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One of the four pillars of good conversation is levity. You needn’t be a comedian, you can but have some funTetra Images, LLC/Alamy Conversation lies at the heart of our relationships – yet many of us find it surprisingly hard to talk to others. We may feel anxious at the thought of making small talk with strangers and struggle to connect with the people who are closest to us. If that sounds familiar, Alison Wood Brooks hopes to help. She is a professor at Harvard Business School, where she teaches an oversubscribed course called “TALK: How to talk gooder in business and life”, and the author of a new book, Talk: The science of conversation and the art of being ourselves. Both offer four key principles for more meaningful exchanges. Conversations are inherently unpredictable, says Wood Brooks, but they follow certain rules – and knowing their architecture makes us more comfortable with what is outside of our control. New Scientist asked her about the best ways to apply this research to our own chats. David Robson: Talking about talking feels quite meta. Do you ever find yourself critiquing your own performance? Alison Wood Brooks: There are so many levels of “meta-ness”. I have often felt like I’m floating over the room, watching conversations unfold, even as I’m involved in them myself. I teach a course at Harvard, and [my students] all get to experience this feeling as well. There can be an uncomfortable period of hypervigilance, but I hope that dissipates over time as they develop better habits. There is a famous quote from Charlie Parker, who was a jazz saxophonist. He said something like, “Practise, practise, practise, and then when you get on stage, let it all go and just wail.” I think that’s my approach to conversation. Even when you’re hyper-aware of conversation dynamics, you have to remember the true delight of being with another human mind, and never lose the magic of being together. Think ahead, but once you’re talking, let it all go and just wail. Reading your book, I learned that a good way to enliven a conversation is to ask someone why they are passionate about what they do. So, where does your passion for conversation come from? I have two answers to this question. One is professional. Early in my professorship at Harvard, I had been studying emotions by exploring how people talk about their feelings and the balance between what we feel inside and how we express that to others. And I realised I just had this deep, profound interest in figuring out how people talk to each other about everything, not just their feelings. We now have scientific tools that allow us to capture conversations and analyse them at large scale. Natural language processing, machine learning, the advent of AI – all this allows us to take huge swathes of transcript data and process it much more efficiently. Receive a weekly dose of discovery in your inbox. Sign up to newsletter The personal answer is that I’m an identical twin, and I spent my whole life, from the moment I opened my newborn eyes, existing next to a person who’s an exact copy of myself. It was like observing myself at very close range, interacting with the world, interacting with other people. I could see when she said and did things well, and I could try to do that myself. And I saw when her jokes failed, or she stumbled over her words – I tried to avoid those mistakes. It was a very fortunate form of feedback that not a lot of people get. And then, as a twin, you’ve got this person sharing a bedroom, sharing all your clothes, going to all the same parties and playing on the same sports teams, so we were just constantly in conversation with each other. You reached this level of shared reality that is so incredible, and I’ve spent the rest of my life trying to help other people get there in their relationships, too. “TALK” cleverly captures your framework for better conversations: topics, asking, levity and kindness. Let’s start at the beginning. How should we decide what to talk about? My first piece of advice is to prepare. Some people do this naturally. They already think about the things that they should talk about with somebody before they see them. They should lean into this habit. Some of my students, however, think it’s crazy. They think preparation will make the conversation seem rigid and forced and overly scripted. But just because you’ve thought ahead about what you might talk about doesn’t mean you have to talk about those things once the conversation is underway. It does mean, however, that you always have an idea waiting for you when you’re not sure what to talk about next. Having just one topic in your back pocket can help you in those anxiety-ridden moments. It makes things more fluent, which is important for establishing a connection. Choosing a topic is not only important at the start of a conversation. We’re constantly making decisions about whether we should stay on one subject, drift to something else or totally shift gears and go somewhere wildly different. Sometimes the topic of conversation is obvious. Even then, knowing when to switch to a new one can be trickyMartin Parr/Magnum Photos What’s your advice when making these decisions? There are three very clear signs that suggest that it’s time to switch topics. The first is longer mutual pauses. The second is more uncomfortable laughter, which we use to fill the space that we would usually fill excitedly with good content. And the third sign is redundancy. Once you start repeating things that have already been said on the topic, it’s a sign that you should move to something else. After