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]

html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd" Japanese architect Shigeru Ban has created the Blue Ocean Dome (BOD) for the Osaka-Kansai Expo 2025, addressing the urgent issue of marine plastic pollution and raising crucial awareness about it.Named Blue Ocean Dome, the pavilion stands out with its innovative design, comprising three distinct dome types: Dome A, Dome B, and Dome C. Each dome is specifically crafted to host captivating installations and dynamic exhibitions, promising an unforgettable experience for all visitors throughout the event. Image © Taiki FukaoThe project was commissioned by the Zero Emissions Research and Initiatives (ZERI), a global network of creative minds, seeking solutions to the ever increasing problems of the world.Rather than outright rejecting plastic, the pavilion inspires deep reflection on how we use and manage materials, highlighting our critical responsibility to make sustainable choices for the future.The BOD merges traditional and modern materials—like bamboo, paper, and carbon fiber reinforced plastic (CFRP)—to unlock new and innovative architectural possibilities.Dome A, serving as the striking entrance, is expertly crafted from laminated bamboo. This innovative design not only showcases the beauty of bamboo but also tackles the pressing issue of abandoned bamboo groves in Japan, which pose a risk to land stability due to their shallow root systems.Utilizing raw bamboo for structural purposes is often difficult; however, through advanced processing, it is transformed into thin, laminated boards that boast strength even greater than that of conventional wood. These boards have been skillfully fashioned into a remarkable 19-meter dome, drawing inspiration from traditional Japanese bamboo hats. This project brilliantly turns an environmental challenge into a sustainable architectural solution, highlighting the potential of bamboo as a valuable resource.Dome B stands as the central and largest structure of its kind, boasting a remarkable diameter of 42 meters. It is primarily constructed from Carbon Fiber Reinforced Polymer (CFRP), a cutting-edge material revered for its extraordinary strength-to-weight ratio—four times stronger than steel yet only one-fifth the weight. While CFRP is predominantly seen in industries such as aerospace and automotive due to its high cost, its application in architecture is pioneering.In this project, the choice of CFRP was not just advantageous; it was essential. The primary goal was to minimize the foundation weight on the reclaimed land of the Expo site, making sustainability a top priority. To mitigate the environmental consequences of deep foundation piles, the structure had to be lighter than the soil excavated for its foundation. CFRP not only met this stringent requirement but also ensured the dome's structural integrity, showcasing a perfect marriage of innovation and environmental responsibility.Dome C, with its impressive 19-meter diameter, is crafted entirely from paper tubes that are 100% recyclable after use. Its innovative design features a three-dimensional truss structure, connected by elegant wooden spheres, evoking the beauty of molecular structures.To champion sustainability and minimize waste following the six-month Expo, the entire BOD pavilion has been meticulously designed for effortless disassembly and relocation. It is anchored by a robust steel foundation system and boasts a modular design that allows it to be conveniently packed into standard shipping containers. After the Expo concludes, this remarkable pavilion will be transported to the Maldives, where it will be transformed into a stunning resort facility, breathing new life into its design and purpose.Recently, Shigeru Ban's Paper Log House was revealed at Philip Johnson's Glass House Venue. In addition, Ban installed his Paper Partition Shelters (PPS) for the victims of the Turkey-Syria earthquake in Mersin and Hatay provinces of Turkey.All images © Hiroyuki Hirai unless otherwise stated.> via Shigeru Ban Architects 

The transformation of Hangzhou’s old steelworks into a park is a tribute to China’s industrial past in a city of the future The congressional hearing about Chinese AI engine DeepSeek held in the US this April has propelled Hangzhou, the heart of China’s new digital economy, to the headlines. With companies such as DeepSeek, Unitree and Alibaba – whose payment app allowed me to get on the metro without needing to buy a ticket – headquartered in Hangzhou, China’s future in AI, robotics and automation is emanating from this city. Getting off the metro in the suburban area of Gongshu, the sun was shining on an old steelworks, overgrown with vines and flowers now that it is being transformed by Jiakun Architects and TLS Landscape Architecture into the Grand Canal Steelworks Park. The unfolding trade war might help to accelerate China’s journey into an automated future, leaving the world of factories behind, yet this new public space shows an impulse to commemorate the country’s economic history, and the forces that have shaped its contemporary built environment. Starting in Hangzhou and travelling more than 1,700km to Beijing, the Grand Canal is an engineering project built 2,500 years ago to connect the different regions of eastern China. The country’s geography means