as a business? It’s a thought I’ve had percolating, but I don’t know. Will it be something else?
    One of the 2024 sunrise gatherings organised by Accept & Proceed founder David Johnston
    I do know that there’s major learnings around authentic community building for brands. We should do away with these buckets we put people into, of age group and location. They aren’t very true. It’s fascinating to see the breadth of people who come to these gatherings.
    Me and Laura were thinking at some point of moving out of London, but I think these sunrise gatherings are now my reason to stay. It’s the thing I didn’t know I needed until I had it. They have made London complete for me.
    There’s something so ancient about watching our star rise, and the reminder that we are actually just animals crawling upon the surface of a planet of mud. That’s what’s real. But it can be hard to remember that when you’re sitting at your computer in the studio.
    These gatherings help me better understand creativity’s true potential, for brands, for the world, and for us.

    Design disciplines in this article

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    What to read next

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    Turning Points: Cultural branding agency EDIT

    Brand Identity
    20 Nov, 2024
    #turning #points #accept #ampamp #proceed
    Turning Points: Accept & Proceed
    12 June, 2025 In our turning points series, design studios share some of the key moments that shaped their business. This week, we meet Accept & Proceed. Accept & Proceed is a London based brand and design studio that works with clients like NASA, Nike and LEGO. Founder David Johnston talks us through some of the decisions that defined his business. In 2006, Johnston took the leap to start his own business, armed with a good name and a willingness to bend the truth about his team… I’d gone through my career learning from big organisations, and one small organisation, and I felt like I wasn’t happy where I was. It was my dad who encouraged me to take a leap of faith and try and go it alone. With nothing more than a month’s wages in the bank and a lot of energy, I decided to go and set up an agency. That really just means giving yourself a name and starting to promote yourself in the world. Accept & Proceed founder David Johnston I think the name itself is a very important thing. I wanted something that was memorable but also layered in meaning. A name that starts with an “a” is very beneficial when you’re being listed in the index of books and things like that. But it became a bit of a compass for the way that we wanted to create work, around accepting the status quo for what it is, but with a continual commitment to proceed nonetheless. Because I didn’t have anyone to work with, in those early months I just made up email addresses of people that didn’t exist. That allowed me to cost projects up for multiple people. That’s obviously a degree of hustle I wouldn’t encourage in everyone, but it meant I was able to charge multiple day rates for projects where I was playing the role of four or five people. Self-initiated projects have long been part of the studio’s DNA and played a key role in building key client relationships. A&P by… was a brief to explore these letterforms without any commercial intent apart from the joy of creative expression. I started reaching out to illustrators and artists and photographers and designers that I really rated, and the things that started coming back were incredible. I was overwhelmed by the amount of energy and passion that people like Mr Bingo and Jason Evans were bringing to this. I think in so many ways, the answer to everything is community. I’ve gone on to work with a lot of the people that created these, and they also became friends. It was an early example of dissolving these illusionary boundaries around what an agency might be, but also expanding and amplifying your potential. The first of Accept & Proceed’s Light Calendars Then in 2006, I was trying to establish our portfolio and I wanted something to send out into the world that would also be an example of how Accept & Proceed thinks about design. I landed on these data visualisations that show the amount of light and darkness that would happen in London in the year ahead. I worked with a freelance designer called Stephen Heath on the first one – he is now our creative director. This kickstarted a 10-year exploration, and they became a rite of passage for new designers that came into the studio, to take that very similar data and express it in completely new ways. It culminated in an exhibition in London in 2016, showing ten years’ of prints. They were a labour of love, but they also meant that every single year we had a number of prints that we could send out to new potential contacts. Still when I go to the global headquarters of Nike in Beaverton in Portland, I’m amazed at how many of these sit in leaders’ offices there. When we first got a finance director, they couldn’t believe how much we’d invested as a business in things like this – we even had our own gallery for a while. It doesn’t make sense from a purely numbers mindset, but if you put things out there for authentic reasons, there are ripple effects over time. In 2017, the studio became a B-corp, the fourth creative agency in the UK to get this accreditation. Around 2016, I couldn’t help but look around – as we probably all have at varying points over the last 10 years – and wondered, what the fuck is going on? All these systems are not fit for purpose for the future – financial systems, food systems, relationship systems, energy systems. They’re not working. And I was like shit, are we part of the problem? Accept & Proceed’s work for the NASA Jet Propulsion Laboratory I’ve always thought of brand as a piece of technology that can fundamentally change our actions and the world around us. That comes with a huge responsibility. We probably paid four months’ wages of two people full-time just to get accredited, so it’s quite a high bar. But I like that the programme shackles you to this idea of improvement. You can’t rest on your laurels if you want to be re-accredited. It’s like the way design works as an iterative process – you have to keep getting better. In 2019, Johnston and his team started thinking seriously about the studio’s own brand, and created a punchy, nuanced new positioning. We got to a point where we’d proven we could help brands achieve their commercial aims. But we wanted to hold a position ourselves, not just be a conduit between a brand and its audience. It still amazes me that so few agencies actually stand for anything. We realised that all the things – vision, mission, principles – that we’ve been creating for brands for years, we hadn’t done for ourselves. It’s a bit like when you see a hairdresser with a really dodgy haircut. But it’s hard to cut your own hair. So we went through that process, which was really difficult, and we landed on “Design for the future” as our promise to the world. And if you’re going to have that as a promise, you better be able to describe the world you’re creating through your work, which we call “the together world.” Accept & Proceed’s work for Second Sea We stand at this most incredible moment in history where the latest technology and science is catching up with ancient wisdom, to know that we must become more entangled, more together, more whole. And we’ve assessed five global shifts that are happening in order to be able to take us towards a more together world through our work – interbeing, reciprocity, healing, resilience and liberation. The year before last, we lost three global rebrand projects based on our positioning. Every one of them said to me, “You’re right but we’re not ready.” But this year, I think the product market fit of what we’ve been saying for the last five years is really starting to mesh. We’re working with Arc’teryx on their 2030 landscape, evolving Nike’s move to zero, and working with LEGO on what their next 100 years might look like, which is mind-boggling work. I don’t think we could have won any of those opportunities had we not been talking for quite a long time about design for the future. In 2023, Johnston started a sunrise gathering on Hackney Marshes, which became a very significant part of his life. I had the flu and I had a vision in my dreamy fluey state of a particular spot on Hackney Marshes where people were gathering and watching the sunrise. I happened to tell my friend, the poet Thomas Sharp this, and he said, “That’s a premonition. You have to make it happen.” The first year there were five of us – this year there were 300 people for the spring equinox in March. I don’t fully know what these gatherings will lead to. Will Accept & Proceed start to introduce the seasons to the way we operate as a business? It’s a thought I’ve had percolating, but I don’t know. Will it be something else? One of the 2024 sunrise gatherings organised by Accept & Proceed founder David Johnston I do know that there’s major learnings around authentic community building for brands. We should do away with these buckets we put people into, of age group and location. They aren’t very true. It’s fascinating to see the breadth of people who come to these gatherings. Me and Laura were thinking at some point of moving out of London, but I think these sunrise gatherings are now my reason to stay. It’s the thing I didn’t know I needed until I had it. They have made London complete for me. There’s something so ancient about watching our star rise, and the reminder that we are actually just animals crawling upon the surface of a planet of mud. That’s what’s real. But it can be hard to remember that when you’re sitting at your computer in the studio. These gatherings help me better understand creativity’s true potential, for brands, for the world, and for us. Design disciplines in this article Brands in this article What to read next Features Turning Points: Cultural branding agency EDIT Brand Identity 20 Nov, 2024 #turning #points #accept #ampamp #proceed
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    Turning Points: Accept & Proceed
    12 June, 2025 In our turning points series, design studios share some of the key moments that shaped their business. This week, we meet Accept & Proceed. Accept & Proceed is a London based brand and design studio that works with clients like NASA, Nike and LEGO. Founder David Johnston talks us through some of the decisions that defined his business. In 2006, Johnston took the leap to start his own business, armed with a good name and a willingness to bend the truth about his team… I’d gone through my career learning from big organisations, and one small organisation, and I felt like I wasn’t happy where I was. It was my dad who encouraged me to take a leap of faith and try and go it alone. With nothing more than a month’s wages in the bank and a lot of energy, I decided to go and set up an agency. That really just means giving yourself a name and starting to promote yourself in the world. Accept & Proceed founder David Johnston I think the name itself is a very important thing. I wanted something that was memorable but also layered in meaning. A name that starts with an “a” is very beneficial when you’re being listed in the index of books and things like that. But it became a bit of a compass for the way that we wanted to create work, around accepting the status quo for what it is, but with a continual commitment to proceed nonetheless. Because I didn’t have anyone to work with, in those early months I just made up email addresses of people that didn’t exist. That allowed me to cost projects up for multiple people. That’s obviously a degree of hustle I wouldn’t encourage in everyone, but it meant I was able to charge multiple day rates for projects where I was playing the role of four or five people. Self-initiated projects have long been part of the studio’s DNA and played a key role in building key client relationships. A&P by… was a brief to explore these letterforms without any commercial intent apart from the joy of creative expression. I started reaching out to illustrators and artists and photographers and designers that I really rated, and the things that started coming back were incredible. I was overwhelmed by the amount of energy and passion that people like Mr Bingo and Jason Evans were bringing to this. I think in so many ways, the answer to everything is community. I’ve gone on to work with a lot of the people that created these, and they also became friends. It was an early example of dissolving these illusionary boundaries around what an agency might be, but also expanding and amplifying your potential. The first of Accept & Proceed’s Light Calendars Then in 2006, I was trying to establish our portfolio and I wanted something to send out into the world that would also be an example of how Accept & Proceed thinks about design. I landed on these data visualisations that show the amount of light and darkness that would happen in London in the year ahead. I worked with a freelance designer called Stephen Heath on the first one – he is now our creative director. This kickstarted a 10-year exploration, and they became a rite of passage for new designers that came into the studio, to take that very similar data and express it in completely new ways. It culminated in an exhibition in London in 2016, showing ten years’ of prints. They were a labour of love, but they also meant that every single year we had a number of prints that we could send out to new potential contacts. Still when I go to the global headquarters of Nike in Beaverton in Portland, I’m amazed at how many of these sit in leaders’ offices there. When we first got a finance director, they couldn’t believe how much we’d invested as a business in things like this – we even had our own gallery for a while. It doesn’t make sense from a purely numbers mindset, but if you put things out there for authentic reasons, there are ripple effects over time. In 2017, the studio became a B-corp, the fourth creative agency in the UK to get this accreditation. Around 2016, I couldn’t help but look around – as we probably all have at varying points over the last 10 years – and wondered, what the fuck is going on? All these systems are not fit for purpose for the future – financial systems, food systems, relationship systems, energy systems. They’re not working. And I was like shit, are we part of the problem? Accept & Proceed’s work for the NASA Jet Propulsion Laboratory I’ve always thought of brand as a piece of technology that can fundamentally change our actions and the world around us. That comes with a huge responsibility. We probably paid four months’ wages of two people full-time just to get accredited, so it’s quite a high bar. But I like that the programme shackles you to this idea of improvement. You can’t rest on your laurels if you want to be re-accredited. It’s like the way design works as an iterative process – you have to keep getting better. In 2019, Johnston and his team started thinking seriously about the studio’s own brand, and created a punchy, nuanced new positioning. We got to a point where we’d proven we could help brands achieve their commercial aims. But we wanted to hold a position ourselves, not just be a conduit between a brand and its audience. It still amazes me that so few agencies actually stand for anything. We realised that all the things – vision, mission, principles – that we’ve been creating for brands for years, we hadn’t done for ourselves. It’s a bit like when you see a hairdresser with a really dodgy haircut. But it’s hard to cut your own hair. So we went through that process, which was really difficult, and we landed on “Design for the future” as our promise to the world. And if you’re going to have that as a promise, you better be able to describe the world you’re creating through your work, which we call “the together world.” Accept & Proceed’s work for Second Sea We stand at this most incredible moment in history where the latest technology and science is catching up with ancient wisdom, to know that we must become more entangled, more together, more whole. And we’ve assessed five global shifts that are happening in order to be able to take us towards a more together world through our work – interbeing, reciprocity, healing, resilience and liberation. The year before last, we lost three global rebrand projects based on our positioning. Every one of them said to me, “You’re right but we’re not ready.” But this year, I think the product market fit of what we’ve been saying for the last five years is really starting to mesh. We’re working with Arc’teryx on their 2030 landscape, evolving Nike’s move to zero, and working with LEGO on what their next 100 years might look like, which is mind-boggling work. I don’t think we could have won any of those opportunities had we not been talking for quite a long time about design for the future. In 2023, Johnston started a sunrise gathering on Hackney Marshes, which became a very significant part of his life. I had the flu and I had a vision in my dreamy fluey state of a particular spot on Hackney Marshes where people were gathering and watching the sunrise. I happened to tell my friend, the poet Thomas Sharp this, and he said, “That’s a premonition. You have to make it happen.” The first year there were five of us – this year there were 300 people for the spring equinox in March. I don’t fully know what these gatherings will lead to. Will Accept & Proceed start to introduce the seasons to the way we operate as a business? It’s a thought I’ve had percolating, but I don’t know. Will it be something else? One of the 2024 sunrise gatherings organised by Accept & Proceed founder David Johnston I do know that there’s major learnings around authentic community building for brands. We should do away with these buckets we put people into, of age group and location. They aren’t very true. It’s fascinating to see the breadth of people who come to these gatherings. Me and Laura were thinking at some point of moving out of London, but I think these sunrise gatherings are now my reason to stay. It’s the thing I didn’t know I needed until I had it. They have made London complete for me. There’s something so ancient about watching our star rise, and the reminder that we are actually just animals crawling upon the surface of a planet of mud. That’s what’s real. But it can be hard to remember that when you’re sitting at your computer in the studio. These gatherings help me better understand creativity’s true potential, for brands, for the world, and for us. Design disciplines in this article Brands in this article What to read next Features Turning Points: Cultural branding agency EDIT Brand Identity 20 Nov, 2024
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  • How AI is reshaping the future of healthcare and medical research

