• What an exhilarating experience at the National Animation Film Festival! I had the pleasure of diving into the fascinating world of "Atomik Tour," a brilliant stop-motion short by the talented Bruno Collet. This 12-minute gem takes us on a thrilling journey through dark tourism, exploring the eerie yet captivating aspects of the forbidden zone. It's amazing how art can transform our perceptions of the world, even the darker sides! Let's celebrate creativity and the magic of storytelling that brings us together!

    Don’t miss out on this incredible film that sparks curiosity and wonder! Keep dreaming, keep creating!

    #AtomikTour #DarkTourism #Stop
    🎉✨ What an exhilarating experience at the National Animation Film Festival! 🎥🌟 I had the pleasure of diving into the fascinating world of "Atomik Tour," a brilliant stop-motion short by the talented Bruno Collet. 🎨💫 This 12-minute gem takes us on a thrilling journey through dark tourism, exploring the eerie yet captivating aspects of the forbidden zone. 🏴‍☠️👌 It's amazing how art can transform our perceptions of the world, even the darker sides! Let's celebrate creativity and the magic of storytelling that brings us together! 🌈🙌 Don’t miss out on this incredible film that sparks curiosity and wonder! Keep dreaming, keep creating! #AtomikTour #DarkTourism #Stop
    Dark Tourism et Stop-Motion : les coulisses d’Atomik Tour de Bruno Collet
    A l’occasion du Festival National du Film d’Animation, le réalisateur Bruno Collet (connu entre autres pour son court-métrage Mémorable) et une partie de son équipe ont proposé un retour sur Atomik Tour. Ce court métrage est un film fanta
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  • The New Street Fighter Movie Has The Most Bizarre Cast

    Fighting game adaptations are some of the campiest, most absurd films in the already generally pretty absurd game-to-movie oeuvre. Films like the Mortal Kombat series, Dead or Alive, and the 1994 Street Fighter all possess a kind of so-bad-they’re-good ridiculousness that has made them cult classic guilty pleasures.…Read more...
    The New Street Fighter Movie Has The Most Bizarre Cast Fighting game adaptations are some of the campiest, most absurd films in the already generally pretty absurd game-to-movie oeuvre. Films like the Mortal Kombat series, Dead or Alive, and the 1994 Street Fighter all possess a kind of so-bad-they’re-good ridiculousness that has made them cult classic guilty pleasures.…Read more...
    KOTAKU.COM
    The New Street Fighter Movie Has The Most Bizarre Cast
    Fighting game adaptations are some of the campiest, most absurd films in the already generally pretty absurd game-to-movie oeuvre. Films like the Mortal Kombat series, Dead or Alive, and the 1994 Street Fighter all possess a kind of so-bad-they’re-go
    2 Commentarios 0 Acciones
  • At last, physicists at the University of Liège have cracked the code: water landscapes created with 3D printing! Because why enjoy a simple drink when you can have a miniature ocean on your table? Forget about the days of just swimming in water; now we can marvel at the aesthetic pleasure of tiny, printed spines dancing on the surface. Who knew physics could be so… artistic? Next up, they'll probably figure out how to print clouds into our living rooms. Get ready for some very confused houseplants.

    #3DPrinting #WaterLandscapes #PhysicsArt #UniversityOfLiege #InnovativeScience
    At last, physicists at the University of Liège have cracked the code: water landscapes created with 3D printing! Because why enjoy a simple drink when you can have a miniature ocean on your table? Forget about the days of just swimming in water; now we can marvel at the aesthetic pleasure of tiny, printed spines dancing on the surface. Who knew physics could be so… artistic? Next up, they'll probably figure out how to print clouds into our living rooms. Get ready for some very confused houseplants. #3DPrinting #WaterLandscapes #PhysicsArt #UniversityOfLiege #InnovativeScience
    Físicos de la Universidad de Lieja crean paisajes líquidos gracias a la impresión 3D
    ¿Y si pudiéramos convertir el agua en un paisaje? Físicos de la Universidad de Lieja, en Bélgica, en colaboración la Universidad Brown (EE.UU.), lo han logrado. A partir de espinas milimétricas impresas en 3D, consiguieron manipular la superficie del
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  • Oh, IMAX, the grand illusion of reality turned up to eleven! Who knew that watching a two-hour movie could feel like a NASA launch, complete with a symphony of surround sound that could wake the dead? For those who haven't had the pleasure, IMAX is not just a cinema; it’s an experience that makes you feel like you’re inside the movie—right before you realize you’re just trapped in a ridiculously oversized chair, too small for your popcorn bucket.

    Let’s talk about those gigantic screens. You know, the ones that make your living room TV look like a postage stamp? Apparently, the idea is to engulf you in the film so much that you forget about the existential dread of your daily life. Because honestly, who needs a therapist when you can sit in a dark room, surrounded by strangers, with a screen larger than your future looming in front of you?

    And don’t get me started on the “revolutionary technology.” IMAX is synonymous with larger-than-life images, but let's face it—it's just fancy pixels. I mean, how many different ways can you capture a superhero saving the world at this point? Yet, somehow, they manage to convince us that we need to watch it all in the world’s biggest format, because watching it on a normal screen would be akin to watching it through a keyhole, right?

    Then there’s the sound. IMAX promises "the most immersive audio experience." Yes, because nothing says relaxation like feeling like you’re in the middle of a battle scene with explosions that could shake the very foundations of your soul. You know, I used to think my neighbors were loud, but now I realize they could never compete with the sound of a spaceship crashing at full volume. Thanks, IMAX, for redefining the meaning of “loud neighbors.”

    And let’s not forget the tickets. A small mortgage payment for an evening of cinematic bliss! Who needs to save for retirement when you can experience the thrill of a blockbuster in a seat that costs more than your last three grocery bills combined? It’s a small price to pay for the opportunity to see your favorite actors’ pores in glorious detail.

    In conclusion, if you haven’t yet experienced the wonder that is IMAX, prepare yourself for a rollercoaster of emotions and a potential existential crisis. Because nothing says “reality” quite like watching a fictional world unfold on a screen so big it makes your own life choices seem trivial. So, grab your credit card, put on your 3D glasses, and let’s dive into the cinematic abyss of IMAX—where reality takes a backseat, and your wallet weeps in despair.