rivers flow from west to east: from higher elevations, culminating in the Himalayas, to the basin that is the country’s eastern seaboard. Historically, it was difficult to transport goods from mercantile centres in the south, including Hangzhou and Suzhou, to the political centre in Beijing up north. As a civil engineering project, the Grand Canal rivals the Great Wall, but if the Great Wall aims to protect China from the outside, the Grand Canal articulates Chinese commerce from the inside. The historic waterway has been an important conduit of economic and cultural exchange, enabling the movement of people and goods such as grain, silk, wine, salt and gravel across the country. It became a UNESCO World Heritage site in 2014. The state‑owned enterprise collective was founded, and the physical facility of Hangzhou steelworks built, in the 1950s during the Great Leap Forward, when China strove for self‑sufficiency, and wended its way through the country’s economic trajectory: first the economic chaos of the 1960s, then the reforms and opening up in the 1980s. Steel remains an important industry today in China, home to more than half of the world’s production, but the listing of the Grand Canal enabled city leaders to move production to a new site and decommission the Hangzhou steelworks. External mandates, including entry into the World Trade Organization, the Beijing Olympics and UNESCO listings, have been instrumentalised in the country to pursue a range of internal interests, particularly economical and real estate ones.  In 2016, the factory was shut down in 150 days, in what the company describes as a ‘heroic’ effort, and the site attracted tourists of industrial ruins. In the competition brief, Hangzhou planners asked for ‘as much of the existing blast furnaces and buildings’ as possible to be preserved. When I arrived in China in 2008, Chinese cities were notorious for heritage demolition, but today urban planners and architects increasingly work to preserve historical buildings. Just like several industrial sites in Beijing and Shanghai have been transformed into major public and cultural spaces in the past decade, in the Yangtze River Delta – of which Hangzhou is a major hub – several industrial sites along the Grand Canal’s course are being given a new lease of life. Today, the three blast furnaces of Hangzhou steelworks remain, with the silhouettes of their smokestacks easily recognisable from a distance. The project preserves as much as possible of the aesthetics of a steel mill with none of the danger or dust, ready to welcome instead new community facilities and cultural programmes in a vast and restored piece of landscape. Situated in a former working‑class district that has been gentrifying and welcoming young families, the new park is becoming a popular venue for music festivals, flower viewing in springtime and year‑round picnics – when I visited, parents were teaching their children to ride a bicycle, and students from Zhejiang University, about a kilometre from the park, were having lunch on the grass. New programmes accommodated in the old coke oven and steel mills will include a series of exhibition halls and spaces welcoming a wide range of cultural and artistic workshops as well as events – the project’s first phase has just completed but tenant organisations have not yet moved in, and works are ongoing to the north of the park. On the day of my visit, a student art exhibition was on display near one of the furnaces, with works made from detritus from the site, including old packing containers. The rehabilitated buildings also provide a range of commercial units, where cafés, restaurants, shops, a bookshop, ice cream shop and a gym have already opened their doors to visitors.  Several structures were deemed structurally unsafe and required demolition, such as the old iron casting building. The architects proposed to partially reconstruct it on its original footprint; the much more open structure, built with reclaimed bricks, now houses a semi‑outdoor garden. Material choices evoke the site’s industrial past: weathered steel, exposed concrete and large expanses of glazing dominate the landscape. The widespread use of red, including in an elevated walkway that traverses the park – at times vaguely reminiscent of a Japanese torii gate in the space below – gives a warm and reassuring earthiness to the otherwise industrial colour palette. Elements selected by the designers underwent sanitisation and detoxification before being reused. The landscaping includes old machinery parts and boulders; recuperated steel panels are for instance inlaid into the paving while pipes for pouring molten steel have been turned into a fountain. The train tracks that once transported material continue to run through the site, providing paths in between the new patches of vegetation, planted with local grasses as well as Japanese maples, camphors and persimmon trees. As Jiawen Chen from TLS describes it, the aesthetic feels ‘wild, but not weedy or abandoned’. The landscape architects’ inspiration came from the site itself after the steelworks’ closure, she explains, once vegetation had begun to reclaim it. Contaminated soil was replaced with clean local soil – at a depth between 0.5 and 1.5 metres, in line with Chinese regulations. The removed soil was sent to specialised facilities for purification, while severely contaminated layers were sealed with concrete. TLS proposed phytoremediation (using plants to detoxify soil) in selected areas of the site ‘as a symbolic and educational gesture’, Chen explains, but ‘the client preferred to be cautious’. From the eastern end of the park, hiking trails lead to the mountain and its Buddhist temples. The old steel mill’s grounds fade seamlessly into the hills. Standing in what it is still a construction site, a sign suggests there will soon be a rowing centre here.  