    Transcript       
    PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”          
    This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.   
    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?    
    In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.  The book passage I read at the top is from “Chapter 10: The Big Black Bag.” 
    In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.   
    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open. 
    As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.  
    Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home. 
    Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.     
    Here’s my conversation with Bill Gates and Sébastien Bubeck. 
    LEE: Bill, welcome. 
    BILL GATES: Thank you. 
    LEE: Seb … 
    SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here. 
    LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening? 
    And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?  
    GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines. 
    And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.  
    And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weaknessthat, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning. 
    LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that? 
    GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, … 
    LEE: Right.  
    GATES: … that is a bit weird.  
    LEE: Yeah. 
    GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training. 
    LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. 
    BUBECK: Yes.  
    LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSRto join and start investigating this thing seriously. And the first person I pulled in was you. 
    BUBECK: Yeah. 
    LEE: And so what were your first encounters? Because I actually don’t remember what happened then. 
    BUBECK: Oh, I remember it very well.My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3. 
    I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1. 
    So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair.And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts. 
    So this was really, to me, the first moment where I saw some understanding in those models.  
    LEE: So this was, just to get the timing right, that was before I pulled you into the tent. 
    BUBECK: That was before. That was like a year before. 
    LEE: Right.  
    BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4. 
    So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.  
    So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x. 
    And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?  
    LEE: Yeah.
    BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.  
    LEE:One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine. 
    And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.  
    And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.  
    I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book. 
    But the main purpose of this conversation isn’t to reminisce aboutor indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements. 
    But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today? 
    You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.  
    Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork? 
    GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.  
    It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision. 
    But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view. 
    LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients.Does that make sense to you? 
    BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong? 
    Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.  
    Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them. 
    And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT. And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.  
    Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way. 
    It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine. 
    LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all? 
    GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that. 
    The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa,
    So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.  
    LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking? 
    GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.  
    The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.  
    LEE: Right.  
    GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.  
    LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication. 
    BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE, for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI. 
    It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for. 
    LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes. 
    I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?  
    That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential.What’s up with that? 
    BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back thatversion of GPT-4o, so now we don’t have the sycophant version out there. 
    Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF, where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad. 
    But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model. 
    So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model. 
    LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and … 
    BUBECK: It’s a very difficult, very difficult balance. 
    LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models? 
    GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there. 
    Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?  
    Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there.
    LEE: Yeah.
    GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake. 
    LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on. 
    BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGIthat kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything. 
    That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects.So it’s … I think it’s an important example to have in mind. 
    LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two? 
    BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it. 
    LEE: So we have about three hours of stuff to talk about, but our time is actually running low.
    BUBECK: Yes, yes, yes.  
    LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now? 
    GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.  
    The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities. 
    And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period. 
    LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers? 
    GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them. 
    LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.  
    I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why. 
    BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and seeproduced what you wanted. So I absolutely agree with that.  
    And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini. So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.  
    LEE: Yeah. 
    BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.  
    Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not. 
    Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision. 
    LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist … 
    BUBECK: Yeah.
    LEE: … or an endocrinologist might not.
    BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know.
    LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today? 
    BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later. 
    And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …  
    LEE: Will AI prescribe your medicines? Write your prescriptions? 
    BUBECK: I think yes. I think yes. 
    LEE: OK. Bill? 
    GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate?
    And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelectedjust on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries. 
    You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that. 
    LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.  
    I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  
    GATES: Yeah. Thanks, you guys. 
    BUBECK: Thank you, Peter. Thanks, Bill. 
    LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.   
    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.  
    And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.  
    One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.  
    HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings. 
    You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.  
    If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  
    I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.  
    Until next time.  
    #how #reshaping #future #healthcare #medical
    How AI is reshaping the future of healthcare and medical research
    Transcript        PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”           This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.  The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.      Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weaknessthat, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent.  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSRto join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well.My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair.And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE:One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce aboutor indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients.Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT. And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE, for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential.What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back thatversion of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF, where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGIthat kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects.So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and seeproduced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini. So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelectedjust on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.   GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.   I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   #how #reshaping #future #healthcare #medical
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    How AI is reshaping the future of healthcare and medical research
    Transcript [MUSIC]      [BOOK PASSAGE]   PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”   [END OF BOOK PASSAGE]     [THEME MUSIC]     This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.   [THEME MUSIC FADES] The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.    [TRANSITION MUSIC]   Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weakness [LAUGHTER] that, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. [LAUGHS]  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSR [Microsoft Research] to join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well. [LAUGHS] My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair. [LAUGHTER] And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE: [LAUGHS] One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce about [LAUGHS] or indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients. [LAUGHTER] Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT (opens in new tab). And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE [United States Medical Licensing Examination], for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential. [LAUGHTER] What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back that [LAUGHS] version of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF [reinforcement learning from human feedback], where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGI [artificial general intelligence] that kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects. [LAUGHTER] So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and see [if you have] produced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab). So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelected [LAUGHTER] just on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  [TRANSITION MUSIC]  GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  [THEME MUSIC]  I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   [MUSIC FADES]
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  • Trump’s military parade is a warning