an average conversation, most people feel like they’ve covered the right number of topics. But if you ask people after conversations that didn’t go well, they’ll more often say that they didn’t talk about enough things, rather than that they talked about too many things. This suggests that a common mistake is lingering too long on a topic after you’ve squeezed all the juice out of it. The second element of TALK is asking questions. I think a lot of us have heard the advice to ask more questions, yet many people don’t apply it. Why do you think that is? Many years of research have shown that the human mind is remarkably egocentric. Often, we are so focused on our own perspective that we forget to even ask someone else to share what’s in their mind. Another reason is fear. You’re interested in the other person, and you know you should ask them questions, but you’re afraid of being too intrusive, or that you will reveal your own incompetence, because you feel you should know the answer already. What kinds of questions should we be asking – and avoiding? In the book, I talk about the power of follow-up questions that build on anything that your partner has just said. It shows that you heard them, that you care and that you want to know more. Even one follow-up question can springboard us away from shallow talk into something deeper and more meaningful. There are, however, some bad patterns of question asking, such as “boomerasking”. Michael Yeomans [at Imperial College London] and I have a recent paper about this, and oh my gosh, it’s been such fun to study. It’s a play on the word boomerang: it comes back to the person who threw it. If I ask you what you had for breakfast, and you tell me you had Special K and banana, and then I say, “Well, let me tell you about my breakfast, because, boy, was it delicious” – that’s boomerasking. Sometimes it’s a thinly veiled way of bragging or complaining, but sometimes I think people are genuinely interested to hear from their partner, but then the partner’s answer reminds them so much of their own life that they can’t help but start sharing their perspective. In our research, we have found that this makes your partner feel like you weren’t interested in their perspective, so it seems very insincere. Sharing your own perspective is important. It’s okay at some point to bring the conversation back to yourself. But don’t do it so soon that it makes your partner feel like you didn’t hear their answer or care about it. Research by Alison Wood Brooks includes a recent study on “boomerasking”, a pitfall you should avoid to make conversations flowJanelle Bruno What are the benefits of levity? When we think of conversations that haven’t gone well, we often think of moments of hostility, anger or disagreement, but a quiet killer of conversation is boredom. Levity is the antidote. These small moments of sparkle or fizz can pull us back in and make us feel engaged with each other again. Our research has shown that we give status and respect to people who make us feel good, so much so that in a group of people, a person who can land even one appropriate joke is more likely to be voted as the leader. And the joke doesn’t even need to be very funny! It’s the fact that they were confident enough to try it and competent enough to read the room. Do you have any practical steps that people can apply to generate levity, even if they’re not a natural comedian? Levity is not just about being funny. In fact, aiming to be a comedian is not the right goal. When we watch stand-up on Netflix, comedians have rehearsed those jokes and honed them and practised them for a long time, and they’re delivering them in a monologue to an audience. It’s a completely different task from a live conversation. In real dialogue, what everybody is looking for is to feel engaged, and that doesn’t require particularly funny jokes or elaborate stories. When you see opportunities to make it fun or lighten the mood, that’s what you need to grab. It can come through a change to a new, fresh topic, or calling back to things that you talked about earlier in the conversation or earlier in your relationship. These callbacks – which sometimes do refer to something funny – are such a nice way of showing that you’ve listened and remembered. A levity move could also involve giving sincere compliments to other people. When you think nice things, when you admire someone, make sure you say it out loud. This brings us to the last element of TALK: kindness. Why do we so often fail to be as kind as we would like? Wobbles in kindness often come back to our egocentrism. Research shows that we underestimate how much other people’s perspectives differ from our own, and we forget that we have the tools to ask other people directly in conversation for their perspective. Being a kinder conversationalist is about trying to focus on your partner’s perspective and then figuring what they need and helping them to get it. Finally, what is your number one tip for readers to have a better conversation the next time they speak to someone? Every conversation is surprisingly tricky and complex. When things don’t go perfectly, give yourself and others more grace. There will be trips and stumbles and then a little grace can go very, very far. Topics:

In April 2025, at the ITER Private Sector Fusion Workshop in Cadarache, something remarkable unfolded. In a room filled with scientists, engineers and software visionaries, the line between big science and commercial innovation began to blur.   Three organisations – Microsoft Research, Arena and Brigantium Engineering – shared how artificial intelligence (AI), already transforming everything from language models to logistics, is now stepping into a new role: helping humanity to unlock the power of nuclear fusion.  Each presenter addressed a different part of the puzzle, but the message was the same: AI isn’t just a buzzword anymore. It’s becoming a real tool – practical, powerful and indispensable – for big science and engineering projects, including fusion.  “If we think of the agricultural revolution and the industrial revolution, the AI revolution is next – and it’s coming at a pace which is unprecedented,” said Kenji Takeda, director of research incubations at Microsoft Research.  Microsoft’s collaboration with ITER is already in motion. Just a month before the workshop, the two teams signed a Memorandum of Understanding (MoU) to explore how AI can accelerate research and development. This follows ITER’s initial use of Microsoft technology to empower their teams. A chatbot in Azure OpenAI service was developed to help staff navigate technical knowledge, on more than a million ITER documents, using natural conversation. GitHub Copilot assists with coding, while AI helps to resolve IT support tickets – those everyday but essential tasks that keep the lights on.  But Microsoft’s vision goes deeper. Fusion demands materials that can survive extreme conditions – heat, radiation, pressure – and that’s where AI shows a different kind of potential. MatterGen, a Microsoft Research generative AI model for materials, designs entirely new materials based on specific properties. “It’s like ChatGPT,” said Takeda, “but instead of ‘Write me a poem’, we ask it to design a material that can survive as the first wall of a fusion reactor.”  The next step? MatterSim – a simulation tool that predicts how these imagined materials will behave in the real world. By combining generation and simulation, Microsoft hopes to uncover materials that don’t yet exist in any catalogue.  While Microsoft tackles the atomic scale, Arena is focused on a different challenge: speeding up hardware development. As general manager Michael Frei put it: “Software innovation happens in seconds. In hardware, that loop can take months – or years.”  Arena’s answer is Atlas, a multimodal AI platform that acts as an extra set of hands – and eyes – for engineers. It can read data sheets, interpret lab results, analyse circuit diagrams and even interact with lab equipment through software interfaces. “Instead of adjusting an oscilloscope manually,” said Frei, “you can just say, ‘Verify the I2C [inter integrated circuit] protocol’, and Atlas gets it done.”  It doesn’t stop there. Atlas can write and adapt firmware on the fly, responding to real-time conditions. That means tighter feedback loops, faster prototyping and fewer late nights in the lab. Arena aims to make building hardware feel a little more like writing software – fluid, fast and assisted by smart tools.  Fusion, of course, isn’t just about atoms and code – it’s also about construction. Gigantic, one-of-a-kind machines don’t build themselves. That’s where Brigantium Engineering comes in. Founder Lynton Sutton explained how his team uses “4D planning” – a marriage of 3D CAD models and detailed construction schedules – to visualise how everything comes together over time. “Gantt charts are hard to interpret. 3D models are static. Our job is to bring those together,” he said.  The result is a time-lapse-style animation that shows the construction process step by step. It’s proven invaluable for safety reviews and stakeholder meetings. Rather than poring over spreadsheets, teams can simply watch the plan come to life.  And there’s more. Brigantium is bringing these models into virtual reality using Unreal Engine – the same one behind many video games. One recent model recreated ITER’s tokamak pit using drone footage and photogrammetry. The experience is fully interactive and can even run in a web browser. “We’ve really improved the quality of the visualisation,” said Sutton. “It’s a lot smoother; the textures look a lot better. Eventually, we’ll have this running through a web browser, so anybody on the team can just click on a web link to navigate this 4D model.”  Looking forward, Sutton believes AI could help automate the painstaking work of syncing schedules with 3D models. One day, these simulations could reach all the way down to individual bolts and fasteners – not just with impressive visuals, but with critical tools for preventing delays.  Despite the different approaches, one theme ran through all three presentations: AI isn’t just a tool for office productivity. It’s becoming a partner in creativity, problem-solving and even scientific discovery.  Takeda mentioned that Microsoft is experimenting with “world models” inspired by how video games simulate physics. These models learn about the physical world by watching pixels in the form of videos of real phenomena such as plasma behaviour. “Our thesis is that if you showed this AI videos of plasma, it might learn the physics of plasmas,” he said.  It sounds futuristic, but the logic holds. The more AI can learn from the world, the more it can help us understand it – and perhaps even master it. At its heart, the message from the workshop was simple: AI isn’t here to replace the scientist, the engineer or the planner; it’s here to help, and to make their work faster, more flexible and maybe a little more fun. As Takeda put it: “Those are just a few examples of how AI is starting to be used at ITER. And it’s just the start of that journey.”  If these early steps are any indication, that journey won’t just be faster – it might also be more inspired. 