    #IMAX #CinematicExperience #RealityCheck #MovieMagic #TooBigToFail
    Oh, IMAX, the grand illusion of reality turned up to eleven! Who knew that watching a two-hour movie could feel like a NASA launch, complete with a symphony of surround sound that could wake the dead? For those who haven't had the pleasure, IMAX is not just a cinema; it’s an experience that makes you feel like you’re inside the movie—right before you realize you’re just trapped in a ridiculously oversized chair, too small for your popcorn bucket. Let’s talk about those gigantic screens. You know, the ones that make your living room TV look like a postage stamp? Apparently, the idea is to engulf you in the film so much that you forget about the existential dread of your daily life. Because honestly, who needs a therapist when you can sit in a dark room, surrounded by strangers, with a screen larger than your future looming in front of you? And don’t get me started on the “revolutionary technology.” IMAX is synonymous with larger-than-life images, but let's face it—it's just fancy pixels. I mean, how many different ways can you capture a superhero saving the world at this point? Yet, somehow, they manage to convince us that we need to watch it all in the world’s biggest format, because watching it on a normal screen would be akin to watching it through a keyhole, right? Then there’s the sound. IMAX promises "the most immersive audio experience." Yes, because nothing says relaxation like feeling like you’re in the middle of a battle scene with explosions that could shake the very foundations of your soul. You know, I used to think my neighbors were loud, but now I realize they could never compete with the sound of a spaceship crashing at full volume. Thanks, IMAX, for redefining the meaning of “loud neighbors.” And let’s not forget the tickets. A small mortgage payment for an evening of cinematic bliss! Who needs to save for retirement when you can experience the thrill of a blockbuster in a seat that costs more than your last three grocery bills combined? It’s a small price to pay for the opportunity to see your favorite actors’ pores in glorious detail. In conclusion, if you haven’t yet experienced the wonder that is IMAX, prepare yourself for a rollercoaster of emotions and a potential existential crisis. Because nothing says “reality” quite like watching a fictional world unfold on a screen so big it makes your own life choices seem trivial. So, grab your credit card, put on your 3D glasses, and let’s dive into the cinematic abyss of IMAX—where reality takes a backseat, and your wallet weeps in despair. #IMAX #CinematicExperience #RealityCheck #MovieMagic #TooBigToFail
    IMAX : tout ce que vous devez savoir
    IMAX est mondialement reconnu pour ses écrans gigantesques, mais cette technologie révolutionnaire ne se limite […] Cet article IMAX : tout ce que vous devez savoir a été publié sur REALITE-VIRTUELLE.COM.
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  • Hey there, beautiful souls!

    Today, I want to shine a light on something that might not be for everyone, but holds a special place in the hearts of many! Let's talk about the **Hot Octopuss Pulse Duo**!

    Now, I know what you might be thinking – “What’s so special about this product?” Well, let me tell you! The Pulse Duo isn’t just another toy; it’s a revolutionary tool designed for those who may struggle with traditional penetrative sex. And that’s absolutely okay! Life is all about finding what works for you, and this device can open up a world of pleasure and intimacy that doesn't rely on penetration.

    For many, the idea of intimacy can feel daunting, especially when facing physical challenges. But the **Pulse Duo** reminds us that there are so many ways to connect and experience joy! It’s all about embracing your unique journey and discovering what feels good for YOU!

    Imagine the possibilities! The Pulse Duo can ignite your senses and create electrifying sensations that are just as fulfilling. Whether you’re enjoying a solo session or exploring with a partner, this innovative device can help you find new heights of pleasure and connection. It’s all about celebrating your body and what it can do!

    Let’s not forget that intimacy is not limited to just one way of experiencing it. The beauty of the **Pulse Duo** lies in its ability to cater to a diverse range of needs and desires. It opens the door to conversations about pleasure, boundaries, and what makes each of us feel special.

    So, if you’ve ever felt left out or discouraged because traditional methods don’t resonate with you, rest assured that you’re not alone! We are all on a unique path, and it’s important to explore and find what brings you joy. Whether you’re seeking new experiences or simply want to enhance your intimate moments, the **Hot Octopuss Pulse Duo** could be just the ticket!

    Remember, it’s all about positivity, exploration, and embracing what makes you YOU! Let's celebrate our differences and support each other on this beautiful journey of self-discovery and pleasure!

    Stay radiant and keep shining, my friends! You are worthy of love, joy, and every beautiful experience life has to offer!

    #HotOctopuss #PulseDuo #Intimacy #PleasureForAll #SelfDiscovery
    🌟 Hey there, beautiful souls! 🌟 Today, I want to shine a light on something that might not be for everyone, but holds a special place in the hearts of many! Let's talk about the **Hot Octopuss Pulse Duo**! 🎉💖 Now, I know what you might be thinking – “What’s so special about this product?” Well, let me tell you! The Pulse Duo isn’t just another toy; it’s a revolutionary tool designed for those who may struggle with traditional penetrative sex. And that’s absolutely okay! Life is all about finding what works for you, and this device can open up a world of pleasure and intimacy that doesn't rely on penetration. 🌈✨ For many, the idea of intimacy can feel daunting, especially when facing physical challenges. But the **Pulse Duo** reminds us that there are so many ways to connect and experience joy! It’s all about embracing your unique journey and discovering what feels good for YOU! 💪💕 Imagine the possibilities! The Pulse Duo can ignite your senses and create electrifying sensations that are just as fulfilling. 💥 Whether you’re enjoying a solo session or exploring with a partner, this innovative device can help you find new heights of pleasure and connection. It’s all about celebrating your body and what it can do! 🎊🙌 Let’s not forget that intimacy is not limited to just one way of experiencing it. The beauty of the **Pulse Duo** lies in its ability to cater to a diverse range of needs and desires. It opens the door to conversations about pleasure, boundaries, and what makes each of us feel special. 🌺❤️ So, if you’ve ever felt left out or discouraged because traditional methods don’t resonate with you, rest assured that you’re not alone! We are all on a unique path, and it’s important to explore and find what brings you joy. Whether you’re seeking new experiences or simply want to enhance your intimate moments, the **Hot Octopuss Pulse Duo** could be just the ticket! 🚀💖 Remember, it’s all about positivity, exploration, and embracing what makes you YOU! Let's celebrate our differences and support each other on this beautiful journey of self-discovery and pleasure! 🌟✨ Stay radiant and keep shining, my friends! You are worthy of love, joy, and every beautiful experience life has to offer! 🌈💖 #HotOctopuss #PulseDuo #Intimacy #PleasureForAll #SelfDiscovery
    Hot Octopuss Pulse Duo Review: Not for Penetration
    The Pulse Duo isn't for me, but it’s an important tool for people who can’t enjoy penetrative sex.
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  • iPad Air vs reMarkable Paper Pro: Which tablet is best for note taking? [Updated]

    Over the past few months, I’ve had the pleasure of testing out the reMarkable Paper Pro. You can read my full review here, but in short, it gets everything right about the note taking experience.
    Despite being an e-ink tablet, it does get quite pricey. However, there are certainly some fantastic parts of the experience that make it worth comparing to an iPad Air, depending on what you’re looking for in a note taking device for school, work, or whatever else.