    Donald Trump’s military parade in Washington this weekend — a show of force in the capital that just happens to take place on the president’s birthday — smacks of authoritarian Dear Leader-style politics.Yet as disconcerting as the imagery of tanks rolling down Constitution Avenue will be, it’s not even close to Trump’s most insidious assault on the US military’s historic and democratically essential nonpartisan ethos.In fact, it’s not even the most worrying thing he’s done this week.On Tuesday, the president gave a speech at Fort Bragg, an Army base home to Special Operations Command. While presidential speeches to soldiers are not uncommon — rows of uniformed troops make a great backdrop for a foreign policy speech — they generally avoid overt partisan attacks and campaign-style rhetoric. The soldiers, for their part, are expected to be studiously neutral, laughing at jokes and such, but remaining fully impassive during any policy conversation.That’s not what happened at Fort Bragg. Trump’s speech was a partisan tirade that targeted “radical left” opponents ranging from Joe Biden to Los Angeles Mayor Karen Bass. He celebrated his deployment of Marines to Los Angeles, proposed jailing people for burning the American flag, and called on soldiers to be “aggressive” toward the protesters they encountered.The soldiers, for their part, cheered Trump and booed his enemies — as they were seemingly expected to. Reporters at Military.com, a military news service, uncovered internal communications from 82nd Airborne leadership suggesting that the crowd was screened for their political opinions.“If soldiers have political views that are in opposition to the current administration and they don’t want to be in the audience then they need to speak with their leadership and get swapped out,” one note read.To call this unusual is an understatement. I spoke with four different experts on civil-military relations, two of whom teach at the Naval War College, about the speech and its implications. To a person, they said it was a step towards politicizing the military with no real precedent in modern American history.“That is, I think, a really big red flag because it means the military’s professional ethic is breaking down internally,” says Risa Brooks, a professor at Marquette University. “Its capacity to maintain that firewall against civilian politicization may be faltering.”This may sound alarmist — like an overreading of a one-off incident — but it’s part of a bigger pattern. The totality of Trump administration policies, ranging from the parade in Washington to the LA troop deployment to Secretary of Defense Pete Hegseth’s firing of high-ranking women and officers of color, suggests a concerted effort to erode the military’s professional ethos and turn it into an institution subservient to the Trump administration’s whims. This is a signal policy aim of would-be dictators, who wish to head off the risk of a coup and ensure the armed forces’ political reliability if they are needed to repress dissent in a crisis.Steve Saideman, a professor at Carleton University, put together a list of eight different signs that a military is being politicized in this fashion. The Trump administration has exhibited six out of the eight.“The biggest theme is that we are seeing a number of checks on the executive fail at the same time — and that’s what’s making individual events seem more alarming than they might otherwise,” says Jessica Blankshain, a professor at the Naval War College.That Trump is trying to politicize the military does not mean he has succeeded. There are several signs, including Trump’s handpicked chair of the Joint Chiefs repudiating the president’s claims of a migrant invasion during congressional testimony, that the US military is resisting Trump’s politicization.But the events in Fort Bragg and Washington suggest that we are in the midst of a quiet crisis in civil-military relations in the United States — one whose implications for American democracy’s future could well be profound.The Trump crisis in civil-military relations, explainedA military is, by sheer fact of its existence, a threat to any civilian government. If you have an institution that controls the overwhelming bulk of weaponry in a society, it always has the physical capacity to seize control of the government at gunpoint. A key question for any government is how to convince the armed forces that they cannot or should not take power for themselves.Democracies typically do this through a process called “professionalization.” Soldiers are rigorously taught to think of themselves as a class of public servants, people trained to perform a specific job within defined parameters. Their ultimate loyalty is not to their generals or even individual presidents, but rather to the people and the constitutional order.Samuel Huntington, the late Harvard political scientist, is the canonical theorist of a professional military. In his book The Soldier and the State, he described optimal professionalization as a system of “objective control”: one in which the military retains autonomy in how they fight and plan for wars while deferring to politicians on whether and why to fight in the first place. In effect, they stay out of the politicians’ affairs while the politicians stay out of theirs.The idea of such a system is to emphasize to the military that they are professionals: Their responsibility isn’t deciding when to use force, but only to conduct operations as effectively as possible once ordered to engage in them. There is thus a strict firewall between military affairs, on the one hand, and policy-political affairs on the other.Typically, the chief worry is that the military breaches this bargain: that, for example, a general starts speaking out against elected officials’ policies in ways that undermine civilian control. This is not a hypothetical fear in the United States, with the most famous such example being Gen. Douglas MacArthur’s insubordination during the Korean War. Thankfully, not even MacArthur attempted the worst-case version of military overstep — a coup.But in backsliding democracies like the modern United States, where the chief executive is attempting an anti-democratic power grab, the military poses a very different kind of threat to democracy — in fact, something akin to the exact opposite of the typical scenario.In such cases, the issue isn’t the military inserting itself into politics but rather the civilians dragging them into it in ways that upset the democratic political order. The worst-case scenario is that the military acts on presidential directives to use force against domestic dissenters, destroying democracy not by ignoring civilian orders, but by following them.There are two ways to arrive at such a worst-case scenario, both of which are in evidence in the early days of Trump 2.0.First is politicization: an intentional attack on the constraints against partisan activity inside the professional ranks.Many of Pete Hegseth’s major moves as secretary of defense fit this bill, including his decisions to fire nonwhite and female generals seen as politically unreliable and his effort to undermine the independence of the military’s lawyers. The breaches in protocol at Fort Bragg are both consequences and causes of politicization: They could only happen in an environment of loosened constraint, and they might encourage more overt political action if gone unpunished.The second pathway to breakdown is the weaponization of professionalism against itself. Here, Trump exploits the military’s deference to politicians by ordering it to engage in undemocraticactivities. In practice, this looks a lot like the LA deployments, and, more specifically, the lack of any visible military pushback. While the military readily agreeing to deployments is normally a good sign — that civilian control is holding — these aren’t normal times. And this isn’t a normal deployment, but rather one that comes uncomfortably close to the military being ordered to assist in repressing overwhelmingly peaceful demonstrations against executive abuses of power.“It’s really been pretty uncommon to use the military for law enforcement,” says David Burbach, another Naval War College professor. “This is really bringing the military into frontline law enforcement when. … these are really not huge disturbances.”This, then, is the crisis: an incremental and slow-rolling effort by the Trump administration to erode the norms and procedures designed to prevent the military from being used as a tool of domestic repression. Is it time to panic?Among the experts I spoke with, there was consensus that the military’s professional and nonpartisan ethos was weakening. This isn’t just because of Trump, but his terms — the first to a degree, and now the second acutely — are major stressors.Yet there was no consensus on just how much military nonpartisanship has eroded — that is, how close we are to a moment when the US military might be willing to follow obviously authoritarian orders.For all its faults, the US military’s professional ethos is a really important part of its identity and self-conception. While few soldiers may actually read Sam Huntington or similar scholars, the general idea that they serve the people and the republic is a bedrock principle among the ranks. There is a reason why the United States has never, in over 250 years of governance, experienced a military coup — or even come particularly close to one.In theory, this ethos should also galvanize resistance to Trump’s efforts at politicization. Soldiers are not unthinking automatons: While they are trained to follow commands, they are explicitly obligated to refuse illegal orders, even coming from the president. The more aggressive Trump’s efforts to use the military as a tool of repression gets, the more likely there is to be resistance.Or, at least theoretically.The truth is that we don’t really know how the US military will respond to a situation like this. Like so many of Trump’s second-term policies, their efforts to bend the military to their will are unprecedented — actions with no real parallel in the modern history of the American military. Experts can only make informed guesses, based on their sense of US military culture as well as comparisons to historical and foreign cases.For this reason, there are probably only two things we can say with confidence.First, what we’ve seen so far is not yet sufficient evidence to declare that the military is in Trump’s thrall. The signs of decay are too limited to ground any conclusions that the longstanding professional norm is entirely gone.“We have seen a few things that are potentially alarming about erosion of the military’s non-partisan norm. But not in a way that’s definitive at this point,” Blankshain says.Second, the stressors on this tradition are going to keep piling on. Trump’s record makes it exceptionally clear that he wants the military to serve him personally — and that he, and Hegseth, will keep working to make it so. This means we really are in the midst of a quiet crisis, and will likely remain so for the foreseeable future.“The fact that he’s getting the troops to cheer for booing Democratic leaders at a time when there’s actuallya blue city and a blue state…he is ordering the troops to take a side,” Saideman says. “There may not be a coherent plan behind this. But there are a lot of things going on that are all in the same direction.”See More: Politics
    #trumpampamp8217s #military #parade #warning
    Trump’s military parade is a warning
    Donald Trump’s military parade in Washington this weekend — a show of force in the capital that just happens to take place on the president’s birthday — smacks of authoritarian Dear Leader-style politics.Yet as disconcerting as the imagery of tanks rolling down Constitution Avenue will be, it’s not even close to Trump’s most insidious assault on the US military’s historic and democratically essential nonpartisan ethos.In fact, it’s not even the most worrying thing he’s done this week.On Tuesday, the president gave a speech at Fort Bragg, an Army base home to Special Operations Command. While presidential speeches to soldiers are not uncommon — rows of uniformed troops make a great backdrop for a foreign policy speech — they generally avoid overt partisan attacks and campaign-style rhetoric. The soldiers, for their part, are expected to be studiously neutral, laughing at jokes and such, but remaining fully impassive during any policy conversation.That’s not what happened at Fort Bragg. Trump’s speech was a partisan tirade that targeted “radical left” opponents ranging from Joe Biden to Los Angeles Mayor Karen Bass. He celebrated his deployment of Marines to Los Angeles, proposed jailing people for burning the American flag, and called on soldiers to be “aggressive” toward the protesters they encountered.The soldiers, for their part, cheered Trump and booed his enemies — as they were seemingly expected to. Reporters at Military.com, a military news service, uncovered internal communications from 82nd Airborne leadership suggesting that the crowd was screened for their political opinions.“If soldiers have political views that are in opposition to the current administration and they don’t want to be in the audience then they need to speak with their leadership and get swapped out,” one note read.To call this unusual is an understatement. I spoke with four different experts on civil-military relations, two of whom teach at the Naval War College, about the speech and its implications. To a person, they said it was a step towards politicizing the military with no real precedent in modern American history.“That is, I think, a really big red flag because it means the military’s professional ethic is breaking down internally,” says Risa Brooks, a professor at Marquette University. “Its capacity to maintain that firewall against civilian politicization may be faltering.”This may sound alarmist — like an overreading of a one-off incident — but it’s part of a bigger pattern. The totality of Trump administration policies, ranging from the parade in Washington to the LA troop deployment to Secretary of Defense Pete Hegseth’s firing of high-ranking women and officers of color, suggests a concerted effort to erode the military’s professional ethos and turn it into an institution subservient to the Trump administration’s whims. This is a signal policy aim of would-be dictators, who wish to head off the risk of a coup and ensure the armed forces’ political reliability if they are needed to repress dissent in a crisis.Steve Saideman, a professor at Carleton University, put together a list of eight different signs that a military is being politicized in this fashion. The Trump administration has exhibited six out of the eight.“The biggest theme is that we are seeing a number of checks on the executive fail at the same time — and that’s what’s making individual events seem more alarming than they might otherwise,” says Jessica Blankshain, a professor at the Naval War College.That Trump is trying to politicize the military does not mean he has succeeded. There are several signs, including Trump’s handpicked chair of the Joint Chiefs repudiating the president’s claims of a migrant invasion during congressional testimony, that the US military is resisting Trump’s politicization.But the events in Fort Bragg and Washington suggest that we are in the midst of a quiet crisis in civil-military relations in the United States — one whose implications for American democracy’s future could well be profound.The Trump crisis in civil-military relations, explainedA military is, by sheer fact of its existence, a threat to any civilian government. If you have an institution that controls the overwhelming bulk of weaponry in a society, it always has the physical capacity to seize control of the government at gunpoint. A key question for any government is how to convince the armed forces that they cannot or should not take power for themselves.Democracies typically do this through a process called “professionalization.” Soldiers are rigorously taught to think of themselves as a class of public servants, people trained to perform a specific job within defined parameters. Their ultimate loyalty is not to their generals or even individual presidents, but rather to the people and the constitutional order.Samuel Huntington, the late Harvard political scientist, is the canonical theorist of a professional military. In his book The Soldier and the State, he described optimal professionalization as a system of “objective control”: one in which the military retains autonomy in how they fight and plan for wars while deferring to politicians on whether and why to fight in the first place. In effect, they stay out of the politicians’ affairs while the politicians stay out of theirs.The idea of such a system is to emphasize to the military that they are professionals: Their responsibility isn’t deciding when to use force, but only to conduct operations as effectively as possible once ordered to engage in them. There is thus a strict firewall between military affairs, on the one hand, and policy-political affairs on the other.Typically, the chief worry is that the military breaches this bargain: that, for example, a general starts speaking out against elected officials’ policies in ways that undermine civilian control. This is not a hypothetical fear in the United States, with the most famous such example being Gen. Douglas MacArthur’s insubordination during the Korean War. Thankfully, not even MacArthur attempted the worst-case version of military overstep — a coup.But in backsliding democracies like the modern United States, where the chief executive is attempting an anti-democratic power grab, the military poses a very different kind of threat to democracy — in fact, something akin to the exact opposite of the typical scenario.In such cases, the issue isn’t the military inserting itself into politics but rather the civilians dragging them into it in ways that upset the democratic political order. The worst-case scenario is that the military acts on presidential directives to use force against domestic dissenters, destroying democracy not by ignoring civilian orders, but by following them.There are two ways to arrive at such a worst-case scenario, both of which are in evidence in the early days of Trump 2.0.First is politicization: an intentional attack on the constraints against partisan activity inside the professional ranks.Many of Pete Hegseth’s major moves as secretary of defense fit this bill, including his decisions to fire nonwhite and female generals seen as politically unreliable and his effort to undermine the independence of the military’s lawyers. The breaches in protocol at Fort Bragg are both consequences and causes of politicization: They could only happen in an environment of loosened constraint, and they might encourage more overt political action if gone unpunished.The second pathway to breakdown is the weaponization of professionalism against itself. Here, Trump exploits the military’s deference to politicians by ordering it to engage in undemocraticactivities. In practice, this looks a lot like the LA deployments, and, more specifically, the lack of any visible military pushback. While the military readily agreeing to deployments is normally a good sign — that civilian control is holding — these aren’t normal times. And this isn’t a normal deployment, but rather one that comes uncomfortably close to the military being ordered to assist in repressing overwhelmingly peaceful demonstrations against executive abuses of power.