• Apple WWDC 2025: News and analysis

    Apple’s Worldwide Developers Conference 2025 saw a range of announcements that offered a glimpse into the future of Apple’s software design and artificial intelligencestrategy, highlighted by a new design language called  Liquid Glass and by Apple Intelligence news.

    Liquid Glass is designed to add translucency and dynamic movement to Apple’s user interface across iPhones, iPads, Macs, Apple Watches, and Apple TVs. The overhaul aims to make interactions with elements like buttons and sidebars adapt contextually.

    However, the real news of WWDC could be what we didn’t see.  Analysts had high expectations for Apple’s AI strategy, and while Apple Intelligence was talked about, many market watchers reported that it lacked the innovation that have come from Google’s and Microsoft’s generative AIrollouts.

    The question of whether Apple is playing catch-up lingered at WWDC 2025, and comments from Apple execs about delays to a significant AI overhaul for Siri were apparently interpreted as a setback by investors, leading to a negative reaction and drop in stock price.

    Follow this page for Computerworld‘s coverage of WWDC25.

    WWDC25 news and analysis

    Apple’s AI Revolution: Insights from WWDC

    June 13, 2025: At Apple’s big developer event, developers were served a feast of AI-related updates, including APIs that let them use Apple Intelligence in their apps and ChatGPT-augmentation from within Xcode. As a development environment, Apple has secured its future, with Macs forming the most computationally performant systems you can affordably purchase for the job.

    For developers, Apple’s tools get a lot better for AI

    June 12, 2025: Apple announced one important AI update at WWDC this week, the introduction of support for third-party large language models such as ChatGPT from within Xcode. It’s a big step that should benefit developers, accelerating app development.

    WWDC 25: What’s new for Apple and the enterprise?

    June 11, 2025: Beyond its new Liquid Glass UI and other major improvements across its operating systems, Apple introduced a hoard of changes, tweaks, and enhancements for IT admins at WWDC 2025.

    What we know so far about Apple’s Liquid Glass UI

    June 10, 2025: What Apple has tried to achieve with Liquid Glass is to bring together the optical quality of glass and the fluidity of liquid to emphasize transparency and lighting when using your devices. 

    WWDC first look: How Apple is improving its ecosystem

    June 9, 2025: While the new user interface design Apple execs highlighted at this year’s Worldwide Developers Conferencemight have been a bit of an eye-candy distraction, Apple’s enterprise users were not forgotten.

    Apple infuses AI into the Vision Pro

    June 8, 2025: Sluggish sales of Apple’s Vision Pro mixed reality headset haven’t dampened the company’s enthusiasm for advancing the device’s 3D computing experience, which now incorporates AI to deliver richer context and experiences.

    WWDC: Apple is about to unlock international business

    June 4, 2025: One of the more exciting pre-WWDC rumors is that Apple is preparing to make language problems go away by implementing focused artificial intelligence in Messages, which will apparently be able to translate incoming and outgoing messages on the fly. 
    #apple #wwdc #news #analysis
    Apple WWDC 2025: News and analysis
    Apple’s Worldwide Developers Conference 2025 saw a range of announcements that offered a glimpse into the future of Apple’s software design and artificial intelligencestrategy, highlighted by a new design language called  Liquid Glass and by Apple Intelligence news. Liquid Glass is designed to add translucency and dynamic movement to Apple’s user interface across iPhones, iPads, Macs, Apple Watches, and Apple TVs. The overhaul aims to make interactions with elements like buttons and sidebars adapt contextually. However, the real news of WWDC could be what we didn’t see.  Analysts had high expectations for Apple’s AI strategy, and while Apple Intelligence was talked about, many market watchers reported that it lacked the innovation that have come from Google’s and Microsoft’s generative AIrollouts. The question of whether Apple is playing catch-up lingered at WWDC 2025, and comments from Apple execs about delays to a significant AI overhaul for Siri were apparently interpreted as a setback by investors, leading to a negative reaction and drop in stock price. Follow this page for Computerworld‘s coverage of WWDC25. WWDC25 news and analysis Apple’s AI Revolution: Insights from WWDC June 13, 2025: At Apple’s big developer event, developers were served a feast of AI-related updates, including APIs that let them use Apple Intelligence in their apps and ChatGPT-augmentation from within Xcode. As a development environment, Apple has secured its future, with Macs forming the most computationally performant systems you can affordably purchase for the job. For developers, Apple’s tools get a lot better for AI June 12, 2025: Apple announced one important AI update at WWDC this week, the introduction of support for third-party large language models such as ChatGPT from within Xcode. It’s a big step that should benefit developers, accelerating app development. WWDC 25: What’s new for Apple and the enterprise? June 11, 2025: Beyond its new Liquid Glass UI and other major improvements across its operating systems, Apple introduced a hoard of changes, tweaks, and enhancements for IT admins at WWDC 2025. What we know so far about Apple’s Liquid Glass UI June 10, 2025: What Apple has tried to achieve with Liquid Glass is to bring together the optical quality of glass and the fluidity of liquid to emphasize transparency and lighting when using your devices.  WWDC first look: How Apple is improving its ecosystem June 9, 2025: While the new user interface design Apple execs highlighted at this year’s Worldwide Developers Conferencemight have been a bit of an eye-candy distraction, Apple’s enterprise users were not forgotten. Apple infuses AI into the Vision Pro June 8, 2025: Sluggish sales of Apple’s Vision Pro mixed reality headset haven’t dampened the company’s enthusiasm for advancing the device’s 3D computing experience, which now incorporates AI to deliver richer context and experiences. WWDC: Apple is about to unlock international business June 4, 2025: One of the more exciting pre-WWDC rumors is that Apple is preparing to make language problems go away by implementing focused artificial intelligence in Messages, which will apparently be able to translate incoming and outgoing messages on the fly.  #apple #wwdc #news #analysis
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    Apple WWDC 2025: News and analysis
    Apple’s Worldwide Developers Conference 2025 saw a range of announcements that offered a glimpse into the future of Apple’s software design and artificial intelligence (AI) strategy, highlighted by a new design language called  Liquid Glass and by Apple Intelligence news. Liquid Glass is designed to add translucency and dynamic movement to Apple’s user interface across iPhones, iPads, Macs, Apple Watches, and Apple TVs. The overhaul aims to make interactions with elements like buttons and sidebars adapt contextually. However, the real news of WWDC could be what we didn’t see.  Analysts had high expectations for Apple’s AI strategy, and while Apple Intelligence was talked about, many market watchers reported that it lacked the innovation that have come from Google’s and Microsoft’s generative AI (genAI) rollouts. The question of whether Apple is playing catch-up lingered at WWDC 2025, and comments from Apple execs about delays to a significant AI overhaul for Siri were apparently interpreted as a setback by investors, leading to a negative reaction and drop in stock price. Follow this page for Computerworld‘s coverage of WWDC25. WWDC25 news and analysis Apple’s AI Revolution: Insights from WWDC June 13, 2025: At Apple’s big developer event, developers were served a feast of AI-related updates, including APIs that let them use Apple Intelligence in their apps and ChatGPT-augmentation from within Xcode. As a development environment, Apple has secured its future, with Macs forming the most computationally performant systems you can affordably purchase for the job. For developers, Apple’s tools get a lot better for AI June 12, 2025: Apple announced one important AI update at WWDC this week, the introduction of support for third-party large language models (LLM) such as ChatGPT from within Xcode. It’s a big step that should benefit developers, accelerating app development. WWDC 25: What’s new for Apple and the enterprise? June 11, 2025: Beyond its new Liquid Glass UI and other major improvements across its operating systems, Apple introduced a hoard of changes, tweaks, and enhancements for IT admins at WWDC 2025. What we know so far about Apple’s Liquid Glass UI June 10, 2025: What Apple has tried to achieve with Liquid Glass is to bring together the optical quality of glass and the fluidity of liquid to emphasize transparency and lighting when using your devices.  WWDC first look: How Apple is improving its ecosystem June 9, 2025: While the new user interface design Apple execs highlighted at this year’s Worldwide Developers Conference (WWDC) might have been a bit of an eye-candy distraction, Apple’s enterprise users were not forgotten. Apple infuses AI into the Vision Pro June 8, 2025: Sluggish sales of Apple’s Vision Pro mixed reality headset haven’t dampened the company’s enthusiasm for advancing the device’s 3D computing experience, which now incorporates AI to deliver richer context and experiences. WWDC: Apple is about to unlock international business June 4, 2025: One of the more exciting pre-WWDC rumors is that Apple is preparing to make language problems go away by implementing focused artificial intelligence in Messages, which will apparently be able to translate incoming and outgoing messages on the fly. 
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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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  • Un pionnier de l’IA veut construire des systèmes non nuisibles à l’humanité

    Un pionnier de l’IA veut construire des systèmes non nuisibles à l’humanité L’informaticien Yoshua Bengio annonce la création de LoiZéro, un laboratoire destiné à mettre au point des intelligences artificielles « sûres ». Elles devraient notamment être capables de prévenir les risques liés aux chatbots. Article réservé aux abonnés Yoshua Bengio, professeur titulaire à l’Université de Montréal au Paris Saclay Summit - Choose Science, Saclay, le 12 février 2025. JEAN NICHOLAS GUILLO/REA Yoshua Bengio a de la suite dans les idées. Prix Turing en 2018, directeur scientifique du MILA, l’Institut en intelligence artificiellede Montréal, cet informaticien canadien est réputé pour être l’un des pionniers de l’apprentissage profond, à l’origine du réveil de l’IA depuis une quinzaine d’années. Il est aussi connu pour alerter, depuis plus récemment, sur les risques inhérents à ces technologies, y compris sur des scénarios catastrophe pouvant conduire à l’anéantissement de l’humanité. En janvier, il publiait un vaste travail qu’il avait coordonné pour évaluer les risques. Si le rapport était équilibré, lui-même a une opinion plus tranchée, s’inquiétant d’une possible extinction de masse et appelant au principe de précaution pour freiner le développement actuel. Le 3 juin, il a franchi une nouvelle étape, ne se contentant plus d’alerter. Il lance, en effet, un nouveau laboratoire de recherche privé pour développer des « solutions techniques de systèmes d’IA sûrs par conception ». C’est-à-dire, comme il le détaille au Monde en visio, pour fabriquer des IA « qui ne se retourneront pas contre nous et qui ne pourront pas être utilisées pour nuire ». Il vous reste 73.38% de cet article à lire. La suite est réservée aux abonnés.
    #pionnier #lia #veut #construire #des
    Un pionnier de l’IA veut construire des systèmes non nuisibles à l’humanité
    Un pionnier de l’IA veut construire des systèmes non nuisibles à l’humanité L’informaticien Yoshua Bengio annonce la création de LoiZéro, un laboratoire destiné à mettre au point des intelligences artificielles « sûres ». Elles devraient notamment être capables de prévenir les risques liés aux chatbots. Article réservé aux abonnés Yoshua Bengio, professeur titulaire à l’Université de Montréal au Paris Saclay Summit - Choose Science, Saclay, le 12 février 2025. JEAN NICHOLAS GUILLO/REA Yoshua Bengio a de la suite dans les idées. Prix Turing en 2018, directeur scientifique du MILA, l’Institut en intelligence artificiellede Montréal, cet informaticien canadien est réputé pour être l’un des pionniers de l’apprentissage profond, à l’origine du réveil de l’IA depuis une quinzaine d’années. Il est aussi connu pour alerter, depuis plus récemment, sur les risques inhérents à ces technologies, y compris sur des scénarios catastrophe pouvant conduire à l’anéantissement de l’humanité. En janvier, il publiait un vaste travail qu’il avait coordonné pour évaluer les risques. Si le rapport était équilibré, lui-même a une opinion plus tranchée, s’inquiétant d’une possible extinction de masse et appelant au principe de précaution pour freiner le développement actuel. Le 3 juin, il a franchi une nouvelle étape, ne se contentant plus d’alerter. Il lance, en effet, un nouveau laboratoire de recherche privé pour développer des « solutions techniques de systèmes d’IA sûrs par conception ». C’est-à-dire, comme il le détaille au Monde en visio, pour fabriquer des IA « qui ne se retourneront pas contre nous et qui ne pourront pas être utilisées pour nuire ». Il vous reste 73.38% de cet article à lire. La suite est réservée aux abonnés. #pionnier #lia #veut #construire #des
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    Un pionnier de l’IA veut construire des systèmes non nuisibles à l’humanité
    Un pionnier de l’IA veut construire des systèmes non nuisibles à l’humanité L’informaticien Yoshua Bengio annonce la création de LoiZéro, un laboratoire destiné à mettre au point des intelligences artificielles « sûres ». Elles devraient notamment être capables de prévenir les risques liés aux chatbots. Article réservé aux abonnés Yoshua Bengio, professeur titulaire à l’Université de Montréal au Paris Saclay Summit - Choose Science, Saclay, le 12 février 2025. JEAN NICHOLAS GUILLO/REA Yoshua Bengio a de la suite dans les idées. Prix Turing en 2018, directeur scientifique du MILA, l’Institut en intelligence artificielle (IA) de Montréal, cet informaticien canadien est réputé pour être l’un des pionniers de l’apprentissage profond, à l’origine du réveil de l’IA depuis une quinzaine d’années. Il est aussi connu pour alerter, depuis plus récemment, sur les risques inhérents à ces technologies, y compris sur des scénarios catastrophe pouvant conduire à l’anéantissement de l’humanité. En janvier, il publiait un vaste travail qu’il avait coordonné pour évaluer les risques. Si le rapport était équilibré, lui-même a une opinion plus tranchée, s’inquiétant d’une possible extinction de masse et appelant au principe de précaution pour freiner le développement actuel. Le 3 juin, il a franchi une nouvelle étape, ne se contentant plus d’alerter. Il lance, en effet, un nouveau laboratoire de recherche privé pour développer des « solutions techniques de systèmes d’IA sûrs par conception ». C’est-à-dire, comme il le détaille au Monde en visio, pour fabriquer des IA « qui ne se retourneront pas contre nous et qui ne pourront pas être utilisées pour nuire ». Il vous reste 73.38% de cet article à lire. La suite est réservée aux abonnés.
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  • Fox News AI Newsletter: Scammers can exploit your data from just 1 ChatGPT search

