• Ecommerce SEO is a thing, I guess. If you don't pay attention, you could easily mess it up. Like, seriously, missing mobile optimization or cramming keywords could just ruin everything for you. No one wants frustrated customers, right?

    So, yeah, maybe check out some tips on mistakes to avoid. Or don’t. It’s whatever.

    #EcommerceSEO
    #SEOtips
    #DigitalMarketing
    #OnlineBusiness
    #MarketingMistakes
    Ecommerce SEO is a thing, I guess. If you don't pay attention, you could easily mess it up. Like, seriously, missing mobile optimization or cramming keywords could just ruin everything for you. No one wants frustrated customers, right? So, yeah, maybe check out some tips on mistakes to avoid. Or don’t. It’s whatever. #EcommerceSEO #SEOtips #DigitalMarketing #OnlineBusiness #MarketingMistakes
    Ecommerce SEO Tips: Mistakes to Avoid
    A strong ecommerce SEO strategy can be the difference between standing out and getting lost in the SERPs. With many SEO elements and best practices, it’s easy to stumble into actions that do more harm than good. From overlooking mobile optimization t
    1 Comentários 0 Compartilhamentos 0 Anterior
  • It's infuriating to see so many artists struggling to color digital art while trying to maintain that raw charm of their sketches. Eliza Ivanova's process in Procreate might sound appealing, but let’s be real: the art world is flooded with tutorials that promise to keep your sketch's personal feel intact, yet most fail miserably. Why is it so hard for creators to find a method that actually respects the integrity of their original work? Instead of enhancing, these techniques often strip away the very essence that makes sketches unique. It's time to call out these so-called "methods" that leave artists frustrated and disillusioned. If you want to color your digital art without losing its charm, you need a genuine solution, not just another empty promise
    It's infuriating to see so many artists struggling to color digital art while trying to maintain that raw charm of their sketches. Eliza Ivanova's process in Procreate might sound appealing, but let’s be real: the art world is flooded with tutorials that promise to keep your sketch's personal feel intact, yet most fail miserably. Why is it so hard for creators to find a method that actually respects the integrity of their original work? Instead of enhancing, these techniques often strip away the very essence that makes sketches unique. It's time to call out these so-called "methods" that leave artists frustrated and disillusioned. If you want to color your digital art without losing its charm, you need a genuine solution, not just another empty promise
    WWW.CREATIVEBLOQ.COM
    How to colour digital art without losing your sketch's raw charm
    Eliza Ivanova explains her process for colouring artwork in Prcreate without losing that personal sketchbook feel.
    Like
    Love
    Wow
    Sad
    Angry
    43
    1 Comentários 0 Compartilhamentos 0 Anterior
  • I am beyond frustrated with the current state of the MMORPG Ilysia VR. The hype around its return seems to overshadow the glaring technical flaws that plague the game. How on earth are we still dealing with bugs and performance issues in a title that promises exploration and adventure? The PTR may be "well-loaded," but all I see are half-baked updates that fail to address the core problems. Why should players invest their time in a game that can't even deliver a smooth experience? Stop focusing on "hidden secrets" and start fixing the glaring issues. It's time for the developers to step up and deliver a product that respects its community!

    #IlysiaVR #MMORPG #GameDevelopment #Frustration #PlayerCommunity
    I am beyond frustrated with the current state of the MMORPG Ilysia VR. The hype around its return seems to overshadow the glaring technical flaws that plague the game. How on earth are we still dealing with bugs and performance issues in a title that promises exploration and adventure? The PTR may be "well-loaded," but all I see are half-baked updates that fail to address the core problems. Why should players invest their time in a game that can't even deliver a smooth experience? Stop focusing on "hidden secrets" and start fixing the glaring issues. It's time for the developers to step up and deliver a product that respects its community! #IlysiaVR #MMORPG #GameDevelopment #Frustration #PlayerCommunity
    Le MMORPG Ilysia VR revient en force avec un PTR bien chargé
    Grimpez, explorez, résolvez : trois régions cachent désormais des secrets que seuls les plus curieux […] Cet article Le MMORPG Ilysia VR revient en force avec un PTR bien chargé a été publié sur REALITE-VIRTUELLE.COM.
    Like
    Love
    Wow
    Sad
    48
    1 Comentários 0 Compartilhamentos 0 Anterior
  • It's infuriating how complicated Google makes it to see your reviews and manage them! Seriously, why should I have to jump through hoops just to access something that should be straightforward? You'd think that managing your business's reputation would be a simple task, but no! Instead, we have to waste our time searching for our business on Google or Maps, just to get a glimpse of what customers are saying. This is a complete oversight on Google's part! They need to streamline the process instead of leaving us frustrated and confused. It's 2023; we deserve better than this clunky system!