    Updated June 15th to reflect reMarkable’s new post-tariff pricing.
    Overview
    Since the reMarkable Paper Pro comes in at with the reMarkable Marker Plus included, it likely makes most sense to compare this against Apple’s iPad Air 11-inch. That comes in at without an Apple Pencil, and adding in the Apple Pencil Pro will run you an additional The equivalent iPad setup will run you more than the reMarkable Paper Pro.
    Given the fact that iPad Air‘s regularly go on sale, it’d be fair to say they’re roughly on the same playing field. So, for a reMarkable Paper Pro setup, versus for a comparable iPad Air setup. Which is better for you?
    Obviously, the iPad Air has one key advantage: It runs iOS, has millions of apps available, can browse the web, play games, stream TV shows/movies, and much more. To some, that might end the comparison and make the iPad a clear winner, but I disagree.
    Yes, if you want your tablet to do all of those things for you, the iPad Air is a no brainer. At the end of the day, the iPad Air is a general purpose tablet that’ll do a lot more for you.
    However, if you also have a laptop to accompany your tablet, I’d argue that the iPad Air may fall into a category of slight redundance. Most things you’d want to do on the iPad can be done on a laptop, excluding any sort of touchscreen/stylus reliant features.
    iPads are great, and if you want that – you should pick that. However, I have an alternative argument to offer…
    The reMarkable Paper Pro does one thing really well: note taking. At first thought, you might think: why would I pay so much for a device that only does one thing?
    Well, that’s because it does that one thing really well. There’s also a second side to this argument: focus.
    It’s much easier to focus on what you’re doing when the device isn’t capable of anything else. If you’re taking notes while studying, you could easily see a notification or have the temptation to check notification center. Or, if you’re reading an e-book, you could easily choose to swipe up and get into another app.
    The best thing about the reMarkable Paper Pro is that you can’t easily get lost in the world of modern technology, while still having important technological features like cloud backup of your notes. Plus, you don’t have to worry about carrying around physical paper.
    One last thing – the reMarkable Paper Pro also has rubber feet on the back, so if you place it down flat on a table caseless, you don’t have to worry about scratching it up.
    Spec comparison
    Here’s a quick rundown of all of the key specs between the two devices. reMarkable Paper Pro‘s strengths definitely lie in battery, form factor, and stylus. iPad has some rather neat features with the Apple Pencil Pro, and also clears in the display category. Both devices also offer keyboards for typed notes, though only the iPad offers a trackpad.
    Display– 10.9-inch LCD display– Glossy glass– 2360 × 1640 at 264 ppi– 11.8-inch Color e-ink display– Paper-feeling textured glass– 2160 × 1620 at 229 ppiHardware– 6.1mm thin– Anodized aluminum coating– Weighs 461g w/o Pencil Pro– 5.1mm thin– Textured aluminum edges– Weighs 360g w/ Marker attachedStylus– Magnetically charges from device– Supports tilt/pressure sensitivity– Low latency– Matte plastic build– Squeeze features, double tap gestures– Magnetically charges from device– Supports tilt/pressure sensitivity– Ultra-low latency– Premium textured aluminum build– Built in eraser on the bottomBattery life– Up to 10 hours of web browsing– Recharges to 100% in 2-3 hrs– Up to 14 days of typical usage– Fast charges to 90% in 90 minsPrice–for iPad Air–for Pencil Pro– bundled with Marker Plus
    Wrap up
    All in all, I’m not going to try to convince anyone that wanted to buy an iPad that they should buy a reMarkable Paper Pro. You can’t beat the fact that the iPad Air will do a lot more, for roughly the same cost.
    But, if you’re not buying this to be a primary computing device, I’d argue that the reMarkable Paper Pro is a worthy alternative, especially if you really just want something you can zone in on. The reMarkable Paper Pro feels a lot nicer to write on, has substantially longer battery life, and really masters a minimalist form of digital note taking.
    Buy M3 iPad Air on Amazon:
    Buy reMarkable Paper Pro on Amazon:
    What do you think of these two tablets? Let us know in the comments.

    My favorite Apple accessory recommendations:
    Follow Michael: X/Twitter, Bluesky, Instagram

    Add 9to5Mac to your Google News feed. 

    FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel
    #ipad #air #remarkable #paper #pro
    iPad Air vs reMarkable Paper Pro: Which tablet is best for note taking? [Updated]
    Over the past few months, I’ve had the pleasure of testing out the reMarkable Paper Pro. You can read my full review here, but in short, it gets everything right about the note taking experience. Despite being an e-ink tablet, it does get quite pricey. However, there are certainly some fantastic parts of the experience that make it worth comparing to an iPad Air, depending on what you’re looking for in a note taking device for school, work, or whatever else. Updated June 15th to reflect reMarkable’s new post-tariff pricing. Overview Since the reMarkable Paper Pro comes in at with the reMarkable Marker Plus included, it likely makes most sense to compare this against Apple’s iPad Air 11-inch. That comes in at without an Apple Pencil, and adding in the Apple Pencil Pro will run you an additional The equivalent iPad setup will run you more than the reMarkable Paper Pro. Given the fact that iPad Air‘s regularly go on sale, it’d be fair to say they’re roughly on the same playing field. So, for a reMarkable Paper Pro setup, versus for a comparable iPad Air setup. Which is better for you? Obviously, the iPad Air has one key advantage: It runs iOS, has millions of apps available, can browse the web, play games, stream TV shows/movies, and much more. To some, that might end the comparison and make the iPad a clear winner, but I disagree. Yes, if you want your tablet to do all of those things for you, the iPad Air is a no brainer. At the end of the day, the iPad Air is a general purpose tablet that’ll do a lot more for you. However, if you also have a laptop to accompany your tablet, I’d argue that the iPad Air may fall into a category of slight redundance. Most things you’d want to do on the iPad can be done on a laptop, excluding any sort of touchscreen/stylus reliant features. iPads are great, and if you want that – you should pick that. However, I have an alternative argument to offer… The reMarkable Paper Pro does one thing really well: note taking. At first thought, you might think: why would I pay so much for a device that only does one thing? Well, that’s because it does that one thing really well. There’s also a second side to this argument: focus. It’s much easier to focus on what you’re doing when the device isn’t capable of anything else. If you’re taking notes while studying, you could easily see a notification or have the temptation to check notification center. Or, if you’re reading an e-book, you could easily choose to swipe up and get into another app. The best thing about the reMarkable Paper Pro is that you can’t easily get lost in the world of modern technology, while still having important technological features like cloud backup of your notes. Plus, you don’t have to worry about carrying around physical paper. One last thing – the reMarkable Paper Pro also has rubber feet on the back, so if you place it down flat on a table caseless, you don’t have to worry about scratching it up. Spec comparison Here’s a quick rundown of all of the key specs between the two devices. reMarkable Paper Pro‘s strengths definitely lie in battery, form factor, and stylus. iPad has some rather neat features with the Apple Pencil Pro, and also clears in the display category. Both devices also offer keyboards for typed notes, though only the iPad offers a trackpad. Display– 10.9-inch LCD display– Glossy glass– 2360 × 1640 at 264 ppi– 11.8-inch Color e-ink display– Paper-feeling textured glass– 2160 × 1620 at 229 ppiHardware– 6.1mm thin– Anodized aluminum coating– Weighs 461g w/o Pencil Pro– 5.1mm thin– Textured aluminum edges– Weighs 360g w/ Marker attachedStylus– Magnetically charges from device– Supports tilt/pressure sensitivity– Low latency– Matte plastic build– Squeeze features, double tap gestures– Magnetically charges from device– Supports tilt/pressure sensitivity– Ultra-low latency– Premium textured aluminum build– Built in eraser on the bottomBattery life– Up to 10 hours of web browsing– Recharges to 100% in 2-3 hrs– Up to 14 days of typical usage– Fast charges to 90% in 90 minsPrice–for iPad Air–for Pencil Pro– bundled with Marker Plus Wrap up All in all, I’m not going to try to convince anyone that wanted to buy an iPad that they should buy a reMarkable Paper Pro. You can’t beat the fact that the iPad Air will do a lot more, for roughly the same cost. But, if you’re not buying this to be a primary computing device, I’d argue that the reMarkable Paper Pro is a worthy alternative, especially if you really just want something you can zone in on. The reMarkable Paper Pro feels a lot nicer to write on, has substantially longer battery life, and really masters a minimalist form of digital note taking. Buy M3 iPad Air on Amazon: Buy reMarkable Paper Pro on Amazon: What do you think of these two tablets? Let us know in the comments. My favorite Apple accessory recommendations: Follow Michael: X/Twitter, Bluesky, Instagram Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel #ipad #air #remarkable #paper #pro
    9TO5MAC.COM
    iPad Air vs reMarkable Paper Pro: Which tablet is best for note taking? [Updated]
    Over the past few months, I’ve had the pleasure of testing out the reMarkable Paper Pro. You can read my full review here, but in short, it gets everything right about the note taking experience. Despite being an e-ink tablet, it does get quite pricey. However, there are certainly some fantastic parts of the experience that make it worth comparing to an iPad Air, depending on what you’re looking for in a note taking device for school, work, or whatever else. Updated June 15th to reflect reMarkable’s new post-tariff pricing. Overview Since the reMarkable Paper Pro comes in at $679 with the reMarkable Marker Plus included, it likely makes most sense to compare this against Apple’s iPad Air 11-inch. That comes in at $599 without an Apple Pencil, and adding in the Apple Pencil Pro will run you an additional $129. The equivalent iPad setup will run you $50 more than the reMarkable Paper Pro. Given the fact that iPad Air‘s regularly go on sale, it’d be fair to say they’re roughly on the same playing field. So, $679 for a reMarkable Paper Pro setup, versus $728 for a comparable iPad Air setup. Which is better for you? Obviously, the iPad Air has one key advantage: It runs iOS, has millions of apps available, can browse the web, play games, stream TV shows/movies, and much more. To some, that might end the comparison and make the iPad a clear winner, but I disagree. Yes, if you want your tablet to do all of those things for you, the iPad Air is a no brainer. At the end of the day, the iPad Air is a general purpose tablet that’ll do a lot more for you. However, if you also have a laptop to accompany your tablet, I’d argue that the iPad Air may fall into a category of slight redundance. Most things you’d want to do on the iPad can be done on a laptop, excluding any sort of touchscreen/stylus reliant features. iPads are great, and if you want that – you should pick that. However, I have an alternative argument to offer… The reMarkable Paper Pro does one thing really well: note taking. At first thought, you might think: why would I pay so much for a device that only does one thing? Well, that’s because it does that one thing really well. There’s also a second side to this argument: focus. It’s much easier to focus on what you’re doing when the device isn’t capable of anything else. If you’re taking notes while studying, you could easily see a notification or have the temptation to check notification center. Or, if you’re reading an e-book, you could easily choose to swipe up and get into another app. The best thing about the reMarkable Paper Pro is that you can’t easily get lost in the world of modern technology, while still having important technological features like cloud backup of your notes. Plus, you don’t have to worry about carrying around physical paper. One last thing – the reMarkable Paper Pro also has rubber feet on the back, so if you place it down flat on a table caseless, you don’t have to worry about scratching it up. Spec comparison Here’s a quick rundown of all of the key specs between the two devices. reMarkable Paper Pro‘s strengths definitely lie in battery, form factor, and stylus. iPad has some rather neat features with the Apple Pencil Pro, and also clears in the display category. Both devices also offer keyboards for typed notes, though only the iPad offers a trackpad. Display– 10.9-inch LCD display– Glossy glass– 2360 × 1640 at 264 ppi– 11.8-inch Color e-ink display– Paper-feeling textured glass– 2160 × 1620 at 229 ppiHardware– 6.1mm thin– Anodized aluminum coating– Weighs 461g w/o Pencil Pro– 5.1mm thin– Textured aluminum edges– Weighs 360g w/ Marker attachedStylus– Magnetically charges from device– Supports tilt/pressure sensitivity– Low latency (number unspecified)– Matte plastic build– Squeeze features, double tap gestures– Magnetically charges from device– Supports tilt/pressure sensitivity– Ultra-low latency (12ms)– Premium textured aluminum build– Built in eraser on the bottomBattery life– Up to 10 hours of web browsing– Recharges to 100% in 2-3 hrs– Up to 14 days of typical usage– Fast charges to 90% in 90 minsPrice– $599 ($529 on sale) for iPad Air– $129 ($99 on sale) for Pencil Pro– $679 bundled with Marker Plus Wrap up All in all, I’m not going to try to convince anyone that wanted to buy an iPad that they should buy a reMarkable Paper Pro. You can’t beat the fact that the iPad Air will do a lot more, for roughly the same cost. But, if you’re not buying this to be a primary computing device, I’d argue that the reMarkable Paper Pro is a worthy alternative, especially if you really just want something you can zone in on. The reMarkable Paper Pro feels a lot nicer to write on, has substantially longer battery life, and really masters a minimalist form of digital note taking. Buy M3 iPad Air on Amazon: Buy reMarkable Paper Pro on Amazon: What do you think of these two tablets? Let us know in the comments. My favorite Apple accessory recommendations: Follow Michael: X/Twitter, Bluesky, Instagram Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel
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  • Why an Xbox Video Game Franchise Is a Partner in a Major Exhibit at The Louvre Museum

    While it’s now accepted by many that video games are an art form, it still might be hard to believe that one is featured in an exhibit at the same museum that’s home to Leonardo da Vinci’s “Mona Lisa”: The Louvre in Paris.

    But this week, Xbox and World’s Edge Studio announced a partnership with what is arguably the most prestigious museum in the world for its new exhibition, “Mamluks 1250–1517.”

    Related Stories

    For those who are unaware of how the gaming studios connect to this aspect of the Egyptian Syrian empire: The Mamluks cavalry are among the many units featured in Xbox and World’s Edge Studio’s “Age of Empires” video game franchise. The cavalry is a fan favorite choice in the game centered around traversing the ages and competing against rival empires, particularly in “Age of Empires II: Definitive Edition.”

    Popular on Variety

    Presented at the Louvre until July 28, the exhibit “Mamluks 1250–1517″ recounts “the glorious and unique history of this Egyptian Syrian empire, which represents a golden age for the Near East during the Islamic era,” per its official description. “Bringing together 260 pieces from international collections, the exhibition explores the richness of this singular and lesser-known society through a spectacular and immersive scenography.”

    This marks the first time a video game franchise has collaborated with the Louvre Museum, with installations and events that occur both in person at the museum and online through the “Age of Empires” game:

    Official “Louvre Museum” scenario in Age of Empires II: Definitive Edition
    Players can embody General Baybars and Sultan Qutuz at the really heart of the Ain Jalut battle, which opposed the Mamluk Sultanate to the Mongol Empire. This scenario, speciallycreated for the occasion, is already available in Age of Empires II: Definitive Edition.Exclusive Gaming Night on Twitch Live from the Louvre
    On Thursday, June 12, at 8 PM, streamer and journalist Samuel Etiennewill replay live from the exhibition “Mamluks 1250-1517” at the Louvre the official“Louvre Museum” scenario to relive the famous Battle of Ain Jalut on the game Age of EmpiresII: Definitive Edition, in the presence of Le Louvre Teams and one of the studio’s developers.This is an opportunity to learn more about the history of the Mamluks and their representationin the various episodes of the saga.Cross-Interview: The Louvre x Age of Empires
    To discover more, an interview featuring Adam Isgreen, creative director at World’s Edge, thestudio behind the franchise, and Souraya Noujaïm and Carine Juvin, curators of the exhibition,is available on the YouTube channels of the Louvre and Age of Empires.Mediation and Gaming Sessions at the Museum
    Museum visitors at the Louvre are invited to test the scenario of the Battle of Ain Jalut,specially designed for the Mamluk exhibition, in the presence of a Louvre mediator and anXbox representative during an exceptional series of workshops. The sessions will take place onFridays, June 20, 27, and 4 & 11 of July. All information and registrations are available here:www.louvre.fr

    “World’s Edge is honoured to collaborate with Le Louvre,” head of World’s Edge studio Michael Mann said. “The ‘Age of Empires’ franchise has been bringing history to life for more than 65 million players around the world for almost 30 years. We’ve always believed in the great potential for our games to spark an interest in history and culture. We often hear of teachers using ‘Age of Empires’ to teach history to their students and stories from our players about how ‘Age of Empires’ has driven them to learn more, or even to pursue history academically or as a career. This opportunity to bring the amazing stories of the Mamluks to new audiences through the Louvre’s exhibition is one we’re excited to be a part of. We hope that through the excellent work of the Louvre’s team, the legacy of the Mamluks can be shared around the world, and that people enjoy their stories as they come to life through ‘Age of Empires.'”

    “We are delighted to welcome ‘Age of Empires’ as part of the exhibition Mamluks 1250–1517, through a unique partnership that blends the pleasures of gaming with learning and discovery,” Souraya Noujaim, director of the Department of Islamic Arts and chief curator of the exhibition at le Louvre Museum, said. “It is a way for the museum to engage with diverse audiences and offer a new narrative, one that resonates with contemporary sensitivities, allowing for a deeper understanding of artworks and a greater openness to world history. Beyond the game, the museum experience becomes an opportunity to move from the virtual to the real and uncover the true history of the Mamluks and their unique contribution to universal heritage.”

    See video and images below from the “Age of Empires” in-game event and the in-person exhibit at the Louvre.
    #why #xbox #video #game #franchise
    Why an Xbox Video Game Franchise Is a Partner in a Major Exhibit at The Louvre Museum
    While it’s now accepted by many that video games are an art form, it still might be hard to believe that one is featured in an exhibit at the same museum that’s home to Leonardo da Vinci’s “Mona Lisa”: The Louvre in Paris. But this week, Xbox and World’s Edge Studio announced a partnership with what is arguably the most prestigious museum in the world for its new exhibition, “Mamluks 1250–1517.” Related Stories For those who are unaware of how the gaming studios connect to this aspect of the Egyptian Syrian empire: The Mamluks cavalry are among the many units featured in Xbox and World’s Edge Studio’s “Age of Empires” video game franchise. The cavalry is a fan favorite choice in the game centered around traversing the ages and competing against rival empires, particularly in “Age of Empires II: Definitive Edition.” Popular on Variety Presented at the Louvre until July 28, the exhibit “Mamluks 1250–1517″ recounts “the glorious and unique history of this Egyptian Syrian empire, which represents a golden age for the Near East during the Islamic era,” per its official description. “Bringing together 260 pieces from international collections, the exhibition explores the richness of this singular and lesser-known society through a spectacular and immersive scenography.” This marks the first time a video game franchise has collaborated with the Louvre Museum, with installations and events that occur both in person at the museum and online through the “Age of Empires” game: Official “Louvre Museum” scenario in Age of Empires II: Definitive Edition Players can embody General Baybars and Sultan Qutuz at the really heart of the Ain Jalut battle, which opposed the Mamluk Sultanate to the Mongol Empire. This scenario, speciallycreated for the occasion, is already available in Age of Empires II: Definitive Edition.Exclusive Gaming Night on Twitch Live from the Louvre On Thursday, June 12, at 8 PM, streamer and journalist Samuel Etiennewill replay live from the exhibition “Mamluks 1250-1517” at the Louvre the official“Louvre Museum” scenario to relive the famous Battle of Ain Jalut on the game Age of EmpiresII: Definitive Edition, in the presence of Le Louvre Teams and one of the studio’s developers.This is an opportunity to learn more about the history of the Mamluks and their representationin the various episodes of the saga.Cross-Interview: The Louvre x Age of Empires To discover more, an interview featuring Adam Isgreen, creative director at World’s Edge, thestudio behind the franchise, and Souraya Noujaïm and Carine Juvin, curators of the exhibition,is available on the YouTube channels of the Louvre and Age of Empires.Mediation and Gaming Sessions at the Museum Museum visitors at the Louvre are invited to test the scenario of the Battle of Ain Jalut,specially designed for the Mamluk exhibition, in the presence of a Louvre mediator and anXbox representative during an exceptional series of workshops. The sessions will take place onFridays, June 20, 27, and 4 & 11 of July. All information and registrations are available here:www.louvre.fr “World’s Edge is honoured to collaborate with Le Louvre,” head of World’s Edge studio Michael Mann said. “The ‘Age of Empires’ franchise has been bringing history to life for more than 65 million players around the world for almost 30 years. We’ve always believed in the great potential for our games to spark an interest in history and culture. We often hear of teachers using ‘Age of Empires’ to teach history to their students and stories from our players about how ‘Age of Empires’ has driven them to learn more, or even to pursue history academically or as a career. This opportunity to bring the amazing stories of the Mamluks to new audiences through the Louvre’s exhibition is one we’re excited to be a part of. We hope that through the excellent work of the Louvre’s team, the legacy of the Mamluks can be shared around the world, and that people enjoy their stories as they come to life through ‘Age of Empires.'” “We are delighted to welcome ‘Age of Empires’ as part of the exhibition Mamluks 1250–1517, through a unique partnership that blends the pleasures of gaming with learning and discovery,” Souraya Noujaim, director of the Department of Islamic Arts and chief curator of the exhibition at le Louvre Museum, said. “It is a way for the museum to engage with diverse audiences and offer a new narrative, one that resonates with contemporary sensitivities, allowing for a deeper understanding of artworks and a greater openness to world history. Beyond the game, the museum experience becomes an opportunity to move from the virtual to the real and uncover the true history of the Mamluks and their unique contribution to universal heritage.” See video and images below from the “Age of Empires” in-game event and the in-person exhibit at the Louvre. #why #xbox #video #game #franchise
    VARIETY.COM
    Why an Xbox Video Game Franchise Is a Partner in a Major Exhibit at The Louvre Museum
    While it’s now accepted by many that video games are an art form, it still might be hard to believe that one is featured in an exhibit at the same museum that’s home to Leonardo da Vinci’s “Mona Lisa”: The Louvre in Paris. But this week, Xbox and World’s Edge Studio announced a partnership with what is arguably the most prestigious museum in the world for its new exhibition, “Mamluks 1250–1517.” Related Stories For those who are unaware of how the gaming studios connect to this aspect of the Egyptian Syrian empire: The Mamluks cavalry are among the many units featured in Xbox and World’s Edge Studio’s “Age of Empires” video game franchise. The cavalry is a fan favorite choice in the game centered around traversing the ages and competing against rival empires, particularly in “Age of Empires II: Definitive Edition.” Popular on Variety Presented at the Louvre until July 28, the exhibit “Mamluks 1250–1517″ recounts “the glorious and unique history of this Egyptian Syrian empire, which represents a golden age for the Near East during the Islamic era,” per its official description. “Bringing together 260 pieces from international collections, the exhibition explores the richness of this singular and lesser-known society through a spectacular and immersive scenography.” This marks the first time a video game franchise has collaborated with the Louvre Museum, with installations and events that occur both in person at the museum and online through the “Age of Empires” game: Official “Louvre Museum” scenario in Age of Empires II: Definitive Edition Players can embody General Baybars and Sultan Qutuz at the really heart of the Ain Jalut battle(1260), which opposed the Mamluk Sultanate to the Mongol Empire. This scenario, speciallycreated for the occasion, is already available in Age of Empires II: Definitive Edition (see onhttp://www.ageofempire.com/lelouvre for instructions on finding the map in the game) [LiveTuesday 10th at 9am PT/6pm BST].Exclusive Gaming Night on Twitch Live from the Louvre On Thursday, June 12, at 8 PM, streamer and journalist Samuel Etienne (1.1M FrenchStreamer) will replay live from the exhibition “Mamluks 1250-1517” at the Louvre the official“Louvre Museum” scenario to relive the famous Battle of Ain Jalut on the game Age of EmpiresII: Definitive Edition, in the presence of Le Louvre Teams and one of the studio’s developers.This is an opportunity to learn more about the history of the Mamluks and their representationin the various episodes of the saga.Cross-Interview: The Louvre x Age of Empires To discover more, an interview featuring Adam Isgreen, creative director at World’s Edge, thestudio behind the franchise, and Souraya Noujaïm and Carine Juvin, curators of the exhibition,is available on the YouTube channels of the Louvre and Age of Empires.Mediation and Gaming Sessions at the Museum Museum visitors at the Louvre are invited to test the scenario of the Battle of Ain Jalut,specially designed for the Mamluk exhibition, in the presence of a Louvre mediator and anXbox representative during an exceptional series of workshops. The sessions will take place onFridays, June 20, 27, and 4 & 11 of July. All information and registrations are available here:www.louvre.fr “World’s Edge is honoured to collaborate with Le Louvre,” head of World’s Edge studio Michael Mann said. “The ‘Age of Empires’ franchise has been bringing history to life for more than 65 million players around the world for almost 30 years. We’ve always believed in the great potential for our games to spark an interest in history and culture. We often hear of teachers using ‘Age of Empires’ to teach history to their students and stories from our players about how ‘Age of Empires’ has driven them to learn more, or even to pursue history academically or as a career. This opportunity to bring the amazing stories of the Mamluks to new audiences through the Louvre’s exhibition is one we’re excited to be a part of. We hope that through the excellent work of the Louvre’s team, the legacy of the Mamluks can be shared around the world, and that people enjoy their stories as they come to life through ‘Age of Empires.'” “We are delighted to welcome ‘Age of Empires’ as part of the exhibition Mamluks 1250–1517, through a unique partnership that blends the pleasures of gaming with learning and discovery,” Souraya Noujaim, director of the Department of Islamic Arts and chief curator of the exhibition at le Louvre Museum, said. “It is a way for the museum to engage with diverse audiences and offer a new narrative, one that resonates with contemporary sensitivities, allowing for a deeper understanding of artworks and a greater openness to world history. Beyond the game, the museum experience becomes an opportunity to move from the virtual to the real and uncover the true history of the Mamluks and their unique contribution to universal heritage.” See video and images below from the “Age of Empires” in-game event and the in-person exhibit at the Louvre.
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  • How AI is reshaping the future of healthcare and medical research

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

    After hanging up his daggers as Drax the Destroyer and getting got as Glossu Rabban in Dune: Part Two, Dave Bautista is stepping into video games and animation with a new franchise by the name of Cat Assassin. The wrestler-actor and his production company Dogbone Entertainment will bring to life a new idea from Steve Lerner, who wrote 2022’s feline adventure game Stray. This would-be franchise will comprise a stealth-action video game—influenced by titles such as Assassin’s Creed, Splinter Cell, and Sifu—from developer Titan1Studiosand a “neo-noir adult animated series.” Cat Assassin focuses on Hugh, an expert killer “caught between various cartels and power brokers in a dark and twisted city.” Bautista’s part of the enterprise’s “creative vision,” but at the moment, it’s unclear if that also means he’ll lend his voice to Hugh in either animated or video game form.Titan1 has several TV and game projects in the works, so at the moment, there’s no real window on when to expect Cat Assassin. Still, in a statement on Titan1’s website Bautista called teaming with the company “a pleasure … Their ability to build worlds through animation has been so impressive and they’ve created a truly unique world in this game that I can’t wait to share with players.”

    While the game is seemingly expected for release in October 2027 for PC and several consoles, including the Nintendo Switch 2, Titan1 said more details on the overall franchise’s future is expected “in the coming months.” Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who.
    #dave #bautistas #next #franchise #play
    Dave Bautista’s Next Franchise Play? Becoming a ‘Cat Assassin’
    After hanging up his daggers as Drax the Destroyer and getting got as Glossu Rabban in Dune: Part Two, Dave Bautista is stepping into video games and animation with a new franchise by the name of Cat Assassin. The wrestler-actor and his production company Dogbone Entertainment will bring to life a new idea from Steve Lerner, who wrote 2022’s feline adventure game Stray. This would-be franchise will comprise a stealth-action video game—influenced by titles such as Assassin’s Creed, Splinter Cell, and Sifu—from developer Titan1Studiosand a “neo-noir adult animated series.” Cat Assassin focuses on Hugh, an expert killer “caught between various cartels and power brokers in a dark and twisted city.” Bautista’s part of the enterprise’s “creative vision,” but at the moment, it’s unclear if that also means he’ll lend his voice to Hugh in either animated or video game form.Titan1 has several TV and game projects in the works, so at the moment, there’s no real window on when to expect Cat Assassin. Still, in a statement on Titan1’s website Bautista called teaming with the company “a pleasure … Their ability to build worlds through animation has been so impressive and they’ve created a truly unique world in this game that I can’t wait to share with players.” While the game is seemingly expected for release in October 2027 for PC and several consoles, including the Nintendo Switch 2, Titan1 said more details on the overall franchise’s future is expected “in the coming months.” Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who. #dave #bautistas #next #franchise #play
    GIZMODO.COM
    Dave Bautista’s Next Franchise Play? Becoming a ‘Cat Assassin’
    After hanging up his daggers as Drax the Destroyer and getting got as Glossu Rabban in Dune: Part Two, Dave Bautista is stepping into video games and animation with a new franchise by the name of Cat Assassin. The wrestler-actor and his production company Dogbone Entertainment will bring to life a new idea from Steve Lerner, who wrote 2022’s feline adventure game Stray. This would-be franchise will comprise a stealth-action video game—influenced by titles such as Assassin’s Creed, Splinter Cell, and Sifu—from developer Titan1Studios (Love is a Roguelike, The Events at Unity Farm) and a “neo-noir adult animated series.” Cat Assassin focuses on Hugh, an expert killer “caught between various cartels and power brokers in a dark and twisted city.” Bautista’s part of the enterprise’s “creative vision,” but at the moment, it’s unclear if that also means he’ll lend his voice to Hugh in either animated or video game form. (His current voice work includes the upcoming Army of the Dead animated series and playing himself in WWE games since 2003.) Titan1 has several TV and game projects in the works, so at the moment, there’s no real window on when to expect Cat Assassin. Still, in a statement on Titan1’s website Bautista called teaming with the company “a pleasure … Their ability to build worlds through animation has been so impressive and they’ve created a truly unique world in this game that I can’t wait to share with players.” While the game is seemingly expected for release in October 2027 for PC and several consoles, including the Nintendo Switch 2, Titan1 said more details on the overall franchise’s future is expected “in the coming months.” Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who.
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  • MindsEye review – a dystopian future that plays like it’s from 2012

    There’s a Sphere-alike in Redrock, MindsEye’s open-world version of Las Vegas. It’s pretty much a straight copy of the original: a huge soap bubble, half sunk into the desert floor, with its surface turned into a gigantic TV. Occasionally you’ll pull up near the Sphere while driving an electric vehicle made by Silva, the megacorp that controls this world. You’ll sometimes come to a stop just as an advert for an identical Silva EV plays out on the huge curved screen overhead. The doubling effect can be slightly vertigo-inducing.At these moments, I truly get what MindsEye is trying to do. You’re stuck in the ultimate company town, where oligarchs and other crooks run everything, and there’s no hope of escaping the ecosystem they’ve built. MindsEye gets this all across through a chance encounter, and in a way that’s both light of touch and clever. The rest of the game tends towards the heavy-handed and silly, but it’s nice to glimpse a few instances where everything clicks.With its Spheres and omnipresent EVs, MindsEye looks and sounds like the future. It’s concerned with AI and tech bros and the insidious creep of a corporate dystopia. You play as an amnesiac former-soldier who must work out the precise damage that technology has done to his humanity, while shooting people and robots and drones. And alongside the campaign itself, MindsEye also has a suite of tools for making your own game or levels and publishing them for fellow players. All of this has come from a studio founded by Leslie Benzies, whose production credits include the likes of GTA 5.AI overlords … MindsEye. Photograph: IOI PartnersWhat’s weird, then, is that MindsEye generally plays like the past. Put a finger to the air and the wind is blowing from somewhere around 2012. At heart, this is a roughly hewn cover shooter with an open world that you only really experience when you’re driving between missions. Its topical concerns mainly exist to justify double-crosses and car chases and shootouts, and to explain why you head into battle with a personal drone that can open doors for you and stun nearby enemies.It can be an uncanny experience, drifting back through the years to a time when many third-person games still featured unskippable cut-scenes and cover that could be awkward to unstick yourself from. I should add that there are plenty of reports at the moment of crashes and technical glitches and characters turning up without their faces in place. Playing on a relatively old PC, aside from one crash and a few amusing bugs, I’ve been mostly fine. I’ve just been playing a game that feels equally elderly.This is sometimes less of a criticism than it sounds. There is a definite pleasure to be had in simple run-and-gun missions where you shoot very similar looking people over and over again and pick a path between waypoints. The shooting often feels good, and while it’s a bit of a swizz to have to drive to and from each mission, the cars have a nice fishtaily looseness to them that can, at times, invoke the Valium-tinged glory of the Driver games.Driving between missions … MindsEye. Photograph: Build A Rocket Boy/IOI PartnersAnd for a game that has thought a lot about the point at which AI takes over, the in-game AI around me wasn’t in danger of taking over anything. When I handed over control of my car to the game while tailing an enemy, having been told I should try not to be spotted, the game made sure our bumpers kissed at every intersection. The streets of this particular open world are filled with amusingly unskilled AI drivers. I’d frequently arrive at traffic lights to be greeted by a recent pile-up, so delighted by the off-screen collisions that had scattered road cones and Dumpsters across my path that I almost always stopped to investigate.I even enjoyed the plot’s hokeyness, which features lines such as: “Your DNA has been altered since we last met!” Has it, though? Even so, I became increasingly aware that clever people had spent a good chunk of their working lives making this game. I don’t think they intended to cast me as what is in essence a Deliveroo bullet courier for an off-brand Elon Musk. Or to drop me into an open world that feels thin not because it lacks mission icons and fishing mini-games, but because it’s devoid of convincing human detail.I suspect the problem may actually be a thematically resonant one: a reckless kind of ambition. When I dropped into the level editor I found a tool that’s astonishingly rich and complex, but which also requires a lot of time and effort if you want to make anything really special in it. This is for the mega-fans, surely, the point-one percent. It must have taken serious time to build, and to do all that alongside a campaignis the kind of endeavour that requires a real megacorp behind it.MindsEye is an oddity. For all its failings, I rarely disliked playing it, and yet it’s also difficult to sincerely recommend. Its ideas, its moment-to-moment action and narrative are so thinly conceived that it barely exists. And yet: I’m kind of happy that it does.