    A person using ChatGPT on their phoneWelcome to Fox News’ Artificial Intelligence newsletter with the latest AI technology advancements.IN TODAY'S NEWSLETTER:- Scammers can exploit your data from just one ChatGPT search- Business Insider embraces AI while laying off 21% of workforce- Nvidia, Dell partner with Trump admin to make next-gen supercomputerGUARD YOUR DATA: ChatGPT and other large language modelshave become amazing helpers for everyday tasks. Whether it's summarizing complex ideas, designing a birthday card or even planning your apartment's layout, you can get impressive results with just a simple prompt.NEWS BREAK: Business Insider announced Thursday that the company will be shrinking the size of its newsroom and making layoffs, impacting over a fifth of its staff. Business Insider CEO Barbara Peng said in an internal memo obtained by Fox News Digital that the company is "fully embracing AI," as 70% of the company’s staff currently uses Enterprise ChatGPT, with a goal of 100%.  Sen. Elizabeth Warren and progressives are taking issue with what they see as Nvidia's outsized influence in the AI chip market.HIGH TECH: Nvidia and Dell on Thursday announced a breakthrough supercomputer powered by artificial intelligencewill launch next year to help drive research at the Department of Energy.SETTING THE PACE: Pace University uses AI and scannable QR codes to read graduates' names. Passengers arrive to Terminal B at Newark Liberty International Airport in Newark, New Jersey on May 7, 2025.A-'EYE' IN THE SKY: Transportation Secretary Sean Duffy recently announced that artificial intelligenceis being used to detect and address air traffic risks, following a slew of near-misses and fatal plane crashes across the country.'PROFOUND TRANSFORMATION': Nvidia, a leader in the artificial intelligencespace, saw shares rise 3% in after-hours trading following the announcement. The earnings report showed that first-quarter net income was up 26% from a year ago at nearly billion, with revenue rising to billion, up 69% from last year. Apple logo'AGRI-FICIAL' INTELLIGENCE: John Deere is turning to artificial intelligence to help farmers address labor shortages and enable them to handle other tasks associated with their business.APPLE’S AI RECKONING': OpenAI has just made a move that's turning heads across the tech world. The company is acquiring io, the AI device startup founded by Jony Ive, for nearly billion. This isn't your typical business deal. It's a collaboration between Sam Altman, who leads OpenAI, and the designer responsible for some of Apple's most iconic products, including the iPhone and Apple Watch.STANDING TALL AGAIN: For Caroline Laubach, being a Wandercraft test pilot is about more than just trying out new technology. It's about reclaiming a sense of freedom and connection that many wheelchair users miss. Laubach, a spinal stroke survivor and full-time wheelchair user, has played a key role in demonstrating the personal AI-powered prototype exoskeleton's development, and her experience highlights just how life-changing this device can be. A man using an exoskeleton to walk.BOT BLUNDER: Google’s artificial intelligence chatbot is being slammed for "anti-American" claims about the supposed White supremacist origins of Memorial Day.FOLLOW FOX NEWS ON SOCIAL MEDIASIGN UP FOR OUR OTHER NEWSLETTERSDOWNLOAD OUR APPSWATCH FOX NEWS ONLINEFox News GoSTREAM FOX NATIONFox NationStay up to date on the latest AI technology advancements and learn about the challenges and opportunities AI presents now and for the future with Fox News here. This article was written by Fox News staff.
    #fox #news #newsletter #scammers #can
    Fox News AI Newsletter: Scammers can exploit your data from just 1 ChatGPT search
    A person using ChatGPT on their phoneWelcome to Fox News’ Artificial Intelligence newsletter with the latest AI technology advancements.IN TODAY'S NEWSLETTER:- Scammers can exploit your data from just one ChatGPT search- Business Insider embraces AI while laying off 21% of workforce- Nvidia, Dell partner with Trump admin to make next-gen supercomputerGUARD YOUR DATA: ChatGPT and other large language modelshave become amazing helpers for everyday tasks. Whether it's summarizing complex ideas, designing a birthday card or even planning your apartment's layout, you can get impressive results with just a simple prompt.NEWS BREAK: Business Insider announced Thursday that the company will be shrinking the size of its newsroom and making layoffs, impacting over a fifth of its staff. Business Insider CEO Barbara Peng said in an internal memo obtained by Fox News Digital that the company is "fully embracing AI," as 70% of the company’s staff currently uses Enterprise ChatGPT, with a goal of 100%.  Sen. Elizabeth Warren and progressives are taking issue with what they see as Nvidia's outsized influence in the AI chip market.HIGH TECH: Nvidia and Dell on Thursday announced a breakthrough supercomputer powered by artificial intelligencewill launch next year to help drive research at the Department of Energy.SETTING THE PACE: Pace University uses AI and scannable QR codes to read graduates' names. Passengers arrive to Terminal B at Newark Liberty International Airport in Newark, New Jersey on May 7, 2025.A-'EYE' IN THE SKY: Transportation Secretary Sean Duffy recently announced that artificial intelligenceis being used to detect and address air traffic risks, following a slew of near-misses and fatal plane crashes across the country.'PROFOUND TRANSFORMATION': Nvidia, a leader in the artificial intelligencespace, saw shares rise 3% in after-hours trading following the announcement. The earnings report showed that first-quarter net income was up 26% from a year ago at nearly billion, with revenue rising to billion, up 69% from last year. Apple logo'AGRI-FICIAL' INTELLIGENCE: John Deere is turning to artificial intelligence to help farmers address labor shortages and enable them to handle other tasks associated with their business.APPLE’S AI RECKONING': OpenAI has just made a move that's turning heads across the tech world. The company is acquiring io, the AI device startup founded by Jony Ive, for nearly billion. This isn't your typical business deal. It's a collaboration between Sam Altman, who leads OpenAI, and the designer responsible for some of Apple's most iconic products, including the iPhone and Apple Watch.STANDING TALL AGAIN: For Caroline Laubach, being a Wandercraft test pilot is about more than just trying out new technology. It's about reclaiming a sense of freedom and connection that many wheelchair users miss. Laubach, a spinal stroke survivor and full-time wheelchair user, has played a key role in demonstrating the personal AI-powered prototype exoskeleton's development, and her experience highlights just how life-changing this device can be. A man using an exoskeleton to walk.BOT BLUNDER: Google’s artificial intelligence chatbot is being slammed for "anti-American" claims about the supposed White supremacist origins of Memorial Day.FOLLOW FOX NEWS ON SOCIAL MEDIASIGN UP FOR OUR OTHER NEWSLETTERSDOWNLOAD OUR APPSWATCH FOX NEWS ONLINEFox News GoSTREAM FOX NATIONFox NationStay up to date on the latest AI technology advancements and learn about the challenges and opportunities AI presents now and for the future with Fox News here. This article was written by Fox News staff. #fox #news #newsletter #scammers #can
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    Fox News AI Newsletter: Scammers can exploit your data from just 1 ChatGPT search
    A person using ChatGPT on their phone (Kurt "CyberGuy" Knutsson) Welcome to Fox News’ Artificial Intelligence newsletter with the latest AI technology advancements.IN TODAY'S NEWSLETTER:- Scammers can exploit your data from just one ChatGPT search- Business Insider embraces AI while laying off 21% of workforce- Nvidia, Dell partner with Trump admin to make next-gen supercomputerGUARD YOUR DATA: ChatGPT and other large language models (LLMs) have become amazing helpers for everyday tasks. Whether it's summarizing complex ideas, designing a birthday card or even planning your apartment's layout, you can get impressive results with just a simple prompt.NEWS BREAK: Business Insider announced Thursday that the company will be shrinking the size of its newsroom and making layoffs, impacting over a fifth of its staff. Business Insider CEO Barbara Peng said in an internal memo obtained by Fox News Digital that the company is "fully embracing AI," as 70% of the company’s staff currently uses Enterprise ChatGPT, with a goal of 100%.  Sen. Elizabeth Warren and progressives are taking issue with what they see as Nvidia's outsized influence in the AI chip market. (Justin Sullivan/Getty Images)HIGH TECH: Nvidia and Dell on Thursday announced a breakthrough supercomputer powered by artificial intelligence (AI) will launch next year to help drive research at the Department of Energy (DOE).SETTING THE PACE: Pace University uses AI and scannable QR codes to read graduates' names. Passengers arrive to Terminal B at Newark Liberty International Airport in Newark, New Jersey on May 7, 2025. (KENA BETANCUR/AFP via Getty Images)A-'EYE' IN THE SKY: Transportation Secretary Sean Duffy recently announced that artificial intelligence (AI) is being used to detect and address air traffic risks, following a slew of near-misses and fatal plane crashes across the country.'PROFOUND TRANSFORMATION': Nvidia, a leader in the artificial intelligence (AI) space, saw shares rise 3% in after-hours trading following the announcement. The earnings report showed that first-quarter net income was up 26% from a year ago at nearly $19 billion, with revenue rising to $44 billion, up 69% from last year. Apple logo (Kurt "CyberGuy" Knutsson)'AGRI-FICIAL' INTELLIGENCE: John Deere is turning to artificial intelligence to help farmers address labor shortages and enable them to handle other tasks associated with their business.APPLE’S AI RECKONING': OpenAI has just made a move that's turning heads across the tech world. The company is acquiring io, the AI device startup founded by Jony Ive, for nearly $6.5 billion. This isn't your typical business deal. It's a collaboration between Sam Altman, who leads OpenAI, and the designer responsible for some of Apple's most iconic products, including the iPhone and Apple Watch.STANDING TALL AGAIN: For Caroline Laubach, being a Wandercraft test pilot is about more than just trying out new technology. It's about reclaiming a sense of freedom and connection that many wheelchair users miss. Laubach, a spinal stroke survivor and full-time wheelchair user, has played a key role in demonstrating the personal AI-powered prototype exoskeleton's development, and her experience highlights just how life-changing this device can be. A man using an exoskeleton to walk. (Wandercraft)BOT BLUNDER: Google’s artificial intelligence chatbot is being slammed for "anti-American" claims about the supposed White supremacist origins of Memorial Day.FOLLOW FOX NEWS ON SOCIAL MEDIASIGN UP FOR OUR OTHER NEWSLETTERSDOWNLOAD OUR APPSWATCH FOX NEWS ONLINEFox News GoSTREAM FOX NATIONFox NationStay up to date on the latest AI technology advancements and learn about the challenges and opportunities AI presents now and for the future with Fox News here. This article was written by Fox News staff.
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  • Waste streams across Lagos