    #GoogleReviews #BusinessManagement #CustomerFeedback #TechFail #Frustration
    It's infuriating how complicated Google makes it to see your reviews and manage them! Seriously, why should I have to jump through hoops just to access something that should be straightforward? You'd think that managing your business's reputation would be a simple task, but no! Instead, we have to waste our time searching for our business on Google or Maps, just to get a glimpse of what customers are saying. This is a complete oversight on Google's part! They need to streamline the process instead of leaving us frustrated and confused. It's 2023; we deserve better than this clunky system! #GoogleReviews #BusinessManagement #CustomerFeedback #TechFail #Frustration
    WWW.SEMRUSH.COM
    How to See Your Google Reviews and Easily Manage Them
    You can find Google reviews by searching your business on Google or Maps. Follow these steps.
    Like
    Love
    Wow
    Sad
    60
    1 Comentários 0 Compartilhamentos 0 Anterior

  • ## Introduction

    In recent months, the French government has implemented strict regulations that have led to the closure of popular adult websites such as PornHub, YouPorn, and RedTube. These measures, aimed at controlling the online adult content market, have left many users frustrated and seeking ways to regain access to their favorite sites. This article will explore the implications of these restrictions, the reasons behind them, and how individuals can circumvent these limitations.

    ## The...
    ## Introduction In recent months, the French government has implemented strict regulations that have led to the closure of popular adult websites such as PornHub, YouPorn, and RedTube. These measures, aimed at controlling the online adult content market, have left many users frustrated and seeking ways to regain access to their favorite sites. This article will explore the implications of these restrictions, the reasons behind them, and how individuals can circumvent these limitations. ## The...
    Débloquer PornHub, YouPorn, RedTube en France : retrouvez un accès illimité
    ## Introduction In recent months, the French government has implemented strict regulations that have led to the closure of popular adult websites such as PornHub, YouPorn, and RedTube. These measures, aimed at controlling the online adult content market, have left many users frustrated and seeking ways to regain access to their favorite sites. This article will explore the implications of...
    Like
    Love
    Wow
    Sad
    Angry
    559
    1 Comentários 0 Compartilhamentos 0 Anterior
  • The Video Game History Foundation has decided to keep the legacy of the game Myst alive, which sounds like a big deal for some people. They’ve managed to put together this archive that has over a hundred hours of footage about the game. I guess that’s interesting if you’re into that sort of thing. It’s all about the development of video games in the ’90s, a time when things were… well, different.

    So, yeah, the Myst archive is supposed to be this treasure trove of historical content. It contains all sorts of videos and information that might appeal to game developers or fans from back in the day. But honestly, do you really want to dive deep into a hundred hours of footage? Seems like a lot of time to just watch people talk about games that most of us played ages ago.

    I mean, Myst was a classic, no doubt. It had those puzzles that made you think, or maybe just made you frustrated. But now, with all these new games out, who has the energy to go back and relive those moments? I suppose some people find nostalgia in it. Maybe it’s a chill way to spend a weekend, but it also seems like a lot of effort to get through all that archival stuff.

    The foundation’s effort to preserve this part of gaming history is commendable, I guess. They’re trying to keep the memory of how games were developed alive. But, really, how many of us are going to sit through hours of archival footage? It’s like they’re saying, “Hey, look at all this old stuff!” and I’m just sitting here thinking, “Okay, cool. But… I could also just take a nap.”