“It’s really been pretty uncommon to use the military for law enforcement,” says David Burbach, another Naval War College professor. “This is really bringing the military into frontline law enforcement when. … these are really not huge disturbances.”This, then, is the crisis: an incremental and slow-rolling effort by the Trump administration to erode the norms and procedures designed to prevent the military from being used as a tool of domestic repression. Is it time to panic?Among the experts I spoke with, there was consensus that the military’s professional and nonpartisan ethos was weakening. This isn’t just because of Trump, but his terms — the first to a degree, and now the second acutely — are major stressors.Yet there was no consensus on just how much military nonpartisanship has eroded — that is, how close we are to a moment when the US military might be willing to follow obviously authoritarian orders.For all its faults, the US military’s professional ethos is a really important part of its identity and self-conception. While few soldiers may actually read Sam Huntington or similar scholars, the general idea that they serve the people and the republic is a bedrock principle among the ranks. There is a reason why the United States has never, in over 250 years of governance, experienced a military coup — or even come particularly close to one.In theory, this ethos should also galvanize resistance to Trump’s efforts at politicization. Soldiers are not unthinking automatons: While they are trained to follow commands, they are explicitly obligated to refuse illegal orders, even coming from the president. The more aggressive Trump’s efforts to use the military as a tool of repression gets, the more likely there is to be resistance.Or, at least theoretically.The truth is that we don’t really know how the US military will respond to a situation like this. Like so many of Trump’s second-term policies, their efforts to bend the military to their will are unprecedented — actions with no real parallel in the modern history of the American military. Experts can only make informed guesses, based on their sense of US military culture as well as comparisons to historical and foreign cases.For this reason, there are probably only two things we can say with confidence.First, what we’ve seen so far is not yet sufficient evidence to declare that the military is in Trump’s thrall. The signs of decay are too limited to ground any conclusions that the longstanding professional norm is entirely gone.“We have seen a few things that are potentially alarming about erosion of the military’s non-partisan norm. But not in a way that’s definitive at this point,” Blankshain says.Second, the stressors on this tradition are going to keep piling on. Trump’s record makes it exceptionally clear that he wants the military to serve him personally — and that he, and Hegseth, will keep working to make it so. This means we really are in the midst of a quiet crisis, and will likely remain so for the foreseeable future.“The fact that he’s getting the troops to cheer for booing Democratic leaders at a time when there’s actuallya blue city and a blue state…he is ordering the troops to take a side,” Saideman says. “There may not be a coherent plan behind this. But there are a lot of things going on that are all in the same direction.”See More: Politics #trumpampamp8217s #military #parade #warning
    WWW.VOX.COM
    Trump’s military parade is a warning
    Donald Trump’s military parade in Washington this weekend — a show of force in the capital that just happens to take place on the president’s birthday — smacks of authoritarian Dear Leader-style politics (even though Trump actually got the idea after attending the 2017 Bastille Day parade in Paris).Yet as disconcerting as the imagery of tanks rolling down Constitution Avenue will be, it’s not even close to Trump’s most insidious assault on the US military’s historic and democratically essential nonpartisan ethos.In fact, it’s not even the most worrying thing he’s done this week.On Tuesday, the president gave a speech at Fort Bragg, an Army base home to Special Operations Command. While presidential speeches to soldiers are not uncommon — rows of uniformed troops make a great backdrop for a foreign policy speech — they generally avoid overt partisan attacks and campaign-style rhetoric. The soldiers, for their part, are expected to be studiously neutral, laughing at jokes and such, but remaining fully impassive during any policy conversation.That’s not what happened at Fort Bragg. Trump’s speech was a partisan tirade that targeted “radical left” opponents ranging from Joe Biden to Los Angeles Mayor Karen Bass. He celebrated his deployment of Marines to Los Angeles, proposed jailing people for burning the American flag, and called on soldiers to be “aggressive” toward the protesters they encountered.The soldiers, for their part, cheered Trump and booed his enemies — as they were seemingly expected to. Reporters at Military.com, a military news service, uncovered internal communications from 82nd Airborne leadership suggesting that the crowd was screened for their political opinions.“If soldiers have political views that are in opposition to the current administration and they don’t want to be in the audience then they need to speak with their leadership and get swapped out,” one note read.To call this unusual is an understatement. I spoke with four different experts on civil-military relations, two of whom teach at the Naval War College, about the speech and its implications. To a person, they said it was a step towards politicizing the military with no real precedent in modern American history.“That is, I think, a really big red flag because it means the military’s professional ethic is breaking down internally,” says Risa Brooks, a professor at Marquette University. “Its capacity to maintain that firewall against civilian politicization may be faltering.”This may sound alarmist — like an overreading of a one-off incident — but it’s part of a bigger pattern. The totality of Trump administration policies, ranging from the parade in Washington to the LA troop deployment to Secretary of Defense Pete Hegseth’s firing of high-ranking women and officers of color, suggests a concerted effort to erode the military’s professional ethos and turn it into an institution subservient to the Trump administration’s whims. This is a signal policy aim of would-be dictators, who wish to head off the risk of a coup and ensure the armed forces’ political reliability if they are needed to repress dissent in a crisis.Steve Saideman, a professor at Carleton University, put together a list of eight different signs that a military is being politicized in this fashion. The Trump administration has exhibited six out of the eight.“The biggest theme is that we are seeing a number of checks on the executive fail at the same time — and that’s what’s making individual events seem more alarming than they might otherwise,” says Jessica Blankshain, a professor at the Naval War College (speaking not for the military but in a personal capacity).That Trump is trying to politicize the military does not mean he has succeeded. There are several signs, including Trump’s handpicked chair of the Joint Chiefs repudiating the president’s claims of a migrant invasion during congressional testimony, that the US military is resisting Trump’s politicization.But the events in Fort Bragg and Washington suggest that we are in the midst of a quiet crisis in civil-military relations in the United States — one whose implications for American democracy’s future could well be profound.The Trump crisis in civil-military relations, explainedA military is, by sheer fact of its existence, a threat to any civilian government. If you have an institution that controls the overwhelming bulk of weaponry in a society, it always has the physical capacity to seize control of the government at gunpoint. A key question for any government is how to convince the armed forces that they cannot or should not take power for themselves.Democracies typically do this through a process called “professionalization.” Soldiers are rigorously taught to think of themselves as a class of public servants, people trained to perform a specific job within defined parameters. Their ultimate loyalty is not to their generals or even individual presidents, but rather to the people and the constitutional order.Samuel Huntington, the late Harvard political scientist, is the canonical theorist of a professional military. In his book The Soldier and the State, he described optimal professionalization as a system of “objective control”: one in which the military retains autonomy in how they fight and plan for wars while deferring to politicians on whether and why to fight in the first place. In effect, they stay out of the politicians’ affairs while the politicians stay out of theirs.The idea of such a system is to emphasize to the military that they are professionals: Their responsibility isn’t deciding when to use force, but only to conduct operations as effectively as possible once ordered to engage in them. There is thus a strict firewall between military affairs, on the one hand, and policy-political affairs on the other.Typically, the chief worry is that the military breaches this bargain: that, for example, a general starts speaking out against elected officials’ policies in ways that undermine civilian control. This is not a hypothetical fear in the United States, with the most famous such example being Gen. Douglas MacArthur’s insubordination during the Korean War. Thankfully, not even MacArthur attempted the worst-case version of military overstep — a coup.But in backsliding democracies like the modern United States, where the chief executive is attempting an anti-democratic power grab, the military poses a very different kind of threat to democracy — in fact, something akin to the exact opposite of the typical scenario.In such cases, the issue isn’t the military inserting itself into politics but rather the civilians dragging them into it in ways that upset the democratic political order. The worst-case scenario is that the military acts on presidential directives to use force against domestic dissenters, destroying democracy not by ignoring civilian orders, but by following them.There are two ways to arrive at such a worst-case scenario, both of which are in evidence in the early days of Trump 2.0.First is politicization: an intentional attack on the constraints against partisan activity inside the professional ranks.Many of Pete Hegseth’s major moves as secretary of defense fit this bill, including his decisions to fire nonwhite and female generals seen as politically unreliable and his effort to undermine the independence of the military’s lawyers. The breaches in protocol at Fort Bragg are both consequences and causes of politicization: They could only happen in an environment of loosened constraint, and they might encourage more overt political action if gone unpunished.The second pathway to breakdown is the weaponization of professionalism against itself. Here, Trump exploits the military’s deference to politicians by ordering it to engage in undemocratic (and even questionably legal) activities. In practice, this looks a lot like the LA deployments, and, more specifically, the lack of any visible military pushback. While the military readily agreeing to deployments is normally a good sign — that civilian control is holding — these aren’t normal times. And this isn’t a normal deployment, but rather one that comes uncomfortably close to the military being ordered to assist in repressing overwhelmingly peaceful demonstrations against executive abuses of power.“It’s really been pretty uncommon to use the military for law enforcement,” says David Burbach, another Naval War College professor (also speaking personally). “This is really bringing the military into frontline law enforcement when. … these are really not huge disturbances.”This, then, is the crisis: an incremental and slow-rolling effort by the Trump administration to erode the norms and procedures designed to prevent the military from being used as a tool of domestic repression. Is it time to panic?Among the experts I spoke with, there was consensus that the military’s professional and nonpartisan ethos was weakening. This isn’t just because of Trump, but his terms — the first to a degree, and now the second acutely — are major stressors.Yet there was no consensus on just how much military nonpartisanship has eroded — that is, how close we are to a moment when the US military might be willing to follow obviously authoritarian orders.For all its faults, the US military’s professional ethos is a really important part of its identity and self-conception. While few soldiers may actually read Sam Huntington or similar scholars, the general idea that they serve the people and the republic is a bedrock principle among the ranks. There is a reason why the United States has never, in over 250 years of governance, experienced a military coup — or even come particularly close to one.In theory, this ethos should also galvanize resistance to Trump’s efforts at politicization. Soldiers are not unthinking automatons: While they are trained to follow commands, they are explicitly obligated to refuse illegal orders, even coming from the president. The more aggressive Trump’s efforts to use the military as a tool of repression gets, the more likely there is to be resistance.Or, at least theoretically.The truth is that we don’t really know how the US military will respond to a situation like this. Like so many of Trump’s second-term policies, their efforts to bend the military to their will are unprecedented — actions with no real parallel in the modern history of the American military. Experts can only make informed guesses, based on their sense of US military culture as well as comparisons to historical and foreign cases.For this reason, there are probably only two things we can say with confidence.First, what we’ve seen so far is not yet sufficient evidence to declare that the military is in Trump’s thrall. The signs of decay are too limited to ground any conclusions that the longstanding professional norm is entirely gone.“We have seen a few things that are potentially alarming about erosion of the military’s non-partisan norm. But not in a way that’s definitive at this point,” Blankshain says.Second, the stressors on this tradition are going to keep piling on. Trump’s record makes it exceptionally clear that he wants the military to serve him personally — and that he, and Hegseth, will keep working to make it so. This means we really are in the midst of a quiet crisis, and will likely remain so for the foreseeable future.“The fact that he’s getting the troops to cheer for booing Democratic leaders at a time when there’s actually [a deployment to] a blue city and a blue state…he is ordering the troops to take a side,” Saideman says. “There may not be a coherent plan behind this. But there are a lot of things going on that are all in the same direction.”See More: Politics
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  • Biofuels policy has been a failure for the climate, new report claims