    The Obalende bus terminus is one of Lagos’s most important transport nodes and a ‘graveyard’ for old danfos, which in Yoruba means ‘hurry’. These yellow‑painted minibuses form the backbone of Lagos’s informal transport system and are mostly second‑hand imports from the global north. Located in the heart of Lagos Island, Obalende is one of the first areas to be developed east of the lagoon that splits Lagos into two main halves: the Island and the Mainland. It receives a large portion of urban commuters daily, especially those entering Lagos Island for work.
    Obalende plays a critical role in the cycle of material reuse across the city. The life of a danfo does not end at retirement; it continues through a vast network of informal markets and recyclers that sustain entire communities. Their metal parts are either repurposed to fix other buses or sold as scrap at markets such as Owode Onirin. Located about 25km away on the Lagos Mainland, Owode Onirin, which means ‘money iron market’ in Yoruba, is a major hub for recycled metals. Waste collectors scour the city’s demolition sites for brass and mild steel; they find copper, bronze and aluminium in discarded vehicles. These materials are then processed and sold to companies such as African Foundries and Nigerian Foundries, as well as to local smiths who transform them into building parts, moulds and decorative objects. Sorters, welders and artisans form the backbone of this circular micro‑economy. Their labour breathes new life into discarded matter. 
    Lagos has a State Waste Management Authority, but it is fraught with politicking and inefficient in managing the city’s complex waste cycle. In the absence of intelligent state strategies, it falls on people to engineer solutions. They add armatures, build networks and modulate the static thresholds and borders imposed by the state. Today, these techniques and intelligences, born out of scarcity, are collectively labelled ‘informality’, a term that flattens their ingenuity. 
    Across the streets of Obalende and around its central roundabout, kiosks and pop‑up shops dominate the landscape. Most are constructed from materials such as timber reclaimed from collapsed buildings or fallen fascias, along with salvaged tarpaulins. Stones and concrete blocks found at demolition sites are moulded into anchors using discarded plastic paint buckets, serving as bases for umbrellas offering relief from the scorching Lagos sun. To anticipate flash flooding, many structures are raised slightly above ground on short stilts. Space, which is in short supply, is creatively repurposed to serve different functions at various times of the day; a single location might host breakfast vendors in the morning, fruit sellers in the afternoon and medicine hawkers at night.
    Due to its proximity to the city centre, Obalende experiences constant population shifts. Most entering the city at this node have no means of livelihood and often become salvagers. Under the curling ends of the Third Mainland Bridge, for example, a community of migrants gathers, surviving by scavenging motor parts, sometimes from old danfos, zinc roofing sheets and other materials of meagre value. Discarded mattresses, bedding and mosquito nets are repurposed as shelter beneath the noisy overpass, which becomes both workplace and home. In the absence of supportive state frameworks, communities like those in Obalende create micro‑responses to urban precarity. Their fluid, multifunctional spaces are adaptive and resilient architectures resulting from necessity, survival and material intelligence. 
    ‘Informality as a way of life is inherently circular in its use of space and materials’
    In Lagos, the most populous city in Nigeria and one of the most populated in Africa, two thirds of the population live on less than USa day, according to Amnesty International. This speaks not only to income levels but to multidimensional poverty. Unlike global cities such as Mumbai, Cape Town and Rio de Janeiro, where poorer demographics are largely confined to specific neighbourhoods at the margins, informality in Lagos is not peripheral but integral to how the city functions, defying the rigid thresholds and boundaries of formal urban planning. 
    Across Lagos, self‑sustaining circular economies flourish. Orile, a metal market located on the mainland, is one of the sites where discarded metals from sites in Lagos can be sold as part of a recycling system. Further out in the suburbs of Lagos, also on the mainland, is the Katangua Market, which is the biggest second‑hand clothes market in the city. In Nigeria’s largest hardware technology hub, Computer Village, just south of Lagos in Ikeja, used electrical and electronic equipmentis sold for parts. A TRT World report notes that about 18,300 tonnes of UEEE arrive in Nigeria annually – although the number varies in other studies to as much as 54,000 tonnes smuggled in – with the majority coming from Europe, closely followed by the US and China. 
    Computer Village evolved into a dense network of shops, stalls and kiosks between 1998 and 2000, just before Nigeria adopted early digital cellular network technology. The market sits just minutes from the local airport and the Ikeja High Court, but its edges are fluid, spilling out from the Ikeja Underbridge. Over time, formal plots have dissolved into an evolving mesh of trade; the streets are lined with kiosks and carts, built from repurposed plywood, corrugated metal and tarpaulin, that come and go. Space is not owned but claimed, temporarily held, sublet and reshuffled. 
    Today, Computer Village generates an estimated USbillion in annual revenue. Yet most of the shops lack permanence and are constantly at risk of demolition or displacement. In March this year, over 500 shops were demolished overnight at Owode Onirin; in 2023, shopping complexes at Computer Village were torn down in a similar way. The state has continuously announced plans to relocate Computer Village to Katangua Market, with demolition of parts of Katangua Market itself making way for the move in 2020. Urban development patterns in Lagos prioritise formal sectors while ignoring self‑organised makers and traders. This contributes to spatial exclusion, where such communities are often under threat of eviction and relocation. 
    Discarded devices eventually make their way to landfills. Olusosun, in the very heart of Lagos, is one of Africa’s largest landfills. Over 10,000 tonnes of waste are delivered daily, and more than 5,000 scavengers live and work here, sifting through an artificial mountain of refuse in search of value: aluminium, copper, plastic, cloth. The waste stream, enlarged by the influx of used hardware and fast fashion from the global north, creates both livelihood and hazard. Recent studies have shown that most of the residents in and around the site are exposed to harmful air conditions that affect their lungs. Additionally, the water conditions around the site show infiltration of toxic substances. Scavengers have lost their lives in the process of harvesting metals from discarded electronics. 
    More than a landfill, Olusosun is a stage for the politics of waste in the global south. Poor regulation enables the flow of unserviceable imports; widespread poverty creates demand for cheap, second‑hand goods. The result is a fragile, and at times dangerous, ecosystem where the absence of the state makes room for informal innovation, such as space reuse and temporary architecture, material upcycling and recycling. In Olusosun, metals are often extracted, crushed and smelted through dangerous processes like open burning. Copper and gold harvested from the ashes then make their way back into products and institutions, such as the insets of bronze or aluminium in a piece of furniture that might eventually travel back to the global north. In its usual fashion, the government has promised to decommission the Olusosun site, but little has been seen in terms of an effective plan to repurpose the site under the state’s so‑called ‘advanced waste treatment initiative’.
    Informality as a way of life is inherently circular in its use of space and materials. It embodies adaptability, resilience and an intuitive response to economic and environmental conditions. The self‑built infrastructures in Lagos reveal the creativity and resilience of communities navigating the challenges of urban life. Now is the time for designers, policymakers and community leaders to work together and rethink urban development in a way that is more sustainable and responsive to the needs of the people who make cities thrive. The question is not whether informal economies will continue to exist, but how they can be designed into wider city planning – making them part of the solution, not the problem.

    Featured in the May 2025 issue: Circularity
    Lead image: Olympia De Maismont / AFP / Getty

