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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]

The Minions pose during the world premiere of the film "Despicable Me 4" in New York City, June 9, 2024.  (REUTERS/Kena Betancur) NEWYou can now listen to Fox News articles! Welcome to Fox News’ Artificial Intelligence newsletter with the latest AI technology advancements.IN TODAY’S NEWSLETTER:- Major Hollywood studios sue AI company over copyright infringement in landmark move- Meta's Zuckerberg aiming to dominate AI race with recruiting push for new ‘superintelligence’ team: report- OpenAI says this state will play central role in artificial intelligence development The website of Midjourney, an artificial intelligence (AI) capable of creating AI art, is seen on a smartphone on April 3, 2023, in Berlin, Germany. (Thomas Trutschel/Photothek via Getty Images)'PIRACY IS PIRACY': Two major Hollywood studios are suing Midjourney, a popular AI image generator, over its use and distribution of intellectual property.AI RACE: Meta CEO Mark Zuckerberg is reportedly building a team of experts to develop artificial general intelligence (AGI) that can meet or exceed human capabilities.TECH HUB: New York is poised to play a central role in the development of artificial intelligence (AI), OpenAI executives told key business and civic leaders on Tuesday. Attendees watch a presentation during an event on the Apple campus in Cupertino, Calif., Monday, June 9, 2025.  (AP Photo/Jeff Chiu)APPLE FALLING BEHIND: Apple’s annual Worldwide Developers Conference (WWDC) kicked off on Monday and runs through Friday. But the Cupertino-based company is not making us wait until the end. The major announcements have already been made, and there are quite a few. The headliners are new software versions for Macs, iPhones, iPads and Vision. FROM COAL TO CODE: This week, Amazon announced a $20 billion investment in artificial intelligence infrastructure in the form of new data centers, the largest in the commonwealth's history, according to the eCommerce giant.DIGITAL DEFENSE: A growing number of fire departments across the country are turning to artificial intelligence to help detect and respond to wildfires more quickly. Rep. Darin LaHood, R-Ill., leaves the House Republican Conference meeting at the Capitol Hill Club in Washington on Tuesday, May 17, 2022.  (Bill Clark/CQ-Roll Call, Inc via Getty Images)SHIELD FROM BEIJING: Rep. Darin LaHood, R-Ill., is introducing a new bill Thursday imploring the National Security Administration (NSA) to develop an "AI security playbook" to stay ahead of threats from China and other foreign adversaries. ROBOT RALLY PARTNER: Finding a reliable tennis partner who matches your energy and skill level can be a challenge. Now, with Tenniix, an artificial intelligence-powered tennis robot from T-Apex, players of all abilities have a new way to practice and improve. DIGITAL DANGER ZONE: Scam ads on Facebook have evolved beyond the days of misspelled headlines and sketchy product photos. Today, many are powered by artificial intelligence, fueled by deepfake technology and distributed at scale through Facebook’s own ad system.  Fairfield, Ohio, USA - February 25, 2011 : Chipotle Mexican Grill Logo on brick building. Chipotle is a chain of fast casual restaurants in the United States and Canada that specialize in burritos and tacos. (iStock)'EXPONENTIAL RATE': Artificial intelligence is helping Chipotle rapidly grow its footprint, according to CEO Scott Boatwright. AI TAKEOVER THREAT: The hottest topic nowadays revolves around Artificial Intelligence (AI) and its potential to rapidly and imminently transform the world we live in — economically, socially, politically and even defensively. Regardless of whether you believe that the technology will be able to develop superintelligence and lead a metamorphosis of everything, the possibility that may come to fruition is a catalyst for more far-leftist control.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.