    Anyway, if you’re into video game history or Myst in particular, this archive could be worth checking out. Just don’t expect it to be super exciting. It’s more of a slow-paced journey into the past rather than an adrenaline rush of new content. But hey, if that’s your thing, go for it. I’ll just be here, doing… well, not much.

    #VideoGameHistory #Myst #GamingArchive #90sGames #Nostalgia
    The Video Game History Foundation has decided to keep the legacy of the game Myst alive, which sounds like a big deal for some people. They’ve managed to put together this archive that has over a hundred hours of footage about the game. I guess that’s interesting if you’re into that sort of thing. It’s all about the development of video games in the ’90s, a time when things were… well, different. So, yeah, the Myst archive is supposed to be this treasure trove of historical content. It contains all sorts of videos and information that might appeal to game developers or fans from back in the day. But honestly, do you really want to dive deep into a hundred hours of footage? Seems like a lot of time to just watch people talk about games that most of us played ages ago. I mean, Myst was a classic, no doubt. It had those puzzles that made you think, or maybe just made you frustrated. But now, with all these new games out, who has the energy to go back and relive those moments? I suppose some people find nostalgia in it. Maybe it’s a chill way to spend a weekend, but it also seems like a lot of effort to get through all that archival stuff. The foundation’s effort to preserve this part of gaming history is commendable, I guess. They’re trying to keep the memory of how games were developed alive. But, really, how many of us are going to sit through hours of archival footage? It’s like they’re saying, “Hey, look at all this old stuff!” and I’m just sitting here thinking, “Okay, cool. But… I could also just take a nap.” Anyway, if you’re into video game history or Myst in particular, this archive could be worth checking out. Just don’t expect it to be super exciting. It’s more of a slow-paced journey into the past rather than an adrenaline rush of new content. But hey, if that’s your thing, go for it. I’ll just be here, doing… well, not much. #VideoGameHistory #Myst #GamingArchive #90sGames #Nostalgia
    The Video Game History Foundation preserves the history of Myst in impressive archival feat
    The archive, which extends to over a hundred hours of footage, is a historical treasure trove about developing games in the ‛90s.
    Like
    Love
    Wow
    Angry
    Sad
    525
    1 Comentários 0 Compartilhamentos 0 Anterior
  • The AI execution gap: Why 80% of projects don’t reach production

    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.
    #execution #gap #why #projects #dont
    The AI execution gap: Why 80% of projects don’t reach production
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont
    WWW.ARTIFICIALINTELLIGENCE-NEWS.COM
    The AI execution gap: Why 80% of projects don’t reach production
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)
    Like
    Love
    Wow
    Angry
    Sad
    598
    0 Comentários 0 Compartilhamentos 0 Anterior
  • 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
    WWW.MICROSOFT.COM
    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]
    0 Comentários 0 Compartilhamentos 0 Anterior
  • As a former Xbox 360 owner I don’t understand Xbox today – Reader’s Feature