    Fewer food crops

    Biofuels policy has been a failure for the climate, new report claims

    Report: An expansion of biofuels policy under Trump would lead to more greenhouse gas emissions.

    Georgina Gustin, Inside Climate News



    Jun 14, 2025 7:10 am

    |

    24

    An ethanol production plant on March 20, 2024 near Ravenna, Nebraska.

    Credit:

    David Madison/Getty Images

    An ethanol production plant on March 20, 2024 near Ravenna, Nebraska.

    Credit:

    David Madison/Getty Images

    Story text

    Size

    Small
    Standard
    Large

    Width
    *

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    Wide

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      Learn more

    This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy, and the environment. Sign up for their newsletter here.
    The American Midwest is home to some of the richest, most productive farmland in the world, enabling its transformation into a vast corn- and soy-producing machine—a conversion spurred largely by decades-long policies that support the production of biofuels.
    But a new report takes a big swing at the ethanol orthodoxy of American agriculture, criticizing the industry for causing economic and social imbalances across rural communities and saying that the expansion of biofuels will increase greenhouse gas emissions, despite their purported climate benefits.
    The report, from the World Resources Institute, which has been critical of US biofuel policy in the past, draws from 100 academic studies on biofuel impacts. It concludes that ethanol policy has been largely a failure and ought to be reconsidered, especially as the world needs more land to produce food to meet growing demand.
    “Multiple studies show that US biofuel policies have reshaped crop production, displacing food crops and driving up emissions from land conversion, tillage, and fertilizer use,” said the report’s lead author, Haley Leslie-Bole. “Corn-based ethanol, in particular, has contributed to nutrient runoff, degraded water quality and harmed wildlife habitat. As climate pressures grow, increasing irrigation and refining for first-gen biofuels could deepen water scarcity in already drought-prone parts of the Midwest.”
    The conversion of Midwestern agricultural land has been sweeping. Between 2004 and 2024, ethanol production increased by nearly 500 percent. Corn and soybeans are now grown on 92 and 86 million acres of land respectively—and roughly a third of those crops go to produce ethanol. That means about 30 million acres of land that could be used to grow food crops are instead being used to produce ethanol, despite ethanol only accounting for 6 percent of the country’s transportation fuel.

    The biofuels industry—which includes refiners, corn and soy growers and the influential agriculture lobby writ large—has long insisted that corn- and soy-based biofuels provide an energy-efficient alternative to fossil-based fuels. Congress and the US Department of Agriculture have agreed.
    The country’s primary biofuels policy, the Renewable Fuel Standard, requires that biofuels provide a greenhouse gas reduction over fossil fuels: The law says that ethanol from new plants must deliver a 20 percent reduction in greenhouse gas emissions compared to gasoline.
    In addition to greenhouse gas reductions, the industry and its allies in Congress have also continued to say that ethanol is a primary mainstay of the rural economy, benefiting communities across the Midwest.
    But a growing body of research—much of which the industry has tried to debunk and deride—suggests that ethanol actually may not provide the benefits that policies require. It may, in fact, produce more greenhouse gases than the fossil fuels it was intended to replace. Recent research says that biofuel refiners also emit significant amounts of carcinogenic and dangerous substances, including hexane and formaldehyde, in greater amounts than petroleum refineries.
    The new report points to research saying that increased production of biofuels from corn and soy could actually raise greenhouse gas emissions, largely from carbon emissions linked to clearing land in other countries to compensate for the use of land in the Midwest.
    On top of that, corn is an especially fertilizer-hungry crop requiring large amounts of nitrogen-based fertilizer, which releases huge amounts of nitrous oxide when it interacts with the soil. American farming is, by far, the largest source of domestic nitrous oxide emissions already—about 50 percent. If biofuel policies lead to expanded production, emissions of this enormously powerful greenhouse gas will likely increase, too.