While Jiakun Architects and TLS have prioritised making the site palatable as a public space, the project also brings to life a history that many are likely to have forgotten. Throughout, the park incorporates different elements of China’s economic history, including the life of the Grand Canal and the industrial era. There is, for example, a Maoist steelworker painted on the mural of one of the cafés, as well as historical photographs and drawings of the steelworks peppering the site, framed and hung on the walls. The ambition might be in part to pay homage to steelworkers, but it is hard to imagine them visiting. Gongshu, like the other suburbs of Hangzhou, has seen rapid increases in its property prices.  The steelworks were built during the Maoist era, a time of ‘battling with earth, battling with heaven, battling with humanity’, to borrow Mao’s own words. Ordinary people melted down pots and pans to surpass the UK in steel production, and industry was seen as a sharp break from a traditional Chinese way of life, in which humans aspire to live in harmony with their environment. The priorities of the government today are more conservative, seeking to create a garden city to attract engineers and their families. Hangzhou has long represented the balmy and sophisticated life of China’s south, a land of rice and fish. To the west of the city, not far from the old steelworks, are the ecologically protected Xixi wetlands, and Hangzhou’s urban planning exemplifies the Chinese principle of 天人合一, or nature and humankind as one.  Today, Hangzhou is only 45 minutes from Shanghai by high‑speed train. The two cities feel like extensions of one another, an urban region of 100 million people. The creation of the Grand Canal Steelworks Park reflects the move away from heavy industry that Chinese cities such as Hangzhou are currently making, shifting towards a supposedly cleaner knowledge‑driven economy. Yet the preservation of the steelworks epitomises the sentimental attitude towards the site’s history and acts as a reminder that today’s middle classes are the children of yesterday’s steelworkers, drinking coffee and playing with their own children in grassy lawns next to shuttered blast furnaces.  The park’s second phase is already nearing completion, and the competition for the nearby Grand Canal Museum was won by Herzog & de Meuron in 2020 – the building is under construction, and should open at the end of this year. It is a district rich in history, but the city is resolutely turned towards the future.  2025-06-02 Reuben J Brown Share AR May 2025CircularityBuy Now

The $7.6 billion Hyundai Motor Group Metaplant America, or HMGMA, is one of the newest and most technologically advanced car factories in the world.The plant, located near Savannah, Georgia, opened its doors in March and will be a key production facility for Hyundai's EVs and PHEVs, as well as those belonging to its Genesis luxury brand and sister company Kia.In a recent interview with Business Insider, Genesis North America COO Tedros Mengiste cited the investment as an example of Hyundai's track record for "visionary and strategic, and long-term thinking."I recently took a behind-the-scenes tour of Hyundai's new megafactory packed with autonomous robots and state-of-the-art tech. The Hyundai Metaplant is situated on a 3,000-acre campus in the south Georgia town of Ellabell. Hyundai's Metaplant America. Hyundai Located just 20 miles from the Port of Savannah, one of the busiest in the US, the plant not only gives Hyundai much-needed manufacturing capacity in the US to avoid import tariffs, but it also affords the company the flexibility to export vehicles abroad.It also gives Hyundai the production footprint to compete against rivals like Tesla, GM, and Rivian, which is also building a new factory in Georgia. Driving up to the factory, it's easy to be wowed by the sheer scale of the sprawling complex. The entryway to the Hyundai Motor Group Metaplant America campus in Ellabell, Georgia. Benjamin Zhang/Business Insider It's Hyundai Group's second car factory in the state. The company also operates a $3.2 billion, 2,200-acre facility in West Point, Georgia, that builds Kia EV and ICE SUVs. I drove to the factory in a new 2026 Hyundai Ioniq 9 EV SUV, which is one of the vehicles assembled at the Metaplant. Hyundai Ioniq 9 EVs are parked in front of the lobby at the Hyundai Motor Group Metaplant America in Georgia. Hyundai The only other model assembled at the plant is the Hyundai Ioniq 5 EV. My tour began in the plant's modern main lobby. The Metaplant lobby is modern and pleasant. Benjamin Zhang/Business Insider Hyundai broke ground on the facility in the fall of 2022 and took just two years to complete construction on the main production buildings. The Metaplant site consists of 11 buildings totalling 7.5 million square feet of space. A