• Sony is taking a stand against the sheer audacity of Tencent, suing them over the upcoming game "Light of Motiram," which they dare to call a "slavish clone" of the beloved "Horizon: Zero Dawn." This isn't just a case of resemblance; it's a blatant theft of creativity and innovation! How can we sit back and watch a corporate behemoth like Tencent, known for its aggressive expansion and questionable practices, rip off the hard work of another company? We must demand accountability in the gaming industry! Enough is enough—no more allowing these giants to trample on originality. It’s time for gamers to stand up and support those who create, not those who copy!

    #Sony #Tencent #HorizonZeroD
    Sony is taking a stand against the sheer audacity of Tencent, suing them over the upcoming game "Light of Motiram," which they dare to call a "slavish clone" of the beloved "Horizon: Zero Dawn." This isn't just a case of resemblance; it's a blatant theft of creativity and innovation! How can we sit back and watch a corporate behemoth like Tencent, known for its aggressive expansion and questionable practices, rip off the hard work of another company? We must demand accountability in the gaming industry! Enough is enough—no more allowing these giants to trample on originality. It’s time for gamers to stand up and support those who create, not those who copy! #Sony #Tencent #HorizonZeroD
    KOTAKU.COM
    Sony Suing Tencent Over Upcoming Game It Calls A 'Slavish Clone' Of Horizon: Zero Dawn
    Remember Light of Motiram, that Tencent game that was announced in late 2024 that looked a lot like Horizon: Zero Dawn? Well, Sony Interactive is suing the massive video game publisher and developer for copyright and trademark infringement over that
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  • LibreOffice Explains 'Real Costs' of Upgrading to Microsoft's Windows 11, Urges Taking Control with Linux

    KDE isn't the only organization reaching out to " as Microsoft prepares to end support for Windows 10.

    "Now, The Document Foundation, maker of LibreOffice, has also joined in to support the Endof10 initiative," reports the tech blog Neowin:
    The foundation writes: "You don't have to follow Microsoft's upgrade path. There is a better option that puts control back in the hands of users, institutions, and public bodies: Linux and LibreOffice. Together, these two programmes offer a powerful, privacy-friendly and future-proof alternative to the Windows + Microsoft 365 ecosystem."

    It further adds the "real costs" of upgrading to Windows 11 as it writes:

    "The move to Windows 11 isn't just about security updates. It increases dependence on Microsoft through aggressive cloud integration, forcing users to adopt Microsoft accounts and services. It also leads to higher costs due to subscription and licensing models, and reduces control over how your computer works and how your data is managed. Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.... The end of Windows 10 does not mark the end of choice, but the beginning of a new era. If you are tired of mandatory updates, invasive changes, and being bound by the commercial choices of a single supplier, it is time for a change. Linux and LibreOffice are ready — 2025 is the right year to choose digital freedom!"
    The first words on LibreOffice's announcement? "The countdown has begun...."

    of this story at Slashdot.
    #libreoffice #explains #039real #costs039 #upgrading
    LibreOffice Explains 'Real Costs' of Upgrading to Microsoft's Windows 11, Urges Taking Control with Linux
    KDE isn't the only organization reaching out to " as Microsoft prepares to end support for Windows 10. "Now, The Document Foundation, maker of LibreOffice, has also joined in to support the Endof10 initiative," reports the tech blog Neowin: The foundation writes: "You don't have to follow Microsoft's upgrade path. There is a better option that puts control back in the hands of users, institutions, and public bodies: Linux and LibreOffice. Together, these two programmes offer a powerful, privacy-friendly and future-proof alternative to the Windows + Microsoft 365 ecosystem." It further adds the "real costs" of upgrading to Windows 11 as it writes: "The move to Windows 11 isn't just about security updates. It increases dependence on Microsoft through aggressive cloud integration, forcing users to adopt Microsoft accounts and services. It also leads to higher costs due to subscription and licensing models, and reduces control over how your computer works and how your data is managed. Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.... The end of Windows 10 does not mark the end of choice, but the beginning of a new era. If you are tired of mandatory updates, invasive changes, and being bound by the commercial choices of a single supplier, it is time for a change. Linux and LibreOffice are ready — 2025 is the right year to choose digital freedom!" The first words on LibreOffice's announcement? "The countdown has begun...." of this story at Slashdot. #libreoffice #explains #039real #costs039 #upgrading
    TECH.SLASHDOT.ORG
    LibreOffice Explains 'Real Costs' of Upgrading to Microsoft's Windows 11, Urges Taking Control with Linux
    KDE isn't the only organization reaching out to " as Microsoft prepares to end support for Windows 10. "Now, The Document Foundation, maker of LibreOffice, has also joined in to support the Endof10 initiative," reports the tech blog Neowin: The foundation writes: "You don't have to follow Microsoft's upgrade path. There is a better option that puts control back in the hands of users, institutions, and public bodies: Linux and LibreOffice. Together, these two programmes offer a powerful, privacy-friendly and future-proof alternative to the Windows + Microsoft 365 ecosystem." It further adds the "real costs" of upgrading to Windows 11 as it writes: "The move to Windows 11 isn't just about security updates. It increases dependence on Microsoft through aggressive cloud integration, forcing users to adopt Microsoft accounts and services. It also leads to higher costs due to subscription and licensing models, and reduces control over how your computer works and how your data is managed. Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.... The end of Windows 10 does not mark the end of choice, but the beginning of a new era. If you are tired of mandatory updates, invasive changes, and being bound by the commercial choices of a single supplier, it is time for a change. Linux and LibreOffice are ready — 2025 is the right year to choose digital freedom!" The first words on LibreOffice's announcement? "The countdown has begun...." Read more of this story at Slashdot.
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  • Can Sonic Racing: CrossWorlds Outrun Mario Kart World?