    2025-05-30
    Reuben J Brown

    Share
    #waste #streams #across #lagos
    Waste streams across Lagos
    The Obalende bus terminus is one of Lagos’s most important transport nodes and a ‘graveyard’ for old danfos, which in Yoruba means ‘hurry’. These yellow‑painted minibuses form the backbone of Lagos’s informal transport system and are mostly second‑hand imports from the global north. Located in the heart of Lagos Island, Obalende is one of the first areas to be developed east of the lagoon that splits Lagos into two main halves: the Island and the Mainland. It receives a large portion of urban commuters daily, especially those entering Lagos Island for work. Obalende plays a critical role in the cycle of material reuse across the city. The life of a danfo does not end at retirement; it continues through a vast network of informal markets and recyclers that sustain entire communities. Their metal parts are either repurposed to fix other buses or sold as scrap at markets such as Owode Onirin. Located about 25km away on the Lagos Mainland, Owode Onirin, which means ‘money iron market’ in Yoruba, is a major hub for recycled metals. Waste collectors scour the city’s demolition sites for brass and mild steel; they find copper, bronze and aluminium in discarded vehicles. These materials are then processed and sold to companies such as African Foundries and Nigerian Foundries, as well as to local smiths who transform them into building parts, moulds and decorative objects. Sorters, welders and artisans form the backbone of this circular micro‑economy. Their labour breathes new life into discarded matter.  Lagos has a State Waste Management Authority, but it is fraught with politicking and inefficient in managing the city’s complex waste cycle. In the absence of intelligent state strategies, it falls on people to engineer solutions. They add armatures, build networks and modulate the static thresholds and borders imposed by the state. Today, these techniques and intelligences, born out of scarcity, are collectively labelled ‘informality’, a term that flattens their ingenuity.  Across the streets of Obalende and around its central roundabout, kiosks and pop‑up shops dominate the landscape. Most are constructed from materials such as timber reclaimed from collapsed buildings or fallen fascias, along with salvaged tarpaulins. Stones and concrete blocks found at demolition sites are moulded into anchors using discarded plastic paint buckets, serving as bases for umbrellas offering relief from the scorching Lagos sun. To anticipate flash flooding, many structures are raised slightly above ground on short stilts. Space, which is in short supply, is creatively repurposed to serve different functions at various times of the day; a single location might host breakfast vendors in the morning, fruit sellers in the afternoon and medicine hawkers at night. Due to its proximity to the city centre, Obalende experiences constant population shifts. Most entering the city at this node have no means of livelihood and often become salvagers. Under the curling ends of the Third Mainland Bridge, for example, a community of migrants gathers, surviving by scavenging motor parts, sometimes from old danfos, zinc roofing sheets and other materials of meagre value. Discarded mattresses, bedding and mosquito nets are repurposed as shelter beneath the noisy overpass, which becomes both workplace and home. In the absence of supportive state frameworks, communities like those in Obalende create micro‑responses to urban precarity. Their fluid, multifunctional spaces are adaptive and resilient architectures resulting from necessity, survival and material intelligence.  ‘Informality as a way of life is inherently circular in its use of space and materials’ In Lagos, the most populous city in Nigeria and one of the most populated in Africa, two thirds of the population live on less than USa day, according to Amnesty International. This speaks not only to income levels but to multidimensional poverty. Unlike global cities such as Mumbai, Cape Town and Rio de Janeiro, where poorer demographics are largely confined to specific neighbourhoods at the margins, informality in Lagos is not peripheral but integral to how the city functions, defying the rigid thresholds and boundaries of formal urban planning.  Across Lagos, self‑sustaining circular economies flourish. Orile, a metal market located on the mainland, is one of the sites where discarded metals from sites in Lagos can be sold as part of a recycling system. Further out in the suburbs of Lagos, also on the mainland, is the Katangua Market, which is the biggest second‑hand clothes market in the city. In Nigeria’s largest hardware technology hub, Computer Village, just south of Lagos in Ikeja, used electrical and electronic equipmentis sold for parts. A TRT World report notes that about 18,300 tonnes of UEEE arrive in Nigeria annually – although the number varies in other studies to as much as 54,000 tonnes smuggled in – with the majority coming from Europe, closely followed by the US and China.  Computer Village evolved into a dense network of shops, stalls and kiosks between 1998 and 2000, just before Nigeria adopted early digital cellular network technology. The market sits just minutes from the local airport and the Ikeja High Court, but its edges are fluid, spilling out from the Ikeja Underbridge. Over time, formal plots have dissolved into an evolving mesh of trade; the streets are lined with kiosks and carts, built from repurposed plywood, corrugated metal and tarpaulin, that come and go. Space is not owned but claimed, temporarily held, sublet and reshuffled.  Today, Computer Village generates an estimated USbillion in annual revenue. Yet most of the shops lack permanence and are constantly at risk of demolition or displacement. In March this year, over 500 shops were demolished overnight at Owode Onirin; in 2023, shopping complexes at Computer Village were torn down in a similar way. The state has continuously announced plans to relocate Computer Village to Katangua Market, with demolition of parts of Katangua Market itself making way for the move in 2020. Urban development patterns in Lagos prioritise formal sectors while ignoring self‑organised makers and traders. This contributes to spatial exclusion, where such communities are often under threat of eviction and relocation.  Discarded devices eventually make their way to landfills. Olusosun, in the very heart of Lagos, is one of Africa’s largest landfills. Over 10,000 tonnes of waste are delivered daily, and more than 5,000 scavengers live and work here, sifting through an artificial mountain of refuse in search of value: aluminium, copper, plastic, cloth. The waste stream, enlarged by the influx of used hardware and fast fashion from the global north, creates both livelihood and hazard. Recent studies have shown that most of the residents in and around the site are exposed to harmful air conditions that affect their lungs. Additionally, the water conditions around the site show infiltration of toxic substances. Scavengers have lost their lives in the process of harvesting metals from discarded electronics.  More than a landfill, Olusosun is a stage for the politics of waste in the global south. Poor regulation enables the flow of unserviceable imports; widespread poverty creates demand for cheap, second‑hand goods. The result is a fragile, and at times dangerous, ecosystem where the absence of the state makes room for informal innovation, such as space reuse and temporary architecture, material upcycling and recycling. In Olusosun, metals are often extracted, crushed and smelted through dangerous processes like open burning. Copper and gold harvested from the ashes then make their way back into products and institutions, such as the insets of bronze or aluminium in a piece of furniture that might eventually travel back to the global north. In its usual fashion, the government has promised to decommission the Olusosun site, but little has been seen in terms of an effective plan to repurpose the site under the state’s so‑called ‘advanced waste treatment initiative’. Informality as a way of life is inherently circular in its use of space and materials. It embodies adaptability, resilience and an intuitive response to economic and environmental conditions. The self‑built infrastructures in Lagos reveal the creativity and resilience of communities navigating the challenges of urban life. Now is the time for designers, policymakers and community leaders to work together and rethink urban development in a way that is more sustainable and responsive to the needs of the people who make cities thrive. The question is not whether informal economies will continue to exist, but how they can be designed into wider city planning – making them part of the solution, not the problem. Featured in the May 2025 issue: Circularity Lead image: Olympia De Maismont / AFP / Getty 2025-05-30 Reuben J Brown Share #waste #streams #across #lagos
    WWW.ARCHITECTURAL-REVIEW.COM
    Waste streams across Lagos
    The Obalende bus terminus is one of Lagos’s most important transport nodes and a ‘graveyard’ for old danfos, which in Yoruba means ‘hurry’. These yellow‑painted minibuses form the backbone of Lagos’s informal transport system and are mostly second‑hand imports from the global north. Located in the heart of Lagos Island, Obalende is one of the first areas to be developed east of the lagoon that splits Lagos into two main halves: the Island and the Mainland. It receives a large portion of urban commuters daily, especially those entering Lagos Island for work. Obalende plays a critical role in the cycle of material reuse across the city. The life of a danfo does not end at retirement; it continues through a vast network of informal markets and recyclers that sustain entire communities. Their metal parts are either repurposed to fix other buses or sold as scrap at markets such as Owode Onirin. Located about 25km away on the Lagos Mainland, Owode Onirin, which means ‘money iron market’ in Yoruba, is a major hub for recycled metals. Waste collectors scour the city’s demolition sites for brass and mild steel; they find copper, bronze and aluminium in discarded vehicles. These materials are then processed and sold to companies such as African Foundries and Nigerian Foundries, as well as to local smiths who transform them into building parts, moulds and decorative objects. Sorters, welders and artisans form the backbone of this circular micro‑economy. Their labour breathes new life into discarded matter.  Lagos has a State Waste Management Authority, but it is fraught with politicking and inefficient in managing the city’s complex waste cycle. In the absence of intelligent state strategies, it falls on people to engineer solutions. They add armatures, build networks and modulate the static thresholds and borders imposed by the state. Today, these techniques and intelligences, born out of scarcity, are collectively labelled ‘informality’, a term that flattens their ingenuity.  Across the streets of Obalende and around its central roundabout, kiosks and pop‑up shops dominate the landscape. Most are constructed from materials such as timber reclaimed from collapsed buildings or fallen fascias, along with salvaged tarpaulins. Stones and concrete blocks found at demolition sites are moulded into anchors using discarded plastic paint buckets, serving as bases for umbrellas offering relief from the scorching Lagos sun. To anticipate flash flooding, many structures are raised slightly above ground on short stilts. Space, which is in short supply, is creatively repurposed to serve different functions at various times of the day; a single location might host breakfast vendors in the morning, fruit sellers in the afternoon and medicine hawkers at night. Due to its proximity to the city centre, Obalende experiences constant population shifts. Most entering the city at this node have no means of livelihood and often become salvagers. Under the curling ends of the Third Mainland Bridge, for example, a community of migrants gathers, surviving by scavenging motor parts, sometimes from old danfos, zinc roofing sheets and other materials of meagre value. Discarded mattresses, bedding and mosquito nets are repurposed as shelter beneath the noisy overpass, which becomes both workplace and home. In the absence of supportive state frameworks, communities like those in Obalende create micro‑responses to urban precarity. Their fluid, multifunctional spaces are adaptive and resilient architectures resulting from necessity, survival and material intelligence.  ‘Informality as a way of life is inherently circular in its use of space and materials’ In Lagos, the most populous city in Nigeria and one of the most populated in Africa, two thirds of the population live on less than US$1 a day, according to Amnesty International. This speaks not only to income levels but to multidimensional poverty. Unlike global cities such as Mumbai, Cape Town and Rio de Janeiro, where poorer demographics are largely confined to specific neighbourhoods at the margins, informality in Lagos is not peripheral but integral to how the city functions, defying the rigid thresholds and boundaries of formal urban planning.  Across Lagos, self‑sustaining circular economies flourish. Orile, a metal market located on the mainland, is one of the sites where discarded metals from sites in Lagos can be sold as part of a recycling system. Further out in the suburbs of Lagos, also on the mainland, is the Katangua Market, which is the biggest second‑hand clothes market in the city. In Nigeria’s largest hardware technology hub, Computer Village, just south of Lagos in Ikeja, used electrical and electronic equipment (UEEE) is sold for parts. A TRT World report notes that about 18,300 tonnes of UEEE arrive in Nigeria annually – although the number varies in other studies to as much as 54,000 tonnes smuggled in – with the majority coming from Europe, closely followed by the US and China.  Computer Village evolved into a dense network of shops, stalls and kiosks between 1998 and 2000, just before Nigeria adopted early digital cellular network technology. The market sits just minutes from the local airport and the Ikeja High Court, but its edges are fluid, spilling out from the Ikeja Underbridge. Over time, formal plots have dissolved into an evolving mesh of trade; the streets are lined with kiosks and carts, built from repurposed plywood, corrugated metal and tarpaulin, that come and go. Space is not owned but claimed, temporarily held, sublet and reshuffled.  Today, Computer Village generates an estimated US$2 billion in annual revenue. Yet most of the shops lack permanence and are constantly at risk of demolition or displacement. In March this year, over 500 shops were demolished overnight at Owode Onirin; in 2023, shopping complexes at Computer Village were torn down in a similar way. The state has continuously announced plans to relocate Computer Village to Katangua Market, with demolition of parts of Katangua Market itself making way for the move in 2020. Urban development patterns in Lagos prioritise formal sectors while ignoring self‑organised makers and traders. This contributes to spatial exclusion, where such communities are often under threat of eviction and relocation.  Discarded devices eventually make their way to landfills. Olusosun, in the very heart of Lagos, is one of Africa’s largest landfills. Over 10,000 tonnes of waste are delivered daily, and more than 5,000 scavengers live and work here, sifting through an artificial mountain of refuse in search of value: aluminium, copper, plastic, cloth. The waste stream, enlarged by the influx of used hardware and fast fashion from the global north, creates both livelihood and hazard. Recent studies have shown that most of the residents in and around the site are exposed to harmful air conditions that affect their lungs. Additionally, the water conditions around the site show infiltration of toxic substances. Scavengers have lost their lives in the process of harvesting metals from discarded electronics.  More than a landfill, Olusosun is a stage for the politics of waste in the global south. Poor regulation enables the flow of unserviceable imports; widespread poverty creates demand for cheap, second‑hand goods. The result is a fragile, and at times dangerous, ecosystem where the absence of the state makes room for informal innovation, such as space reuse and temporary architecture, material upcycling and recycling. In Olusosun, metals are often extracted, crushed and smelted through dangerous processes like open burning. Copper and gold harvested from the ashes then make their way back into products and institutions, such as the insets of bronze or aluminium in a piece of furniture that might eventually travel back to the global north. In its usual fashion, the government has promised to decommission the Olusosun site, but little has been seen in terms of an effective plan to repurpose the site under the state’s so‑called ‘advanced waste treatment initiative’. Informality as a way of life is inherently circular in its use of space and materials. It embodies adaptability, resilience and an intuitive response to economic and environmental conditions. The self‑built infrastructures in Lagos reveal the creativity and resilience of communities navigating the challenges of urban life. Now is the time for designers, policymakers and community leaders to work together and rethink urban development in a way that is more sustainable and responsive to the needs of the people who make cities thrive. The question is not whether informal economies will continue to exist, but how they can be designed into wider city planning – making them part of the solution, not the problem. Featured in the May 2025 issue: Circularity Lead image: Olympia De Maismont / AFP / Getty 2025-05-30 Reuben J Brown Share
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  • IE University presents "Alternative Skies" at the 2025 Venice Architecture Biennale