Researchers unexpectedly discovered toxic airborne pollutants in Oklahoma. The image above depicts a field in Oklahoma. Credit: Shutterstock University of Colorado Boulder researchers made the first-ever airborne detection of Medium Chain Chlorinated Paraffins (MCCPs) in the Western Hemisphere. Sometimes, scientific research feels a lot like solving a mystery. Scientists head into the field with a clear goal and a solid hypothesis, but then the data reveals something surprising. That’s when the real detective work begins. This is exactly what happened to a team from the University of Colorado Boulder during a recent field study in rural Oklahoma. They were using a state-of-the-art instrument to track how tiny particles form and grow in the air. But instead of just collecting expected data, they uncovered something completely new: the first-ever airborne detection of Medium Chain Chlorinated Paraffins (MCCPs), a kind of toxic organic pollutant, in the Western Hemisphere. The teams findings were published in ACS Environmental Au. “It’s very exciting as a scientist to find something unexpected like this that we weren’t looking for,” said Daniel Katz, CU Boulder chemistry PhD student and lead author of the study. “We’re starting to learn more about this toxic, organic pollutant that we know is out there, and which we need to understand better.” MCCPs are currently under consideration for regulation by the Stockholm Convention, a global treaty to protect human health from long-standing and widespread chemicals. While the toxic pollutants have been measured in Antarctica and Asia, researchers haven’t been sure how to document them in the Western Hemisphere’s atmosphere until now. From Wastewater to Farmlands MCCPs are used in fluids for metal working and in the construction of PVC and textiles. They are often found in wastewater and as a result, can end up in biosolid fertilizer, also called sewage sludge, which is created when liquid is removed from wastewater in a treatment plant. In Oklahoma, researchers suspect the MCCPs they identified came from biosolid fertilizer in the fields near where they set up their instrument. “When sewage sludges are spread across the fields, those toxic compounds could be released into the air,” Katz said. “We can’t show directly that that’s happening, but we think it’s a reasonable way that they could be winding up in the air. Sewage sludge fertilizers have been shown to release similar compounds.” MCCPs little cousins, Short Chain Chlorinated Paraffins (SCCPs), are currently regulated by the Stockholm Convention, and since 2009, by the EPA here in the United States. Regulation came after studies found the toxic pollutants, which travel far and last a long time in the atmosphere, were harmful to human health. But researchers hypothesize that the regulation of SCCPs may have increased MCCPs in the environment. “We always have these unintended consequences of regulation, where you regulate something, and then there’s still a need for the products that those were in,” said Ellie Browne, CU Boulder chemistry professor, CIRES Fellow, and co-author of the study. “So they get replaced by something.” Measurement of aerosols led to a new and surprising discovery Using a nitrate chemical ionization mass spectrometer, which allows scientists to identify chemical compounds in the air, the team measured air at the agricultural site 24 hours a day for one month. As Katz cataloged the data, he documented the different isotopic patterns in the compounds. The compounds measured by the team had distinct patterns, and he noticed new patterns that he immediately identified as different from the known chemical compounds. With some additional research, he identified them as chlorinated paraffins found in MCCPs. Katz says the makeup of MCCPs are similar to PFAS, long-lasting toxic chemicals that break down slowly over time. Known as “forever chemicals,” their presence in soils recently led the Oklahoma Senate to ban biosolid fertilizer. Now that researchers know how to measure MCCPs, the next step might be to measure the pollutants at different times throughout the year to understand how levels change each season. Many unknowns surrounding MCCPs remain, and there’s much more to learn about their environmental impacts. “We identified them, but we still don’t know exactly what they do when they are in the atmosphere, and they need to be investigated further,” Katz said. “I think it’s important that we continue to have governmental agencies that are capable of evaluating the science and regulating these chemicals as necessary for public health and safety.” Reference: “Real-Time Measurements of Gas-Phase Medium-Chain Chlorinated Paraffins Reveal Daily Changes in Gas-Particle Partitioning Controlled by Ambient Temperature” by Daniel John Katz, Bri Dobson, Mitchell Alton, Harald Stark, Douglas R. Worsnop, Manjula R. Canagaratna and Eleanor C. Browne, 5 June 2025, ACS Environmental Au. DOI: 10.1021/acsenvironau.5c00038 Never miss a breakthrough: Join the SciTechDaily newsletter.