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

In 2024, Egypt completed its 1,155km stretch of the Cairo–Cape Town Highway, a 10,228km‑long road connecting 10 African countries – Egypt, Sudan, South Sudan, Ethiopia, Kenya, Tanzania, Zambia, Zimbabwe, Botswana and South Africa.   The imaginary of ‘Cape to Cairo’ is not new. In 1874, editor of the Daily Telegraph Edwin Arnold proposed a plan to connect the African continent by rail, a project that came to be known as the Cape to Cairo Railway project. Cecil Rhodes expressed his support for the project, seeing it as a means to connect the various ‘possessions’ of the British Empire across Africa, facilitating the movement of troops and natural resources. This railway project was never completed, and in 1970 was overlaid by a very different attempt at connecting the Cape to Cairo, as part of the Trans‑African Highway network. This 56,683km‑long system of highways – some dating from the colonial era, some built as part of the 1970s project, and some only recently built – aimed to create lines of connection across the African continent, from north to south as well as east to west.  Here, postcolonial state power invested in ‘moving the continent’s people and economies from past to future’, as architectural historians Kenny Cupers and Prita Meier write in their 2020 essay ‘Infrastructure between Statehood and Selfhood: The Trans‑African Highway’. The highways were to be built with the support of Kenya’s president Jomo Kenyatta, Ghana’s president Kwame Nkrumah and Ghana’s director of social welfare Robert Gardiner, as well as the United Nations Economic Commission for Africa (UNECA). This project was part of a particular historical moment during which anticolonial ideas animated most of the African continent; alongside trade, this iteration of Cape to Cairo centred social and cultural connection between African peoples. But though largely socialist in ambition, the project nevertheless engaged modernist developmentalist logics that cemented capitalism.  Lead image: Over a century in the making, the final stretches of the Cairo–Cape Town Highway are being finished. Egypt completed the section within its borders last year and a section over the dry Merille River in Kenya was constructed in 2019. Credit: Allan Muturi / SOPA / ZUMA / Alamy. Above: The route from Cairo to Cape Town, outlined in red, belongs to the Trans‑African Highway network, which comprises nine routes, here in black The project failed to fully materialise at the time, but efforts to complete the Trans‑African Highway network have been revived in the last 20 years; large parts are now complete though some links remain unbuilt and many roads are unpaved or hazardous. The most recent attempts to realise this project coincide with a new continental free trade agreement, the agreement on African Continental Free Trade Area (AfCFTA), established in 2019, to increase trade within the continent. The contemporary manifestation of the Cairo–Cape Town Highway – also known as Trans‑African Highway (TAH) 4 – is marked by deepening neoliberal politics. Represented as an opportunity to boost trade and exports, connecting Egypt to African markets that the Egyptian government view as ‘untapped’, the project invokes notions of trade steeped in extraction, reflecting the neoliberal logic underpinning contemporary Egyptian governance; today, the country’s political project, led by Abdel Fattah El Sisi, is oriented towards Egyptian dominance and extraction in relation to the rest of the continent.  Through an allusion to markets ripe for extraction, this language brings to the fore historical forms of domination that have shaped the connections between Egypt and the rest of the continent; previous iterations of connection across the continent often reproduced forms of domination stretching from the north of the African continent to the south, including the Trans‑Saharan slave trade routes across Africa that ended in various North African and Middle Eastern territories. These networks, beginning in the 8th century and lasting until the 20th, produced racialised hierarchies across the continent, shaping North Africa into a comparably privileged space proximate to ‘Arabness’. This was a racialised division based on a civilisational narrative that saw Arabs as superior, but more importantly a political economic division resulting from the slave trade routes that produced huge profits for North Africa and the Middle East. In the contemporary moment, these racialised hierarchies are bound up in political economic dependency on the Arab Gulf states, who are themselves dependent on resource extraction, land grabbing and privatisation across the entire African continent.  ‘The Cairo–Cape Town Highway connects Egypt to African markets viewed as “untapped”, invoking notions steeped in extraction’ However, this imaginary conjured by the Cairo–Cape Town Highway is countered by a network of streets scattered across Africa that traces the web of Egyptian Pan‑African solidarity across the continent. In Lusaka in Zambia, you might find yourself on Nasser Road, as you might in Mwanza in Tanzania or Luanda in Angola. In Mombasa in Kenya, you might be driving down Abdel Nasser Road; in Kampala in Uganda, you might find yourself at Nasser Road University; and in Tunis in Tunisia, you might end up on Gamal Abdel Nasser Street. These street names are a reference to Gamal Abdel Nasser, Egypt’s first postcolonial leader and president between 1956 and 1970.  Read against the contemporary Cairo–Cape Town Highway, these place names signal a different form of connection that brings to life Egyptian Pan‑Africanism, when solidarity was the hegemonic force connecting the continent, coming up against the notion of a natural or timeless ‘great divide’ within Africa. From the memoirs of Egyptian officials who were posted around Africa as conduits of solidarity, to the broadcasts of Radio Cairo that were heard across the continent, to the various conferences attended by anticolonial movements and postcolonial states, Egypt’s orientation towards Pan‑Africanism, beginning in the early 20th century and lasting until the 1970s, was both material and ideological. Figures and movements forged webs of solidarity with their African comrades, imagining an Africa that was united through shared commitments to ending colonialism and capitalist extraction.  The route between Cape Town in South Africa and Cairo in Egypt has long occupied the colonial imaginary. In 1930, Margaret Belcher and Ellen Budgell made the journey, sponsored by car brand Morris and oil company Shell Credit: Fox Photos / Getty The pair made use of the road built by British colonisers in the 19th century, and which forms the basis for the current Cairo–Cape Town Highway. The road was preceded by the 1874 Cape to Cairo Railway project, which connected the colonies of the British Empire Credit: Library of Congress, Geography and Map Division This network of eponymous streets represents attempts to inscribe anticolonial power into the materiality of the city. Street‑naming practices are one way in which the past comes into the present, ‘weaving history into the geographic fabric of everyday life’, as geographer Derek Alderman wrote in his 2002 essay ‘Street Names as Memorial Arenas’. In this vein, the renaming of streets during decolonisation marked a practice of contesting the production of colonial space. In the newly postcolonial city, renaming was a way of ‘claiming the city back’, Alderman continues. While these changes may appear discursive, it is their embedding in material spaces, through signs and maps, that make the names come to life; place names become a part of the everyday through sharing addresses or giving directions. This quality makes them powerful; consciously or unconsciously, they form part of how the spaces of the city are navigated.  These are traces that were once part of a dominant historical narrative; yet when they are encountered in the present, during a different historical moment, they no longer act as expressions of power but instead conjure up a moment that has long passed. A street in Lusaka named after an Egyptian general made more sense 60 years ago than it does today, yet contextualising it recovers a marginalised history of Egyptian Pan‑Africanism.  Markers such as street names or monuments are simultaneously markers of anticolonial struggle as well as expressions of state power – part of an attempt, by political projects such as Nasser’s, to exert their own dominance over cities, towns and villages. That such traces are expressions of both anticolonial hopes and postcolonial state power produces a sense of tension within them. For instance, Nasser’s postcolonial project in Egypt was a contradictory one; it gave life to anticolonial hopes – for instance by breaking away from European capitalism and embracing anticolonial geopolitics – while crushing many parts of the left through repression, censorship and imprisonment. Traces of Nasser found today inscribe both anticolonial promises – those that came to life and those that did not – while reproducing postcolonial power that in most instances ended in dictatorship.  