map of the Hyundai Motor Group Metaplant America in Georgia. Benjamin Zhang/Business Insider The Metaplant is a marvel of vertical integration, with the goal of having as many key components, ranging from battery packs to seats, made on-site. Here's a Hyundai XCIENT hydrogen fuel cell semi truck used to transport parts and supplies to the factory. A Hyundai XCIENT hydrogen fuel cell truck. Benjamin Zhang/Business Insider It's one of 21 emission-free XCIENT trucks deployed around the Metaplant site. The production process starts in the stamping shop, where sheet metal is cut and stamped into parts that will make up the frame of the car. The stamping facility. Benjamin Zhang/Business Insider The sheet metal is supplied by the on-site Hyundai Steel facility. Stamped parts are transported by automated guided vehicles, or AGVs. Autonomous robots are transporting stamped metal parts. Benjamin Zhang/Business Insider The plant employs almost 300 AGVs to shuttle everything from spare parts to partially assembled cars. The stamped metal panels are then stored in these massive racks. Racks full of stamped metal sections of Ioniq 5 and Ioniq 9 EVs. Benjamin Zhang/Business Insider The Metaplant was originally expected to produce up to 300,000 electrified vehicles annually. However, Hyundai announced at the plant's grand opening in March that its capacity will be expanded to 500,000 units in the coming years as part of a new $21 billion investment in US manufacturing. Here are parts of the Ioniq 9, Hyundai's new flagship three-row EV SUV. Parts of the Hyundai Ioniq 9 EV at the Hyundai Motor Group Metaplant America in Georgia. Benjamin Zhang/Business Insider The plant is expected to start production of its first Kia model next year. The next part of the tour is the welding shop. Ioniq 5 EVs at the welding facility at the Hyundai Motor Group Metaplant America in Georgia. Benjamin Zhang/Business Insider Here, the stamped metal pieces are welded together by robot to form the body of the vehicle. The work done by the welding robots is then inspected by the plant's human employees known as Meta Pros. The Hyundai Ioniq 5 and Ioniq 9 EVs are going through quality inspections in the welding shop. Benjamin Zhang/Business Insider The Metplant employees more than 1,300 Meta Pros, nearly 90% of whom were hired locally. There are employee meeting and break areas located along the inspection and assembly areas. Employee break and meeting area at the welding shop. Benjamin Zhang/Business Insider An employee cafeteria with remote ordering capability is located in the main assembly building. In addition to human eyes, the vehicles are also inspected by a pair of Boston Dynamics robot dogs called Spot. Boston Dynamics robot dogs inspecting Hyundai Ioniq 5 EVs. Benjamin Zhang/Business Insider In 2021, Hyundai acquired an 80% stake in Boston Dynamics in a deal that valued the company at $1.1 billion. After the inspections are complete, a robot loads the partially assembled vehicles onto a conveyor system. Ioniq 5 EVs are about to be lifted onto the conveyor belt to the paint shop. Benjamin Zhang/Business Insider Next stop, the paint shop. Unfortunately, my tour did not get access to the paint shop due to concerns that outside visitors may compromise the quality of the paint application. Hyundai EV bodies are moving from the paint shop to the assembly facility at the Hyundai Motor Group Metaplant America in Georgia. Benjamin Zhang/Business Insider After receiving a fresh coat of paint, the vehicles travel through a bridge to the assembly building. Here, the painted bodies are married with their battery packs and skateboard chassis. An Ioniq 5 on the assembly line. Benjamin Zhang/Business Insider Hyundai Mobis produces the skateboard chassis in a building next door to the general assembly facility. The Metaplant's on-site battery factory, operated in a joint venture with LG, is expected to come online next year. The plant currently sources its batteries from Hyundai's other facilities, including one in North Georgia that's a joint venture with SK. The vehicles' interiors are then assembled by hand. The Metaplant assembly line, where human workers are joining in. Benjamin Zhang/Business Insider The further along the production process, the more you see human workers on the assembly line. Partially assembled EVs are shuttled through from area to area by the automated robots. Ioniq 5 EVs at the Hyundai Motor Group Metaplant America in Georgia. Benjamin Zhang/Business Insider The entire facility was immaculately clean, quiet, and felt beautifully choreographed. Assembled vehicles are loaded onto different AGVs that navigate the facility by reading the QR codes embedded into the floor. Hyundai Ioniq 5 EVs after soak testing at the Hyundai Motor Group Metaplant America in Georgia. Benjamin Zhang/Business Insider These AGVs shuttle the vehicles through the plant's various quality control tests. At the end of the assembly line, completed EVs are put through their paces at the on-site test track before being sent to the vehicle preparation center, or VPC, to get them ready for shipping. Completed Hyundai EVs are ready for a dealer's lot. Benjamin Zhang/Business Insider Vehicles destined for dealerships in the region are put on trucks, while those traveling more than 500 miles are shipped by rail at the Metplant's on-site train terminal.