    As a former Xbox 360 owner I don’t understand Xbox today – Reader’s Feature

    GameCentral

    Published June 15, 2025 1:00am

    Xbox 360 is coming up to its 20th anniversaryA reader looks back on the Xbox 360 era and is frustrated at how things have evolved since then, with ROG Xbox Ally and the move towards multiformat releases.
    I though the Xbox Games Showcase on Sunday was pretty good. Like Sony’s State of Play, it was mostly third party games but there was some interesting stuff there and I think overall the vibe was better than from Sony. I liked the look of High On Life 2, There Are No Ghosts At The Grand, and Cronos: The New Dawn the best but there was a lot of potentially cool games – I’d include Keeper, because it looked interestingly weird, but I don’t feel Double Fine are ever very good at gameplay.
    The biggest news out of the event was the new portable with the terrible name: Asus ROG Xbox Ally. I bet you can just imagine some parent asking that for that at shop at Christmas, to buy their kid? Not that that would ever happen because the thing’s going to be stupidly expensive.
    It seemed like a distraction, a small experiment at best, and I didn’t really pay much attention to it, especially as I already have a Steam Deck. But then today I read that Microsoft has cancelled its plans for their next gen portable and that actually this ridiculously named non-Xbox device may end up being the future of gaming for Microsoft.
    I’ve always preferred Xbox as my console as choice, probably because I was always a PC gamer before that. Although now I look back at things I have to admit that I only got the Xbox One out of brand loyalty and I wouldn’t have if I’d been thinking about it more clearly.
    By that point I was in too deep and so I bought the Xbox Series X/S out of muscle memory more than anything, wasn’t I proven to be a chump?
    What frustrates me most about Xbox at the moment is how indecisive it seems. I almost didn’t watch the Xbox Games Showcase because I knew I’d have to see Phil Spencer, or one of his goons, grinning into the camera, as if nothing is wrong. And, of course, that’s exactly what he did, ‘hinting’ about the return of Halo, as if everyone was going to be pumping the air to hear about that.

    Expert, exclusive gaming analysis

    Sign up to the GameCentral newsletter for a unique take on the week in gaming, alongside the latest reviews and more. Delivered to your inbox every Saturday morning.

    News flash, Phil: no one cares. You’ve run that series into the ground, like all the other Xbox exclusives, to the point where they just feel old fashioned and tired. Old school fans don’t care and newer ones definitely don’t. It may sell okay at first on PlayStation 5, but only out of curiosity and as a kind of celebration that Sony has finally defeated Microsoft.
    To all extents and purposes, Xbox is now third party. The only thing that makes them not is that they still make their own console hardware but how long is that going to last? The ROG Ally is made by Asus and if Microsoft don’t make a handheld are they really going to put out a home console instead? That’s going to cost a lot of money in R&D and marketing and everything else, and I don’t know who could argue that it’s got a chance of selling more than the Xbox Series X/S.
    Phil Spencer has been talking about making a handheld for years and yet suddenly it’s not going to happen? Is there anything that is set in stone? I even heard people talking about them going back to having exclusives with the next generation, if it seemed like things were working out.
    I loved my Xbox 360, it’s still my favourite console of all time – the perfect balance between modern and retro games – but its golden era is a long time ago now, well over a decade. Xbox at the time was the new kid on the block, full of new ideas and daring to what Sony wouldn’t or couldn’t. When was the last time Xbox did anything like that? Game Pass probably, and that hasn’t worked out at all well.

    More Trending

    Nothing has, ever since that disastrous Xbox One reveal, and I just don’t understand how a company with basically infinite resources, and which already owns half the games industry, can be such a hopeless mess. I’m just sticking with PC from now and in the future, I’m going to pretend the Xbox 360 was my one and only console.
    By reader Cramersauce

    Xbox One – not a good follow-up to the Xbox 360The reader’s features do not necessarily represent the views of GameCentral or Metro.
    You can submit your own 500 to 600-word reader feature at any time, which if used will be published in the next appropriate weekend slot. Just contact us at gamecentral@metro.co.uk or use our Submit Stuff page and you won’t need to send an email.