Recent efforts to complete the route build on those of the post‑independence era – work on a section north of Nairobi started in 1968 Credit: Associated Press / Alamy The Trans‑African Highway network was conceived in 1970 in the spirit of Pan‑Africanism At that time, the routes did not extend into South Africa, which was in the grip of apartheid. The Trans‑African Highway initiative was motivated by a desire to improve trade and centre cultural links across the continent – an ambition that was even celebrated on postage stamps There have been long‑standing debates about the erasure of the radical anticolonial spirit from the more conservative postcolonial states that emerged; the promises and hopes of anticolonialism, not least among them socialism and a world free of white supremacy, remain largely unrealised. Instead, by the 1970s neoliberalism emerged as a new hegemonic project. The contemporary instantiation of Cape to Cairo highlights just how pervasive neoliberal logics continue to be, despite multiple global financial crises and the 2011 Egyptian revolution demanding ‘bread, freedom, social justice’.  But the network of streets named after anticolonial figures and events across the world is testament to the immense power and promise of anticolonial revolution. Most of the 20th century was characterised by anticolonial struggle, decolonisation and postcolonial nation‑building, as nations across the global south gained independence from European empire and founded their own political projects. Anticolonial traces, present in street and place names, point to the possibility of solidarity as a means of reorienting colonial geographies. They are a reminder that there have been other imaginings of Cape to Cairo, and that things can be – and have been – otherwise. 2025-06-13 Kristina Rapacki Share

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.Tech billionaires are making a risky bet with humanity’s future Sam Altman, Jeff Bezos, Elon Musk, and others may have slightly different goals, but their grand visions for the next decade and beyond are remarkably similar.They include aligning AI with the interests of humanity; creating an artificial superintelligence that will solve all the world’s most pressing problems; merging with that superintelligence to achieve immortality (or something close to it); establishing a permanent, self-­sustaining colony on Mars; and, ultimately, spreading out across the cosmos.Three features play a central role with powering these visions, says Adam Becker, a science writer and astrophysicist: an unshakable certainty that technology can solve any problem, a belief in the necessity of perpetual growth, and a quasi-religious obsession with transcending our physical and biological limits.In his timely new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, Becker reveals how these fantastical visions conceal a darker agenda. Read the full story. —Bryan Gardiner This story is from the next print edition of MIT Technology Review, which explores power—who has it, and who wants it. It’s set to go live on Wednesday June 25, so subscribe & save 25% to read it and get a copy of the issue when it lands! Here’s what food and drug regulation might look like under the Trump administration Earlier this week, two new leaders of the US Food and Drug Administration published a list of priorities for the agency. Both Marty Makary and Vinay Prasad are controversial figures in the science community. They were generally highly respected academics until the covid pandemic, when their contrarian opinions on masking, vaccines, and lockdowns turned many of their colleagues off them. Given all this, along with recent mass firings of FDA employees, lots of people were pretty anxious to see what this list might include—and what we might expect the future of food and drug regulation in the US to look like. So let’s dive into the pair’s plans for new investigations, speedy approvals, and the “unleashing” of AI. —Jessica Hamzelou This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 NASA is investigating leaks on the ISSIt’s postponed launching private astronauts to the station while it evaluates. (WP $)+ Its core component has been springing small air leaks for months. (Reuters)+ Meanwhile, this Chinese probe is en route to a near-Earth asteroid. (Wired $) 2 Undocumented migrants are using social media to warn of ICE raidsThe DIY networks are anonymously reporting police presences across LA. (Wired $)+ Platforms’ relationships with protest activism has changed drastically. (NY Mag $)  3 Google’s AI Overviews is hallucinating about the fatal Air India crashIt incorrectly stated that it involved an Airbus plane, not a Boeing 787. (Ars Technica)+ Why Google’s AI Overviews gets things wrong. (MIT Technology Review) 4 Chinese engineers are sneaking suitcases of hard drives into the countryTo covertly train advanced AI models. (WSJ $)+ The US is cracking down on Huawei’s ability to produce chips. (Bloomberg $)+ What the US-China AI race overlooks. (Rest of World) 5 The National Hurricane Center is joining forces with DeepMindIt’s the first time the center has used AI to predict nature’s worst storms. (NYT $)+ Here’s what we know about hurricanes and climate change. (MIT Technology Review) 6 OpenAI is working on a product with toymaker MattelAI-powered Barbies?! (FT $)+ Nothing is safe from the creep of AI, not even playtime. (LA Times $)+ OpenAI has ambitions to reach billions of users. (Bloomberg $) 7 Chatbots posing as licensed therapists may be breaking the lawDigital rights organizations have filed a complaint to the FTC. (404 Media)+ How do you teach an AI model to give therapy? (MIT Technology Review) 8 Major companies are abandoning their climate commitmentsBut some experts argue this may not be entirely bad. (Bloomberg $)+ Google, Amazon and the problem with Big Tech’s climate claims. (MIT Technology Review) 9 Vibe coding is shaking up software engineeringEven though AI-generated code is inherently unreliable. (Wired $)+ What is vibe coding, exactly? (MIT Technology Review) 10 TikTok really loves hotdogs And who can blame it? (Insider $) Quote of the day “It kind of jams two years of work into two months.” —Andrew Butcher, president of the Maine Connectivity Authority, tells Ars Technica why it’s so difficult to meet the Trump administration’s new plans to increase broadband access in certain states. One more thing The surprising barrier that keeps us from building the housing we needIt’s a tough time to try and buy a home in America. From the beginning of the pandemic to early 2024, US home prices rose by 47%. In large swaths of the country, buying a home is no longer a possibility even for those with middle-class incomes. For many, that marks the end of an American dream built around owning a house. Over the same time, rents have gone up 26%.The reason for the current rise in the cost of housing is clear to most economists: a lack of supply. Simply put, we don’t build enough houses and apartments, and we haven’t for years. But the reality is that even if we ease the endless permitting delays and begin cutting red tape, we will still be faced with a distressing fact: The construction industry is not very efficient when it comes to building stuff. Read the full story. —David Rotman We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + If you’re one of the unlucky people who has triskaidekaphobia, look away now.+ 15-year old Nicholas is preparing to head from his home in the UK to Japan to become a professional sumo wrestler.+ Earlier this week, London played host to 20,000 women in bald caps. But why? ($)+ Why do dads watch TV standing up? I need to know.

Businesses in highly-regulated industries like financial services, insurance, pharmaceuticals, and health care are increasingly turning to AI-powered tools to streamline complex and sensitive tasks. Conversational AI-driven interfaces are helping hospitals to track the location and delivery of a patient’s time-sensitive cancer drugs. Generative AI chatbots are helping insurance customers answer questions and solve problems. And agentic AI systems are emerging to support financial services customers in making complex financial planning and budgeting decisions.  “Over the last 15 years of digital transformation, the orientation in many regulated sectors has been to look at digital technologies as a place to provide more cost-effective and meaningful customer experience and divert customers from higher-cost, more complex channels of service,” says Peter Neufeld, who leads the EY Studio+ digital and customer experience capability at EY for financial services companies in the UK, Europe, the Middle East, and Africa.  DOWNLOAD THE FULL REPORT For many, the “last mile” of the end-to-end customer journey can present a challenge. Services at this stage often involve much more complex interactions than the usual app or self-service portal can handle. This could be dealing with a challenging health diagnosis, addressing late mortgage payments, applying for government benefits, or understanding the lifestyle you can afford in retirement. “When we get into these more complex service needs, there’s a real bias toward human interaction,” says Neufeld. “We want to speak to someone, we want to understand whether we’re making a good decision, or we might want alternative views and perspectives.”  But these high-cost, high-touch interactions can be less than satisfying for customers when handled through a call center if, for example, technical systems are outdated or data sources are disconnected. Those kinds of problems ultimately lead to the possibility of complaints and lost business. Good customer experience is critical for the bottom line. Customers are 3.8 times more likely to make return purchases after a successful experience than after an unsuccessful one, according to Qualtrics. Intuitive AI-driven systems— supported by robust data infrastructure that can efficiently access and share information in real time— can boost the customer experience, even in complex or sensitive situations.  Download the full report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.