A floating pavilion square in plan, topped by a parabolic waffle slab held up with pilotis, will soon debut in Italy at the 19th International Architecture Exhibition of La Biennale di Venezia before traveling to Belém, Brazil, for COP30, the UN’s annual climate conference. AquaPraça, a collaboration between Höweler & Yoon and Carlo Ratti Associati, is conceived as a gathering space for global climate dialogue. In Italy, AquaPraça will accompany programming at the Italian Pavilion. It will be transported later this year across the Atlantic Ocean to the coast of Brazil, where world leaders, climate activists, and architects will convene in November at COP30. The pavilion will have capacity for 150 people. (Courtesy CRA/Höweler & Yoon) A water feature in the center of the pavilion was informed by Archimedes’ principle. (Courtesy CRA/Höweler & Yoon) A rectangular tub, in the center of the pavilion, filled with water helps with buoyancy—a design decision informed by Archimedes’ principle, a law of physics fundamental to fluid mechanics. Carlo Ratti said AquaPraça is likewise rooted in architectural history. “In 1979, Aldo Rossi launched the Teatro del Mondo at the first Biennale Architettura, positing that architecture could engage with the past,” Ratti said in a statement. “Today, AquaPraça shows how architecture can engage with the future—by responding to climate and engaging with nature rather than resisting it.” Höweler & Yoon previously designed a similar concept, dubbed Float Lab, set to open on the Schuylkill River in Philadelphia in 2026. Like Float Lab, AquaPraça visitors will bear witness to dynamic fluctuations of sea level rise at eye level, added Eric Höweler. The 4,000-square-foot ensemble coming to Brazil will have capacity for hosting 150 people for exhibitions, workshops, symposia, and cultural events. AquaPraça’s “sloping surfaces and shifting levels embody a delicate equilibrium,” Höweler said. “It’s a platform, both literal and figurative, for deepening our collective understanding and experience of sea level rise and the impacts of climate change on global cities and communities and seeking collective solutions,” elaborated J. Meejin Yoon.  The floating pavilion is currently under construction in northern Italy by steel construction company Cimolai. It will open in Venice on September 4. Then, ahead of COP30 from November 10–21, it will go to Belém, becoming a “permanent floating landmark in the Amazon—an architectural testament to adaptability and dialogue in the face of climate change.”  Visitors will be at eye level with the water. (Courtesy CRA/Höweler & Yoon) Last year’s COP29 took place in Baku, Azerbaijan, while the G20 summit happened in Rio de Janeiro. At G20, Brazil president Luíz Inácio Lula da Silva announced that, by 2030, there will be “zero deforestation” in Brazil, a huge win for climate activists. “We need to take care of the largest forest reserve in the world,” Lula said last year in Rio de Janeiro, “which is under our care. Trying to make people understand that keeping the forest standing is an economic gain.” AquaPraça is a partnership with Italy’s Ministry of Foreign Affairs and International Cooperation, and the Italian Ministry of Environment and Energy Security. It’s also supported by Brazil’s Ministry of Foreign Affairs, Bloomberg Philanthropies, the World Bank’s Connect4Climate program, CIHEAM Bari, and others.