    The new report concludes that not only will the expansion of ethanol increase greenhouse gas emissions, but it has also failed to provide the social and financial benefits to Midwestern communities that lawmakers and the industry say it has.“The benefits from biofuels remain concentrated in the hands of a few,” Leslie-Bole said. “As subsidies flow, so may the trend of farmland consolidation, increasing inaccessibility of farmland in the Midwest, and locking out emerging or low-resource farmers. This means the benefits of biofuels production are flowing to fewer people, while more are left bearing the costs.”
    New policies being considered in state legislatures and Congress, including additional tax credits and support for biofuel-based aviation fuel, could expand production, potentially causing more land conversion and greenhouse gas emissions, widening the gap between the rural communities and rich agribusinesses at a time when food demand is climbing and, critics say, land should be used to grow food instead.
    President Donald Trump’s tax cut bill, passed by the House and currently being negotiated in the Senate, would not only extend tax credits for biofuels producers, it specifically excludes calculations of emissions from land conversion when determining what qualifies as a low-emission fuel.
    The primary biofuels industry trade groups, including Growth Energy and the Renewable Fuels Association, did not respond to Inside Climate News requests for comment or interviews.
    An employee with the Clean Fuels Alliance America, which represents biodiesel and sustainable aviation fuel producers, not ethanol, said the report vastly overstates the carbon emissions from crop-based fuels by comparing the farmed land to natural landscapes, which no longer exist.
    They also noted that the impact of soy-based fuels in 2024 was more than billion, providing over 100,000 jobs.
    “Ten percent of the value of every bushel of soybeans is linked to biomass-based fuel,” they said.

    Georgina Gustin, Inside Climate News

    24 Comments
    #biofuels #policy #has #been #failure
    Biofuels policy has been a failure for the climate, new report claims
    Fewer food crops Biofuels policy has been a failure for the climate, new report claims Report: An expansion of biofuels policy under Trump would lead to more greenhouse gas emissions. Georgina Gustin, Inside Climate News – Jun 14, 2025 7:10 am | 24 An ethanol production plant on March 20, 2024 near Ravenna, Nebraska. Credit: David Madison/Getty Images An ethanol production plant on March 20, 2024 near Ravenna, Nebraska. Credit: David Madison/Getty Images Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy, and the environment. Sign up for their newsletter here. The American Midwest is home to some of the richest, most productive farmland in the world, enabling its transformation into a vast corn- and soy-producing machine—a conversion spurred largely by decades-long policies that support the production of biofuels. But a new report takes a big swing at the ethanol orthodoxy of American agriculture, criticizing the industry for causing economic and social imbalances across rural communities and saying that the expansion of biofuels will increase greenhouse gas emissions, despite their purported climate benefits. The report, from the World Resources Institute, which has been critical of US biofuel policy in the past, draws from 100 academic studies on biofuel impacts. It concludes that ethanol policy has been largely a failure and ought to be reconsidered, especially as the world needs more land to produce food to meet growing demand. “Multiple studies show that US biofuel policies have reshaped crop production, displacing food crops and driving up emissions from land conversion, tillage, and fertilizer use,” said the report’s lead author, Haley Leslie-Bole. “Corn-based ethanol, in particular, has contributed to nutrient runoff, degraded water quality and harmed wildlife habitat. As climate pressures grow, increasing irrigation and refining for first-gen biofuels could deepen water scarcity in already drought-prone parts of the Midwest.” The conversion of Midwestern agricultural land has been sweeping. Between 2004 and 2024, ethanol production increased by nearly 500 percent. Corn and soybeans are now grown on 92 and 86 million acres of land respectively—and roughly a third of those crops go to produce ethanol. That means about 30 million acres of land that could be used to grow food crops are instead being used to produce ethanol, despite ethanol only accounting for 6 percent of the country’s transportation fuel. The biofuels industry—which includes refiners, corn and soy growers and the influential agriculture lobby writ large—has long insisted that corn- and soy-based biofuels provide an energy-efficient alternative to fossil-based fuels. Congress and the US Department of Agriculture have agreed. The country’s primary biofuels policy, the Renewable Fuel Standard, requires that biofuels provide a greenhouse gas reduction over fossil fuels: The law says that ethanol from new plants must deliver a 20 percent reduction in greenhouse gas emissions compared to gasoline. In addition to greenhouse gas reductions, the industry and its allies in Congress have also continued to say that ethanol is a primary mainstay of the rural economy, benefiting communities across the Midwest. But a growing body of research—much of which the industry has tried to debunk and deride—suggests that ethanol actually may not provide the benefits that policies require. It may, in fact, produce more greenhouse gases than the fossil fuels it was intended to replace. Recent research says that biofuel refiners also emit significant amounts of carcinogenic and dangerous substances, including hexane and formaldehyde, in greater amounts than petroleum refineries. The new report points to research saying that increased production of biofuels from corn and soy could actually raise greenhouse gas emissions, largely from carbon emissions linked to clearing land in other countries to compensate for the use of land in the Midwest. On top of that, corn is an especially fertilizer-hungry crop requiring large amounts of nitrogen-based fertilizer, which releases huge amounts of nitrous oxide when it interacts with the soil. American farming is, by far, the largest source of domestic nitrous oxide emissions already—about 50 percent. If biofuel policies lead to expanded production, emissions of this enormously powerful greenhouse gas will likely increase, too. The new report concludes that not only will the expansion of ethanol increase greenhouse gas emissions, but it has also failed to provide the social and financial benefits to Midwestern communities that lawmakers and the industry say it has.“The benefits from biofuels remain concentrated in the hands of a few,” Leslie-Bole said. “As subsidies flow, so may the trend of farmland consolidation, increasing inaccessibility of farmland in the Midwest, and locking out emerging or low-resource farmers. This means the benefits of biofuels production are flowing to fewer people, while more are left bearing the costs.” New policies being considered in state legislatures and Congress, including additional tax credits and support for biofuel-based aviation fuel, could expand production, potentially causing more land conversion and greenhouse gas emissions, widening the gap between the rural communities and rich agribusinesses at a time when food demand is climbing and, critics say, land should be used to grow food instead. President Donald Trump’s tax cut bill, passed by the House and currently being negotiated in the Senate, would not only extend tax credits for biofuels producers, it specifically excludes calculations of emissions from land conversion when determining what qualifies as a low-emission fuel. The primary biofuels industry trade groups, including Growth Energy and the Renewable Fuels Association, did not respond to Inside Climate News requests for comment or interviews. An employee with the Clean Fuels Alliance America, which represents biodiesel and sustainable aviation fuel producers, not ethanol, said the report vastly overstates the carbon emissions from crop-based fuels by comparing the farmed land to natural landscapes, which no longer exist. They also noted that the impact of soy-based fuels in 2024 was more than billion, providing over 100,000 jobs. “Ten percent of the value of every bushel of soybeans is linked to biomass-based fuel,” they said. Georgina Gustin, Inside Climate News 24 Comments #biofuels #policy #has #been #failure
    ARSTECHNICA.COM
    Biofuels policy has been a failure for the climate, new report claims
    Fewer food crops Biofuels policy has been a failure for the climate, new report claims Report: An expansion of biofuels policy under Trump would lead to more greenhouse gas emissions. Georgina Gustin, Inside Climate News – Jun 14, 2025 7:10 am | 24 An ethanol production plant on March 20, 2024 near Ravenna, Nebraska. Credit: David Madison/Getty Images An ethanol production plant on March 20, 2024 near Ravenna, Nebraska. Credit: David Madison/Getty Images Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy, and the environment. Sign up for their newsletter here. The American Midwest is home to some of the richest, most productive farmland in the world, enabling its transformation into a vast corn- and soy-producing machine—a conversion spurred largely by decades-long policies that support the production of biofuels. But a new report takes a big swing at the ethanol orthodoxy of American agriculture, criticizing the industry for causing economic and social imbalances across rural communities and saying that the expansion of biofuels will increase greenhouse gas emissions, despite their purported climate benefits. The report, from the World Resources Institute, which has been critical of US biofuel policy in the past, draws from 100 academic studies on biofuel impacts. It concludes that ethanol policy has been largely a failure and ought to be reconsidered, especially as the world needs more land to produce food to meet growing demand. “Multiple studies show that US biofuel policies have reshaped crop production, displacing food crops and driving up emissions from land conversion, tillage, and fertilizer use,” said the report’s lead author, Haley Leslie-Bole. “Corn-based ethanol, in particular, has contributed to nutrient runoff, degraded water quality and harmed wildlife habitat. As climate pressures grow, increasing irrigation and refining for first-gen biofuels could deepen water scarcity in already drought-prone parts of the Midwest.” The conversion of Midwestern agricultural land has been sweeping. Between 2004 and 2024, ethanol production increased by nearly 500 percent. Corn and soybeans are now grown on 92 and 86 million acres of land respectively—and roughly a third of those crops go to produce ethanol. That means about 30 million acres of land that could be used to grow food crops are instead being used to produce ethanol, despite ethanol only accounting for 6 percent of the country’s transportation fuel. The biofuels industry—which includes refiners, corn and soy growers and the influential agriculture lobby writ large—has long insisted that corn- and soy-based biofuels provide an energy-efficient alternative to fossil-based fuels. Congress and the US Department of Agriculture have agreed. The country’s primary biofuels policy, the Renewable Fuel Standard, requires that biofuels provide a greenhouse gas reduction over fossil fuels: The law says that ethanol from new plants must deliver a 20 percent reduction in greenhouse gas emissions compared to gasoline. In addition to greenhouse gas reductions, the industry and its allies in Congress have also continued to say that ethanol is a primary mainstay of the rural economy, benefiting communities across the Midwest. But a growing body of research—much of which the industry has tried to debunk and deride—suggests that ethanol actually may not provide the benefits that policies require. It may, in fact, produce more greenhouse gases than the fossil fuels it was intended to replace. Recent research says that biofuel refiners also emit significant amounts of carcinogenic and dangerous substances, including hexane and formaldehyde, in greater amounts than petroleum refineries. The new report points to research saying that increased production of biofuels from corn and soy could actually raise greenhouse gas emissions, largely from carbon emissions linked to clearing land in other countries to compensate for the use of land in the Midwest. On top of that, corn is an especially fertilizer-hungry crop requiring large amounts of nitrogen-based fertilizer, which releases huge amounts of nitrous oxide when it interacts with the soil. American farming is, by far, the largest source of domestic nitrous oxide emissions already—about 50 percent. If biofuel policies lead to expanded production, emissions of this enormously powerful greenhouse gas will likely increase, too. The new report concludes that not only will the expansion of ethanol increase greenhouse gas emissions, but it has also failed to provide the social and financial benefits to Midwestern communities that lawmakers and the industry say it has. (The report defines the Midwest as Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin.) “The benefits from biofuels remain concentrated in the hands of a few,” Leslie-Bole said. “As subsidies flow, so may the trend of farmland consolidation, increasing inaccessibility of farmland in the Midwest, and locking out emerging or low-resource farmers. This means the benefits of biofuels production are flowing to fewer people, while more are left bearing the costs.” New policies being considered in state legislatures and Congress, including additional tax credits and support for biofuel-based aviation fuel, could expand production, potentially causing more land conversion and greenhouse gas emissions, widening the gap between the rural communities and rich agribusinesses at a time when food demand is climbing and, critics say, land should be used to grow food instead. President Donald Trump’s tax cut bill, passed by the House and currently being negotiated in the Senate, would not only extend tax credits for biofuels producers, it specifically excludes calculations of emissions from land conversion when determining what qualifies as a low-emission fuel. The primary biofuels industry trade groups, including Growth Energy and the Renewable Fuels Association, did not respond to Inside Climate News requests for comment or interviews. An employee with the Clean Fuels Alliance America, which represents biodiesel and sustainable aviation fuel producers, not ethanol, said the report vastly overstates the carbon emissions from crop-based fuels by comparing the farmed land to natural landscapes, which no longer exist. They also noted that the impact of soy-based fuels in 2024 was more than $42 billion, providing over 100,000 jobs. “Ten percent of the value of every bushel of soybeans is linked to biomass-based fuel,” they said. Georgina Gustin, Inside Climate News 24 Comments
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  • How addresses are collected and put on people finder sites