    MindsEye is out now; £54.99
    #mindseye #review #dystopian #future #that
    MindsEye review – a dystopian future that plays like it’s from 2012
    There’s a Sphere-alike in Redrock, MindsEye’s open-world version of Las Vegas. It’s pretty much a straight copy of the original: a huge soap bubble, half sunk into the desert floor, with its surface turned into a gigantic TV. Occasionally you’ll pull up near the Sphere while driving an electric vehicle made by Silva, the megacorp that controls this world. You’ll sometimes come to a stop just as an advert for an identical Silva EV plays out on the huge curved screen overhead. The doubling effect can be slightly vertigo-inducing.At these moments, I truly get what MindsEye is trying to do. You’re stuck in the ultimate company town, where oligarchs and other crooks run everything, and there’s no hope of escaping the ecosystem they’ve built. MindsEye gets this all across through a chance encounter, and in a way that’s both light of touch and clever. The rest of the game tends towards the heavy-handed and silly, but it’s nice to glimpse a few instances where everything clicks.With its Spheres and omnipresent EVs, MindsEye looks and sounds like the future. It’s concerned with AI and tech bros and the insidious creep of a corporate dystopia. You play as an amnesiac former-soldier who must work out the precise damage that technology has done to his humanity, while shooting people and robots and drones. And alongside the campaign itself, MindsEye also has a suite of tools for making your own game or levels and publishing them for fellow players. All of this has come from a studio founded by Leslie Benzies, whose production credits include the likes of GTA 5.AI overlords … MindsEye. Photograph: IOI PartnersWhat’s weird, then, is that MindsEye generally plays like the past. Put a finger to the air and the wind is blowing from somewhere around 2012. At heart, this is a roughly hewn cover shooter with an open world that you only really experience when you’re driving between missions. Its topical concerns mainly exist to justify double-crosses and car chases and shootouts, and to explain why you head into battle with a personal drone that can open doors for you and stun nearby enemies.It can be an uncanny experience, drifting back through the years to a time when many third-person games still featured unskippable cut-scenes and cover that could be awkward to unstick yourself from. I should add that there are plenty of reports at the moment of crashes and technical glitches and characters turning up without their faces in place. Playing on a relatively old PC, aside from one crash and a few amusing bugs, I’ve been mostly fine. I’ve just been playing a game that feels equally elderly.This is sometimes less of a criticism than it sounds. There is a definite pleasure to be had in simple run-and-gun missions where you shoot very similar looking people over and over again and pick a path between waypoints. The shooting often feels good, and while it’s a bit of a swizz to have to drive to and from each mission, the cars have a nice fishtaily looseness to them that can, at times, invoke the Valium-tinged glory of the Driver games.Driving between missions … MindsEye. Photograph: Build A Rocket Boy/IOI PartnersAnd for a game that has thought a lot about the point at which AI takes over, the in-game AI around me wasn’t in danger of taking over anything. When I handed over control of my car to the game while tailing an enemy, having been told I should try not to be spotted, the game made sure our bumpers kissed at every intersection. The streets of this particular open world are filled with amusingly unskilled AI drivers. I’d frequently arrive at traffic lights to be greeted by a recent pile-up, so delighted by the off-screen collisions that had scattered road cones and Dumpsters across my path that I almost always stopped to investigate.I even enjoyed the plot’s hokeyness, which features lines such as: “Your DNA has been altered since we last met!” Has it, though? Even so, I became increasingly aware that clever people had spent a good chunk of their working lives making this game. I don’t think they intended to cast me as what is in essence a Deliveroo bullet courier for an off-brand Elon Musk. Or to drop me into an open world that feels thin not because it lacks mission icons and fishing mini-games, but because it’s devoid of convincing human detail.I suspect the problem may actually be a thematically resonant one: a reckless kind of ambition. When I dropped into the level editor I found a tool that’s astonishingly rich and complex, but which also requires a lot of time and effort if you want to make anything really special in it. This is for the mega-fans, surely, the point-one percent. It must have taken serious time to build, and to do all that alongside a campaignis the kind of endeavour that requires a real megacorp behind it.MindsEye is an oddity. For all its failings, I rarely disliked playing it, and yet it’s also difficult to sincerely recommend. Its ideas, its moment-to-moment action and narrative are so thinly conceived that it barely exists. And yet: I’m kind of happy that it does. MindsEye is out now; £54.99 #mindseye #review #dystopian #future #that
    WWW.THEGUARDIAN.COM
    MindsEye review – a dystopian future that plays like it’s from 2012
    There’s a Sphere-alike in Redrock, MindsEye’s open-world version of Las Vegas. It’s pretty much a straight copy of the original: a huge soap bubble, half sunk into the desert floor, with its surface turned into a gigantic TV. Occasionally you’ll pull up near the Sphere while driving an electric vehicle made by Silva, the megacorp that controls this world. You’ll sometimes come to a stop just as an advert for an identical Silva EV plays out on the huge curved screen overhead. The doubling effect can be slightly vertigo-inducing.At these moments, I truly get what MindsEye is trying to do. You’re stuck in the ultimate company town, where oligarchs and other crooks run everything, and there’s no hope of escaping the ecosystem they’ve built. MindsEye gets this all across through a chance encounter, and in a way that’s both light of touch and clever. The rest of the game tends towards the heavy-handed and silly, but it’s nice to glimpse a few instances where everything clicks.With its Spheres and omnipresent EVs, MindsEye looks and sounds like the future. It’s concerned with AI and tech bros and the insidious creep of a corporate dystopia. You play as an amnesiac former-soldier who must work out the precise damage that technology has done to his humanity, while shooting people and robots and drones. And alongside the campaign itself, MindsEye also has a suite of tools for making your own game or levels and publishing them for fellow players. All of this has come from a studio founded by Leslie Benzies, whose production credits include the likes of GTA 5.AI overlords … MindsEye. Photograph: IOI PartnersWhat’s weird, then, is that MindsEye generally plays like the past. Put a finger to the air and the wind is blowing from somewhere around 2012. At heart, this is a roughly hewn cover shooter with an open world that you only really experience when you’re driving between missions. Its topical concerns mainly exist to justify double-crosses and car chases and shootouts, and to explain why you head into battle with a personal drone that can open doors for you and stun nearby enemies.It can be an uncanny experience, drifting back through the years to a time when many third-person games still featured unskippable cut-scenes and cover that could be awkward to unstick yourself from. I should add that there are plenty of reports at the moment of crashes and technical glitches and characters turning up without their faces in place. Playing on a relatively old PC, aside from one crash and a few amusing bugs, I’ve been mostly fine. I’ve just been playing a game that feels equally elderly.This is sometimes less of a criticism than it sounds. There is a definite pleasure to be had in simple run-and-gun missions where you shoot very similar looking people over and over again and pick a path between waypoints. The shooting often feels good, and while it’s a bit of a swizz to have to drive to and from each mission, the cars have a nice fishtaily looseness to them that can, at times, invoke the Valium-tinged glory of the Driver games. (The airborne craft are less fun because they have less character.)Driving between missions … MindsEye. Photograph: Build A Rocket Boy/IOI PartnersAnd for a game that has thought a lot about the point at which AI takes over, the in-game AI around me wasn’t in danger of taking over anything. When I handed over control of my car to the game while tailing an enemy, having been told I should try not to be spotted, the game made sure our bumpers kissed at every intersection. The streets of this particular open world are filled with amusingly unskilled AI drivers. I’d frequently arrive at traffic lights to be greeted by a recent pile-up, so delighted by the off-screen collisions that had scattered road cones and Dumpsters across my path that I almost always stopped to investigate.I even enjoyed the plot’s hokeyness, which features lines such as: “Your DNA has been altered since we last met!” Has it, though? Even so, I became increasingly aware that clever people had spent a good chunk of their working lives making this game. I don’t think they intended to cast me as what is in essence a Deliveroo bullet courier for an off-brand Elon Musk. Or to drop me into an open world that feels thin not because it lacks mission icons and fishing mini-games, but because it’s devoid of convincing human detail.I suspect the problem may actually be a thematically resonant one: a reckless kind of ambition. When I dropped into the level editor I found a tool that’s astonishingly rich and complex, but which also requires a lot of time and effort if you want to make anything really special in it. This is for the mega-fans, surely, the point-one percent. It must have taken serious time to build, and to do all that alongside a campaign (one that tries, at least, to vary things now and then with stealth, trailing and sniper sections) is the kind of endeavour that requires a real megacorp behind it.MindsEye is an oddity. For all its failings, I rarely disliked playing it, and yet it’s also difficult to sincerely recommend. Its ideas, its moment-to-moment action and narrative are so thinly conceived that it barely exists. And yet: I’m kind of happy that it does. MindsEye is out now; £54.99
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