Europa, a moon of Jupiter, has long been one of the most exciting targets in the search for life beyond Earth. Many scientists believe that an ocean lies below its icy surface, potentially hosting geologic activity capable of supporting life, but what happens on the moon’s seafloor is still largely a mystery. Although discussions on Europa are mostly centered around this hidden ocean, the shell of ice that envelops the moon has its own surprises. A study recently published in The Planetary Science Journal suggests that Europa’s surface ice is constantly changing. The evidence explored in the study paints a better picture of Europa’s outermost layer, and it may even reveal the interior processes that shape the moon’s unique structure. Europa's Surface IceEuropa has the smoothest surface out of any known object in our Solar System, but it’s far from lacking variety. The surface is rife with distinct geologic features, such as ridges, plains, and cracks, that cross over each other. Their disorderly appearance is linked to a fitting name, “chaos terrain.”Some regions with chaos terrain also provide insight on Europa’s surface ice. Most of Europa’s surface is made of amorphous ice, which lacks a crystalline structure. Scientists previously believed that Europa’s surface was entirely covered by a thin layer of amorphous ice, and that below this was crystalline ice (the form that most ice on Earth takes). However, the researchers involved with the new study have confirmed that certain areas of Europa’s surface contain crystalline ice, aligning with spectral data captured by the James Webb Space Telescope (JWST). This same ice also appears below the surface in these regions as well. “We think that the surface is fairly porous and warm enough in some areas to allow the ice to recrystallize rapidly,” said lead author Richard Cartwright, a spectroscopist at Johns Hopkins University, in a statement.Activity in the OceanA few other factors have convinced the researchers that an ocean exists below Europa's icy surface. The regions where ice recrystallizes show evidence of sodium chloride (what we know as table salt), carbon dioxide, and hydrogen peroxide. “Our data showed strong indications that what we are seeing must be sourced from the interior, perhaps from a subsurface ocean nearly 20 miles (30 kilometers) beneath Europa’s thick icy shell,” said author Ujjwal Raut, a program manager at the Southwest Research Institute. “This region of fractured surface materials could point to geologic processes pushing subsurface materials up from below.”The Europa Clipper's MissionAlthough Europa and its subsurface ocean will be a crucial target for future space exploration, some scientists have expressed doubts regarding its capacity to sustain life. A series of obstacles could make finding life on Europa more difficult. At an American Geophysical Union conference last year, scientists reported that the ice layer covering the moon's surface is thicker than expected, indicating that there may not be enough heat or activity in the subsurface ocean to support life. Scientists aren’t yet sure if an abundance of hydrothermal vents or seafloor volcanoes sit at the bottom of the ocean — these features have been crucial in driving life on our own planet. Observations of Europa haven’t fully confirmed the existence of plumes, either, which would be a clear sign that material from the ocean could be transported to the surface. About 5 years from now, in 2030, scientists will get an unprecedented view of Europa as NASA's Europa Clipper approaches the icy moon. Launched last October, the Europa Clipper will reveal many secrets that still surround the moon's surface and the ocean below. Among its various objectives, the mission will look for plumes, which would be able to eject microbes — if they truly do exist on the moon — into space for the Europa Clipper to examine. Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:The Planetary Science Journal. JWST Reveals Spectral Tracers of Recent Surface Modification on EuropaThe Planetary Society. Europa, Jupiter’s possible watery moonThe Planetary Society. Could Europa Clipper find life?Jack Knudson is an assistant editor at Discover with a strong interest in environmental science and history. Before joining Discover in 2023, he studied journalism at the Scripps College of Communication at Ohio University and previously interned at Recycling Today magazine.