    Published
    June 14, 2025 10:00am EDT close Top lawmaker on cybersecurity panel talks threats to US agriculture Senate Armed Services Committee member Mike Rounds, R-S.D., speaks to Fox News Digital NEWYou can now listen to Fox News articles!
    Your home address might be easier to find online than you think. A quick search of your name could turn up past and current locations, all thanks to people finder sites. These data broker sites quietly collect and publish personal details without your consent, making your privacy vulnerable with just a few clicks.Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide — free when you join. A woman searching for herself online.How your address gets exposed online and who’s using itIf you’ve ever searched for your name and found personal details, like your address, on unfamiliar websites, you’re not alone. People finder platforms collect this information from public records and third-party data brokers, then publish and share it widely. They often link your address to other details such as phone numbers, email addresses and even relatives.11 EASY WAYS TO PROTECT YOUR ONLINE PRIVACY IN 2025While this data may already be public in various places, these sites make it far easier to access and monetize it at scale. In one recent breach, more than 183 million login credentials were exposed through an unsecured database. Many of these records were linked to physical addresses, raising concerns about how multiple sources of personal data can be combined and exploited.Although people finder sites claim to help reconnect friends or locate lost contacts, they also make sensitive personal information available to anyone willing to pay. This includes scammers, spammers and identity thieves who use it for fraud, harassment, and targeted scams. A woman searching for herself online.How do people search sites get your home address?First, let’s define two sources of information; public and private databases that people search sites use to get your detailed profile, including your home address. They run an automated search on these databases with key information about you and add your home address from the search results. 1. Public sourcesYour home address can appear in:Property deeds: When you buy or sell a home, your name and address become part of the public record.Voter registration: You need to list your address when voting.Court documents: Addresses appear in legal filings or lawsuits.Marriage and divorce records: These often include current or past addresses.Business licenses and professional registrations: If you own a business or hold a license, your address can be listed.WHAT IS ARTIFICIAL INTELLIGENCE?These records are legal to access, and people finder sites collect and repackage them into detailed personal profiles.2. Private sourcesOther sites buy your data from companies you’ve interacted with:Online purchases: When you buy something online, your address is recorded and can be sold to marketing companies.Subscriptions and memberships: Magazines, clubs and loyalty programs often share your information.Social media platforms: Your location or address details can be gathered indirectly from posts, photos or shared information.Mobile apps and websites: Some apps track your location.People finder sites buy this data from other data brokers and combine it with public records to build complete profiles that include address information. A woman searching for herself online.What are the risks of having your address on people finder sites?The Federal Trade Commissionadvises people to request the removal of their private data, including home addresses, from people search sites due to the associated risks of stalking, scamming and other crimes.People search sites are a goldmine for cybercriminals looking to target and profile potential victims as well as plan comprehensive cyberattacks. Losses due to targeted phishing attacks increased by 33% in 2024, according to the FBI. So, having your home address publicly accessible can lead to several risks:Stalking and harassment: Criminals can easily find your home address and threaten you.Identity theft: Scammers can use your address and other personal information to impersonate you or fraudulently open accounts.Unwanted contact: Marketers and scammers can use your address to send junk mail or phishing or brushing scams.Increased financial risks: Insurance companies or lenders can use publicly available address information to unfairly decide your rates or eligibility.Burglary and home invasion: Criminals can use your location to target your home when you’re away or vulnerable.How to protect your home addressThe good news is that you can take steps to reduce the risks and keep your address private. However, keep in mind that data brokers and people search sites can re-list your information after some time, so you might need to request data removal periodically.I recommend a few ways to delete your private information, including your home address, from such websites.1. Use personal data removal services: Data brokers can sell your home address and other personal data to multiple businesses and individuals, so the key is to act fast. If you’re looking for an easier way to protect your privacy, a data removal service can do the heavy lifting for you, automatically requesting data removal from brokers and tracking compliance.While no service can guarantee the complete removal of your data from the internet, a data removal service is really a smart choice. They aren’t cheap — and neither is your privacy. These services do all the work for you by actively monitoring and systematically erasing your personal information from hundreds of websites. It’s what gives me peace of mind and has proven to be the most effective way to erase your personal data from the internet. By limiting the information available, you reduce the risk of scammers cross-referencing data from breaches with information they might find on the dark web, making it harder for them to target you. Check out my top picks for data removal services here. Get a free scan to find out if your personal information is already out on the web2. Opt out manually : Use a free scanner provided by a data removal service to check which people search sites that list your address. Then, visit each of these websites and look for an opt-out procedure or form: keywords like "opt out," "delete my information," etc., point the way.Follow each site’s opt-out process carefully, and confirm they’ve removed all your personal info, otherwise, it may get relisted.3. Monitor your digital footprint: I recommend regularly searching online for your name to see if your location is publicly available. If only your social media profile pops up, there’s no need to worry. However, people finder sites tend to relist your private information, including your home address, after some time.4. Limit sharing your address online: Be careful about sharing your home address on social media, online forms and apps. Review privacy settings regularly, and only provide your address when absolutely necessary. Also, adjust your phone settings so that apps don’t track your location.Kurt’s key takeawaysYour home address is more vulnerable than you think. People finder sites aggregate data from public records and private sources to display your address online, often without your knowledge or consent. This can lead to serious privacy and safety risks. Taking proactive steps to protect your home address is essential. Do it manually or use a data removal tool for an easier process. By understanding how your location is collected and taking measures to remove your address from online sites, you can reclaim control over your personal data.CLICK HERE TO GET THE FOX NEWS APPHow do you feel about companies making your home address so easy to find? Let us know by writing us at Cyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.
    #how #addresses #are #collected #put
    How addresses are collected and put on people finder sites
    Published June 14, 2025 10:00am EDT close Top lawmaker on cybersecurity panel talks threats to US agriculture Senate Armed Services Committee member Mike Rounds, R-S.D., speaks to Fox News Digital NEWYou can now listen to Fox News articles! Your home address might be easier to find online than you think. A quick search of your name could turn up past and current locations, all thanks to people finder sites. These data broker sites quietly collect and publish personal details without your consent, making your privacy vulnerable with just a few clicks.Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide — free when you join. A woman searching for herself online.How your address gets exposed online and who’s using itIf you’ve ever searched for your name and found personal details, like your address, on unfamiliar websites, you’re not alone. People finder platforms collect this information from public records and third-party data brokers, then publish and share it widely. They often link your address to other details such as phone numbers, email addresses and even relatives.11 EASY WAYS TO PROTECT YOUR ONLINE PRIVACY IN 2025While this data may already be public in various places, these sites make it far easier to access and monetize it at scale. In one recent breach, more than 183 million login credentials were exposed through an unsecured database. Many of these records were linked to physical addresses, raising concerns about how multiple sources of personal data can be combined and exploited.Although people finder sites claim to help reconnect friends or locate lost contacts, they also make sensitive personal information available to anyone willing to pay. This includes scammers, spammers and identity thieves who use it for fraud, harassment, and targeted scams. A woman searching for herself online.How do people search sites get your home address?First, let’s define two sources of information; public and private databases that people search sites use to get your detailed profile, including your home address. They run an automated search on these databases with key information about you and add your home address from the search results. 1. Public sourcesYour home address can appear in:Property deeds: When you buy or sell a home, your name and address become part of the public record.Voter registration: You need to list your address when voting.Court documents: Addresses appear in legal filings or lawsuits.Marriage and divorce records: These often include current or past addresses.Business licenses and professional registrations: If you own a business or hold a license, your address can be listed.WHAT IS ARTIFICIAL INTELLIGENCE?These records are legal to access, and people finder sites collect and repackage them into detailed personal profiles.2. Private sourcesOther sites buy your data from companies you’ve interacted with:Online purchases: When you buy something online, your address is recorded and can be sold to marketing companies.Subscriptions and memberships: Magazines, clubs and loyalty programs often share your information.Social media platforms: Your location or address details can be gathered indirectly from posts, photos or shared information.Mobile apps and websites: Some apps track your location.People finder sites buy this data from other data brokers and combine it with public records to build complete profiles that include address information. A woman searching for herself online.What are the risks of having your address on people finder sites?The Federal Trade Commissionadvises people to request the removal of their private data, including home addresses, from people search sites due to the associated risks of stalking, scamming and other crimes.People search sites are a goldmine for cybercriminals looking to target and profile potential victims as well as plan comprehensive cyberattacks. Losses due to targeted phishing attacks increased by 33% in 2024, according to the FBI. So, having your home address publicly accessible can lead to several risks:Stalking and harassment: Criminals can easily find your home address and threaten you.Identity theft: Scammers can use your address and other personal information to impersonate you or fraudulently open accounts.Unwanted contact: Marketers and scammers can use your address to send junk mail or phishing or brushing scams.Increased financial risks: Insurance companies or lenders can use publicly available address information to unfairly decide your rates or eligibility.Burglary and home invasion: Criminals can use your location to target your home when you’re away or vulnerable.How to protect your home addressThe good news is that you can take steps to reduce the risks and keep your address private. However, keep in mind that data brokers and people search sites can re-list your information after some time, so you might need to request data removal periodically.I recommend a few ways to delete your private information, including your home address, from such websites.1. Use personal data removal services: Data brokers can sell your home address and other personal data to multiple businesses and individuals, so the key is to act fast. If you’re looking for an easier way to protect your privacy, a data removal service can do the heavy lifting for you, automatically requesting data removal from brokers and tracking compliance.While no service can guarantee the complete removal of your data from the internet, a data removal service is really a smart choice. They aren’t cheap — and neither is your privacy. These services do all the work for you by actively monitoring and systematically erasing your personal information from hundreds of websites. It’s what gives me peace of mind and has proven to be the most effective way to erase your personal data from the internet. By limiting the information available, you reduce the risk of scammers cross-referencing data from breaches with information they might find on the dark web, making it harder for them to target you. Check out my top picks for data removal services here. Get a free scan to find out if your personal information is already out on the web2. Opt out manually : Use a free scanner provided by a data removal service to check which people search sites that list your address. Then, visit each of these websites and look for an opt-out procedure or form: keywords like "opt out," "delete my information," etc., point the way.Follow each site’s opt-out process carefully, and confirm they’ve removed all your personal info, otherwise, it may get relisted.3. Monitor your digital footprint: I recommend regularly searching online for your name to see if your location is publicly available. If only your social media profile pops up, there’s no need to worry. However, people finder sites tend to relist your private information, including your home address, after some time.4. Limit sharing your address online: Be careful about sharing your home address on social media, online forms and apps. Review privacy settings regularly, and only provide your address when absolutely necessary. Also, adjust your phone settings so that apps don’t track your location.Kurt’s key takeawaysYour home address is more vulnerable than you think. People finder sites aggregate data from public records and private sources to display your address online, often without your knowledge or consent. This can lead to serious privacy and safety risks. Taking proactive steps to protect your home address is essential. Do it manually or use a data removal tool for an easier process. By understanding how your location is collected and taking measures to remove your address from online sites, you can reclaim control over your personal data.CLICK HERE TO GET THE FOX NEWS APPHow do you feel about companies making your home address so easy to find? Let us know by writing us at Cyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com. #how #addresses #are #collected #put
    WWW.FOXNEWS.COM
    How addresses are collected and put on people finder sites
    Published June 14, 2025 10:00am EDT close Top lawmaker on cybersecurity panel talks threats to US agriculture Senate Armed Services Committee member Mike Rounds, R-S.D., speaks to Fox News Digital NEWYou can now listen to Fox News articles! Your home address might be easier to find online than you think. A quick search of your name could turn up past and current locations, all thanks to people finder sites. These data broker sites quietly collect and publish personal details without your consent, making your privacy vulnerable with just a few clicks.Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide — free when you join. A woman searching for herself online. (Kurt "CyberGuy" Knutsson)How your address gets exposed online and who’s using itIf you’ve ever searched for your name and found personal details, like your address, on unfamiliar websites, you’re not alone. People finder platforms collect this information from public records and third-party data brokers, then publish and share it widely. They often link your address to other details such as phone numbers, email addresses and even relatives.11 EASY WAYS TO PROTECT YOUR ONLINE PRIVACY IN 2025While this data may already be public in various places, these sites make it far easier to access and monetize it at scale. In one recent breach, more than 183 million login credentials were exposed through an unsecured database. Many of these records were linked to physical addresses, raising concerns about how multiple sources of personal data can be combined and exploited.Although people finder sites claim to help reconnect friends or locate lost contacts, they also make sensitive personal information available to anyone willing to pay. This includes scammers, spammers and identity thieves who use it for fraud, harassment, and targeted scams. A woman searching for herself online. (Kurt "CyberGuy" Knutsson)How do people search sites get your home address?First, let’s define two sources of information; public and private databases that people search sites use to get your detailed profile, including your home address. They run an automated search on these databases with key information about you and add your home address from the search results. 1. Public sourcesYour home address can appear in:Property deeds: When you buy or sell a home, your name and address become part of the public record.Voter registration: You need to list your address when voting.Court documents: Addresses appear in legal filings or lawsuits.Marriage and divorce records: These often include current or past addresses.Business licenses and professional registrations: If you own a business or hold a license, your address can be listed.WHAT IS ARTIFICIAL INTELLIGENCE (AI)?These records are legal to access, and people finder sites collect and repackage them into detailed personal profiles.2. Private sourcesOther sites buy your data from companies you’ve interacted with:Online purchases: When you buy something online, your address is recorded and can be sold to marketing companies.Subscriptions and memberships: Magazines, clubs and loyalty programs often share your information.Social media platforms: Your location or address details can be gathered indirectly from posts, photos or shared information.Mobile apps and websites: Some apps track your location.People finder sites buy this data from other data brokers and combine it with public records to build complete profiles that include address information. A woman searching for herself online. (Kurt "CyberGuy" Knutsson)What are the risks of having your address on people finder sites?The Federal Trade Commission (FTC) advises people to request the removal of their private data, including home addresses, from people search sites due to the associated risks of stalking, scamming and other crimes.People search sites are a goldmine for cybercriminals looking to target and profile potential victims as well as plan comprehensive cyberattacks. Losses due to targeted phishing attacks increased by 33% in 2024, according to the FBI. So, having your home address publicly accessible can lead to several risks:Stalking and harassment: Criminals can easily find your home address and threaten you.Identity theft: Scammers can use your address and other personal information to impersonate you or fraudulently open accounts.Unwanted contact: Marketers and scammers can use your address to send junk mail or phishing or brushing scams.Increased financial risks: Insurance companies or lenders can use publicly available address information to unfairly decide your rates or eligibility.Burglary and home invasion: Criminals can use your location to target your home when you’re away or vulnerable.How to protect your home addressThe good news is that you can take steps to reduce the risks and keep your address private. However, keep in mind that data brokers and people search sites can re-list your information after some time, so you might need to request data removal periodically.I recommend a few ways to delete your private information, including your home address, from such websites.1. Use personal data removal services: Data brokers can sell your home address and other personal data to multiple businesses and individuals, so the key is to act fast. If you’re looking for an easier way to protect your privacy, a data removal service can do the heavy lifting for you, automatically requesting data removal from brokers and tracking compliance.While no service can guarantee the complete removal of your data from the internet, a data removal service is really a smart choice. They aren’t cheap — and neither is your privacy. These services do all the work for you by actively monitoring and systematically erasing your personal information from hundreds of websites. It’s what gives me peace of mind and has proven to be the most effective way to erase your personal data from the internet. By limiting the information available, you reduce the risk of scammers cross-referencing data from breaches with information they might find on the dark web, making it harder for them to target you. Check out my top picks for data removal services here. Get a free scan to find out if your personal information is already out on the web2. Opt out manually : Use a free scanner provided by a data removal service to check which people search sites that list your address. Then, visit each of these websites and look for an opt-out procedure or form: keywords like "opt out," "delete my information," etc., point the way.Follow each site’s opt-out process carefully, and confirm they’ve removed all your personal info, otherwise, it may get relisted.3. Monitor your digital footprint: I recommend regularly searching online for your name to see if your location is publicly available. If only your social media profile pops up, there’s no need to worry. However, people finder sites tend to relist your private information, including your home address, after some time.4. Limit sharing your address online: Be careful about sharing your home address on social media, online forms and apps. Review privacy settings regularly, and only provide your address when absolutely necessary. Also, adjust your phone settings so that apps don’t track your location.Kurt’s key takeawaysYour home address is more vulnerable than you think. People finder sites aggregate data from public records and private sources to display your address online, often without your knowledge or consent. This can lead to serious privacy and safety risks. Taking proactive steps to protect your home address is essential. Do it manually or use a data removal tool for an easier process. By understanding how your location is collected and taking measures to remove your address from online sites, you can reclaim control over your personal data.CLICK HERE TO GET THE FOX NEWS APPHow do you feel about companies making your home address so easy to find? Let us know by writing us at Cyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.
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