    html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" ";
    At the 19th International Architecture Exhibition of Venice Architecture Biennale, IE University is showcasing "Alternative Skies." The 19th International Architecture Exhibition of Venice Architecture Biennale which is curated by Carlo Ratti, features displays by Wesam Al Asali, a professor and researcher at the IE School of Architecture and Design; Sigrid Adriaenssens, director of Princeton University's Form Finding Lab and Keller Center for Innovation in Engineering Education; Romina Canna, director of d-Lab at IE University; and Robin Oval, professor at Institut Polytéchnique de Paris. In order to enhance their educational experience, students from the IE School of Architecture and Design have had the chance to work over the previous few months and participate in the installation's production.In order to reevaluate the distinctions between design, workmanship, and natural materials, the "Alternative Skies" project focuses on horizontal architectural features, specifically the roof as a symbolic place and an architectural construct. It calls attention to underappreciated vernacular building techniques and emphasizes how communal knowledge can be used to create environmentally and culturally sensitive architecture. "Alternative Skies" invites us to look upward and rethink our building practices.” He explained that the installation opens a dialogue between vernacular knowledge and emerging technologies, involving masters of traditional construction in Spain—Salvador Gomis, a tile vaulting specialist; Ángel María Martín, a geometrist and master of traditional Spanish carpentry; and Carlos Fontales, a basketry expert," said Wesam Al Asali, the project’s lead. “The project reflects our interest in exploring how design and fabrication technologies can draw on the many intelligences of craft, culture, and nature,” added Al Asali."Patterns in craft emerged through hands-on experimentation and tacit knowledge—shaping materials to meet human needs with elegance and efficiency. Today, we use physics, mathematics, and engineered design to reimagine and scale these crafted artifacts for future-oriented large structures," said Sigrid Adriaenssens.The "Arcade" is a vaulted structure that is 7.5 meters long and was created using three different roof and floor systems techniques. The "Alternative Skies Archive" below it employs traditional crafts to examine the relationship between natural materials and collective building knowledge.The "Arcade" has three full-scale vaulted systems and was created by Sigrid Adriaenssens and Wesam Al Asali as part of their joint project "Structural Crafts." These include a classic interlaced timber shell that combines attractive geometry and structural performance, a segmented tile vault that was created as a prefabricated modular system utilizing panelized building techniques, and a woven willow roof that was put together with the use of Augmented Reality tools. More than just a structural component, the suspended Arcade is a spatial representation of how design and production technologies convert implicit knowledge—such as patterns, pressures, and material intelligence—into architectural form.Two parallel cabinets frame the "Alternative Skies Archive" beneath the Arcade, inviting viewing and education. This learning environment was created in collaboration with the design laboratoryat the IE School of Architecture and Design under the direction of Romina Canna. Students from the Bachelor of Architectural Studies program collaborated with IWLab, a practice that Al Asali co-founded. From Syria's corbelled domes to Egypt's clay dovecotes, the exploring area showcases a variety of regional roofing customs, showcasing the inventiveness and resourcefulness of place-based methods. The "Archive" is an example of how local expertise and modern creativity may coexist to create sustainable, well-founded architecture.Romina Canna highlighted the alignment between the project and the IE School of Architecture and Design d-Lab's mission: "At our design laboratory, we explore design as a means of connecting disciplinary knowledge with other realms of meaning and production. In "Alternative Skies", we developed a narrative that reveals both existing and potential links between traditional knowledge and techniques, material intelligence, and design innovation."Intelligens Natural Artificial Collective is the theme of Carlo Ratti's 19th International Architecture Exhibition at Venice Architecture Biennale, which runs from May 10 to November 23. The show, which is organized around four sub-themes—Transdisciplinarity, Living Lab, Space for Ideas, and Circularity Protocol—aims to connect technology, nature, and teamwork.Ratti highlights that creativity, interdisciplinary collaboration, and inclusivity are essential for the advancement of architecture in the modern day. This edition investigates how architecture might use various forms of intelligence to adapt to an environment that is changing quickly. By showcasing the potential for incorporating community handicraft and natural intelligence into modern architectural thought, "Alternative Skies" significantly advances this discussion.With support from the Princeton Institute for International and Regional Studies, IE School of Architecture and Design, Research Office IE University, and IE Foundation, "Alternative Skies" is able to participate in the 19th International Architecture Exhibition of La Biennale di Venezia.PlanVault boards tileVault boards timberVault boards willowThe 19th International Architecture Exhibition will take place from 10 May to 23 November 2025 at the Giardini, the Arsenale and various venues in Venice, Italy.Find out all exhibition news on WAC's Venice Architecture Biennale page. Exhibition factsConcept: Wesam Al AsaliDesign team: Wesam Al Asali, Sigrid Adriaenssens, Romina Canna, Robin Oval.Authorial Collaborators IWLab: Marah Sharabati, Joelle Deeb, Sadek Jooriahd-Lab: Marta Garcia Salamanca, Malena Gronda Garrigues, Michaela Zavacká, Alaa Belal, Hayk Areg Khachikyan Supported by: Princeton Institute for International and Regional Studies, IE School of Architecture and Design, Research Office IE University, IE FoundationTechnical Collaborators: Salvador Gomis Aviñó, Angel Maria Martín López, Carlos Fontales Ortíz, ETSAMaderaAknowledgments to: Alejandro García Hermida, Kinda Ghannoum, Alessandro Dell'Endice, IE University Fab Lab, Maintenance Team IE UniversityAll images © Luis Díaz Díaz.All drawings © Wesam Al Asali.> via IE University
    #university #presents #quotalternative #skiesquot #venice
    IE University presents "Alternative Skies" at the 2025 Venice Architecture Biennale
    html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "; At the 19th International Architecture Exhibition of Venice Architecture Biennale, IE University is showcasing "Alternative Skies." The 19th International Architecture Exhibition of Venice Architecture Biennale which is curated by Carlo Ratti, features displays by Wesam Al Asali, a professor and researcher at the IE School of Architecture and Design; Sigrid Adriaenssens, director of Princeton University's Form Finding Lab and Keller Center for Innovation in Engineering Education; Romina Canna, director of d-Lab at IE University; and Robin Oval, professor at Institut Polytéchnique de Paris. In order to enhance their educational experience, students from the IE School of Architecture and Design have had the chance to work over the previous few months and participate in the installation's production.In order to reevaluate the distinctions between design, workmanship, and natural materials, the "Alternative Skies" project focuses on horizontal architectural features, specifically the roof as a symbolic place and an architectural construct. It calls attention to underappreciated vernacular building techniques and emphasizes how communal knowledge can be used to create environmentally and culturally sensitive architecture. "Alternative Skies" invites us to look upward and rethink our building practices.” He explained that the installation opens a dialogue between vernacular knowledge and emerging technologies, involving masters of traditional construction in Spain—Salvador Gomis, a tile vaulting specialist; Ángel María Martín, a geometrist and master of traditional Spanish carpentry; and Carlos Fontales, a basketry expert," said Wesam Al Asali, the project’s lead. “The project reflects our interest in exploring how design and fabrication technologies can draw on the many intelligences of craft, culture, and nature,” added Al Asali."Patterns in craft emerged through hands-on experimentation and tacit knowledge—shaping materials to meet human needs with elegance and efficiency. Today, we use physics, mathematics, and engineered design to reimagine and scale these crafted artifacts for future-oriented large structures," said Sigrid Adriaenssens.The "Arcade" is a vaulted structure that is 7.5 meters long and was created using three different roof and floor systems techniques. The "Alternative Skies Archive" below it employs traditional crafts to examine the relationship between natural materials and collective building knowledge.The "Arcade" has three full-scale vaulted systems and was created by Sigrid Adriaenssens and Wesam Al Asali as part of their joint project "Structural Crafts." These include a classic interlaced timber shell that combines attractive geometry and structural performance, a segmented tile vault that was created as a prefabricated modular system utilizing panelized building techniques, and a woven willow roof that was put together with the use of Augmented Reality tools. More than just a structural component, the suspended Arcade is a spatial representation of how design and production technologies convert implicit knowledge—such as patterns, pressures, and material intelligence—into architectural form.Two parallel cabinets frame the "Alternative Skies Archive" beneath the Arcade, inviting viewing and education. This learning environment was created in collaboration with the design laboratoryat the IE School of Architecture and Design under the direction of Romina Canna. Students from the Bachelor of Architectural Studies program collaborated with IWLab, a practice that Al Asali co-founded. From Syria's corbelled domes to Egypt's clay dovecotes, the exploring area showcases a variety of regional roofing customs, showcasing the inventiveness and resourcefulness of place-based methods. The "Archive" is an example of how local expertise and modern creativity may coexist to create sustainable, well-founded architecture.Romina Canna highlighted the alignment between the project and the IE School of Architecture and Design d-Lab's mission: "At our design laboratory, we explore design as a means of connecting disciplinary knowledge with other realms of meaning and production. In "Alternative Skies", we developed a narrative that reveals both existing and potential links between traditional knowledge and techniques, material intelligence, and design innovation."Intelligens Natural Artificial Collective is the theme of Carlo Ratti's 19th International Architecture Exhibition at Venice Architecture Biennale, which runs from May 10 to November 23. The show, which is organized around four sub-themes—Transdisciplinarity, Living Lab, Space for Ideas, and Circularity Protocol—aims to connect technology, nature, and teamwork.Ratti highlights that creativity, interdisciplinary collaboration, and inclusivity are essential for the advancement of architecture in the modern day. This edition investigates how architecture might use various forms of intelligence to adapt to an environment that is changing quickly. By showcasing the potential for incorporating community handicraft and natural intelligence into modern architectural thought, "Alternative Skies" significantly advances this discussion.With support from the Princeton Institute for International and Regional Studies, IE School of Architecture and Design, Research Office IE University, and IE Foundation, "Alternative Skies" is able to participate in the 19th International Architecture Exhibition of La Biennale di Venezia.PlanVault boards tileVault boards timberVault boards willowThe 19th International Architecture Exhibition will take place from 10 May to 23 November 2025 at the Giardini, the Arsenale and various venues in Venice, Italy.Find out all exhibition news on WAC's Venice Architecture Biennale page. Exhibition factsConcept: Wesam Al AsaliDesign team: Wesam Al Asali, Sigrid Adriaenssens, Romina Canna, Robin Oval.Authorial Collaborators IWLab: Marah Sharabati, Joelle Deeb, Sadek Jooriahd-Lab: Marta Garcia Salamanca, Malena Gronda Garrigues, Michaela Zavacká, Alaa Belal, Hayk Areg Khachikyan Supported by: Princeton Institute for International and Regional Studies, IE School of Architecture and Design, Research Office IE University, IE FoundationTechnical Collaborators: Salvador Gomis Aviñó, Angel Maria Martín López, Carlos Fontales Ortíz, ETSAMaderaAknowledgments to: Alejandro García Hermida, Kinda Ghannoum, Alessandro Dell'Endice, IE University Fab Lab, Maintenance Team IE UniversityAll images © Luis Díaz Díaz.All drawings © Wesam Al Asali.> via IE University #university #presents #quotalternative #skiesquot #venice
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    IE University presents "Alternative Skies" at the 2025 Venice Architecture Biennale
    html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd" At the 19th International Architecture Exhibition of Venice Architecture Biennale, IE University is showcasing "Alternative Skies." The 19th International Architecture Exhibition of Venice Architecture Biennale which is curated by Carlo Ratti, features displays by Wesam Al Asali, a professor and researcher at the IE School of Architecture and Design; Sigrid Adriaenssens, director of Princeton University's Form Finding Lab and Keller Center for Innovation in Engineering Education; Romina Canna, director of d-Lab at IE University; and Robin Oval, professor at Institut Polytéchnique de Paris. In order to enhance their educational experience, students from the IE School of Architecture and Design have had the chance to work over the previous few months and participate in the installation's production.In order to reevaluate the distinctions between design, workmanship, and natural materials, the "Alternative Skies" project focuses on horizontal architectural features, specifically the roof as a symbolic place and an architectural construct. It calls attention to underappreciated vernacular building techniques and emphasizes how communal knowledge can be used to create environmentally and culturally sensitive architecture. "Alternative Skies" invites us to look upward and rethink our building practices.” He explained that the installation opens a dialogue between vernacular knowledge and emerging technologies, involving masters of traditional construction in Spain—Salvador Gomis, a tile vaulting specialist; Ángel María Martín, a geometrist and master of traditional Spanish carpentry; and Carlos Fontales, a basketry expert," said Wesam Al Asali, the project’s lead. “The project reflects our interest in exploring how design and fabrication technologies can draw on the many intelligences of craft, culture, and nature,” added Al Asali."Patterns in craft emerged through hands-on experimentation and tacit knowledge—shaping materials to meet human needs with elegance and efficiency. Today, we use physics, mathematics, and engineered design to reimagine and scale these crafted artifacts for future-oriented large structures," said Sigrid Adriaenssens.The "Arcade" is a vaulted structure that is 7.5 meters long and was created using three different roof and floor systems techniques. The "Alternative Skies Archive" below it employs traditional crafts to examine the relationship between natural materials and collective building knowledge.The "Arcade" has three full-scale vaulted systems and was created by Sigrid Adriaenssens and Wesam Al Asali as part of their joint project "Structural Crafts." These include a classic interlaced timber shell that combines attractive geometry and structural performance, a segmented tile vault that was created as a prefabricated modular system utilizing panelized building techniques, and a woven willow roof that was put together with the use of Augmented Reality tools. More than just a structural component, the suspended Arcade is a spatial representation of how design and production technologies convert implicit knowledge—such as patterns, pressures, and material intelligence—into architectural form.Two parallel cabinets frame the "Alternative Skies Archive" beneath the Arcade, inviting viewing and education. This learning environment was created in collaboration with the design laboratory (d-Lab) at the IE School of Architecture and Design under the direction of Romina Canna. Students from the Bachelor of Architectural Studies program collaborated with IWLab, a practice that Al Asali co-founded. From Syria's corbelled domes to Egypt's clay dovecotes, the exploring area showcases a variety of regional roofing customs, showcasing the inventiveness and resourcefulness of place-based methods. The "Archive" is an example of how local expertise and modern creativity may coexist to create sustainable, well-founded architecture.Romina Canna highlighted the alignment between the project and the IE School of Architecture and Design d-Lab's mission: "At our design laboratory, we explore design as a means of connecting disciplinary knowledge with other realms of meaning and production. In "Alternative Skies", we developed a narrative that reveals both existing and potential links between traditional knowledge and techniques, material intelligence, and design innovation."Intelligens Natural Artificial Collective is the theme of Carlo Ratti's 19th International Architecture Exhibition at Venice Architecture Biennale, which runs from May 10 to November 23. The show, which is organized around four sub-themes—Transdisciplinarity, Living Lab, Space for Ideas, and Circularity Protocol—aims to connect technology, nature, and teamwork.Ratti highlights that creativity, interdisciplinary collaboration, and inclusivity are essential for the advancement of architecture in the modern day. This edition investigates how architecture might use various forms of intelligence to adapt to an environment that is changing quickly. By showcasing the potential for incorporating community handicraft and natural intelligence into modern architectural thought, "Alternative Skies" significantly advances this discussion.With support from the Princeton Institute for International and Regional Studies, IE School of Architecture and Design, Research Office IE University, and IE Foundation, "Alternative Skies" is able to participate in the 19th International Architecture Exhibition of La Biennale di Venezia.PlanVault boards tileVault boards timberVault boards willowThe 19th International Architecture Exhibition will take place from 10 May to 23 November 2025 at the Giardini, the Arsenale and various venues in Venice, Italy.Find out all exhibition news on WAC's Venice Architecture Biennale page. Exhibition factsConcept: Wesam Al AsaliDesign team: Wesam Al Asali, Sigrid Adriaenssens, Romina Canna, Robin Oval.Authorial Collaborators IWLab: Marah Sharabati, Joelle Deeb, Sadek Jooriahd-Lab (IE School of Architecture and Design): Marta Garcia Salamanca, Malena Gronda Garrigues, Michaela Zavacká, Alaa Belal, Hayk Areg Khachikyan Supported by: Princeton Institute for International and Regional Studies, IE School of Architecture and Design, Research Office IE University, IE FoundationTechnical Collaborators: Salvador Gomis Aviñó (CERCAA), Angel Maria Martín López (La Escuela de Carpintería de lo Blanco de Narros del Castillo), Carlos Fontales Ortíz, ETSAMaderaAknowledgments to: Alejandro García Hermida, Kinda Ghannoum, Alessandro Dell'Endice, IE University Fab Lab, Maintenance Team IE UniversityAll images © Luis Díaz Díaz.All drawings © Wesam Al Asali.> via IE University
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  • Labour puts Humphrey AI to work for council admin

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    Labour puts Humphrey AI to work for council admin
    A tool built on the government’s Humphrey AI toolset is being piloted by 25 councils to take notes during meetings

    Published: 23 May 2025 15:45

    The UK government has announced that its artificial intelligencesuite, Humphrey, is being trialled by a number of local councils.
    Its AI tool, Minute, takes notes in meetings, and was recently used in one chaired by prime minister Keir Starmer.