Requisition #: 16890 Our Mission: Powering Innovation That Drives Human Advancement When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Innovate With Ansys, Power Your Career. Summary / Role Purpose The R&D Engineer II contributes to the development of software products and supporting systems. In this role, the R&D Engineer II will collaborate with a team of expert professionals to understand customer requirements and accomplish development objectives. Key Duties and Responsibilities Performs moderately complex development activities, including the design, implementation, maintenance, testing and documentation of software modules and sub-systems Understands and employs best practices Performs moderately complex bug verification, release testing and beta support for assigned products. Researches problems discovered by QA or product support and develops solutions Understands the marketing requirements for a product, including target environment, performance criteria and competitive issues Works under the general supervision of a development manager Minimum Education/Certification Requirements and Experience BS in Computer Science, Applied Mathematics, Engineering, or other natural science disciplines with 3-5 years' experience or MS with minimum 2 years experience Working experience within technical software development proven by academic, research, or industry projects. Good understanding and skills in object-oriented programming Experience with Java and C# / .NET Role can be remote, must be based on the East Coast due to timezone Preferred Qualifications and Skills Experience with C++, Python, in addition to Java and C# / .NET Knowledge of Task-Based Asynchronous design pattern (TAP) Exposure to model-based systems engineering concepts Working knowledge of SysML Know-how on cloud computing technologies like micro-service architectures, RPC frameworks (e.g., gRPC), REST APIs, etc. Knowledge of software security best practices Experience working on an Agile software development team Technical knowledge and experience with various engineering tools and methodologies, such as Finite Element simulation, CAD modeling, and Systems Architecture modelling is a plus Ability to assist more junior developers on an as-needed basis Ability to learn quickly and to collaborate with others in a geographically distributed team Excellent communication and interpersonal skills At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement Our Commitments:Amaze with innovative products and solutionsMake our customers incredibly successfulAct with integrityEnsure employees thrive and shareholders prosper Our Values:Adaptability: Be open, welcome what's nextCourage: Be courageous, move forward passionatelyGenerosity: Be generous, share, listen, serveAuthenticity: Be you, make us stronger Our Actions:We commit to audacious goalsWe work seamlessly as a teamWe demonstrate masteryWe deliver outstanding resultsVALUES IN ACTION Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they are valued and can thrive. Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers. We're proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. WELCOME WHAT'S NEXT IN YOUR CAREER AT ANSYS At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high - met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here . At Ansys, it's about the learning, the discovery, and the collaboration. It's about the "what's next" as much as the "mission accomplished." And it's about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.CREATING A PLACE WE'RE PROUD TO BEAnsys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek's Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America's Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified (China, Greece, France, India, Japan, Korea, Spain, Sweden, Taiwan, and U.K.).For more information, please visit us at Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.Ansys does not accept unsolicited referrals for vacancies, and any unsolicited referral will become the property of Ansys. 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While it’s now accepted by many that video games are an art form, it still might be hard to believe that one is featured in an exhibit at the same museum that’s home to Leonardo da Vinci’s “Mona Lisa”: The Louvre in Paris. But this week, Xbox and World’s Edge Studio announced a partnership with what is arguably the most prestigious museum in the world for its new exhibition, “Mamluks 1250–1517.” Related Stories For those who are unaware of how the gaming studios connect to this aspect of the Egyptian Syrian empire: The Mamluks cavalry are among the many units featured in Xbox and World’s Edge Studio’s “Age of Empires” video game franchise. The cavalry is a fan favorite choice in the game centered around traversing the ages and competing against rival empires, particularly in “Age of Empires II: Definitive Edition.” Popular on Variety Presented at the Louvre until July 28, the exhibit “Mamluks 1250–1517″ recounts “the glorious and unique history of this Egyptian Syrian empire, which represents a golden age for the Near East during the Islamic era,” per its official description. “Bringing together 260 pieces from international collections, the exhibition explores the richness of this singular and lesser-known society through a spectacular and immersive scenography.” This marks the first time a video game franchise has collaborated with the Louvre Museum, with installations and events that occur both in person at the museum and online through the “Age of Empires” game: Official “Louvre Museum” scenario in Age of Empires II: Definitive Edition Players can embody General Baybars and Sultan Qutuz at the really heart of the Ain Jalut battle(1260), which opposed the Mamluk Sultanate to the Mongol Empire. This scenario, speciallycreated for the occasion, is already available in Age of Empires II: Definitive Edition (see onhttp://www.ageofempire.com/lelouvre for instructions on finding the map in the game) [LiveTuesday 10th at 9am PT/6pm BST].Exclusive Gaming Night on Twitch Live from the Louvre On Thursday, June 12, at 8 PM, streamer and journalist Samuel Etienne (1.1M FrenchStreamer) will replay live from the exhibition “Mamluks 1250-1517” at the Louvre the official“Louvre Museum” scenario to relive the famous Battle of Ain Jalut on the game Age of EmpiresII: Definitive Edition, in the presence of Le Louvre Teams and one of the studio’s developers.This is an opportunity to learn more about the history of the Mamluks and their representationin the various episodes of the saga.Cross-Interview: The Louvre x Age of Empires To discover more, an interview featuring Adam Isgreen, creative director at World’s Edge, thestudio behind the franchise, and Souraya Noujaïm and Carine Juvin, curators of the exhibition,is available on the YouTube channels of the Louvre and Age of Empires.Mediation and Gaming Sessions at the Museum Museum visitors at the Louvre are invited to test the scenario of the Battle of Ain Jalut,specially designed for the Mamluk exhibition, in the presence of a Louvre mediator and anXbox representative during an exceptional series of workshops. The sessions will take place onFridays, June 20, 27, and 4 & 11 of July. All information and registrations are available here:www.louvre.fr “World’s Edge is honoured to collaborate with Le Louvre,” head of World’s Edge studio Michael Mann said. “The ‘Age of Empires’ franchise has been bringing history to life for more than 65 million players around the world for almost 30 years. We’ve always believed in the great potential for our games to spark an interest in history and culture. We often hear of teachers using ‘Age of Empires’ to teach history to their students and stories from our players about how ‘Age of Empires’ has driven them to learn more, or even to pursue history academically or as a career. This opportunity to bring the amazing stories of the Mamluks to new audiences through the Louvre’s exhibition is one we’re excited to be a part of. We hope that through the excellent work of the Louvre’s team, the legacy of the Mamluks can be shared around the world, and that people enjoy their stories as they come to life through ‘Age of Empires.'” “We are delighted to welcome ‘Age of Empires’ as part of the exhibition Mamluks 1250–1517, through a unique partnership that blends the pleasures of gaming with learning and discovery,” Souraya Noujaim, director of the Department of Islamic Arts and chief curator of the exhibition at le Louvre Museum, said. “It is a way for the museum to engage with diverse audiences and offer a new narrative, one that resonates with contemporary sensitivities, allowing for a deeper understanding of artworks and a greater openness to world history. Beyond the game, the museum experience becomes an opportunity to move from the virtual to the real and uncover the true history of the Mamluks and their unique contribution to universal heritage.” See video and images below from the “Age of Empires” in-game event and the in-person exhibit at the Louvre.

Nature, Published online: 13 June 2025; doi:10.1038/d41586-025-01862-xEarth’s magnetic field seems to correlate with conditions that helped complex life to thrive — a discovery that could aid the search for life on distant exoplanets.

Samsung Galaxy Watch 8 series is expected to make its debut later this year alongside the next-generation foldables. As we wait for the official unveil, renders of the purported smartwatches have surfaced which corroborate previous leaks and suggest that Samsung could bring the unique “Squircle” design of the Galaxy Watch Ultra to other models this year. According to a report, a total of three smartwatches may be launched as part of the Galaxy Watch 8 lineup.Squircle Design of Samsung Galaxy Watch 8 SeriesAndroid Headlines shared exclusive renders of the Samsung Galaxy Watch 8 series in a report. The publication claims that three smartwatches will be introduced this year — Galaxy Watch 8, Galaxy Watch 8 Classic, and a Galaxy Watch Ultra successor, and all three models will sport a squircle design which debuted last year with the Galaxy Watch Ultra.Photo Credit: Android HeadlinesIt is expected to comprise a circular display housed inside a square dial. Renders also suggest that the standard Galaxy Watch 8 will have a two-button design, while the Watch 8 Classic and 2025 Watch Ultra models could be equipped with three buttons, including a Quick button which may be accented in a different colour.Notably, it is a customisable button which is designed to toggle a workout mode or for playing a siren on the Galaxy Watch Ultra.The purported Galaxy Watch 8 Classic is also shown sporting a rotating physical dial, similar to other Classic models of the past. This usually gives it a more traditional watch aesthetic while retaining smartwatch functionality.This report corroborates a previous discovery in the animation files of the leaked One UI 8 Watch which hinted towards the implementation of the squircle design across Samsung's next generation Galaxy Watch lineup. The unreleased firmware contained references of two smartwatches — the Galaxy Watch 8 and Galaxy Watch 8 Classic. Further, the report also suggested that the smartwatches could also benefit from stronger vibrations, aided by extended vibration pattern support akin to the flagship Galaxy Watch Ultra.

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]