The Last of Us showrunners discuss where season three will go Once spore unto the breach. Image credit: HBO News by Victoria Phillips Kennedy News Reporter Published on May 26, 2025 The Last of Us' cast and crew have shed some insight into where the show will go during its third season. Please note, there will be major spoilers for The Last of Us season two finale below. The Death of Console Exclusives Is Inevitable and I Don't Know How I Feel About It. Watch on YouTube Earlier today, The Last of Us' second season wrapped. It ended with a cliffhanger, showing Kaitlyn Dever's Abby shooting Jesse dead, before she points her gun at Ellie. She fires again, and the screen cuts to black. We are then transported back to "Seattle: Day One", but this time we are not seeing events through the eyes of Ellie. This time, we are with Abby, who walks out into the massive football stadium the WLF have made their base camp. Speaking about season two's finale during a press conference held earlier this month, showrunners Craig Mazin and Neil Druckmann said they don't currently know how much viewers can expect to see Ellie, Dina, Tommy and Jesse during the show's third season. However, even if these characters aren't on screen as much as they were during season two, their presence will still be felt. Image credit: HBO "Even if I thought I knew now exactly how it was going to go, I'm experienced enough to know that two weeks from now we may have a different idea of how it should go," Mazin said (thanks, Variety). "All I can say is we haven't seen the last of Kaitlyn Dever and we haven't seen the last of Bella Ramsey, and we haven't seen the last of Isabela Merced, and we haven't seen the last of a lot of people who are currently dead in the story." Meanwhile, Mazin affirmed The Last of Us season three will provide more clarity to some of the events that were playing off in the background of season two, including the WLF's war with the Seraphites. "Those questions are correct and will be answered," Mazin noted. "How did that war start? Why? How did the Seraphites start? Who is [their] prophet? What happened to her? What does Isaac want? What's happening at the end of Episode 7? What is this explosion? All of it will become clear." Image credit: HBO Now, don't get your hopes up here, but during this same conference Druckman didn't rule out Pedro Pascal making a return as Joel via flashbacks. In season one, we saw Anna Torv's Tess pop up again, despite her character being killed off earlier in the show. Meanwhile, season two featured an episode made up almost entirely of flashbacks, which included the introduction of Joel's father, a character not seen in the games. "I wouldn't have guessed we would have a short story about Joel's dad before we wrote the season, so there you go," Druckmann said of that scene, adding: "You can't predict these things." Image credit: HBO In a separate interview with the publication, Ramsey added they "most likely" expect their presence in the show to be smaller than in previous seasons when series three rolls around. "I haven't seen any scripts, but yes, I do expect that," Ramsey said. "I think that I'm going to be there, but not a whole bunch. We've had conversations about that. I sort of have a rough idea of what it's going to be, but I can't tell you." For more on the show, you can check out my discussion feature: The Last of Us season two wraps with episode seven, but was it a satisfying finale?

Sen. Maggie Hassan has asked Spotify about its moderation policies after a BI investigation. Evelyn Hockstein/Pool via AP 2025-05-24T18:31:51Z Save Saved Read in app This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? Sen. Maggie Hassan is demanding answers from Spotify over fake podcasts that pushed opioids. In a letter, Hassan asked Spotify to "take action" on the phony content. Hassan's letter comes after a BI investigation found 200 podcasts on Spotify peddling opioids. A senator is demanding answers from Spotify about its handling of fake podcasts that promoted opioids and other prescription drugs.In the wake of a Business Insider investigation that found 200 phony podcasts on Spotify advertising the sale of pills, often without a prescription, Sen. Maggie Hassan of New Hampshire urged the digital music and podcast company to moderate its content better.Some of the podcasts were removed after BI previously flagged them to Spotify."I urge you to take action to prevent fake podcasts that facilitate the illicit sale of drugs — including those that could contain fentanyl — from appearing on your platform," the two-term Democratic lawmaker said in a letter to Spotify CEO Daniel Ek."Addressing these threats requires an all-hands-on-deck approach, and based on recent reports, Spotify has not exercised the level of diligence needed," she continued.In response to BI's investigation earlier this month, a Spotify spokesperson said: "The content in question has been removed because it violates our Platform Rules. We are constantly working to detect and remove violating content across our service." In response to Hassan's letter, a company spokesperson on Saturday referred BI to its earlier statement.Many lawmakers across the United States have long sought to address the scourge of opioid abuse, which increasingly comes in the form of fentanyl.Fentanyl trafficking is a major issue for President Donald Trump, who has accused Mexico, Canada, and China of allowing the drug to be transported into the United States. Trump imposed tariffs on those countries in part to force them to do more to stem the flow of fentanyl.In her letter, Hassan, a former governor, spoke of the "heart-wrenching conversations" that she's had with constituents in her state who've lost family members or friends to drug overdoses."The scale of the fentanyl crisis requires cooperation among law enforcement, online platforms, and international partners to protect our communities," she said.Hassan also asked Spotify to detail its moderation tools and policies and inquired about the number of drug-related podcasts it has had to remove. She asked whether the platform received any revenue from the removed podcasts.The lawmaker, who serves on the Senate Homeland Security and Governmental Affairs committee, gave Spotify until June 12 to respond to her inquiries."We are constantly working to detect and remove violating content across our service," a Spotify spokesperson said in response to BI's investigation.Read the full letter here: