• PlayStation Plus Game Catalog for June: FBC: Firebreak, Battlefield 2042, Five Nights at Freddy’s: Help Wanted 2 and more

    This month, join forces to tackle the paranormal crises of a mysterious federal agency under siege in the cooperative first-person shooter FBC: Firebreak, lead your team to victory in the iconic all-out warfare of Battlefield 2042, test your skills as a new Fazbear employee managing and maintaining the eerie pizzeria of Five Nights at Freddy’s: Help Wanted 2 or live for the thrill of the hunt in the realistic hunting open world theHunter: Call of the Wild. All of these titles and more are available in June’s PlayStation Plus Game Catalog lineup*.   

    Meanwhile, PS2’s Deus Ex: The Conspiracy merges action-RPG, stealth and FPS gameplay in PlayStation Plus Premium.   

    All titles will be available to play on June 17.  

    PlayStation Plus Extra and Premium | Game Catalog 

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    FBC: Firebreak | PS5

    Launching on the PlayStation Plus Game Catalog this month is FBC: Firebreak, a cooperative first-person shooter set within a mysterious federal agency under assault by otherworldly forces. Return to the strange and unexpected world of Control or venture in for the first time in this standalone, multiplayer experience. As a years-long siege on the agency’s headquarters reaches its boiling point, only Firebreak—the Bureau’s most versatile unit—has the gear and the guts to plunge into the building’s strangest crises, restore order, contain the chaos, and fight to reclaim control. Join forces with friends or strangers to tackle each job as a well-oiled crew. Survival in this three-player cooperative FPS hinges on quick thinking and seamless teamwork as you scramble to tame raging paranatural crises across a variety of unexpected locations.   

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    Battlefield 2042 | PS4, PS5

    Battlefield 2042 is a first-person shooter that marks the return to the iconic all-out warfare of the franchise. With the help of a cutting-edge arsenal, engage in intense, immersive multiplayer battles. Lead your team to victory in both large all-out warfare and close quarters combat on maps from the world of 2042 and classic Battlefield titles. Find your playstyle in class-based gameplay and take on several experiences comprising elevated versions of Conquest and Breakthrough. Explore Battlefield Portal, a platform where players can discover, create, and share unexpected battles from Battlefield’s past and present.

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    Five Nights at Freddy’s: Help Wanted 2 | PS5

    Five Nights at Freddy’s: Help Wanted 2 is the sequel to the terrifying VR experience that brought new life to the iconic horror franchise. As a brand new Fazbear employee you’ll have to prove you have what it takes to excel in all aspects of Pizzeria management and maintenance. Find out if you have what it takes to be a Fazbear Entertainment Superstar!

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    theHunter: Call of the Wild | PS4

    Discover an atmospheric hunting game like no other in this realistic, stunning open world – regularly updated in collaboration with its community. Immerse yourself in the single player campaign, or share the ultimate hunting experience with friends. Roam freely across meticulously crafted environments and explore a diverse range of regions and biomes, each with its own unique flora and fauna. Experience the intricacies of complex animal behavior, dynamic weather events, full day and night cycles, simulated ballistics, highly realistic acoustics, and scents carried by the wind. Select from a variety of weapons, ammunition, and equipment to create the ultimate hunting experience. With a diverse range of wildlife, including Jackrabbits, Mallard Ducks, Black Bears, Elk, and Moose, you will need to strategically match prey to weaponry to successfully track, lure, and ambush animals based on their unique behavior and environment.

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    We Love Katamari Reroll + Royal Reverie | PS4, PS5

    We Love Katamari Damacy, the second title in the Katamari series released in 2005, has been remastered with redesigned graphics and a revamped in-game UI. The King of the Cosmos accidentally destroyed all the stars in the universe. He sent his son, the Prince, to Earth and ordered him to create a large katamari. Roll the katamari to make it bigger and bigger, rolling up all the things on the earth. You can roll up anything from paper clips and snacks in the house, to telephone poles and buildings in the town, to even living creatures such as people and animals. Once the katamari is complete, it will turn into a star that colors the night sky. You cannot roll up anything larger than the current size of your katamari, so the key is to think in advance about the order in which you roll things up around the stage. In Royal Reverie, roll up katamari as the King of All Cosmos in his boyhood!

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    Eiyuden Chronicles: Hundred Heroes | PS4, PS5

    Directed and produced by the creator of treasured JRPG series Suikoden, Eiyuden Chronicle: Hundred Heroes provides a contemporary take on the classic JRPG experience. In the land of Allraan, two friends from different backgrounds are united by a war waged by the power-hungry Galdean Empire. Explore a diverse, magical world populated by humans, beastmen, elves and desert people. Meet and recruit over 100 unique characters, each with their own vivid voice acting and intricate backstories. Over four years in the making, and funded by the most successful Kickstarter videogame campaign of 2020, Eiyuden Chronicle: Hundred Heroes features turn-based battles, a staggering selection of heroes and a thrilling story to discover.

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    Train Sim World 5 | PS4, PS5

    The rails are yours in Train Sim World 5! Take on new challenges and new roles as you master the tracks and trains of iconic cities across 3 new routes. Immerse yourself in the ultimate rail hobby and embark on your next journey. Be swept off your feet with the commuter mayhem of the West Coast main line with the Northwestern Class 350, the twisting Kinzigtalbahn with the tilting DB BR 411 ICE-T, or the sun-soaked tracks of the San Bernardino line and its Metrolink movements, powered by the MP36 & F125. 

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    Endless Dungeon | PS4, PS5

    Endless Dungeon is a unique blend of roguelite, tactical action, and tower defense set in the award-winning Endless Universe. Plunge into an abandoned space station alone or with friends in co-op, recruit a team of shipwrecked heroes, and protect your crystal against never-ending waves of monsters… or die trying, get reloaded, and try again. You’re stranded on an abandoned space station chock-full of monsters and mysteries. To get out you’ll have to reach The Core, but you can’t do that without your crystal bot. That scuttling critter is your key to surviving the procedurally generated rooms of this space ruin. Sadly, it’s also a fragile soul, and every monster in the place wants a piece of it. You’re going to have to think quick, plan well, place your turrets, and then… fireworks! Bugs, bots and blobs will stop at nothing to turn you and that crystal into dust and debris. With a large choice of weapons and turrets, the right gear will be the difference between life and death.

    PlayStation Plus Premium 

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    Deus Ex: The Conspiracy | PS4, PS5This is an emulation of the classic PS2 title, Deus Ex: The Conspiracy, playable on PS4 and PS5 for the first time. The year is 2052 and the world is a dangerous and chaotic place. Terrorists operate openly – killing thousands; drugs, disease and pollution kill even more. The world’s economies are close to collapse and the gap between the insanely wealthy and the desperately poor grows ever wider. Worst of all, an age- old conspiracy bent on world domination has decided that the time is right to emerge from the shadows and take control. 

    *PlayStation Plus Game Catalog and PlayStation Plus Premium/Deluxe lineups may differ by region. Please check PlayStation Store on release day. 
    #playstation #plus #game #catalog #june
    PlayStation Plus Game Catalog for June: FBC: Firebreak, Battlefield 2042, Five Nights at Freddy’s: Help Wanted 2 and more
    This month, join forces to tackle the paranormal crises of a mysterious federal agency under siege in the cooperative first-person shooter FBC: Firebreak, lead your team to victory in the iconic all-out warfare of Battlefield 2042, test your skills as a new Fazbear employee managing and maintaining the eerie pizzeria of Five Nights at Freddy’s: Help Wanted 2 or live for the thrill of the hunt in the realistic hunting open world theHunter: Call of the Wild. All of these titles and more are available in June’s PlayStation Plus Game Catalog lineup*.    Meanwhile, PS2’s Deus Ex: The Conspiracy merges action-RPG, stealth and FPS gameplay in PlayStation Plus Premium.    All titles will be available to play on June 17.   PlayStation Plus Extra and Premium | Game Catalog  View and download image Download the image close Close Download this image FBC: Firebreak | PS5 Launching on the PlayStation Plus Game Catalog this month is FBC: Firebreak, a cooperative first-person shooter set within a mysterious federal agency under assault by otherworldly forces. Return to the strange and unexpected world of Control or venture in for the first time in this standalone, multiplayer experience. As a years-long siege on the agency’s headquarters reaches its boiling point, only Firebreak—the Bureau’s most versatile unit—has the gear and the guts to plunge into the building’s strangest crises, restore order, contain the chaos, and fight to reclaim control. Join forces with friends or strangers to tackle each job as a well-oiled crew. Survival in this three-player cooperative FPS hinges on quick thinking and seamless teamwork as you scramble to tame raging paranatural crises across a variety of unexpected locations.    View and download image Download the image close Close Download this image Battlefield 2042 | PS4, PS5 Battlefield 2042 is a first-person shooter that marks the return to the iconic all-out warfare of the franchise. With the help of a cutting-edge arsenal, engage in intense, immersive multiplayer battles. Lead your team to victory in both large all-out warfare and close quarters combat on maps from the world of 2042 and classic Battlefield titles. Find your playstyle in class-based gameplay and take on several experiences comprising elevated versions of Conquest and Breakthrough. Explore Battlefield Portal, a platform where players can discover, create, and share unexpected battles from Battlefield’s past and present. View and download image Download the image close Close Download this image Five Nights at Freddy’s: Help Wanted 2 | PS5 Five Nights at Freddy’s: Help Wanted 2 is the sequel to the terrifying VR experience that brought new life to the iconic horror franchise. As a brand new Fazbear employee you’ll have to prove you have what it takes to excel in all aspects of Pizzeria management and maintenance. Find out if you have what it takes to be a Fazbear Entertainment Superstar! View and download image Download the image close Close Download this image theHunter: Call of the Wild | PS4 Discover an atmospheric hunting game like no other in this realistic, stunning open world – regularly updated in collaboration with its community. Immerse yourself in the single player campaign, or share the ultimate hunting experience with friends. Roam freely across meticulously crafted environments and explore a diverse range of regions and biomes, each with its own unique flora and fauna. Experience the intricacies of complex animal behavior, dynamic weather events, full day and night cycles, simulated ballistics, highly realistic acoustics, and scents carried by the wind. Select from a variety of weapons, ammunition, and equipment to create the ultimate hunting experience. With a diverse range of wildlife, including Jackrabbits, Mallard Ducks, Black Bears, Elk, and Moose, you will need to strategically match prey to weaponry to successfully track, lure, and ambush animals based on their unique behavior and environment. View and download image Download the image close Close Download this image We Love Katamari Reroll + Royal Reverie | PS4, PS5 We Love Katamari Damacy, the second title in the Katamari series released in 2005, has been remastered with redesigned graphics and a revamped in-game UI. The King of the Cosmos accidentally destroyed all the stars in the universe. He sent his son, the Prince, to Earth and ordered him to create a large katamari. Roll the katamari to make it bigger and bigger, rolling up all the things on the earth. You can roll up anything from paper clips and snacks in the house, to telephone poles and buildings in the town, to even living creatures such as people and animals. Once the katamari is complete, it will turn into a star that colors the night sky. You cannot roll up anything larger than the current size of your katamari, so the key is to think in advance about the order in which you roll things up around the stage. In Royal Reverie, roll up katamari as the King of All Cosmos in his boyhood! View and download image Download the image close Close Download this image Eiyuden Chronicles: Hundred Heroes | PS4, PS5 Directed and produced by the creator of treasured JRPG series Suikoden, Eiyuden Chronicle: Hundred Heroes provides a contemporary take on the classic JRPG experience. In the land of Allraan, two friends from different backgrounds are united by a war waged by the power-hungry Galdean Empire. Explore a diverse, magical world populated by humans, beastmen, elves and desert people. Meet and recruit over 100 unique characters, each with their own vivid voice acting and intricate backstories. Over four years in the making, and funded by the most successful Kickstarter videogame campaign of 2020, Eiyuden Chronicle: Hundred Heroes features turn-based battles, a staggering selection of heroes and a thrilling story to discover. View and download image Download the image close Close Download this image Train Sim World 5 | PS4, PS5 The rails are yours in Train Sim World 5! Take on new challenges and new roles as you master the tracks and trains of iconic cities across 3 new routes. Immerse yourself in the ultimate rail hobby and embark on your next journey. Be swept off your feet with the commuter mayhem of the West Coast main line with the Northwestern Class 350, the twisting Kinzigtalbahn with the tilting DB BR 411 ICE-T, or the sun-soaked tracks of the San Bernardino line and its Metrolink movements, powered by the MP36 & F125.  View and download image Download the image close Close Download this image Endless Dungeon | PS4, PS5 Endless Dungeon is a unique blend of roguelite, tactical action, and tower defense set in the award-winning Endless Universe. Plunge into an abandoned space station alone or with friends in co-op, recruit a team of shipwrecked heroes, and protect your crystal against never-ending waves of monsters… or die trying, get reloaded, and try again. You’re stranded on an abandoned space station chock-full of monsters and mysteries. To get out you’ll have to reach The Core, but you can’t do that without your crystal bot. That scuttling critter is your key to surviving the procedurally generated rooms of this space ruin. Sadly, it’s also a fragile soul, and every monster in the place wants a piece of it. You’re going to have to think quick, plan well, place your turrets, and then… fireworks! Bugs, bots and blobs will stop at nothing to turn you and that crystal into dust and debris. With a large choice of weapons and turrets, the right gear will be the difference between life and death. PlayStation Plus Premium  View and download image Download the image close Close Download this image Deus Ex: The Conspiracy | PS4, PS5This is an emulation of the classic PS2 title, Deus Ex: The Conspiracy, playable on PS4 and PS5 for the first time. The year is 2052 and the world is a dangerous and chaotic place. Terrorists operate openly – killing thousands; drugs, disease and pollution kill even more. The world’s economies are close to collapse and the gap between the insanely wealthy and the desperately poor grows ever wider. Worst of all, an age- old conspiracy bent on world domination has decided that the time is right to emerge from the shadows and take control.  *PlayStation Plus Game Catalog and PlayStation Plus Premium/Deluxe lineups may differ by region. Please check PlayStation Store on release day.  #playstation #plus #game #catalog #june
    BLOG.PLAYSTATION.COM
    PlayStation Plus Game Catalog for June: FBC: Firebreak, Battlefield 2042, Five Nights at Freddy’s: Help Wanted 2 and more
    This month, join forces to tackle the paranormal crises of a mysterious federal agency under siege in the cooperative first-person shooter FBC: Firebreak, lead your team to victory in the iconic all-out warfare of Battlefield 2042, test your skills as a new Fazbear employee managing and maintaining the eerie pizzeria of Five Nights at Freddy’s: Help Wanted 2 or live for the thrill of the hunt in the realistic hunting open world theHunter: Call of the Wild. All of these titles and more are available in June’s PlayStation Plus Game Catalog lineup*.    Meanwhile, PS2’s Deus Ex: The Conspiracy merges action-RPG, stealth and FPS gameplay in PlayStation Plus Premium.    All titles will be available to play on June 17.   PlayStation Plus Extra and Premium | Game Catalog  View and download image Download the image close Close Download this image FBC: Firebreak | PS5 Launching on the PlayStation Plus Game Catalog this month is FBC: Firebreak, a cooperative first-person shooter set within a mysterious federal agency under assault by otherworldly forces. Return to the strange and unexpected world of Control or venture in for the first time in this standalone, multiplayer experience. As a years-long siege on the agency’s headquarters reaches its boiling point, only Firebreak—the Bureau’s most versatile unit—has the gear and the guts to plunge into the building’s strangest crises, restore order, contain the chaos, and fight to reclaim control. Join forces with friends or strangers to tackle each job as a well-oiled crew. Survival in this three-player cooperative FPS hinges on quick thinking and seamless teamwork as you scramble to tame raging paranatural crises across a variety of unexpected locations.    View and download image Download the image close Close Download this image Battlefield 2042 | PS4, PS5 Battlefield 2042 is a first-person shooter that marks the return to the iconic all-out warfare of the franchise. With the help of a cutting-edge arsenal, engage in intense, immersive multiplayer battles. Lead your team to victory in both large all-out warfare and close quarters combat on maps from the world of 2042 and classic Battlefield titles. Find your playstyle in class-based gameplay and take on several experiences comprising elevated versions of Conquest and Breakthrough. Explore Battlefield Portal, a platform where players can discover, create, and share unexpected battles from Battlefield’s past and present. View and download image Download the image close Close Download this image Five Nights at Freddy’s: Help Wanted 2 | PS5 Five Nights at Freddy’s: Help Wanted 2 is the sequel to the terrifying VR experience that brought new life to the iconic horror franchise. As a brand new Fazbear employee you’ll have to prove you have what it takes to excel in all aspects of Pizzeria management and maintenance. Find out if you have what it takes to be a Fazbear Entertainment Superstar! View and download image Download the image close Close Download this image theHunter: Call of the Wild | PS4 Discover an atmospheric hunting game like no other in this realistic, stunning open world – regularly updated in collaboration with its community. Immerse yourself in the single player campaign, or share the ultimate hunting experience with friends. Roam freely across meticulously crafted environments and explore a diverse range of regions and biomes, each with its own unique flora and fauna. Experience the intricacies of complex animal behavior, dynamic weather events, full day and night cycles, simulated ballistics, highly realistic acoustics, and scents carried by the wind. Select from a variety of weapons, ammunition, and equipment to create the ultimate hunting experience. With a diverse range of wildlife, including Jackrabbits, Mallard Ducks, Black Bears, Elk, and Moose, you will need to strategically match prey to weaponry to successfully track, lure, and ambush animals based on their unique behavior and environment. View and download image Download the image close Close Download this image We Love Katamari Reroll + Royal Reverie | PS4, PS5 We Love Katamari Damacy, the second title in the Katamari series released in 2005, has been remastered with redesigned graphics and a revamped in-game UI. The King of the Cosmos accidentally destroyed all the stars in the universe. He sent his son, the Prince, to Earth and ordered him to create a large katamari. Roll the katamari to make it bigger and bigger, rolling up all the things on the earth. You can roll up anything from paper clips and snacks in the house, to telephone poles and buildings in the town, to even living creatures such as people and animals. Once the katamari is complete, it will turn into a star that colors the night sky. You cannot roll up anything larger than the current size of your katamari, so the key is to think in advance about the order in which you roll things up around the stage. In Royal Reverie, roll up katamari as the King of All Cosmos in his boyhood! View and download image Download the image close Close Download this image Eiyuden Chronicles: Hundred Heroes | PS4, PS5 Directed and produced by the creator of treasured JRPG series Suikoden, Eiyuden Chronicle: Hundred Heroes provides a contemporary take on the classic JRPG experience. In the land of Allraan, two friends from different backgrounds are united by a war waged by the power-hungry Galdean Empire. Explore a diverse, magical world populated by humans, beastmen, elves and desert people. Meet and recruit over 100 unique characters, each with their own vivid voice acting and intricate backstories. Over four years in the making, and funded by the most successful Kickstarter videogame campaign of 2020, Eiyuden Chronicle: Hundred Heroes features turn-based battles, a staggering selection of heroes and a thrilling story to discover. View and download image Download the image close Close Download this image Train Sim World 5 | PS4, PS5 The rails are yours in Train Sim World 5! Take on new challenges and new roles as you master the tracks and trains of iconic cities across 3 new routes. Immerse yourself in the ultimate rail hobby and embark on your next journey. Be swept off your feet with the commuter mayhem of the West Coast main line with the Northwestern Class 350, the twisting Kinzigtalbahn with the tilting DB BR 411 ICE-T, or the sun-soaked tracks of the San Bernardino line and its Metrolink movements, powered by the MP36 & F125.  View and download image Download the image close Close Download this image Endless Dungeon | PS4, PS5 Endless Dungeon is a unique blend of roguelite, tactical action, and tower defense set in the award-winning Endless Universe. Plunge into an abandoned space station alone or with friends in co-op, recruit a team of shipwrecked heroes, and protect your crystal against never-ending waves of monsters… or die trying, get reloaded, and try again. You’re stranded on an abandoned space station chock-full of monsters and mysteries. To get out you’ll have to reach The Core, but you can’t do that without your crystal bot. That scuttling critter is your key to surviving the procedurally generated rooms of this space ruin. Sadly, it’s also a fragile soul, and every monster in the place wants a piece of it. You’re going to have to think quick, plan well, place your turrets, and then… fireworks! Bugs, bots and blobs will stop at nothing to turn you and that crystal into dust and debris. With a large choice of weapons and turrets, the right gear will be the difference between life and death. PlayStation Plus Premium  View and download image Download the image close Close Download this image Deus Ex: The Conspiracy | PS4, PS5This is an emulation of the classic PS2 title, Deus Ex: The Conspiracy, playable on PS4 and PS5 for the first time. The year is 2052 and the world is a dangerous and chaotic place. Terrorists operate openly – killing thousands; drugs, disease and pollution kill even more. The world’s economies are close to collapse and the gap between the insanely wealthy and the desperately poor grows ever wider. Worst of all, an age- old conspiracy bent on world domination has decided that the time is right to emerge from the shadows and take control.  *PlayStation Plus Game Catalog and PlayStation Plus Premium/Deluxe lineups may differ by region. Please check PlayStation Store on release day. 
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  • Ants Do Poop and They Even Use Toilets to Fertilize Their Own Gardens

    Key Takeaways on Ant PoopDo ants poop? Yes. Any creature that eats will poop and ants are no exception. Because ants live in close quarters, they need to protect the colony from their feces so bacteria and fungus doesn't infect their health. This is why they use toilet chambers. Whether they isolate it in a toilet chamber or kick it to the curb, ants don’t keep their waste around. But some ants find a use for that stuff. One such species is the leafcutter ant that takes little clippings of leaves and uses these leaves to grow a very particular fungus that they then eat.Like urban humans, ants live in close quarters. Ant colonies can be home to thousands, even tens of thousands of individuals, depending on the species. And like any creature that eats, ants poop. When you combine close quarters and loads of feces, you have a recipe for disease, says Jessica Ware, curator and division chair of Invertebrate Zoology at the American Museum of Natural History. “Ant poop can harbor bacteria, and because it contains partly undigested food, it can grow bacteria and fungus that could threaten the health of the colony,” Ware says. But ant colonies aren’t seething beds of disease. That’s because ants are scrupulous about hygiene.Ants Do Poop and Ant Toilets Are RealAnt colony underground with ant chambers.To keep themselves and their nests clean, ants have evolved some interesting housekeeping strategies. Some types of ants actually have toilets — or at least something we might call toilets. Their nests are very complicated, with lots of different tunnels and chambers, explains Ware, and one of those chambers is a toilet chamber. Ants don’t visit the toilet when they feel the call of nature. Instead, worker ants who are on latrine duty collect the poop and carry it to the toilet chamber, which is located far away from other parts of the nest. What Does Ant Poop Look Like? This isn’t as messy a chore as it sounds. Like most insects, ants are water-limited, says Ware, so they try to get as much liquid out of their food as possible. This results in small, hard, usually black or brownish pellets of poop. The poop is dry and hard enough so that for ant species that don’t have indoor toilet chambers, the workers can just kick the poop out of the nest.Ants Use Poop as FertilizerWhether they isolate it in a toilet chamber or kick it to the curb, ants don’t keep their waste around. Well, at least most types of ants don’t. Some ants find a use for that stuff. One such species is the leafcutter ant. “They basically take little clippings of leaves and use these leaves to grow a very particular fungus that they then eat,” says Ware. “They don't eat the leaves, they eat the fungus.” And yep, they use their poop to fertilize their crops. “They’re basically gardeners,” Ware says. If you’d like to see leafcutter ants at work in their gardens and you happen to be in the New York City area, drop by the American Museum of Natural History. They have a large colony of fungus-gardening ants on display.Other Insects That Use ToiletsAnts may have toilets, but termites have even wilder ways of dealing with their wastes. Termites and ants might seem similar at first sight, but they aren’t closely related. Ants are more closely related to bees, while termites are more closely related to cockroaches, explains Aram Mikaelyan, an entomologist at North Carolina State University who studies the co-evolution of insects and their gut microbiomes. So ants’ and termites’ styles of social living evolved independently, and their solutions to the waste problem are quite different.“Termites have found a way to not distance themselves from the feces,” says Mikaelyan. “Instead, they use the feces itself as building material.” They’re able to do this because they feed on wood, Mikaelyan explains. When wood passes through the termites’ digestive systems into the poop, it enables a type of bacteria called Actinobacteria. These bacteria are the source of many antibiotics that humans use.So that unusual building material acts as a disinfectant. Mikaelyan describes it as “a living disinfectant wall, like a Clorox wall, almost.”Insect HygieneIt may seem surprising that ants and termites are so tidy and concerned with hygiene, but it’s really not uncommon. “Insects in general are cleaner than we think,” says Ware. “We often think of insects as being really gross, but most insects don’t want to lie in their own filth.”Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:The American Society of Microbiology. The Leaf-cutter Ant’s 50 Million Years of FarmingAvery Hurt is a freelance science journalist. In addition to writing for Discover, she writes regularly for a variety of outlets, both print and online, including National Geographic, Science News Explores, Medscape, and WebMD. She’s the author of Bullet With Your Name on It: What You Will Probably Die From and What You Can Do About It, Clerisy Press 2007, as well as several books for young readers. Avery got her start in journalism while attending university, writing for the school newspaper and editing the student non-fiction magazine. Though she writes about all areas of science, she is particularly interested in neuroscience, the science of consciousness, and AI–interests she developed while earning a degree in philosophy.
    #ants #poop #they #even #use
    Ants Do Poop and They Even Use Toilets to Fertilize Their Own Gardens
    Key Takeaways on Ant PoopDo ants poop? Yes. Any creature that eats will poop and ants are no exception. Because ants live in close quarters, they need to protect the colony from their feces so bacteria and fungus doesn't infect their health. This is why they use toilet chambers. Whether they isolate it in a toilet chamber or kick it to the curb, ants don’t keep their waste around. But some ants find a use for that stuff. One such species is the leafcutter ant that takes little clippings of leaves and uses these leaves to grow a very particular fungus that they then eat.Like urban humans, ants live in close quarters. Ant colonies can be home to thousands, even tens of thousands of individuals, depending on the species. And like any creature that eats, ants poop. When you combine close quarters and loads of feces, you have a recipe for disease, says Jessica Ware, curator and division chair of Invertebrate Zoology at the American Museum of Natural History. “Ant poop can harbor bacteria, and because it contains partly undigested food, it can grow bacteria and fungus that could threaten the health of the colony,” Ware says. But ant colonies aren’t seething beds of disease. That’s because ants are scrupulous about hygiene.Ants Do Poop and Ant Toilets Are RealAnt colony underground with ant chambers.To keep themselves and their nests clean, ants have evolved some interesting housekeeping strategies. Some types of ants actually have toilets — or at least something we might call toilets. Their nests are very complicated, with lots of different tunnels and chambers, explains Ware, and one of those chambers is a toilet chamber. Ants don’t visit the toilet when they feel the call of nature. Instead, worker ants who are on latrine duty collect the poop and carry it to the toilet chamber, which is located far away from other parts of the nest. What Does Ant Poop Look Like? This isn’t as messy a chore as it sounds. Like most insects, ants are water-limited, says Ware, so they try to get as much liquid out of their food as possible. This results in small, hard, usually black or brownish pellets of poop. The poop is dry and hard enough so that for ant species that don’t have indoor toilet chambers, the workers can just kick the poop out of the nest.Ants Use Poop as FertilizerWhether they isolate it in a toilet chamber or kick it to the curb, ants don’t keep their waste around. Well, at least most types of ants don’t. Some ants find a use for that stuff. One such species is the leafcutter ant. “They basically take little clippings of leaves and use these leaves to grow a very particular fungus that they then eat,” says Ware. “They don't eat the leaves, they eat the fungus.” And yep, they use their poop to fertilize their crops. “They’re basically gardeners,” Ware says. If you’d like to see leafcutter ants at work in their gardens and you happen to be in the New York City area, drop by the American Museum of Natural History. They have a large colony of fungus-gardening ants on display.Other Insects That Use ToiletsAnts may have toilets, but termites have even wilder ways of dealing with their wastes. Termites and ants might seem similar at first sight, but they aren’t closely related. Ants are more closely related to bees, while termites are more closely related to cockroaches, explains Aram Mikaelyan, an entomologist at North Carolina State University who studies the co-evolution of insects and their gut microbiomes. So ants’ and termites’ styles of social living evolved independently, and their solutions to the waste problem are quite different.“Termites have found a way to not distance themselves from the feces,” says Mikaelyan. “Instead, they use the feces itself as building material.” They’re able to do this because they feed on wood, Mikaelyan explains. When wood passes through the termites’ digestive systems into the poop, it enables a type of bacteria called Actinobacteria. These bacteria are the source of many antibiotics that humans use.So that unusual building material acts as a disinfectant. Mikaelyan describes it as “a living disinfectant wall, like a Clorox wall, almost.”Insect HygieneIt may seem surprising that ants and termites are so tidy and concerned with hygiene, but it’s really not uncommon. “Insects in general are cleaner than we think,” says Ware. “We often think of insects as being really gross, but most insects don’t want to lie in their own filth.”Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:The American Society of Microbiology. The Leaf-cutter Ant’s 50 Million Years of FarmingAvery Hurt is a freelance science journalist. In addition to writing for Discover, she writes regularly for a variety of outlets, both print and online, including National Geographic, Science News Explores, Medscape, and WebMD. She’s the author of Bullet With Your Name on It: What You Will Probably Die From and What You Can Do About It, Clerisy Press 2007, as well as several books for young readers. Avery got her start in journalism while attending university, writing for the school newspaper and editing the student non-fiction magazine. Though she writes about all areas of science, she is particularly interested in neuroscience, the science of consciousness, and AI–interests she developed while earning a degree in philosophy. #ants #poop #they #even #use
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    Ants Do Poop and They Even Use Toilets to Fertilize Their Own Gardens
    Key Takeaways on Ant PoopDo ants poop? Yes. Any creature that eats will poop and ants are no exception. Because ants live in close quarters, they need to protect the colony from their feces so bacteria and fungus doesn't infect their health. This is why they use toilet chambers. Whether they isolate it in a toilet chamber or kick it to the curb, ants don’t keep their waste around. But some ants find a use for that stuff. One such species is the leafcutter ant that takes little clippings of leaves and uses these leaves to grow a very particular fungus that they then eat.Like urban humans, ants live in close quarters. Ant colonies can be home to thousands, even tens of thousands of individuals, depending on the species. And like any creature that eats, ants poop. When you combine close quarters and loads of feces, you have a recipe for disease, says Jessica Ware, curator and division chair of Invertebrate Zoology at the American Museum of Natural History. “Ant poop can harbor bacteria, and because it contains partly undigested food, it can grow bacteria and fungus that could threaten the health of the colony,” Ware says. But ant colonies aren’t seething beds of disease. That’s because ants are scrupulous about hygiene.Ants Do Poop and Ant Toilets Are RealAnt colony underground with ant chambers. (Image Credit: Lidok_L/Shutterstock)To keep themselves and their nests clean, ants have evolved some interesting housekeeping strategies. Some types of ants actually have toilets — or at least something we might call toilets. Their nests are very complicated, with lots of different tunnels and chambers, explains Ware, and one of those chambers is a toilet chamber. Ants don’t visit the toilet when they feel the call of nature. Instead, worker ants who are on latrine duty collect the poop and carry it to the toilet chamber, which is located far away from other parts of the nest. What Does Ant Poop Look Like? This isn’t as messy a chore as it sounds. Like most insects, ants are water-limited, says Ware, so they try to get as much liquid out of their food as possible. This results in small, hard, usually black or brownish pellets of poop. The poop is dry and hard enough so that for ant species that don’t have indoor toilet chambers, the workers can just kick the poop out of the nest.Ants Use Poop as FertilizerWhether they isolate it in a toilet chamber or kick it to the curb, ants don’t keep their waste around. Well, at least most types of ants don’t. Some ants find a use for that stuff. One such species is the leafcutter ant. “They basically take little clippings of leaves and use these leaves to grow a very particular fungus that they then eat,” says Ware. “They don't eat the leaves, they eat the fungus.” And yep, they use their poop to fertilize their crops. “They’re basically gardeners,” Ware says. If you’d like to see leafcutter ants at work in their gardens and you happen to be in the New York City area, drop by the American Museum of Natural History. They have a large colony of fungus-gardening ants on display.Other Insects That Use ToiletsAnts may have toilets, but termites have even wilder ways of dealing with their wastes. Termites and ants might seem similar at first sight, but they aren’t closely related. Ants are more closely related to bees, while termites are more closely related to cockroaches, explains Aram Mikaelyan, an entomologist at North Carolina State University who studies the co-evolution of insects and their gut microbiomes. So ants’ and termites’ styles of social living evolved independently, and their solutions to the waste problem are quite different.“Termites have found a way to not distance themselves from the feces,” says Mikaelyan. “Instead, they use the feces itself as building material.” They’re able to do this because they feed on wood, Mikaelyan explains. When wood passes through the termites’ digestive systems into the poop, it enables a type of bacteria called Actinobacteria. These bacteria are the source of many antibiotics that humans use. (Leafcutter ants also use Actinobacteria to keep their fungus gardens free of parasites.) So that unusual building material acts as a disinfectant. Mikaelyan describes it as “a living disinfectant wall, like a Clorox wall, almost.”Insect HygieneIt may seem surprising that ants and termites are so tidy and concerned with hygiene, but it’s really not uncommon. “Insects in general are cleaner than we think,” says Ware. “We often think of insects as being really gross, but most insects don’t want to lie in their own filth.”Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:The American Society of Microbiology. The Leaf-cutter Ant’s 50 Million Years of FarmingAvery Hurt is a freelance science journalist. In addition to writing for Discover, she writes regularly for a variety of outlets, both print and online, including National Geographic, Science News Explores, Medscape, and WebMD. She’s the author of Bullet With Your Name on It: What You Will Probably Die From and What You Can Do About It, Clerisy Press 2007, as well as several books for young readers. Avery got her start in journalism while attending university, writing for the school newspaper and editing the student non-fiction magazine. Though she writes about all areas of science, she is particularly interested in neuroscience, the science of consciousness, and AI–interests she developed while earning a degree in philosophy.
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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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  • How a US agriculture agency became key in the fight against bird flu

    A dangerous strain of bird flu is spreading in US livestockMediaMedium/Alamy
    Since Donald Trump assumed office in January, the leading US public health agency has pulled back preparations for a potential bird flu pandemic. But as it steps back, another government agency is stepping up.

    While the US Department of Health and Human Servicespreviously held regular briefings on its efforts to prevent a wider outbreak of a deadly bird flu virus called H5N1 in people, it largely stopped once Trump took office. It has also cancelled funding for a vaccine that would have targeted the virus. In contrast, the US Department of Agriculturehas escalated its fight against H5N1’s spread in poultry flocks and dairy herds, including by funding the development of livestock vaccines.
    This particular virus – a strain of avian influenza called H5N1 – poses a significant threat to humans, having killed about half of the roughly 1000 people worldwide who tested positive for it since 2003. While the pathogen spreads rapidly in birds, it is poorly adapted to infecting humans and isn’t known to transmit between people. But that could change if it acquires mutations that allow it to spread more easily among mammals – a risk that increases with each mammalian infection.
    The possibility of H5N1 evolving to become more dangerous to people has grown significantly since March 2024, when the virus jumped from migratory birds to dairy cows in Texas. More than 1,070 herds across 17 states have been affected since then.
    H5N1 also infects poultry, placing the virus in closer proximity to people. Since 2022, nearly 175 million domestic birds have been culled in the US due to H5N1, and almost all of the 71 people who have tested positive for it had direct contact with livestock.

    Get the most essential health and fitness news in your inbox every Saturday.

    Sign up to newsletter

    “We need to take this seriously because whenconstantly is spreading, it’s constantly spilling over into humans,” says Seema Lakdawala at Emory University in Georgia. The virus has already killed a person in the US and a child in Mexico this year.
    Still, cases have declined under Trump. The last recorded human case was in February, and the number of affected poultry flocks fell 95 per cent between then and June. Outbreaks in dairy herds have also stabilised.
    It isn’t clear what is behind the decline. Lakdawala believes it is partly due to a lull in bird migration, which reduces opportunities for the virus to spread from wild birds to livestock. It may also reflect efforts by the USDA to contain outbreaks on farms. In February, the USDA unveiled a billion plan for tackling H5N1, including strengthening farmers’ defences against the virus, such as through free biosecurity assessments. Of the 150 facilities that have undergone assessment, only one has experienced an H5N1 outbreak.
    Under Trump, the USDA also continued its National Milk Testing Strategy, which mandates farms provide raw milk samples for influenza testing. If a farm is positive for H5N1, it must allow the USDA to monitor livestock and implement measures to contain the virus. The USDA launched the programme in December and has since ramped up participation to 45 states.
    “The National Milk Testing Strategy is a fantastic system,” says Erin Sorrell at Johns Hopkins University in Maryland. Along with the USDA’s efforts to improve biosecurity measures on farms, milk testing is crucial for containing the outbreak, says Sorrell.

    But while the USDA has bolstered its efforts against H5N1, the HHS doesn’t appear to have followed suit. In fact, the recent drop in human cases may reflect decreased surveillance due to workforce cuts, says Sorrell. In April, the HHS laid off about 10,000 employees, including 90 per cent of staff at the National Institute for Occupational Safety and Health, an office that helps investigate H5N1 outbreaks in farm workers.
    “There is an old saying that if you don’t test for something, you can’t find it,” says Sorrell. Yet a spokesperson for the US Centers for Disease Control and Preventionsays its guidance and surveillance efforts have not changed. “State and local health departments continue to monitor for illness in persons exposed to sick animals,” they told New Scientist. “CDC remains committed to rapidly communicating information as needed about H5N1.”
    The USDA and HHS also diverge on vaccination. While the USDA has allocated million toward developing vaccines and other solutions for preventing H5N1’s spread in livestock, the HHS cancelled million in contracts for influenza vaccine development. The contracts – terminated on 28 May – were with the pharmaceutical company Moderna to develop vaccines targeting flu subtypes, including H5N1, that could cause future pandemics. The news came the same day Moderna reported nearly 98 per cent of the roughly 300 participants who received two doses of the H5 vaccine in a clinical trial had antibody levels believed to be protective against the virus.
    The US has about five million H5N1 vaccine doses stockpiled, but these are made using eggs and cultured cells, which take longer to produce than mRNA-based vaccines like Moderna’s. The Moderna vaccine would have modernised the stockpile and enabled the government to rapidly produce vaccines in the event of a pandemic, says Sorrell. “It seems like a very effective platform and would have positioned the US and others to be on good footing if and when we needed a vaccine for our general public,” she says.

    The HHS cancelled the contracts due to concerns about mRNA vaccines, which Robert F Kennedy Jr – the country’s highest-ranking public health official – has previously cast doubt on. “The reality is that mRNA technology remains under-tested, and we are not going to spend taxpayer dollars repeating the mistakes of the last administration,” said HHS communications director Andrew Nixon in a statement to New Scientist.
    However, mRNA technology isn’t new. It has been in development for more than half a century and numerous clinical trials have shown mRNA vaccines are safe. While they do carry the risk of side effects – the majority of which are mild – this is true of almost every medical treatment. In a press release, Moderna said it would explore alternative funding paths for the programme.
    “My stance is that we should not be looking to take anything off the table, and that includes any type of vaccine regimen,” says Lakdawala.
    “Vaccines are the most effective way to counter an infectious disease,” says Sorrell. “And so having that in your arsenal and ready to go just give you more options.”
    Topics:
    #how #agriculture #agency #became #key
    How a US agriculture agency became key in the fight against bird flu
    A dangerous strain of bird flu is spreading in US livestockMediaMedium/Alamy Since Donald Trump assumed office in January, the leading US public health agency has pulled back preparations for a potential bird flu pandemic. But as it steps back, another government agency is stepping up. While the US Department of Health and Human Servicespreviously held regular briefings on its efforts to prevent a wider outbreak of a deadly bird flu virus called H5N1 in people, it largely stopped once Trump took office. It has also cancelled funding for a vaccine that would have targeted the virus. In contrast, the US Department of Agriculturehas escalated its fight against H5N1’s spread in poultry flocks and dairy herds, including by funding the development of livestock vaccines. This particular virus – a strain of avian influenza called H5N1 – poses a significant threat to humans, having killed about half of the roughly 1000 people worldwide who tested positive for it since 2003. While the pathogen spreads rapidly in birds, it is poorly adapted to infecting humans and isn’t known to transmit between people. But that could change if it acquires mutations that allow it to spread more easily among mammals – a risk that increases with each mammalian infection. The possibility of H5N1 evolving to become more dangerous to people has grown significantly since March 2024, when the virus jumped from migratory birds to dairy cows in Texas. More than 1,070 herds across 17 states have been affected since then. H5N1 also infects poultry, placing the virus in closer proximity to people. Since 2022, nearly 175 million domestic birds have been culled in the US due to H5N1, and almost all of the 71 people who have tested positive for it had direct contact with livestock. Get the most essential health and fitness news in your inbox every Saturday. Sign up to newsletter “We need to take this seriously because whenconstantly is spreading, it’s constantly spilling over into humans,” says Seema Lakdawala at Emory University in Georgia. The virus has already killed a person in the US and a child in Mexico this year. Still, cases have declined under Trump. The last recorded human case was in February, and the number of affected poultry flocks fell 95 per cent between then and June. Outbreaks in dairy herds have also stabilised. It isn’t clear what is behind the decline. Lakdawala believes it is partly due to a lull in bird migration, which reduces opportunities for the virus to spread from wild birds to livestock. It may also reflect efforts by the USDA to contain outbreaks on farms. In February, the USDA unveiled a billion plan for tackling H5N1, including strengthening farmers’ defences against the virus, such as through free biosecurity assessments. Of the 150 facilities that have undergone assessment, only one has experienced an H5N1 outbreak. Under Trump, the USDA also continued its National Milk Testing Strategy, which mandates farms provide raw milk samples for influenza testing. If a farm is positive for H5N1, it must allow the USDA to monitor livestock and implement measures to contain the virus. The USDA launched the programme in December and has since ramped up participation to 45 states. “The National Milk Testing Strategy is a fantastic system,” says Erin Sorrell at Johns Hopkins University in Maryland. Along with the USDA’s efforts to improve biosecurity measures on farms, milk testing is crucial for containing the outbreak, says Sorrell. But while the USDA has bolstered its efforts against H5N1, the HHS doesn’t appear to have followed suit. In fact, the recent drop in human cases may reflect decreased surveillance due to workforce cuts, says Sorrell. In April, the HHS laid off about 10,000 employees, including 90 per cent of staff at the National Institute for Occupational Safety and Health, an office that helps investigate H5N1 outbreaks in farm workers. “There is an old saying that if you don’t test for something, you can’t find it,” says Sorrell. Yet a spokesperson for the US Centers for Disease Control and Preventionsays its guidance and surveillance efforts have not changed. “State and local health departments continue to monitor for illness in persons exposed to sick animals,” they told New Scientist. “CDC remains committed to rapidly communicating information as needed about H5N1.” The USDA and HHS also diverge on vaccination. While the USDA has allocated million toward developing vaccines and other solutions for preventing H5N1’s spread in livestock, the HHS cancelled million in contracts for influenza vaccine development. The contracts – terminated on 28 May – were with the pharmaceutical company Moderna to develop vaccines targeting flu subtypes, including H5N1, that could cause future pandemics. The news came the same day Moderna reported nearly 98 per cent of the roughly 300 participants who received two doses of the H5 vaccine in a clinical trial had antibody levels believed to be protective against the virus. The US has about five million H5N1 vaccine doses stockpiled, but these are made using eggs and cultured cells, which take longer to produce than mRNA-based vaccines like Moderna’s. The Moderna vaccine would have modernised the stockpile and enabled the government to rapidly produce vaccines in the event of a pandemic, says Sorrell. “It seems like a very effective platform and would have positioned the US and others to be on good footing if and when we needed a vaccine for our general public,” she says. The HHS cancelled the contracts due to concerns about mRNA vaccines, which Robert F Kennedy Jr – the country’s highest-ranking public health official – has previously cast doubt on. “The reality is that mRNA technology remains under-tested, and we are not going to spend taxpayer dollars repeating the mistakes of the last administration,” said HHS communications director Andrew Nixon in a statement to New Scientist. However, mRNA technology isn’t new. It has been in development for more than half a century and numerous clinical trials have shown mRNA vaccines are safe. While they do carry the risk of side effects – the majority of which are mild – this is true of almost every medical treatment. In a press release, Moderna said it would explore alternative funding paths for the programme. “My stance is that we should not be looking to take anything off the table, and that includes any type of vaccine regimen,” says Lakdawala. “Vaccines are the most effective way to counter an infectious disease,” says Sorrell. “And so having that in your arsenal and ready to go just give you more options.” Topics: #how #agriculture #agency #became #key
    WWW.NEWSCIENTIST.COM
    How a US agriculture agency became key in the fight against bird flu
    A dangerous strain of bird flu is spreading in US livestockMediaMedium/Alamy Since Donald Trump assumed office in January, the leading US public health agency has pulled back preparations for a potential bird flu pandemic. But as it steps back, another government agency is stepping up. While the US Department of Health and Human Services (HHS) previously held regular briefings on its efforts to prevent a wider outbreak of a deadly bird flu virus called H5N1 in people, it largely stopped once Trump took office. It has also cancelled funding for a vaccine that would have targeted the virus. In contrast, the US Department of Agriculture (USDA) has escalated its fight against H5N1’s spread in poultry flocks and dairy herds, including by funding the development of livestock vaccines. This particular virus – a strain of avian influenza called H5N1 – poses a significant threat to humans, having killed about half of the roughly 1000 people worldwide who tested positive for it since 2003. While the pathogen spreads rapidly in birds, it is poorly adapted to infecting humans and isn’t known to transmit between people. But that could change if it acquires mutations that allow it to spread more easily among mammals – a risk that increases with each mammalian infection. The possibility of H5N1 evolving to become more dangerous to people has grown significantly since March 2024, when the virus jumped from migratory birds to dairy cows in Texas. More than 1,070 herds across 17 states have been affected since then. H5N1 also infects poultry, placing the virus in closer proximity to people. Since 2022, nearly 175 million domestic birds have been culled in the US due to H5N1, and almost all of the 71 people who have tested positive for it had direct contact with livestock. Get the most essential health and fitness news in your inbox every Saturday. Sign up to newsletter “We need to take this seriously because when [H5N1] constantly is spreading, it’s constantly spilling over into humans,” says Seema Lakdawala at Emory University in Georgia. The virus has already killed a person in the US and a child in Mexico this year. Still, cases have declined under Trump. The last recorded human case was in February, and the number of affected poultry flocks fell 95 per cent between then and June. Outbreaks in dairy herds have also stabilised. It isn’t clear what is behind the decline. Lakdawala believes it is partly due to a lull in bird migration, which reduces opportunities for the virus to spread from wild birds to livestock. It may also reflect efforts by the USDA to contain outbreaks on farms. In February, the USDA unveiled a $1 billion plan for tackling H5N1, including strengthening farmers’ defences against the virus, such as through free biosecurity assessments. Of the 150 facilities that have undergone assessment, only one has experienced an H5N1 outbreak. Under Trump, the USDA also continued its National Milk Testing Strategy, which mandates farms provide raw milk samples for influenza testing. If a farm is positive for H5N1, it must allow the USDA to monitor livestock and implement measures to contain the virus. The USDA launched the programme in December and has since ramped up participation to 45 states. “The National Milk Testing Strategy is a fantastic system,” says Erin Sorrell at Johns Hopkins University in Maryland. Along with the USDA’s efforts to improve biosecurity measures on farms, milk testing is crucial for containing the outbreak, says Sorrell. But while the USDA has bolstered its efforts against H5N1, the HHS doesn’t appear to have followed suit. In fact, the recent drop in human cases may reflect decreased surveillance due to workforce cuts, says Sorrell. In April, the HHS laid off about 10,000 employees, including 90 per cent of staff at the National Institute for Occupational Safety and Health, an office that helps investigate H5N1 outbreaks in farm workers. “There is an old saying that if you don’t test for something, you can’t find it,” says Sorrell. Yet a spokesperson for the US Centers for Disease Control and Prevention (CDC) says its guidance and surveillance efforts have not changed. “State and local health departments continue to monitor for illness in persons exposed to sick animals,” they told New Scientist. “CDC remains committed to rapidly communicating information as needed about H5N1.” The USDA and HHS also diverge on vaccination. While the USDA has allocated $100 million toward developing vaccines and other solutions for preventing H5N1’s spread in livestock, the HHS cancelled $776 million in contracts for influenza vaccine development. The contracts – terminated on 28 May – were with the pharmaceutical company Moderna to develop vaccines targeting flu subtypes, including H5N1, that could cause future pandemics. The news came the same day Moderna reported nearly 98 per cent of the roughly 300 participants who received two doses of the H5 vaccine in a clinical trial had antibody levels believed to be protective against the virus. The US has about five million H5N1 vaccine doses stockpiled, but these are made using eggs and cultured cells, which take longer to produce than mRNA-based vaccines like Moderna’s. The Moderna vaccine would have modernised the stockpile and enabled the government to rapidly produce vaccines in the event of a pandemic, says Sorrell. “It seems like a very effective platform and would have positioned the US and others to be on good footing if and when we needed a vaccine for our general public,” she says. The HHS cancelled the contracts due to concerns about mRNA vaccines, which Robert F Kennedy Jr – the country’s highest-ranking public health official – has previously cast doubt on. “The reality is that mRNA technology remains under-tested, and we are not going to spend taxpayer dollars repeating the mistakes of the last administration,” said HHS communications director Andrew Nixon in a statement to New Scientist. However, mRNA technology isn’t new. It has been in development for more than half a century and numerous clinical trials have shown mRNA vaccines are safe. While they do carry the risk of side effects – the majority of which are mild – this is true of almost every medical treatment. In a press release, Moderna said it would explore alternative funding paths for the programme. “My stance is that we should not be looking to take anything off the table, and that includes any type of vaccine regimen,” says Lakdawala. “Vaccines are the most effective way to counter an infectious disease,” says Sorrell. “And so having that in your arsenal and ready to go just give you more options.” Topics:
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  • Climate Change Is Ruining Cheese, Scientists and Farmers Warn

    Climate change is making everything worse — including apparently threatening the dairy that makes our precious cheese.In interviews with Science News, veterinary researchers and dairy farmers alike warned that changes to the climate that affect cows are impacting not only affects the nutritional value of the cheeses produced from their milk, but also the color, texture, and even taste.Researchers from the Université Clermont Auvergne, which is located in the mountainous Central France region that produces a delicious firm cheese known as Cantal, explained in a new paper for the Journal of Dairy Science that grass shortages caused by climate change can greatly affect how cows' milk, and the subsequent cheese created from it, tastes.At regular intervals throughout a five-month testing period in 2021, the scientists sampled milk from two groups of cows, each containing 20 cows from two different breeds that were either allowed to graze on grass like normal or only graze part-time while being fed a supplemental diet that featured corn and other concentrated foods.As the researchers found, the corn-fed cohort consistently produced the same amount of milk and less methane than their grass-fed counterparts — but the taste of the resulting milk products was less savory and rich than the grass-fed bovines.Moreover, the milk from the grass-fed cows contained more omega-3 fatty acids, which are good for the heart, and lactic acids, which act as probiotics."Farmers are looking for feed with better yields than grass or that are more resilient to droughts," explained Matthieu Bouchon, the fittingly-named lead author of the study.Still, those same farmers want to know how supplementing their cows' feed will change the nutritional value and taste, Bouchon said — and one farmer who spoke to Science News affirmed anecdotally, this effect is bearing out in other parts of the world, too."We were having lots of problems with milk protein and fat content due to the heat," Gustavo Abijaodi, a dairy farmer in Brazil, told the website. "If we can stabilize heat effects, the cattle will respond with better and more nutritious milk."The heat also seems to be getting to the way cows eat and behave as well."Cows produce heat to digest food — so if they are already feeling hot, they’ll eat less to lower their temperature," noted Marina Danes, a dairy scientist at Brazil's Federal University of Lavras. "This process spirals into immunosuppression, leaving the animal vulnerable to disease."Whether it's the food quality or the heat affecting the cows, the effects are palpable — or, in this case, edible."If climate change progresses the way it’s going, we’ll feel it in our cheese," remarked Bouchon, the French researcher.More on cattle science: Brazilian "Supercows" Reportedly Close to Achieving World DominationShare This Article
    #climate #change #ruining #cheese #scientists
    Climate Change Is Ruining Cheese, Scientists and Farmers Warn
    Climate change is making everything worse — including apparently threatening the dairy that makes our precious cheese.In interviews with Science News, veterinary researchers and dairy farmers alike warned that changes to the climate that affect cows are impacting not only affects the nutritional value of the cheeses produced from their milk, but also the color, texture, and even taste.Researchers from the Université Clermont Auvergne, which is located in the mountainous Central France region that produces a delicious firm cheese known as Cantal, explained in a new paper for the Journal of Dairy Science that grass shortages caused by climate change can greatly affect how cows' milk, and the subsequent cheese created from it, tastes.At regular intervals throughout a five-month testing period in 2021, the scientists sampled milk from two groups of cows, each containing 20 cows from two different breeds that were either allowed to graze on grass like normal or only graze part-time while being fed a supplemental diet that featured corn and other concentrated foods.As the researchers found, the corn-fed cohort consistently produced the same amount of milk and less methane than their grass-fed counterparts — but the taste of the resulting milk products was less savory and rich than the grass-fed bovines.Moreover, the milk from the grass-fed cows contained more omega-3 fatty acids, which are good for the heart, and lactic acids, which act as probiotics."Farmers are looking for feed with better yields than grass or that are more resilient to droughts," explained Matthieu Bouchon, the fittingly-named lead author of the study.Still, those same farmers want to know how supplementing their cows' feed will change the nutritional value and taste, Bouchon said — and one farmer who spoke to Science News affirmed anecdotally, this effect is bearing out in other parts of the world, too."We were having lots of problems with milk protein and fat content due to the heat," Gustavo Abijaodi, a dairy farmer in Brazil, told the website. "If we can stabilize heat effects, the cattle will respond with better and more nutritious milk."The heat also seems to be getting to the way cows eat and behave as well."Cows produce heat to digest food — so if they are already feeling hot, they’ll eat less to lower their temperature," noted Marina Danes, a dairy scientist at Brazil's Federal University of Lavras. "This process spirals into immunosuppression, leaving the animal vulnerable to disease."Whether it's the food quality or the heat affecting the cows, the effects are palpable — or, in this case, edible."If climate change progresses the way it’s going, we’ll feel it in our cheese," remarked Bouchon, the French researcher.More on cattle science: Brazilian "Supercows" Reportedly Close to Achieving World DominationShare This Article #climate #change #ruining #cheese #scientists
    FUTURISM.COM
    Climate Change Is Ruining Cheese, Scientists and Farmers Warn
    Climate change is making everything worse — including apparently threatening the dairy that makes our precious cheese.In interviews with Science News, veterinary researchers and dairy farmers alike warned that changes to the climate that affect cows are impacting not only affects the nutritional value of the cheeses produced from their milk, but also the color, texture, and even taste.Researchers from the Université Clermont Auvergne, which is located in the mountainous Central France region that produces a delicious firm cheese known as Cantal, explained in a new paper for the Journal of Dairy Science that grass shortages caused by climate change can greatly affect how cows' milk, and the subsequent cheese created from it, tastes.At regular intervals throughout a five-month testing period in 2021, the scientists sampled milk from two groups of cows, each containing 20 cows from two different breeds that were either allowed to graze on grass like normal or only graze part-time while being fed a supplemental diet that featured corn and other concentrated foods.As the researchers found, the corn-fed cohort consistently produced the same amount of milk and less methane than their grass-fed counterparts — but the taste of the resulting milk products was less savory and rich than the grass-fed bovines.Moreover, the milk from the grass-fed cows contained more omega-3 fatty acids, which are good for the heart, and lactic acids, which act as probiotics."Farmers are looking for feed with better yields than grass or that are more resilient to droughts," explained Matthieu Bouchon, the fittingly-named lead author of the study.Still, those same farmers want to know how supplementing their cows' feed will change the nutritional value and taste, Bouchon said — and one farmer who spoke to Science News affirmed anecdotally, this effect is bearing out in other parts of the world, too."We were having lots of problems with milk protein and fat content due to the heat," Gustavo Abijaodi, a dairy farmer in Brazil, told the website. "If we can stabilize heat effects, the cattle will respond with better and more nutritious milk."The heat also seems to be getting to the way cows eat and behave as well."Cows produce heat to digest food — so if they are already feeling hot, they’ll eat less to lower their temperature," noted Marina Danes, a dairy scientist at Brazil's Federal University of Lavras. "This process spirals into immunosuppression, leaving the animal vulnerable to disease."Whether it's the food quality or the heat affecting the cows, the effects are palpable — or, in this case, edible."If climate change progresses the way it’s going, we’ll feel it in our cheese," remarked Bouchon, the French researcher.More on cattle science: Brazilian "Supercows" Reportedly Close to Achieving World DominationShare This Article
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