    Part of Humphrey, the package of AI tools built to help civil servants deliver for ministers and the public more effectively, uses generative AI to turn meetings into notes, and provides tools for correcting summaries. The government found that early tests using Minute showed that officials saved an hour of admin per one-hour meeting.

    The Department for Science, Innovation and Technologysaid Minute can help speed up actions after planning meetings, allowing officers to focus on the task at hand, rather than paperwork, and make informed decisions to get homes built. It’s currently being trailed by 25 local councils.

    Among the ways it’s being used is to help streamline burdensome admin tasks in the planning process as part of the government’s plans to build 1.5 million homes by 2030.

    Lords minister for housing and local government Sharon Taylor said: “Local councils are on the frontline of housing delivery, and we’re backing them with cutting-edge AI technology like Minute so officers can spend less time buried in admin and more time helping to get Britain building.

    “This is alongside our landmark reforms to deliver 1.5 million homes, including the Planning and Infrastructure Bill, which will get working people and families into secure homes and boost economic growth right across the country,” she said.

    stories about public sector AI

    Humphrey AI tool powers Scottish Parliament consultation: AI-powered Consult tool has helped the Scottish Parliament to organise feedback from a public consultation into themes.
    Major obstacles facing Labour’s AI opportunity action plan: Skills, data held in legacy tech and a lack of leadership are among the areas discussed during a recent Public Accounts Committee session.

    Minute can also be used to take notes in meetings between social care workers and their supervisors, allowing workers to focus on offering more support instead of being bogged down by bureaucracy.  

    The Minute trial ties in with a broader government initiative to help local councils use technology to improve essential services they are responsible for delivering to local residents. To fulfil one of the actions in the 50-point AI Opportunities Plan of Action, which was published in January, the government has also introduced an AI Knowledge Hub for sharing examples of how local councils are using technology so others can learn from them – such as an AI assistant that speeds up the reporting of fly-tipping and graffiti in central London.
    In 2024, a Local Government Associationsurvey found that the majority of councils who took part in the pollwere using or exploring how they would use AI. The areas where most respondents had realised benefits from using AI were staff productivity, service efficienciesand cost savings.
    However, the LGA reported that the five biggest barriers to deploying AI identified by respondents were a lack of funding, a lack of staff capabilities, a lack of staff capacity, a lack of sufficient governance and a lack of clear use cases.

    The government’s own State of digital government review, published earlier this year, reported that each of the 320 local authorities in England negotiate technology contracts with big tech companies independently – when many are buying exactly the same tools – making this spending much less effective. The trials with AI-based tools built on Humphrey and the AI Knowledge Hub represent an attempt by the government to reduce the barriers to deploying AI across the public sector.

    AI and digital government minister Feryal Clark said: “From parking permits and planning permission, local councils handle some of the services that impact our daily lives most. For too long, they have been left to fend for themselves when keeping up with rapid innovations in AI and digital technology – when we know it has huge potential to help solve many of the challenges they face.

    Clark said the government was going to work with local councils to help them buy and build the technology they need to deliver Labour’s Plan for Change and support their local communities more effectively. 

    In The Current Issue:

    UK critical systems at risk from ‘digital divide’ created by AI threats
    UK at risk of Russian cyber and physical attacks as Ukraine seeks peace deal
    Standard Chartered grounds AI ambitions in data governance

    Download Current Issue

    SAP Sapphire 2025: Developers take centre stage as AI integration deepens
    – CW Developer Network

    Microsoft entices developers to build more Windows AI apps
    – Cliff Saran's Enterprise blog

    View All Blogs
    #labour #puts #humphrey #work #council
    Labour puts Humphrey AI to work for council admin
    Flyalone - Adobe News Labour puts Humphrey AI to work for council admin A tool built on the government’s Humphrey AI toolset is being piloted by 25 councils to take notes during meetings Published: 23 May 2025 15:45 The UK government has announced that its artificial intelligencesuite, Humphrey, is being trialled by a number of local councils. Its AI tool, Minute, takes notes in meetings, and was recently used in one chaired by prime minister Keir Starmer. Part of Humphrey, the package of AI tools built to help civil servants deliver for ministers and the public more effectively, uses generative AI to turn meetings into notes, and provides tools for correcting summaries. The government found that early tests using Minute showed that officials saved an hour of admin per one-hour meeting. The Department for Science, Innovation and Technologysaid Minute can help speed up actions after planning meetings, allowing officers to focus on the task at hand, rather than paperwork, and make informed decisions to get homes built. It’s currently being trailed by 25 local councils. Among the ways it’s being used is to help streamline burdensome admin tasks in the planning process as part of the government’s plans to build 1.5 million homes by 2030. Lords minister for housing and local government Sharon Taylor said: “Local councils are on the frontline of housing delivery, and we’re backing them with cutting-edge AI technology like Minute so officers can spend less time buried in admin and more time helping to get Britain building. “This is alongside our landmark reforms to deliver 1.5 million homes, including the Planning and Infrastructure Bill, which will get working people and families into secure homes and boost economic growth right across the country,” she said. stories about public sector AI Humphrey AI tool powers Scottish Parliament consultation: AI-powered Consult tool has helped the Scottish Parliament to organise feedback from a public consultation into themes. Major obstacles facing Labour’s AI opportunity action plan: Skills, data held in legacy tech and a lack of leadership are among the areas discussed during a recent Public Accounts Committee session. Minute can also be used to take notes in meetings between social care workers and their supervisors, allowing workers to focus on offering more support instead of being bogged down by bureaucracy.   The Minute trial ties in with a broader government initiative to help local councils use technology to improve essential services they are responsible for delivering to local residents. To fulfil one of the actions in the 50-point AI Opportunities Plan of Action, which was published in January, the government has also introduced an AI Knowledge Hub for sharing examples of how local councils are using technology so others can learn from them – such as an AI assistant that speeds up the reporting of fly-tipping and graffiti in central London. In 2024, a Local Government Associationsurvey found that the majority of councils who took part in the pollwere using or exploring how they would use AI. The areas where most respondents had realised benefits from using AI were staff productivity, service efficienciesand cost savings. However, the LGA reported that the five biggest barriers to deploying AI identified by respondents were a lack of funding, a lack of staff capabilities, a lack of staff capacity, a lack of sufficient governance and a lack of clear use cases. The government’s own State of digital government review, published earlier this year, reported that each of the 320 local authorities in England negotiate technology contracts with big tech companies independently – when many are buying exactly the same tools – making this spending much less effective. The trials with AI-based tools built on Humphrey and the AI Knowledge Hub represent an attempt by the government to reduce the barriers to deploying AI across the public sector. AI and digital government minister Feryal Clark said: “From parking permits and planning permission, local councils handle some of the services that impact our daily lives most. For too long, they have been left to fend for themselves when keeping up with rapid innovations in AI and digital technology – when we know it has huge potential to help solve many of the challenges they face. Clark said the government was going to work with local councils to help them buy and build the technology they need to deliver Labour’s Plan for Change and support their local communities more effectively.  In The Current Issue: UK critical systems at risk from ‘digital divide’ created by AI threats UK at risk of Russian cyber and physical attacks as Ukraine seeks peace deal Standard Chartered grounds AI ambitions in data governance Download Current Issue SAP Sapphire 2025: Developers take centre stage as AI integration deepens – CW Developer Network Microsoft entices developers to build more Windows AI apps – Cliff Saran's Enterprise blog View All Blogs #labour #puts #humphrey #work #council
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    Labour puts Humphrey AI to work for council admin
    Flyalone - Adobe News Labour puts Humphrey AI to work for council admin A tool built on the government’s Humphrey AI toolset is being piloted by 25 councils to take notes during meetings Published: 23 May 2025 15:45 The UK government has announced that its artificial intelligence (AI) suite, Humphrey, is being trialled by a number of local councils. Its AI tool, Minute, takes notes in meetings, and was recently used in one chaired by prime minister Keir Starmer. Part of Humphrey, the package of AI tools built to help civil servants deliver for ministers and the public more effectively, uses generative AI to turn meetings into notes, and provides tools for correcting summaries. The government found that early tests using Minute showed that officials saved an hour of admin per one-hour meeting. The Department for Science, Innovation and Technology (DSIT) said Minute can help speed up actions after planning meetings, allowing officers to focus on the task at hand, rather than paperwork, and make informed decisions to get homes built. It’s currently being trailed by 25 local councils. Among the ways it’s being used is to help streamline burdensome admin tasks in the planning process as part of the government’s plans to build 1.5 million homes by 2030. Lords minister for housing and local government Sharon Taylor said: “Local councils are on the frontline of housing delivery, and we’re backing them with cutting-edge AI technology like Minute so officers can spend less time buried in admin and more time helping to get Britain building. “This is alongside our landmark reforms to deliver 1.5 million homes, including the Planning and Infrastructure Bill, which will get working people and families into secure homes and boost economic growth right across the country,” she said. Read more stories about public sector AI Humphrey AI tool powers Scottish Parliament consultation: AI-powered Consult tool has helped the Scottish Parliament to organise feedback from a public consultation into themes. Major obstacles facing Labour’s AI opportunity action plan: Skills, data held in legacy tech and a lack of leadership are among the areas discussed during a recent Public Accounts Committee session. Minute can also be used to take notes in meetings between social care workers and their supervisors, allowing workers to focus on offering more support instead of being bogged down by bureaucracy.   The Minute trial ties in with a broader government initiative to help local councils use technology to improve essential services they are responsible for delivering to local residents. To fulfil one of the actions in the 50-point AI Opportunities Plan of Action, which was published in January, the government has also introduced an AI Knowledge Hub for sharing examples of how local councils are using technology so others can learn from them – such as an AI assistant that speeds up the reporting of fly-tipping and graffiti in central London. In 2024, a Local Government Association (LGA) survey found that the majority of councils who took part in the poll (85%) were using or exploring how they would use AI. The areas where most respondents had realised benefits from using AI were staff productivity (35%), service efficiencies (32%) and cost savings (22%). However, the LGA reported that the five biggest barriers to deploying AI identified by respondents were a lack of funding (64%), a lack of staff capabilities (53%), a lack of staff capacity (50%), a lack of sufficient governance and a lack of clear use cases (41% each). The government’s own State of digital government review, published earlier this year, reported that each of the 320 local authorities in England negotiate technology contracts with big tech companies independently – when many are buying exactly the same tools – making this spending much less effective. The trials with AI-based tools built on Humphrey and the AI Knowledge Hub represent an attempt by the government to reduce the barriers to deploying AI across the public sector. AI and digital government minister Feryal Clark said: “From parking permits and planning permission, local councils handle some of the services that impact our daily lives most. For too long, they have been left to fend for themselves when keeping up with rapid innovations in AI and digital technology – when we know it has huge potential to help solve many of the challenges they face. Clark said the government was going to work with local councils to help them buy and build the technology they need to deliver Labour’s Plan for Change and support their local communities more effectively.  In The Current Issue: UK critical systems at risk from ‘digital divide’ created by AI threats UK at risk of Russian cyber and physical attacks as Ukraine seeks peace deal Standard Chartered grounds AI ambitions in data governance Download Current Issue SAP Sapphire 2025: Developers take centre stage as AI integration deepens – CW Developer Network Microsoft entices developers to build more Windows AI apps – Cliff Saran's Enterprise blog View All Blogs
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  • Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the Present

    Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the PresentSave this picture!Andamio Vivo - Perú. Image © Gonzalo Vera Tudela De MontreuilThe 19th edition of the Venice Architecture Biennale officially opened to the public on May 10, becoming a significant international platform for exploring the current state of global architecture and sparking conversations about the challenges the discipline faces today—both shared and specific to each territory. This year’s theme, "Intelligens. Natural. Artificial. Collective," proposed by general curator and Italian architect Carlo Ratti, invites reflection on architecture’s interconnection with other fields—such as art, artificial intelligence, and technology—while also emphasizing the importance of territories, landscapes, and, above all, the people who collectively shape our built environment.In this context, the national participations of Latin American countries have enriched the international exhibition with contributions deeply rooted in their local cultures and identities. Argentina, Brazil, Chile, Mexico, Peru, and Uruguay represented Central and South America in Venice. Across their proposals, several shared themes emerged—most notably, the idea that contemporary architecture must consciously reconnect with its territory and draw from its history in order to build more thoughtfully today. Along these lines, the installations explored the re-signification of local elements and ancestral knowledge, adapting them to contemporary challenges and contexts.Brazil and Mexico centered their exhibitions on an in-depth investigation of land recording and mapping, addressing the use of ancestral construction technologies in relation to agriculture and the natural landscape. Both explored how these traditional techniques can be adapted to contemporary contexts. Uruguay, recognizing that over half of its territory is composed of water, emphasized the importance of considering this resource as an integral part of the country’s history, culture, and development. Peru and Argentina, meanwhile, focused on the re-signification of unique local elements—the silobag, emblematic of the Argentine countryside, and totora, a plant traditionally used in various forms of construction in Peru. In both pavilions, these materials were prominently featured, evoking the cultural and symbolic significance they carry. Finally, Chile’s participation presented a reflective and thought-provoking working table that examined recent debates around artificial intelligence policies established in the country. Related Article Between Algorithms and Ancestral Knowledge: Expanding the Concept of Architectural Intelligence Siestario - Argentina
    this picture!this picture!Upon entering Siestario, the Argentine Pavilion located in the Arsenale of Venice, visitors are immersed in a space of soft light and evocative soundscapes. At the center, serving as the undisputed focal point, is a large pink inflatable bag that instinctively invites repose. This is a silobag—a storage element commonly used in the Argentine countryside for preserving grain, especially soy, and emblematic of the country’s export-driven economy. In this context, the silobag functions not only as a spatial gesture but also as a temporal one: an invitation to pause and reflect amid the pace of the Biennale.In this way, architects Marco Zampieron and Juan Manuel Pachué succeed in decontextualizing this characteristic element—deeply rooted in national identity—by re-signifying its function and placing it within a space of critique and questioning. The result is effective: visitors are drawn to the installation, climb onto it, rest, and surrender to the experience, surrounded by images and sounds that induce a dreamlike drowsiness.invenção - BrazilSave this picture!this picture!Brazil’s exhibition, curated by Luciana Saboia, Eder Alencar, and Matheus Seco—members of Plano Coletivo—is divided into two rooms, presenting research on the knowledge drawn from the lands of the Amazon. The installation establishes a dialogue between ancestral wisdom and contemporary urban infrastructure through exhibition elements that also serve as the structural system of the display.In the first room, lined with biodegradable wooden panels, maps and documents are spread across the floor, evoking the direct relationship that Indigenous peoples of the Amazon have with their land. In the second, a curated selection of architectural and urban infrastructure projects illustrates how these traditional forms of knowledge—deeply connected to Brazilian territory—are transformed into collective knowledge, capable of adapting to contemporary projects while preserving this cultural heritage.This balance between local culture, territory, and contemporary challenges is expressed almost literally through a minimalist and precise installation, composed of vertical panels and a suspended table made of reforested wood, both connected by tensioned steel cables. The balance is achieved through stone counterweights and a central metal tube that distributes the forces, turning the table into a structural element that redefines the spatial experience of the room.Reflective Intelligences - ChileSave this picture!this picture!The Chilean Pavilion presents a powerful proposal: upon entering the room, a central table—the main exhibition element—reflects a series of videos, essays, and images on its water surface. These works focus on archival research exploring the country’s growing role in the development of artificial intelligence, data center buildings, and the impact this has on the territory and, above all, its inhabitants.Serena Dambrosio, Nicolás Díaz Bejarano, and Linda Schilling Cuellar, the architects behind the pavilion, conceive the table not only as a physical support but also as a reference to the political tool of the "roundtable" used by the Chilean government to introduce policies and regulations around AI. In this case, the use of the water’s reflection invites visitors to reflect on what this technological development truly entails, questioning the exclusion of communities and environmental factors in these decision-making spaces. In this way, the table within the pavilion becomes a fertile ground for fostering collective dialogue among all key stakeholders: architects, researchers, communities, and policymakers.Chinampa Veneta - MéxicoSave this picture!this picture!The experience of entering the Mexican Pavilion, located in the Arsenale at the Biennale, is completely immersive. Visitors are welcomed by a recreation of a chinampa—an ancient cultivation system that involves creating platforms of earth over water to form small agricultural islands—which immediately captures attention through its lush vegetation, the scent of damp soil, and the sounds of water. The rest of the room, where vegetables, flowers, and medicinal herbs planted in the central chinampa are also expected to grow, is arranged to mimic the canals of Xochimilco, drawing a parallel with Venice itself, famously built over water.With this installation, the curatorial team—comprising Estudio Ignacio Urquiza and Ana Paula de Alba, Estudio María Marín de Buen, ILWT, Locus, Lucio Usobiaga Hegewisch & Nathalia Muguet, and Pedro&Juana—proposes revisiting these traditional chinampa agricultural systems to reflect on their adaptation in the present as a sustainable response, thanks to their self-irrigation system, within the context of droughts and global climate crisis. It also stands as evidence of a collective system bridging the natural and the built environment, as well as sustained care over time.Living Scaffolding - Perúthis picture!this picture!The Peruvian Pavilion, with Alex Hudtwalcker as chief curator and Sebastián Cillóniz, José Ignacio Beteta, and Gianfranco Morales as associate curators, is presented at the Biennale’s Arsenale with Living Scaffolding, a proposal centered around a monumental structure built from totora reed wood. This installation brings to Venice the ancestral knowledge of the Uros and Aymara peoples of Lake Titicaca, who for centuries have used totora to construct floating habitable islands, homes, boats, and other essential elements for life on the lake.Over time, the refinement of this ancient technique incorporated other essential components—such as ropes and logs—that contribute to the stability and buoyancy of the structures. All this knowledge is materialized in an installation that can be fully experienced: visitors enter and walk through the scaffolding, exploring its construction system from within.Living Scaffolding highlights the technical precision and enduring relevance of this tradition, which in the contemporary context takes on a new meaning connected to collectivity, material memory, and the possibility of reactivating ancestral techniques as a response to today’s challenges. 53,86% Uruguay Land of Water - Uruguay this picture!this picture!Curated by architects Ken Sei Fong and Katia Sei Fong, alongside visual artist Luis Sei Fong, the Uruguayan Pavilion explores the country’s relationship with its maritime territory, which accounts for just over half of its total surface area. Located in its own building within the Biennale’s Giardini, the pavilion features a poetic and musical installation: a wavy ceiling from which amethyst stones hang, dripping water that strikes metal containers on the floor. This sensory and sonic experience invites visitors to contemplate water as a thread that weaves together the country’s memory, identity, and development.The installation presents a critique of the global water management model, emphasizing that, as a finite and increasingly scarce resource, it is essential to establish policies and regulations for its preservation. In this context, architecture plays a key role: it can not only offer innovative solutions but also promote conscious planning around water in cities and territories, acting as a bridge between the way we inhabit and the way we collectively manage this vital resource.this picture!Latin America’s participation in the 2025 Venice Biennale reveals that architecture is not only a design discipline but also a powerful critical and cultural tool. Each pavilion, rooted in its specific territorial context and local cultural identity, enacts a form of resistance by exploring ancestral knowledge, natural resources, and contemporary technologies as collective ways of knowing—learning from the past to build better today. In a global context marked by environmental crises, inequalities, and technological transformations, these architectural and deeply reflective endeavors construct new and reimagined narratives, where the local is no longer intrinsic to a fixed context but rather knowledge that expands, connects, and adapts to shared new realities.this picture!