    Mario Kart World is one of the year's hottest games, but its pivot to an open world setting, while peeling back kart customization options, opened a massive rift for Sonic Racing: CrossWorlds to drift into. And Sega is determined to do everything possible to make its kart racer the one to beat by including numerous guest characters and cross-platform, multiplayer contests. I took Sonic Racing: CrossWorlds for a test drive at the Summer Game Fest, and it's a strong contender racing game of the year.Sonic Racing: CrossWorlds' Deep Kart CustomizationThe biggest difference between Sonic Racing: CrossWorlds and Mario Kart World is that Sega's title focuses on kart customization. I'm not just talking about colors and tires; CrossWorlds introduces Gadgets, add-ons that augment your car, giving your whip helpful abilities to bring into the race. Each ride has a license plate with six slots where you can slot your chosen Gadgets. A Gadget can take up one, two, or three slots, so the idea is to find a mix that pairs well with character traits. There's a surprising amount of depth for people who want to min/max their favorite anthropomorphic animal.I chose Sonic, a speed character, and added a Gadget that started him with two boosts, a Gadget that improved his speed while trailing an opponent, and a Gadget that improved acceleration. There were so many Gadgets that I could have easily spent my entire demo session building a car to match my playstyle. I envision people happily getting lost in the weeds before participating in their first race.Gameplay: This Ain't Mario Kart WorldAlthough it's not an open world like Mario Kart World, Sonic Racing: CrossWorlds injects a unique spin on traditional kart racing. The familiar trappings are all here, such as rings to boost your top speed. Each Grand Prix consists of three maps, but the gimmick at play is stage transitions. Recommended by Our EditorsAbout a third of the way down a course, a giant ring-portal opens, presenting a new world and track. The shift in tone and terrain keeps the races fast-paced and unpredictable. I particularly liked how whoever is in first place can sometimes choose which CrossWorlds track to go down, controlling the tempo. With every race completion, you earn credits based on your performance that you can cash in for new car parts.In a stark contrast to Mario Kart World, Sonic Racing: CrossWorlds is far more aggressive, even on lower difficulties. At the start of each grand prix, the game assigns you a rival—this is the character to beat, and the one who taunts you all match. Beat them all, and you can race high-powered Super variants.Just about everything caused you to lose rings: bumping into other players, the walls, and, of course, getting hit by items. The series' trademark rubberband AI is still in place, too. Even in the press demo, I wasn't safe from taking four items back to back and being knocked off the stage mere feet away from the finish line.The demo didn't include the new characters that debuted at the Summer Game Fest, but I studied the character screen to see who else could be coming to the game. Including the 12 Sonic characters available in the demo, I counted a whopping 64 character slots. They include Hatsune Miku, Joker, Ichiban Kasuga, and Steve. However, I hope to see other classic Sega IPs like in previous Sonic Racing titles.Platforms and Release DateWill Sega do what Nintendon't? I had an exhilarating time playing Sonic Racing: CrossWorld, and I can't wait to see more wild track compositions. Sonic Racing: CrossWorlds will be available on Nintendo Switch, PC, PlayStation 4, PlayStation 5, Xbox One, and Xbox Series X/S on Sept. 25, 2025. A Nintendo Switch 2 version is planned for later in the year.
    #can #sonic #racing #crossworlds #outrun
    Can Sonic Racing: CrossWorlds Outrun Mario Kart World?
    Mario Kart World is one of the year's hottest games, but its pivot to an open world setting, while peeling back kart customization options, opened a massive rift for Sonic Racing: CrossWorlds to drift into. And Sega is determined to do everything possible to make its kart racer the one to beat by including numerous guest characters and cross-platform, multiplayer contests. I took Sonic Racing: CrossWorlds for a test drive at the Summer Game Fest, and it's a strong contender racing game of the year.Sonic Racing: CrossWorlds' Deep Kart CustomizationThe biggest difference between Sonic Racing: CrossWorlds and Mario Kart World is that Sega's title focuses on kart customization. I'm not just talking about colors and tires; CrossWorlds introduces Gadgets, add-ons that augment your car, giving your whip helpful abilities to bring into the race. Each ride has a license plate with six slots where you can slot your chosen Gadgets. A Gadget can take up one, two, or three slots, so the idea is to find a mix that pairs well with character traits. There's a surprising amount of depth for people who want to min/max their favorite anthropomorphic animal.I chose Sonic, a speed character, and added a Gadget that started him with two boosts, a Gadget that improved his speed while trailing an opponent, and a Gadget that improved acceleration. There were so many Gadgets that I could have easily spent my entire demo session building a car to match my playstyle. I envision people happily getting lost in the weeds before participating in their first race.Gameplay: This Ain't Mario Kart WorldAlthough it's not an open world like Mario Kart World, Sonic Racing: CrossWorlds injects a unique spin on traditional kart racing. The familiar trappings are all here, such as rings to boost your top speed. Each Grand Prix consists of three maps, but the gimmick at play is stage transitions. Recommended by Our EditorsAbout a third of the way down a course, a giant ring-portal opens, presenting a new world and track. The shift in tone and terrain keeps the races fast-paced and unpredictable. I particularly liked how whoever is in first place can sometimes choose which CrossWorlds track to go down, controlling the tempo. With every race completion, you earn credits based on your performance that you can cash in for new car parts.In a stark contrast to Mario Kart World, Sonic Racing: CrossWorlds is far more aggressive, even on lower difficulties. At the start of each grand prix, the game assigns you a rival—this is the character to beat, and the one who taunts you all match. Beat them all, and you can race high-powered Super variants.Just about everything caused you to lose rings: bumping into other players, the walls, and, of course, getting hit by items. The series' trademark rubberband AI is still in place, too. Even in the press demo, I wasn't safe from taking four items back to back and being knocked off the stage mere feet away from the finish line.The demo didn't include the new characters that debuted at the Summer Game Fest, but I studied the character screen to see who else could be coming to the game. Including the 12 Sonic characters available in the demo, I counted a whopping 64 character slots. They include Hatsune Miku, Joker, Ichiban Kasuga, and Steve. However, I hope to see other classic Sega IPs like in previous Sonic Racing titles.Platforms and Release DateWill Sega do what Nintendon't? I had an exhilarating time playing Sonic Racing: CrossWorld, and I can't wait to see more wild track compositions. Sonic Racing: CrossWorlds will be available on Nintendo Switch, PC, PlayStation 4, PlayStation 5, Xbox One, and Xbox Series X/S on Sept. 25, 2025. A Nintendo Switch 2 version is planned for later in the year. #can #sonic #racing #crossworlds #outrun
    ME.PCMAG.COM
    Can Sonic Racing: CrossWorlds Outrun Mario Kart World?
    Mario Kart World is one of the year's hottest games, but its pivot to an open world setting, while peeling back kart customization options, opened a massive rift for Sonic Racing: CrossWorlds to drift into. And Sega is determined to do everything possible to make its kart racer the one to beat by including numerous guest characters and cross-platform, multiplayer contests. I took Sonic Racing: CrossWorlds for a test drive at the Summer Game Fest, and it's a strong contender racing game of the year.Sonic Racing: CrossWorlds' Deep Kart CustomizationThe biggest difference between Sonic Racing: CrossWorlds and Mario Kart World is that Sega's title focuses on kart customization. I'm not just talking about colors and tires; CrossWorlds introduces Gadgets, add-ons that augment your car, giving your whip helpful abilities to bring into the race. (Credit: Sega)Each ride has a license plate with six slots where you can slot your chosen Gadgets. A Gadget can take up one, two, or three slots, so the idea is to find a mix that pairs well with character traits. There's a surprising amount of depth for people who want to min/max their favorite anthropomorphic animal.I chose Sonic, a speed character, and added a Gadget that started him with two boosts (one slot), a Gadget that improved his speed while trailing an opponent (two slots), and a Gadget that improved acceleration (three slots). There were so many Gadgets that I could have easily spent my entire demo session building a car to match my playstyle. I envision people happily getting lost in the weeds before participating in their first race.(Credit: Sega)Gameplay: This Ain't Mario Kart WorldAlthough it's not an open world like Mario Kart World, Sonic Racing: CrossWorlds injects a unique spin on traditional kart racing. The familiar trappings are all here, such as rings to boost your top speed. Each Grand Prix consists of three maps, but the gimmick at play is stage transitions. Recommended by Our EditorsAbout a third of the way down a course, a giant ring-portal opens, presenting a new world and track (hence the name "CrossWorlds"). The shift in tone and terrain keeps the races fast-paced and unpredictable. I particularly liked how whoever is in first place can sometimes choose which CrossWorlds track to go down, controlling the tempo. With every race completion, you earn credits based on your performance that you can cash in for new car parts.In a stark contrast to Mario Kart World, Sonic Racing: CrossWorlds is far more aggressive, even on lower difficulties. At the start of each grand prix, the game assigns you a rival—this is the character to beat, and the one who taunts you all match. Beat them all, and you can race high-powered Super variants.Just about everything caused you to lose rings: bumping into other players, the walls, and, of course, getting hit by items. The series' trademark rubberband AI is still in place, too. Even in the press demo, I wasn't safe from taking four items back to back and being knocked off the stage mere feet away from the finish line.(Credit: Sega)The demo didn't include the new characters that debuted at the Summer Game Fest, but I studied the character screen to see who else could be coming to the game. Including the 12 Sonic characters available in the demo, I counted a whopping 64 character slots. They include Hatsune Miku (the ultra-popular Vocaloid), Joker (from Persona 5), Ichiban Kasuga (from Like a Dragon), and Steve (from Minecraft). However, I hope to see other classic Sega IPs like in previous Sonic Racing titles.Platforms and Release DateWill Sega do what Nintendon't? I had an exhilarating time playing Sonic Racing: CrossWorld, and I can't wait to see more wild track compositions. Sonic Racing: CrossWorlds will be available on Nintendo Switch, PC, PlayStation 4, PlayStation 5, Xbox One, and Xbox Series X/S on Sept. 25, 2025. A Nintendo Switch 2 version is planned for later in the year.
    0 Yorumlar 0 hisse senetleri 0 önizleme
  • 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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  • My unexpected Pride icon: Link from the Zelda games, a non-binary hero who helped me work out who I was

    Growing up steeped in the aggressive gender stereotypes of the 1990s was a real trip for most queer millennials, but I think gamers had it especially hard. Almost all video game characters were hypermasculine military men, unrealistically curvaceous fantasy women wearing barely enough armour to cover their nipples, or cartoon animals. Most of these characters catered exclusively to straight teenage boys; overt queer representation in games was pretty much nonexistent until the mid 2010s. Before that, we had to take what we could get. And what I had was Link, from The Legend of Zelda.Link. Composite: Guardian Design; Zuma Press/AlamyLink is a boy, but he didn’t really look like one. He wore a green tunic and a serious expression under a mop of blond hair. He is the adventurous, mostly silent hero of the Zelda games, unassuming and often vulnerable, but also resourceful, daring and handy with a sword. In most of the early Zelda games, he is a kid of about 10, but even when he grew into a teenager in 1998’s Ocarina of Time on the Nintendo 64, he didn’t become a furious lump of muscle. He stayed androgynous, in his tunic and tights. As a kid, I would dress up like him for Halloween, carefully centre-parting my blond fringe. Link may officially be a boy, but for me he has always been a non-binary icon.As time has gone on and game graphics have evolved, Link has stayed somewhat gender-ambiguous. Gay guys and gender-fluid types alike appreciate his ageless twink energy. And given the total lack of thought that most game developers gave to players who weren’t straight and male, I felt vindicated when I found out that this was intentional. In 2016, the Zelda series’ producer Eiji Aonuma told Time magazine that the development team had experimented a little with Link’s gender presentation over the years, but that he felt that the character’s androgyny was part of who he was.“back during the Ocarina of Time days, I wanted Link to be gender neutral,” he said. “I wanted the player to think: ‘Maybe Link is a boy or a girl.’ If you saw Link as a guy, he’d have more of a feminine touch. Or vice versa … I’ve always thought that for either female or male players, I wanted them to be able to relate to Link.”As it turns out, Link appeals perhaps most of all to those of us somewhere in between. In 2023, the tech blog io9 spoke to many transgender and non-binary people who saw something of themselves in Link: he has acquired a reputation as an egg-cracker, a fictional character who prompts a realisation about your own gender identity.Despite their outdated reputation as a pursuit for adolescent boys, video games have always been playgrounds for gender experimentation and expression. There are legions of trans, non-binary and gender non-conforming people who first started exploring their identity with customisable game characters in World of Warcraft, or gender-swapping themselves in The Sims – the digital equivalent of dressing up. Video games are the closest you can come to stepping into a new body for a bit and seeing how it feels.It is no surprise to me that a lot of queer people are drawn to video games. A 2024 survey by GLAAD found that 17% of gamers identify as LGBTQ+, a huge number compared with the general population. It may be because people who play games skew younger – 40 and below – but I also think it’s because gender is all about play. What fun it is to mess with the rules, subvert people’s expectations and create your own character. It is as empowering as any world-saving quest.
    #unexpected #pride #icon #link #zelda
    My unexpected Pride icon: Link from the Zelda games, a non-binary hero who helped me work out who I was
    Growing up steeped in the aggressive gender stereotypes of the 1990s was a real trip for most queer millennials, but I think gamers had it especially hard. Almost all video game characters were hypermasculine military men, unrealistically curvaceous fantasy women wearing barely enough armour to cover their nipples, or cartoon animals. Most of these characters catered exclusively to straight teenage boys; overt queer representation in games was pretty much nonexistent until the mid 2010s. Before that, we had to take what we could get. And what I had was Link, from The Legend of Zelda.Link. Composite: Guardian Design; Zuma Press/AlamyLink is a boy, but he didn’t really look like one. He wore a green tunic and a serious expression under a mop of blond hair. He is the adventurous, mostly silent hero of the Zelda games, unassuming and often vulnerable, but also resourceful, daring and handy with a sword. In most of the early Zelda games, he is a kid of about 10, but even when he grew into a teenager in 1998’s Ocarina of Time on the Nintendo 64, he didn’t become a furious lump of muscle. He stayed androgynous, in his tunic and tights. As a kid, I would dress up like him for Halloween, carefully centre-parting my blond fringe. Link may officially be a boy, but for me he has always been a non-binary icon.As time has gone on and game graphics have evolved, Link has stayed somewhat gender-ambiguous. Gay guys and gender-fluid types alike appreciate his ageless twink energy. And given the total lack of thought that most game developers gave to players who weren’t straight and male, I felt vindicated when I found out that this was intentional. In 2016, the Zelda series’ producer Eiji Aonuma told Time magazine that the development team had experimented a little with Link’s gender presentation over the years, but that he felt that the character’s androgyny was part of who he was.“back during the Ocarina of Time days, I wanted Link to be gender neutral,” he said. “I wanted the player to think: ‘Maybe Link is a boy or a girl.’ If you saw Link as a guy, he’d have more of a feminine touch. Or vice versa … I’ve always thought that for either female or male players, I wanted them to be able to relate to Link.”As it turns out, Link appeals perhaps most of all to those of us somewhere in between. In 2023, the tech blog io9 spoke to many transgender and non-binary people who saw something of themselves in Link: he has acquired a reputation as an egg-cracker, a fictional character who prompts a realisation about your own gender identity.Despite their outdated reputation as a pursuit for adolescent boys, video games have always been playgrounds for gender experimentation and expression. There are legions of trans, non-binary and gender non-conforming people who first started exploring their identity with customisable game characters in World of Warcraft, or gender-swapping themselves in The Sims – the digital equivalent of dressing up. Video games are the closest you can come to stepping into a new body for a bit and seeing how it feels.It is no surprise to me that a lot of queer people are drawn to video games. A 2024 survey by GLAAD found that 17% of gamers identify as LGBTQ+, a huge number compared with the general population. It may be because people who play games skew younger – 40 and below – but I also think it’s because gender is all about play. What fun it is to mess with the rules, subvert people’s expectations and create your own character. It is as empowering as any world-saving quest. #unexpected #pride #icon #link #zelda
    WWW.THEGUARDIAN.COM
    My unexpected Pride icon: Link from the Zelda games, a non-binary hero who helped me work out who I was
    Growing up steeped in the aggressive gender stereotypes of the 1990s was a real trip for most queer millennials, but I think gamers had it especially hard. Almost all video game characters were hypermasculine military men, unrealistically curvaceous fantasy women wearing barely enough armour to cover their nipples, or cartoon animals. Most of these characters catered exclusively to straight teenage boys (or, I guess, furries); overt queer representation in games was pretty much nonexistent until the mid 2010s. Before that, we had to take what we could get. And what I had was Link, from The Legend of Zelda.Link. Composite: Guardian Design; Zuma Press/AlamyLink is a boy, but he didn’t really look like one. He wore a green tunic and a serious expression under a mop of blond hair. He is the adventurous, mostly silent hero of the Zelda games, unassuming and often vulnerable, but also resourceful, daring and handy with a sword. In most of the early Zelda games, he is a kid of about 10, but even when he grew into a teenager in 1998’s Ocarina of Time on the Nintendo 64, he didn’t become a furious lump of muscle. He stayed androgynous, in his tunic and tights. As a kid, I would dress up like him for Halloween, carefully centre-parting my blond fringe. Link may officially be a boy, but for me he has always been a non-binary icon.As time has gone on and game graphics have evolved, Link has stayed somewhat gender-ambiguous. Gay guys and gender-fluid types alike appreciate his ageless twink energy. And given the total lack of thought that most game developers gave to players who weren’t straight and male, I felt vindicated when I found out that this was intentional. In 2016, the Zelda series’ producer Eiji Aonuma told Time magazine that the development team had experimented a little with Link’s gender presentation over the years, but that he felt that the character’s androgyny was part of who he was.“[Even] back during the Ocarina of Time days, I wanted Link to be gender neutral,” he said. “I wanted the player to think: ‘Maybe Link is a boy or a girl.’ If you saw Link as a guy, he’d have more of a feminine touch. Or vice versa … I’ve always thought that for either female or male players, I wanted them to be able to relate to Link.”As it turns out, Link appeals perhaps most of all to those of us somewhere in between. In 2023, the tech blog io9 spoke to many transgender and non-binary people who saw something of themselves in Link: he has acquired a reputation as an egg-cracker, a fictional character who prompts a realisation about your own gender identity.Despite their outdated reputation as a pursuit for adolescent boys, video games have always been playgrounds for gender experimentation and expression. There are legions of trans, non-binary and gender non-conforming people who first started exploring their identity with customisable game characters in World of Warcraft, or gender-swapping themselves in The Sims – the digital equivalent of dressing up. Video games are the closest you can come to stepping into a new body for a bit and seeing how it feels.It is no surprise to me that a lot of queer people are drawn to video games. A 2024 survey by GLAAD found that 17% of gamers identify as LGBTQ+, a huge number compared with the general population. It may be because people who play games skew younger – 40 and below – but I also think it’s because gender is all about play. What fun it is to mess with the rules, subvert people’s expectations and create your own character. It is as empowering as any world-saving quest.
    0 Yorumlar 0 hisse senetleri 0 önizleme
  • 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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  • Humpback Whales Are Approaching People to Blow Rings. What Are They Trying to Say?

    A bubble ring created by a humpback whale named Thorn. Image © Dan Knaub, The Video Company
    Humpback Whales Are Approaching People to Blow Rings. What Are They Trying to Say?
    June 13, 2025
    NatureSocial Issues
    Grace Ebert

    After the “orca uprising” captivated anti-capitalists around the world in 2023, scientists are intrigued by another form of marine mammal communication.
    A study released this month by the SETI Institute and the University of California at Davis dives into a newly documented phenomenon of humpback whales blowing bubble rings while interacting with humans. In contrast to the orcas’ aggressive behavior, researchers say the humpbacks appear to be friendly, relaxed, and even curious.
    Bubbles aren’t new to these aquatic giants, which typically release various shapes when corraling prey and courting mates. This study follows 12 distinct incidents involving 11 whales producing 39 rings, most of which have approached boats near Hawaii, the Dominican Republic, Mo’orea, and the U.S. Atlantic coast on their own.
    The impact of this research reaches far beyond the oceans, though. Deciphering these non-verbal messages could aid in potential extraterrestrial communication, as they can help to “develop filters that aid in parsing cosmic signals for signs of extraterrestrial life,” a statement says.
    “Because of current limitations on technology, an important assumption of the search for extraterrestrial intelligence is that extraterrestrial intelligence and life will be interested in making contact and so target human receivers,” said Dr. Laurance Doyle, a SETI Institute scientist who co-wrote the paper. “This important assumption is certainly supported by the independent evolution of curious behavior in humpback whales.”A composite image of at least one bubble ring from each interaction
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    #humpback #whales #are #approaching #people
    Humpback Whales Are Approaching People to Blow Rings. What Are They Trying to Say?
    A bubble ring created by a humpback whale named Thorn. Image © Dan Knaub, The Video Company Humpback Whales Are Approaching People to Blow Rings. What Are They Trying to Say? June 13, 2025 NatureSocial Issues Grace Ebert After the “orca uprising” captivated anti-capitalists around the world in 2023, scientists are intrigued by another form of marine mammal communication. A study released this month by the SETI Institute and the University of California at Davis dives into a newly documented phenomenon of humpback whales blowing bubble rings while interacting with humans. In contrast to the orcas’ aggressive behavior, researchers say the humpbacks appear to be friendly, relaxed, and even curious. Bubbles aren’t new to these aquatic giants, which typically release various shapes when corraling prey and courting mates. This study follows 12 distinct incidents involving 11 whales producing 39 rings, most of which have approached boats near Hawaii, the Dominican Republic, Mo’orea, and the U.S. Atlantic coast on their own. The impact of this research reaches far beyond the oceans, though. Deciphering these non-verbal messages could aid in potential extraterrestrial communication, as they can help to “develop filters that aid in parsing cosmic signals for signs of extraterrestrial life,” a statement says. “Because of current limitations on technology, an important assumption of the search for extraterrestrial intelligence is that extraterrestrial intelligence and life will be interested in making contact and so target human receivers,” said Dr. Laurance Doyle, a SETI Institute scientist who co-wrote the paper. “This important assumption is certainly supported by the independent evolution of curious behavior in humpback whales.”A composite image of at least one bubble ring from each interaction Previous articleNext article #humpback #whales #are #approaching #people
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    Humpback Whales Are Approaching People to Blow Rings. What Are They Trying to Say?
    A bubble ring created by a humpback whale named Thorn. Image © Dan Knaub, The Video Company Humpback Whales Are Approaching People to Blow Rings. What Are They Trying to Say? June 13, 2025 NatureSocial Issues Grace Ebert After the “orca uprising” captivated anti-capitalists around the world in 2023, scientists are intrigued by another form of marine mammal communication. A study released this month by the SETI Institute and the University of California at Davis dives into a newly documented phenomenon of humpback whales blowing bubble rings while interacting with humans. In contrast to the orcas’ aggressive behavior, researchers say the humpbacks appear to be friendly, relaxed, and even curious. Bubbles aren’t new to these aquatic giants, which typically release various shapes when corraling prey and courting mates. This study follows 12 distinct incidents involving 11 whales producing 39 rings, most of which have approached boats near Hawaii, the Dominican Republic, Mo’orea, and the U.S. Atlantic coast on their own. The impact of this research reaches far beyond the oceans, though. Deciphering these non-verbal messages could aid in potential extraterrestrial communication, as they can help to “develop filters that aid in parsing cosmic signals for signs of extraterrestrial life,” a statement says. “Because of current limitations on technology, an important assumption of the search for extraterrestrial intelligence is that extraterrestrial intelligence and life will be interested in making contact and so target human receivers,” said Dr. Laurance Doyle, a SETI Institute scientist who co-wrote the paper. “This important assumption is certainly supported by the independent evolution of curious behavior in humpback whales.” (via PetaPixel) A composite image of at least one bubble ring from each interaction Previous articleNext article
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  • Call of Duty bosses offer top gunplay tips for Ballerina movie fans in Black Ops 6

    Activision has included movie assassin Ballerina in the latest multiplayer version of Black Ops 6 and here they are exclusively offering Daily Star readers their top tips to become a gun heroTech17:04, 13 Jun 2025Ballerina in Call of DutyJohn Wick movie Ballerina has take over Call of Duty and the bods behind the game have given us some top tips on how to become the shooter king.The movie follows actress Ana de Armas as protagonist badass assassin Eve Macarro as she takes on all kinds of baddies alongside Keanu Reeve’s anti-hero John Wick.‌And to celebrate the silver screen success, Activision has included Eve, aka Ballerina, into Black Ops 6 as a playable online character cmplete with her own guns, finishing move and load screen as a downloadable add-on.‌But how can you, like Ana herself, become the star of mass destruction in the PS5 and Xbox game?The makers of Call of Duty have offered Daily Star readers these exclusive tips for online multiplayer mayhem in the game…Fire your guns as Ana de ArmasArticle continues belowEmbrace the Ruska Rogue : Don the "Ruska Rogue" Operator Skin for a sleek, black tactical look that screams stealth and deadly efficiency. Blend into the shadows and strike with calculated precision. You can adopt a minimalist, all-black approach to your loadout and Operator selection, channeling Wick's understated lethality. Remember, sometimes the most dangerous players are the ones you barely notice.Know Your Armoury : Equip the "Relevé" AK-74, "Plié" Saug, or "Arabesque" 9MM PM, all featuring High Table Coin Tracers and Death FX. Not only will you look good, but you'll also send a message: your enemies are just currency in your quest for victory . Aim for headshots to maximise your impact and conserve ammo. Use the AK-74 for mid-range engagements, the Saug for close-quarters dominance, and the 9MM PM as a reliable sidearm.‌Dance With Death, Literally: Master the "Dance With Death" Finishing Move. Nothing is more demoralising than eliminating an opponent with a graceful, yet brutal, execution. Use it strategically when you're sure you're safe from interruption. However, don't get caught up in the theatrics . Like Macarro, seek tactical reloads after every engagement, ensuring you're always ready for the next threat.Black Ops 6 has a fantastic multiplayer suite‌Pirouette to Victory : Utilise the "Pirouette" Emote to taunt your fallen foes. A well-timed pirouette can tilt the mental game in your favour, especially after a clutch play. But remember, survival is paramount . Know when to disengage and reposition, just like Ballerina. A tactical retreat can be just as effective as a head-on assault.Article continues belowCharm Your Way to the Top : Equip the "Keepsake" Weapon Charm as a reminder of your mission and to add a touch of personal flair to your weapon. Small details can make a big difference in your gameplay and standing in the lobby. Furthermore, master your weapon . An assassin knows the ins and outs of every firearm he or she uses. Practice with different weapons to find what suits your play style and become proficient with it.Know Your Role: Just like a ballerina needs to know their choreography, understand your role on the team. Are you the aggressive fragger, the objective player, or the support specialist? Tailor your loadout and play style to maximise your contribution to the team's success. Remember, even the most elegant ballerina needs a solid foundation. Use cover, listen for footsteps, and anticipate enemy movements. Situational awareness is key to survival.
    #call #duty #bosses #offer #top
    Call of Duty bosses offer top gunplay tips for Ballerina movie fans in Black Ops 6
    Activision has included movie assassin Ballerina in the latest multiplayer version of Black Ops 6 and here they are exclusively offering Daily Star readers their top tips to become a gun heroTech17:04, 13 Jun 2025Ballerina in Call of DutyJohn Wick movie Ballerina has take over Call of Duty and the bods behind the game have given us some top tips on how to become the shooter king.The movie follows actress Ana de Armas as protagonist badass assassin Eve Macarro as she takes on all kinds of baddies alongside Keanu Reeve’s anti-hero John Wick.‌And to celebrate the silver screen success, Activision has included Eve, aka Ballerina, into Black Ops 6 as a playable online character cmplete with her own guns, finishing move and load screen as a downloadable add-on.‌But how can you, like Ana herself, become the star of mass destruction in the PS5 and Xbox game?The makers of Call of Duty have offered Daily Star readers these exclusive tips for online multiplayer mayhem in the game…Fire your guns as Ana de ArmasArticle continues belowEmbrace the Ruska Rogue : Don the "Ruska Rogue" Operator Skin for a sleek, black tactical look that screams stealth and deadly efficiency. Blend into the shadows and strike with calculated precision. You can adopt a minimalist, all-black approach to your loadout and Operator selection, channeling Wick's understated lethality. Remember, sometimes the most dangerous players are the ones you barely notice.Know Your Armoury : Equip the "Relevé" AK-74, "Plié" Saug, or "Arabesque" 9MM PM, all featuring High Table Coin Tracers and Death FX. Not only will you look good, but you'll also send a message: your enemies are just currency in your quest for victory . Aim for headshots to maximise your impact and conserve ammo. Use the AK-74 for mid-range engagements, the Saug for close-quarters dominance, and the 9MM PM as a reliable sidearm.‌Dance With Death, Literally: Master the "Dance With Death" Finishing Move. Nothing is more demoralising than eliminating an opponent with a graceful, yet brutal, execution. Use it strategically when you're sure you're safe from interruption. However, don't get caught up in the theatrics . Like Macarro, seek tactical reloads after every engagement, ensuring you're always ready for the next threat.Black Ops 6 has a fantastic multiplayer suite‌Pirouette to Victory : Utilise the "Pirouette" Emote to taunt your fallen foes. A well-timed pirouette can tilt the mental game in your favour, especially after a clutch play. But remember, survival is paramount . Know when to disengage and reposition, just like Ballerina. A tactical retreat can be just as effective as a head-on assault.Article continues belowCharm Your Way to the Top : Equip the "Keepsake" Weapon Charm as a reminder of your mission and to add a touch of personal flair to your weapon. Small details can make a big difference in your gameplay and standing in the lobby. Furthermore, master your weapon . An assassin knows the ins and outs of every firearm he or she uses. Practice with different weapons to find what suits your play style and become proficient with it.Know Your Role: Just like a ballerina needs to know their choreography, understand your role on the team. Are you the aggressive fragger, the objective player, or the support specialist? Tailor your loadout and play style to maximise your contribution to the team's success. Remember, even the most elegant ballerina needs a solid foundation. Use cover, listen for footsteps, and anticipate enemy movements. Situational awareness is key to survival. #call #duty #bosses #offer #top
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    Call of Duty bosses offer top gunplay tips for Ballerina movie fans in Black Ops 6
    Activision has included movie assassin Ballerina in the latest multiplayer version of Black Ops 6 and here they are exclusively offering Daily Star readers their top tips to become a gun heroTech17:04, 13 Jun 2025Ballerina in Call of DutyJohn Wick movie Ballerina has take over Call of Duty and the bods behind the game have given us some top tips on how to become the shooter king.The movie follows actress Ana de Armas as protagonist badass assassin Eve Macarro as she takes on all kinds of baddies alongside Keanu Reeve’s anti-hero John Wick.‌And to celebrate the silver screen success, Activision has included Eve, aka Ballerina, into Black Ops 6 as a playable online character cmplete with her own guns, finishing move and load screen as a downloadable add-on.‌But how can you, like Ana herself, become the star of mass destruction in the PS5 and Xbox game?The makers of Call of Duty have offered Daily Star readers these exclusive tips for online multiplayer mayhem in the game…Fire your guns as Ana de Armas(Image: Activision)Article continues belowEmbrace the Ruska Rogue : Don the "Ruska Rogue" Operator Skin for a sleek, black tactical look that screams stealth and deadly efficiency. Blend into the shadows and strike with calculated precision. You can adopt a minimalist, all-black approach to your loadout and Operator selection, channeling Wick's understated lethality. Remember, sometimes the most dangerous players are the ones you barely notice.Know Your Armoury : Equip the "Relevé" AK-74, "Plié" Saug, or "Arabesque" 9MM PM, all featuring High Table Coin Tracers and Death FX. Not only will you look good, but you'll also send a message: your enemies are just currency in your quest for victory . Aim for headshots to maximise your impact and conserve ammo. Use the AK-74 for mid-range engagements, the Saug for close-quarters dominance, and the 9MM PM as a reliable sidearm.‌Dance With Death, Literally (and Tactically Reload) : Master the "Dance With Death" Finishing Move. Nothing is more demoralising than eliminating an opponent with a graceful, yet brutal, execution. Use it strategically when you're sure you're safe from interruption. However, don't get caught up in the theatrics . Like Macarro, seek tactical reloads after every engagement, ensuring you're always ready for the next threat.Black Ops 6 has a fantastic multiplayer suite‌Pirouette to Victory : Utilise the "Pirouette" Emote to taunt your fallen foes (sparingly, of course!). A well-timed pirouette can tilt the mental game in your favour, especially after a clutch play. But remember, survival is paramount . Know when to disengage and reposition, just like Ballerina. A tactical retreat can be just as effective as a head-on assault.Article continues belowCharm Your Way to the Top : Equip the "Keepsake" Weapon Charm as a reminder of your mission and to add a touch of personal flair to your weapon. Small details can make a big difference in your gameplay and standing in the lobby. Furthermore, master your weapon . An assassin knows the ins and outs of every firearm he or she uses. Practice with different weapons to find what suits your play style and become proficient with it.Know Your Role (and Your Surroundings) : Just like a ballerina needs to know their choreography, understand your role on the team. Are you the aggressive fragger, the objective player, or the support specialist? Tailor your loadout and play style to maximise your contribution to the team's success. Remember, even the most elegant ballerina needs a solid foundation. Use cover, listen for footsteps, and anticipate enemy movements. Situational awareness is key to survival.
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  • Five Climate Issues to Watch When Trump Goes to Canada

    June 13, 20255 min readFive Climate Issues to Watch When Trump Goes to CanadaPresident Trump will attend the G7 summit on Sunday in a nation he threatened to annex. He will also be an outlier on climate issuesBy Sara Schonhardt & E&E News Saul Loeb/AFP via Getty ImagesCLIMATEWIRE | The world’s richest nations are gathering Sunday in the Canadian Rockies for a summit that could reveal whether President Donald Trump's policies are shaking global climate efforts.The Group of Seven meeting comes at a challenging time for international climate policy. Trump’s tariff seesaw has cast a shade over the global economy, and his domestic policies have threatened billions of dollars in funding for clean energy programs. Those pressures are colliding with record-breaking temperatures worldwide and explosive demand for energy, driven by power-hungry data centers linked to artificial intelligence technologies.On top of that, Trump has threatened to annex the host of the meeting — Canada — and members of his Cabinet have taken swipes at Europe’s use of renewable energy. Rather than being aligned with much of the world's assertion that fossil fuels should be tempered, Trump embraces the opposite position — drill for more oil and gas and keep burning coal, while repealing environmental regulations on the biggest sources of U.S. carbon pollution.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.Those moves illustrate his rejection of climate science and underscore his outlying positions on global warming in the G7.Here are five things to know about the summit.Who will be there?The group comprises Canada, France, Germany, Italy, Japan, the United Kingdom and the United States — plus the European Union. Together they account for more than 40 percent of gross domestic product globally and around a quarter of all energy-related carbon dioxide pollution, according to the International Energy Agency. The U.S. is the only one among them that is not trying to hit a carbon reduction goal.Some emerging economies have also been invited, including Mexico, India, South Africa and Brazil, the host of this year’s COP30 climate talks in November.Ahead of the meeting, the office of Canada's prime minister, Mark Carney, said he and Brazilian President Luiz Inácio Lula da Silva agreed to strengthen cooperation on energy security and critical minerals. White House press secretary Karoline Leavitt said Trump would be having "quite a few" bilateral meetings but that his schedule was in flux.The G7 first came together 50 years ago following the Arab oil embargo. Since then, its seven members have all joined the United Nations Framework Convention on Climate Change and the Paris Agreement. The U.S. is the only nation in the group that has withdrawn from the Paris Agreement, which counts almost every country in the world as a signatory.What’s on the table?Among Canada’s top priorities as host are strengthening energy security and fortifying critical mineral supply chains. Carney would also like to see some agreement on joint wildfire action.Expanding supply chains for critical minerals — and competing more aggressively with China over those resources — could be areas of common ground among the leaders. Climate change is expected to remain divisive. Looming over the discussions will be tariffs — which Trump has applied across the board — because they will have an impact on the clean energy transition.“I think probably the majority of the conversation will be less about climate per se, or certainly not using climate action as the frame, but more about energy transition and infrastructure as a way of kind of bridging the known gaps between most of the G7 and where the United States is right now,” said Dan Baer, director of the Europe program at the Carnegie Endowment for International Peace.What are the possible outcomes?The leaders could issue a communique at the end of their meeting, but those statements are based on consensus, something that would be difficult to reach without other G7 countries capitulating to Trump. Bloomberg reported Wednesday that nations won’t try to reach a joint agreement, in part because bridging gaps on climate change could be too hard.Instead, Carney could issue a chair’s summary or joint statements based on certain issues.The question is how far Canada will go to accommodate the U.S., which could try to roll back past statements on advancing clean energy, said Andrew Light, former assistant secretary of Energy for international affairs, who led ministerial-level negotiations for the G7.“They might say, rather than watering everything down that we accomplished in the last four years, we just do a chair's statement, which summarizes the debate,” Light said. “That will show you that you didn't get consensus, but you also didn't get capitulation.”What to watch forIf there is a communique, Light says he’ll be looking for whether there is tougher language on China and any signal of support for science and the Paris Agreement. During his first term, Trump refused to support the Paris accord in the G7 and G20 declarations.The statement could avoid climate and energy issues entirely. But if it backtracks on those issues, that could be a sign that countries made a deal by trading climate-related language for something else, Light said.Baer of Carnegie said a statement framed around energy security and infrastructure could be seen as a “pragmatic adaptation” to the U.S. administration, rather than an indication that other leaders aren’t concerned about climate change.Climate activists have lower expectations.“Realistically, we can expect very little, if any, mention of climate change,” said Caroline Brouillette, executive director of Climate Action Network Canada.“The message we should be expecting from those leaders is that climate action remains a priority for the rest of the G7 … whether it's on the transition away from fossil fuels and supporting developing countries through climate finance,” she said. “Especially now that the U.S. is stepping back, we need countries, including Canada, to be stepping up.”Best- and worst-case scenariosThe challenge for Carney will be preventing any further rupture with Trump, analysts said.In 2018, Trump made a hasty exit from the G7 summit, also in Canada that year, due largely to trade disagreements. He retracted his support for the joint statement.“The best,realistic case outcome is that things don't get worse,” said Baer.The worst-case scenario? Some kind of “highly personalized spat” that could add to the sense of disorder, he added.“I think the G7 on the one hand has the potential to be more important than ever, as fewer and fewer platforms for international cooperation seem to be able to take action,” Baer said. “So it's both very important and also I don't have super-high expectations.”Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2025. E&E News provides essential news for energy and environment professionals.
    #five #climate #issues #watch #when
    Five Climate Issues to Watch When Trump Goes to Canada
    June 13, 20255 min readFive Climate Issues to Watch When Trump Goes to CanadaPresident Trump will attend the G7 summit on Sunday in a nation he threatened to annex. He will also be an outlier on climate issuesBy Sara Schonhardt & E&E News Saul Loeb/AFP via Getty ImagesCLIMATEWIRE | The world’s richest nations are gathering Sunday in the Canadian Rockies for a summit that could reveal whether President Donald Trump's policies are shaking global climate efforts.The Group of Seven meeting comes at a challenging time for international climate policy. Trump’s tariff seesaw has cast a shade over the global economy, and his domestic policies have threatened billions of dollars in funding for clean energy programs. Those pressures are colliding with record-breaking temperatures worldwide and explosive demand for energy, driven by power-hungry data centers linked to artificial intelligence technologies.On top of that, Trump has threatened to annex the host of the meeting — Canada — and members of his Cabinet have taken swipes at Europe’s use of renewable energy. Rather than being aligned with much of the world's assertion that fossil fuels should be tempered, Trump embraces the opposite position — drill for more oil and gas and keep burning coal, while repealing environmental regulations on the biggest sources of U.S. carbon pollution.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.Those moves illustrate his rejection of climate science and underscore his outlying positions on global warming in the G7.Here are five things to know about the summit.Who will be there?The group comprises Canada, France, Germany, Italy, Japan, the United Kingdom and the United States — plus the European Union. Together they account for more than 40 percent of gross domestic product globally and around a quarter of all energy-related carbon dioxide pollution, according to the International Energy Agency. The U.S. is the only one among them that is not trying to hit a carbon reduction goal.Some emerging economies have also been invited, including Mexico, India, South Africa and Brazil, the host of this year’s COP30 climate talks in November.Ahead of the meeting, the office of Canada's prime minister, Mark Carney, said he and Brazilian President Luiz Inácio Lula da Silva agreed to strengthen cooperation on energy security and critical minerals. White House press secretary Karoline Leavitt said Trump would be having "quite a few" bilateral meetings but that his schedule was in flux.The G7 first came together 50 years ago following the Arab oil embargo. Since then, its seven members have all joined the United Nations Framework Convention on Climate Change and the Paris Agreement. The U.S. is the only nation in the group that has withdrawn from the Paris Agreement, which counts almost every country in the world as a signatory.What’s on the table?Among Canada’s top priorities as host are strengthening energy security and fortifying critical mineral supply chains. Carney would also like to see some agreement on joint wildfire action.Expanding supply chains for critical minerals — and competing more aggressively with China over those resources — could be areas of common ground among the leaders. Climate change is expected to remain divisive. Looming over the discussions will be tariffs — which Trump has applied across the board — because they will have an impact on the clean energy transition.“I think probably the majority of the conversation will be less about climate per se, or certainly not using climate action as the frame, but more about energy transition and infrastructure as a way of kind of bridging the known gaps between most of the G7 and where the United States is right now,” said Dan Baer, director of the Europe program at the Carnegie Endowment for International Peace.What are the possible outcomes?The leaders could issue a communique at the end of their meeting, but those statements are based on consensus, something that would be difficult to reach without other G7 countries capitulating to Trump. Bloomberg reported Wednesday that nations won’t try to reach a joint agreement, in part because bridging gaps on climate change could be too hard.Instead, Carney could issue a chair’s summary or joint statements based on certain issues.The question is how far Canada will go to accommodate the U.S., which could try to roll back past statements on advancing clean energy, said Andrew Light, former assistant secretary of Energy for international affairs, who led ministerial-level negotiations for the G7.“They might say, rather than watering everything down that we accomplished in the last four years, we just do a chair's statement, which summarizes the debate,” Light said. “That will show you that you didn't get consensus, but you also didn't get capitulation.”What to watch forIf there is a communique, Light says he’ll be looking for whether there is tougher language on China and any signal of support for science and the Paris Agreement. During his first term, Trump refused to support the Paris accord in the G7 and G20 declarations.The statement could avoid climate and energy issues entirely. But if it backtracks on those issues, that could be a sign that countries made a deal by trading climate-related language for something else, Light said.Baer of Carnegie said a statement framed around energy security and infrastructure could be seen as a “pragmatic adaptation” to the U.S. administration, rather than an indication that other leaders aren’t concerned about climate change.Climate activists have lower expectations.“Realistically, we can expect very little, if any, mention of climate change,” said Caroline Brouillette, executive director of Climate Action Network Canada.“The message we should be expecting from those leaders is that climate action remains a priority for the rest of the G7 … whether it's on the transition away from fossil fuels and supporting developing countries through climate finance,” she said. “Especially now that the U.S. is stepping back, we need countries, including Canada, to be stepping up.”Best- and worst-case scenariosThe challenge for Carney will be preventing any further rupture with Trump, analysts said.In 2018, Trump made a hasty exit from the G7 summit, also in Canada that year, due largely to trade disagreements. He retracted his support for the joint statement.“The best,realistic case outcome is that things don't get worse,” said Baer.The worst-case scenario? Some kind of “highly personalized spat” that could add to the sense of disorder, he added.“I think the G7 on the one hand has the potential to be more important than ever, as fewer and fewer platforms for international cooperation seem to be able to take action,” Baer said. “So it's both very important and also I don't have super-high expectations.”Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2025. E&E News provides essential news for energy and environment professionals. #five #climate #issues #watch #when
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    Five Climate Issues to Watch When Trump Goes to Canada
    June 13, 20255 min readFive Climate Issues to Watch When Trump Goes to CanadaPresident Trump will attend the G7 summit on Sunday in a nation he threatened to annex. He will also be an outlier on climate issuesBy Sara Schonhardt & E&E News Saul Loeb/AFP via Getty ImagesCLIMATEWIRE | The world’s richest nations are gathering Sunday in the Canadian Rockies for a summit that could reveal whether President Donald Trump's policies are shaking global climate efforts.The Group of Seven meeting comes at a challenging time for international climate policy. Trump’s tariff seesaw has cast a shade over the global economy, and his domestic policies have threatened billions of dollars in funding for clean energy programs. Those pressures are colliding with record-breaking temperatures worldwide and explosive demand for energy, driven by power-hungry data centers linked to artificial intelligence technologies.On top of that, Trump has threatened to annex the host of the meeting — Canada — and members of his Cabinet have taken swipes at Europe’s use of renewable energy. Rather than being aligned with much of the world's assertion that fossil fuels should be tempered, Trump embraces the opposite position — drill for more oil and gas and keep burning coal, while repealing environmental regulations on the biggest sources of U.S. carbon pollution.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.Those moves illustrate his rejection of climate science and underscore his outlying positions on global warming in the G7.Here are five things to know about the summit.Who will be there?The group comprises Canada, France, Germany, Italy, Japan, the United Kingdom and the United States — plus the European Union. Together they account for more than 40 percent of gross domestic product globally and around a quarter of all energy-related carbon dioxide pollution, according to the International Energy Agency. The U.S. is the only one among them that is not trying to hit a carbon reduction goal.Some emerging economies have also been invited, including Mexico, India, South Africa and Brazil, the host of this year’s COP30 climate talks in November.Ahead of the meeting, the office of Canada's prime minister, Mark Carney, said he and Brazilian President Luiz Inácio Lula da Silva agreed to strengthen cooperation on energy security and critical minerals. White House press secretary Karoline Leavitt said Trump would be having "quite a few" bilateral meetings but that his schedule was in flux.The G7 first came together 50 years ago following the Arab oil embargo. Since then, its seven members have all joined the United Nations Framework Convention on Climate Change and the Paris Agreement. The U.S. is the only nation in the group that has withdrawn from the Paris Agreement, which counts almost every country in the world as a signatory.What’s on the table?Among Canada’s top priorities as host are strengthening energy security and fortifying critical mineral supply chains. Carney would also like to see some agreement on joint wildfire action.Expanding supply chains for critical minerals — and competing more aggressively with China over those resources — could be areas of common ground among the leaders. Climate change is expected to remain divisive. Looming over the discussions will be tariffs — which Trump has applied across the board — because they will have an impact on the clean energy transition.“I think probably the majority of the conversation will be less about climate per se, or certainly not using climate action as the frame, but more about energy transition and infrastructure as a way of kind of bridging the known gaps between most of the G7 and where the United States is right now,” said Dan Baer, director of the Europe program at the Carnegie Endowment for International Peace.What are the possible outcomes?The leaders could issue a communique at the end of their meeting, but those statements are based on consensus, something that would be difficult to reach without other G7 countries capitulating to Trump. Bloomberg reported Wednesday that nations won’t try to reach a joint agreement, in part because bridging gaps on climate change could be too hard.Instead, Carney could issue a chair’s summary or joint statements based on certain issues.The question is how far Canada will go to accommodate the U.S., which could try to roll back past statements on advancing clean energy, said Andrew Light, former assistant secretary of Energy for international affairs, who led ministerial-level negotiations for the G7.“They might say, rather than watering everything down that we accomplished in the last four years, we just do a chair's statement, which summarizes the debate,” Light said. “That will show you that you didn't get consensus, but you also didn't get capitulation.”What to watch forIf there is a communique, Light says he’ll be looking for whether there is tougher language on China and any signal of support for science and the Paris Agreement. During his first term, Trump refused to support the Paris accord in the G7 and G20 declarations.The statement could avoid climate and energy issues entirely. But if it backtracks on those issues, that could be a sign that countries made a deal by trading climate-related language for something else, Light said.Baer of Carnegie said a statement framed around energy security and infrastructure could be seen as a “pragmatic adaptation” to the U.S. administration, rather than an indication that other leaders aren’t concerned about climate change.Climate activists have lower expectations.“Realistically, we can expect very little, if any, mention of climate change,” said Caroline Brouillette, executive director of Climate Action Network Canada.“The message we should be expecting from those leaders is that climate action remains a priority for the rest of the G7 … whether it's on the transition away from fossil fuels and supporting developing countries through climate finance,” she said. “Especially now that the U.S. is stepping back, we need countries, including Canada, to be stepping up.”Best- and worst-case scenariosThe challenge for Carney will be preventing any further rupture with Trump, analysts said.In 2018, Trump made a hasty exit from the G7 summit, also in Canada that year, due largely to trade disagreements. He retracted his support for the joint statement.“The best, [most] realistic case outcome is that things don't get worse,” said Baer.The worst-case scenario? Some kind of “highly personalized spat” that could add to the sense of disorder, he added.“I think the G7 on the one hand has the potential to be more important than ever, as fewer and fewer platforms for international cooperation seem to be able to take action,” Baer said. “So it's both very important and also I don't have super-high expectations.”Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2025. E&E News provides essential news for energy and environment professionals.
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