    GameCentral
    Sign up for exclusive analysis, latest releases, and bonus community content.
    This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply. Your information will be used in line with our Privacy Policy
    #former #xbox #owner #dont #understand
    As a former Xbox 360 owner I don’t understand Xbox today – Reader’s Feature
    As a former Xbox 360 owner I don’t understand Xbox today – Reader’s Feature GameCentral Published June 15, 2025 1:00am Xbox 360 is coming up to its 20th anniversaryA reader looks back on the Xbox 360 era and is frustrated at how things have evolved since then, with ROG Xbox Ally and the move towards multiformat releases. I though the Xbox Games Showcase on Sunday was pretty good. Like Sony’s State of Play, it was mostly third party games but there was some interesting stuff there and I think overall the vibe was better than from Sony. I liked the look of High On Life 2, There Are No Ghosts At The Grand, and Cronos: The New Dawn the best but there was a lot of potentially cool games – I’d include Keeper, because it looked interestingly weird, but I don’t feel Double Fine are ever very good at gameplay. The biggest news out of the event was the new portable with the terrible name: Asus ROG Xbox Ally. I bet you can just imagine some parent asking that for that at shop at Christmas, to buy their kid? Not that that would ever happen because the thing’s going to be stupidly expensive. It seemed like a distraction, a small experiment at best, and I didn’t really pay much attention to it, especially as I already have a Steam Deck. But then today I read that Microsoft has cancelled its plans for their next gen portable and that actually this ridiculously named non-Xbox device may end up being the future of gaming for Microsoft. I’ve always preferred Xbox as my console as choice, probably because I was always a PC gamer before that. Although now I look back at things I have to admit that I only got the Xbox One out of brand loyalty and I wouldn’t have if I’d been thinking about it more clearly. By that point I was in too deep and so I bought the Xbox Series X/S out of muscle memory more than anything, wasn’t I proven to be a chump? What frustrates me most about Xbox at the moment is how indecisive it seems. I almost didn’t watch the Xbox Games Showcase because I knew I’d have to see Phil Spencer, or one of his goons, grinning into the camera, as if nothing is wrong. And, of course, that’s exactly what he did, ‘hinting’ about the return of Halo, as if everyone was going to be pumping the air to hear about that. Expert, exclusive gaming analysis Sign up to the GameCentral newsletter for a unique take on the week in gaming, alongside the latest reviews and more. Delivered to your inbox every Saturday morning. News flash, Phil: no one cares. You’ve run that series into the ground, like all the other Xbox exclusives, to the point where they just feel old fashioned and tired. Old school fans don’t care and newer ones definitely don’t. It may sell okay at first on PlayStation 5, but only out of curiosity and as a kind of celebration that Sony has finally defeated Microsoft. To all extents and purposes, Xbox is now third party. The only thing that makes them not is that they still make their own console hardware but how long is that going to last? The ROG Ally is made by Asus and if Microsoft don’t make a handheld are they really going to put out a home console instead? That’s going to cost a lot of money in R&D and marketing and everything else, and I don’t know who could argue that it’s got a chance of selling more than the Xbox Series X/S. Phil Spencer has been talking about making a handheld for years and yet suddenly it’s not going to happen? Is there anything that is set in stone? I even heard people talking about them going back to having exclusives with the next generation, if it seemed like things were working out. I loved my Xbox 360, it’s still my favourite console of all time – the perfect balance between modern and retro games – but its golden era is a long time ago now, well over a decade. Xbox at the time was the new kid on the block, full of new ideas and daring to what Sony wouldn’t or couldn’t. When was the last time Xbox did anything like that? Game Pass probably, and that hasn’t worked out at all well. More Trending Nothing has, ever since that disastrous Xbox One reveal, and I just don’t understand how a company with basically infinite resources, and which already owns half the games industry, can be such a hopeless mess. I’m just sticking with PC from now and in the future, I’m going to pretend the Xbox 360 was my one and only console. By reader Cramersauce Xbox One – not a good follow-up to the Xbox 360The reader’s features do not necessarily represent the views of GameCentral or Metro. You can submit your own 500 to 600-word reader feature at any time, which if used will be published in the next appropriate weekend slot. Just contact us at gamecentral@metro.co.uk or use our Submit Stuff page and you won’t need to send an email. GameCentral Sign up for exclusive analysis, latest releases, and bonus community content. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply. Your information will be used in line with our Privacy Policy #former #xbox #owner #dont #understand