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    About this authorPaula PintosAuthor•••
    Cite: Pintos, Paula. "Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the Present"23 May 2025. ArchDaily. Accessed . < ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否
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    #latin #america #venice #biennale #exploring
    Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the Present
    Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the PresentSave this picture!Andamio Vivo - Perú. Image © Gonzalo Vera Tudela De MontreuilThe 19th edition of the Venice Architecture Biennale officially opened to the public on May 10, becoming a significant international platform for exploring the current state of global architecture and sparking conversations about the challenges the discipline faces today—both shared and specific to each territory. This year’s theme, "Intelligens. Natural. Artificial. Collective," proposed by general curator and Italian architect Carlo Ratti, invites reflection on architecture’s interconnection with other fields—such as art, artificial intelligence, and technology—while also emphasizing the importance of territories, landscapes, and, above all, the people who collectively shape our built environment.In this context, the national participations of Latin American countries have enriched the international exhibition with contributions deeply rooted in their local cultures and identities. Argentina, Brazil, Chile, Mexico, Peru, and Uruguay represented Central and South America in Venice. Across their proposals, several shared themes emerged—most notably, the idea that contemporary architecture must consciously reconnect with its territory and draw from its history in order to build more thoughtfully today. Along these lines, the installations explored the re-signification of local elements and ancestral knowledge, adapting them to contemporary challenges and contexts.Brazil and Mexico centered their exhibitions on an in-depth investigation of land recording and mapping, addressing the use of ancestral construction technologies in relation to agriculture and the natural landscape. Both explored how these traditional techniques can be adapted to contemporary contexts. Uruguay, recognizing that over half of its territory is composed of water, emphasized the importance of considering this resource as an integral part of the country’s history, culture, and development. Peru and Argentina, meanwhile, focused on the re-signification of unique local elements—the silobag, emblematic of the Argentine countryside, and totora, a plant traditionally used in various forms of construction in Peru. In both pavilions, these materials were prominently featured, evoking the cultural and symbolic significance they carry. Finally, Chile’s participation presented a reflective and thought-provoking working table that examined recent debates around artificial intelligence policies established in the country. Related Article Between Algorithms and Ancestral Knowledge: Expanding the Concept of Architectural Intelligence Siestario - Argentina this picture!this picture!Upon entering Siestario, the Argentine Pavilion located in the Arsenale of Venice, visitors are immersed in a space of soft light and evocative soundscapes. At the center, serving as the undisputed focal point, is a large pink inflatable bag that instinctively invites repose. This is a silobag—a storage element commonly used in the Argentine countryside for preserving grain, especially soy, and emblematic of the country’s export-driven economy. In this context, the silobag functions not only as a spatial gesture but also as a temporal one: an invitation to pause and reflect amid the pace of the Biennale.In this way, architects Marco Zampieron and Juan Manuel Pachué succeed in decontextualizing this characteristic element—deeply rooted in national identity—by re-signifying its function and placing it within a space of critique and questioning. The result is effective: visitors are drawn to the installation, climb onto it, rest, and surrender to the experience, surrounded by images and sounds that induce a dreamlike drowsiness.invenção - BrazilSave this picture!this picture!Brazil’s exhibition, curated by Luciana Saboia, Eder Alencar, and Matheus Seco—members of Plano Coletivo—is divided into two rooms, presenting research on the knowledge drawn from the lands of the Amazon. The installation establishes a dialogue between ancestral wisdom and contemporary urban infrastructure through exhibition elements that also serve as the structural system of the display.In the first room, lined with biodegradable wooden panels, maps and documents are spread across the floor, evoking the direct relationship that Indigenous peoples of the Amazon have with their land. In the second, a curated selection of architectural and urban infrastructure projects illustrates how these traditional forms of knowledge—deeply connected to Brazilian territory—are transformed into collective knowledge, capable of adapting to contemporary projects while preserving this cultural heritage.This balance between local culture, territory, and contemporary challenges is expressed almost literally through a minimalist and precise installation, composed of vertical panels and a suspended table made of reforested wood, both connected by tensioned steel cables. The balance is achieved through stone counterweights and a central metal tube that distributes the forces, turning the table into a structural element that redefines the spatial experience of the room.Reflective Intelligences - ChileSave this picture!this picture!The Chilean Pavilion presents a powerful proposal: upon entering the room, a central table—the main exhibition element—reflects a series of videos, essays, and images on its water surface. These works focus on archival research exploring the country’s growing role in the development of artificial intelligence, data center buildings, and the impact this has on the territory and, above all, its inhabitants.Serena Dambrosio, Nicolás Díaz Bejarano, and Linda Schilling Cuellar, the architects behind the pavilion, conceive the table not only as a physical support but also as a reference to the political tool of the "roundtable" used by the Chilean government to introduce policies and regulations around AI. In this case, the use of the water’s reflection invites visitors to reflect on what this technological development truly entails, questioning the exclusion of communities and environmental factors in these decision-making spaces. In this way, the table within the pavilion becomes a fertile ground for fostering collective dialogue among all key stakeholders: architects, researchers, communities, and policymakers.Chinampa Veneta - MéxicoSave this picture!this picture!The experience of entering the Mexican Pavilion, located in the Arsenale at the Biennale, is completely immersive. Visitors are welcomed by a recreation of a chinampa—an ancient cultivation system that involves creating platforms of earth over water to form small agricultural islands—which immediately captures attention through its lush vegetation, the scent of damp soil, and the sounds of water. The rest of the room, where vegetables, flowers, and medicinal herbs planted in the central chinampa are also expected to grow, is arranged to mimic the canals of Xochimilco, drawing a parallel with Venice itself, famously built over water.With this installation, the curatorial team—comprising Estudio Ignacio Urquiza and Ana Paula de Alba, Estudio María Marín de Buen, ILWT, Locus, Lucio Usobiaga Hegewisch & Nathalia Muguet, and Pedro&Juana—proposes revisiting these traditional chinampa agricultural systems to reflect on their adaptation in the present as a sustainable response, thanks to their self-irrigation system, within the context of droughts and global climate crisis. It also stands as evidence of a collective system bridging the natural and the built environment, as well as sustained care over time.Living Scaffolding - Perúthis picture!this picture!The Peruvian Pavilion, with Alex Hudtwalcker as chief curator and Sebastián Cillóniz, José Ignacio Beteta, and Gianfranco Morales as associate curators, is presented at the Biennale’s Arsenale with Living Scaffolding, a proposal centered around a monumental structure built from totora reed wood. This installation brings to Venice the ancestral knowledge of the Uros and Aymara peoples of Lake Titicaca, who for centuries have used totora to construct floating habitable islands, homes, boats, and other essential elements for life on the lake.Over time, the refinement of this ancient technique incorporated other essential components—such as ropes and logs—that contribute to the stability and buoyancy of the structures. All this knowledge is materialized in an installation that can be fully experienced: visitors enter and walk through the scaffolding, exploring its construction system from within.Living Scaffolding highlights the technical precision and enduring relevance of this tradition, which in the contemporary context takes on a new meaning connected to collectivity, material memory, and the possibility of reactivating ancestral techniques as a response to today’s challenges. 53,86% Uruguay Land of Water - Uruguay this picture!this picture!Curated by architects Ken Sei Fong and Katia Sei Fong, alongside visual artist Luis Sei Fong, the Uruguayan Pavilion explores the country’s relationship with its maritime territory, which accounts for just over half of its total surface area. Located in its own building within the Biennale’s Giardini, the pavilion features a poetic and musical installation: a wavy ceiling from which amethyst stones hang, dripping water that strikes metal containers on the floor. This sensory and sonic experience invites visitors to contemplate water as a thread that weaves together the country’s memory, identity, and development.The installation presents a critique of the global water management model, emphasizing that, as a finite and increasingly scarce resource, it is essential to establish policies and regulations for its preservation. In this context, architecture plays a key role: it can not only offer innovative solutions but also promote conscious planning around water in cities and territories, acting as a bridge between the way we inhabit and the way we collectively manage this vital resource.this picture!Latin America’s participation in the 2025 Venice Biennale reveals that architecture is not only a design discipline but also a powerful critical and cultural tool. Each pavilion, rooted in its specific territorial context and local cultural identity, enacts a form of resistance by exploring ancestral knowledge, natural resources, and contemporary technologies as collective ways of knowing—learning from the past to build better today. In a global context marked by environmental crises, inequalities, and technological transformations, these architectural and deeply reflective endeavors construct new and reimagined narratives, where the local is no longer intrinsic to a fixed context but rather knowledge that expands, connects, and adapts to shared new realities.this picture! Image gallerySee allShow less About this authorPaula PintosAuthor••• Cite: Pintos, Paula. "Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the Present"23 May 2025. ArchDaily. Accessed . < ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream #latin #america #venice #biennale #exploring
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    Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the Present
    Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the PresentSave this picture!Andamio Vivo - Perú. Image © Gonzalo Vera Tudela De MontreuilThe 19th edition of the Venice Architecture Biennale officially opened to the public on May 10, becoming a significant international platform for exploring the current state of global architecture and sparking conversations about the challenges the discipline faces today—both shared and specific to each territory. This year’s theme, "Intelligens. Natural. Artificial. Collective," proposed by general curator and Italian architect Carlo Ratti, invites reflection on architecture’s interconnection with other fields—such as art, artificial intelligence, and technology—while also emphasizing the importance of territories, landscapes, and, above all, the people who collectively shape our built environment.In this context, the national participations of Latin American countries have enriched the international exhibition with contributions deeply rooted in their local cultures and identities. Argentina, Brazil, Chile, Mexico, Peru, and Uruguay represented Central and South America in Venice. Across their proposals, several shared themes emerged—most notably, the idea that contemporary architecture must consciously reconnect with its territory and draw from its history in order to build more thoughtfully today. Along these lines, the installations explored the re-signification of local elements and ancestral knowledge, adapting them to contemporary challenges and contexts.Brazil and Mexico centered their exhibitions on an in-depth investigation of land recording and mapping, addressing the use of ancestral construction technologies in relation to agriculture and the natural landscape. Both explored how these traditional techniques can be adapted to contemporary contexts. Uruguay, recognizing that over half of its territory is composed of water, emphasized the importance of considering this resource as an integral part of the country’s history, culture, and development. Peru and Argentina, meanwhile, focused on the re-signification of unique local elements—the silobag, emblematic of the Argentine countryside, and totora, a plant traditionally used in various forms of construction in Peru. In both pavilions, these materials were prominently featured, evoking the cultural and symbolic significance they carry. Finally, Chile’s participation presented a reflective and thought-provoking working table that examined recent debates around artificial intelligence policies established in the country. Related Article Between Algorithms and Ancestral Knowledge: Expanding the Concept of Architectural Intelligence Siestario - Argentina Save this picture!Save this picture!Upon entering Siestario, the Argentine Pavilion located in the Arsenale of Venice, visitors are immersed in a space of soft light and evocative soundscapes. At the center, serving as the undisputed focal point, is a large pink inflatable bag that instinctively invites repose. This is a silobag—a storage element commonly used in the Argentine countryside for preserving grain, especially soy, and emblematic of the country’s export-driven economy. In this context, the silobag functions not only as a spatial gesture but also as a temporal one: an invitation to pause and reflect amid the pace of the Biennale.In this way, architects Marco Zampieron and Juan Manuel Pachué succeed in decontextualizing this characteristic element—deeply rooted in national identity—by re-signifying its function and placing it within a space of critique and questioning. The result is effective: visitors are drawn to the installation, climb onto it, rest, and surrender to the experience, surrounded by images and sounds that induce a dreamlike drowsiness.(re) invenção - BrazilSave this picture!Save this picture!Brazil’s exhibition, curated by Luciana Saboia, Eder Alencar, and Matheus Seco—members of Plano Coletivo—is divided into two rooms, presenting research on the knowledge drawn from the lands of the Amazon. The installation establishes a dialogue between ancestral wisdom and contemporary urban infrastructure through exhibition elements that also serve as the structural system of the display.In the first room, lined with biodegradable wooden panels, maps and documents are spread across the floor, evoking the direct relationship that Indigenous peoples of the Amazon have with their land. In the second, a curated selection of architectural and urban infrastructure projects illustrates how these traditional forms of knowledge—deeply connected to Brazilian territory—are transformed into collective knowledge, capable of adapting to contemporary projects while preserving this cultural heritage.This balance between local culture, territory, and contemporary challenges is expressed almost literally through a minimalist and precise installation, composed of vertical panels and a suspended table made of reforested wood, both connected by tensioned steel cables. The balance is achieved through stone counterweights and a central metal tube that distributes the forces, turning the table into a structural element that redefines the spatial experience of the room.Reflective Intelligences - ChileSave this picture!Save this picture!The Chilean Pavilion presents a powerful proposal: upon entering the room, a central table—the main exhibition element—reflects a series of videos, essays, and images on its water surface. These works focus on archival research exploring the country’s growing role in the development of artificial intelligence, data center buildings, and the impact this has on the territory and, above all, its inhabitants.Serena Dambrosio, Nicolás Díaz Bejarano, and Linda Schilling Cuellar, the architects behind the pavilion, conceive the table not only as a physical support but also as a reference to the political tool of the "roundtable" used by the Chilean government to introduce policies and regulations around AI. In this case, the use of the water’s reflection invites visitors to reflect on what this technological development truly entails, questioning the exclusion of communities and environmental factors in these decision-making spaces. In this way, the table within the pavilion becomes a fertile ground for fostering collective dialogue among all key stakeholders: architects, researchers, communities, and policymakers.Chinampa Veneta - MéxicoSave this picture!Save this picture!The experience of entering the Mexican Pavilion, located in the Arsenale at the Biennale, is completely immersive. Visitors are welcomed by a recreation of a chinampa—an ancient cultivation system that involves creating platforms of earth over water to form small agricultural islands—which immediately captures attention through its lush vegetation, the scent of damp soil, and the sounds of water. The rest of the room, where vegetables, flowers, and medicinal herbs planted in the central chinampa are also expected to grow, is arranged to mimic the canals of Xochimilco, drawing a parallel with Venice itself, famously built over water.With this installation, the curatorial team—comprising Estudio Ignacio Urquiza and Ana Paula de Alba, Estudio María Marín de Buen, ILWT, Locus, Lucio Usobiaga Hegewisch & Nathalia Muguet, and Pedro&Juana—proposes revisiting these traditional chinampa agricultural systems to reflect on their adaptation in the present as a sustainable response, thanks to their self-irrigation system, within the context of droughts and global climate crisis. It also stands as evidence of a collective system bridging the natural and the built environment, as well as sustained care over time.Living Scaffolding - PerúSave this picture!Save this picture!The Peruvian Pavilion, with Alex Hudtwalcker as chief curator and Sebastián Cillóniz, José Ignacio Beteta, and Gianfranco Morales as associate curators, is presented at the Biennale’s Arsenale with Living Scaffolding, a proposal centered around a monumental structure built from totora reed wood. This installation brings to Venice the ancestral knowledge of the Uros and Aymara peoples of Lake Titicaca, who for centuries have used totora to construct floating habitable islands, homes, boats, and other essential elements for life on the lake.Over time, the refinement of this ancient technique incorporated other essential components—such as ropes and logs—that contribute to the stability and buoyancy of the structures. All this knowledge is materialized in an installation that can be fully experienced: visitors enter and walk through the scaffolding, exploring its construction system from within.Living Scaffolding highlights the technical precision and enduring relevance of this tradition, which in the contemporary context takes on a new meaning connected to collectivity, material memory, and the possibility of reactivating ancestral techniques as a response to today’s challenges. 53,86% Uruguay Land of Water - Uruguay Save this picture!Save this picture!Curated by architects Ken Sei Fong and Katia Sei Fong, alongside visual artist Luis Sei Fong, the Uruguayan Pavilion explores the country’s relationship with its maritime territory, which accounts for just over half of its total surface area. Located in its own building within the Biennale’s Giardini, the pavilion features a poetic and musical installation: a wavy ceiling from which amethyst stones hang, dripping water that strikes metal containers on the floor. This sensory and sonic experience invites visitors to contemplate water as a thread that weaves together the country’s memory, identity, and development.The installation presents a critique of the global water management model, emphasizing that, as a finite and increasingly scarce resource, it is essential to establish policies and regulations for its preservation. In this context, architecture plays a key role: it can not only offer innovative solutions but also promote conscious planning around water in cities and territories, acting as a bridge between the way we inhabit and the way we collectively manage this vital resource.Save this picture!Latin America’s participation in the 2025 Venice Biennale reveals that architecture is not only a design discipline but also a powerful critical and cultural tool. Each pavilion, rooted in its specific territorial context and local cultural identity, enacts a form of resistance by exploring ancestral knowledge, natural resources, and contemporary technologies as collective ways of knowing—learning from the past to build better today. In a global context marked by environmental crises, inequalities, and technological transformations, these architectural and deeply reflective endeavors construct new and reimagined narratives, where the local is no longer intrinsic to a fixed context but rather knowledge that expands, connects, and adapts to shared new realities.Save this picture! Image gallerySee allShow less About this authorPaula PintosAuthor••• Cite: Pintos, Paula. "Latin America at the 2025 Venice Biennale: Exploring Territory, Memory, and Ancestral Knowledge to Build the Present" [Latinoamérica en la Bienal de Venecia 2025: territorio, memoria y saberes ancestrales para construir el presente] 23 May 2025. ArchDaily. Accessed . <https://www.archdaily.com/1030213/latin-america-at-the-2025-venice-biennale-exploring-territory-memory-and-ancestral-knowledge-to-build-the-present&gt ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream
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