    METRO.CO.UK
    As a former Xbox 360 owner I don’t understand Xbox today – Reader’s Feature
    As a former Xbox 360 owner I don’t understand Xbox today – Reader’s Feature GameCentral Published June 15, 2025 1:00am Xbox 360 is coming up to its 20th anniversary (Microsoft) A reader looks back on the Xbox 360 era and is frustrated at how things have evolved since then, with ROG Xbox Ally and the move towards multiformat releases. I though the Xbox Games Showcase on Sunday was pretty good. Like Sony’s State of Play, it was mostly third party games but there was some interesting stuff there and I think overall the vibe was better than from Sony. I liked the look of High On Life 2, There Are No Ghosts At The Grand, and Cronos: The New Dawn the best but there was a lot of potentially cool games – I’d include Keeper, because it looked interestingly weird, but I don’t feel Double Fine are ever very good at gameplay. The biggest news out of the event was the new portable with the terrible name: Asus ROG Xbox Ally. I bet you can just imagine some parent asking that for that at shop at Christmas, to buy their kid? Not that that would ever happen because the thing’s going to be stupidly expensive. It seemed like a distraction, a small experiment at best, and I didn’t really pay much attention to it, especially as I already have a Steam Deck. But then today I read that Microsoft has cancelled its plans for their next gen portable and that actually this ridiculously named non-Xbox device may end up being the future of gaming for Microsoft. I’ve always preferred Xbox as my console as choice, probably because I was always a PC gamer before that. Although now I look back at things I have to admit that I only got the Xbox One out of brand loyalty and I wouldn’t have if I’d been thinking about it more clearly. By that point I was in too deep and so I bought the Xbox Series X/S out of muscle memory more than anything, wasn’t I proven to be a chump? What frustrates me most about Xbox at the moment is how indecisive it seems. I almost didn’t watch the Xbox Games Showcase because I knew I’d have to see Phil Spencer, or one of his goons, grinning into the camera, as if nothing is wrong. And, of course, that’s exactly what he did, ‘hinting’ about the return of Halo, as if everyone was going to be pumping the air to hear about that. Expert, exclusive gaming analysis Sign up to the GameCentral newsletter for a unique take on the week in gaming, alongside the latest reviews and more. Delivered to your inbox every Saturday morning. News flash, Phil: no one cares. You’ve run that series into the ground, like all the other Xbox exclusives, to the point where they just feel old fashioned and tired. Old school fans don’t care and newer ones definitely don’t. It may sell okay at first on PlayStation 5, but only out of curiosity and as a kind of celebration that Sony has finally defeated Microsoft. To all extents and purposes, Xbox is now third party. The only thing that makes them not is that they still make their own console hardware but how long is that going to last? The ROG Ally is made by Asus and if Microsoft don’t make a handheld are they really going to put out a home console instead? That’s going to cost a lot of money in R&D and marketing and everything else, and I don’t know who could argue that it’s got a chance of selling more than the Xbox Series X/S. Phil Spencer has been talking about making a handheld for years and yet suddenly it’s not going to happen? Is there anything that is set in stone? I even heard people talking about them going back to having exclusives with the next generation, if it seemed like things were working out. I loved my Xbox 360, it’s still my favourite console of all time – the perfect balance between modern and retro games – but its golden era is a long time ago now, well over a decade. Xbox at the time was the new kid on the block, full of new ideas and daring to what Sony wouldn’t or couldn’t. When was the last time Xbox did anything like that? Game Pass probably, and that hasn’t worked out at all well. More Trending Nothing has, ever since that disastrous Xbox One reveal, and I just don’t understand how a company with basically infinite resources, and which already owns half the games industry, can be such a hopeless mess. I’m just sticking with PC from now and in the future, I’m going to pretend the Xbox 360 was my one and only console. By reader Cramersauce Xbox One – not a good follow-up to the Xbox 360 (Microsoft) The reader’s features do not necessarily represent the views of GameCentral or Metro. You can submit your own 500 to 600-word reader feature at any time, which if used will be published in the next appropriate weekend slot. Just contact us at gamecentral@metro.co.uk or use our Submit Stuff page and you won’t need to send an email. GameCentral Sign up for exclusive analysis, latest releases, and bonus community content. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply. Your information will be used in line with our Privacy Policy
    0 Comentários 0 Compartilhamentos 0 Anterior
CGShares https://cgshares.com