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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]

A bubble ring created by a humpback whale named Thorn. Image © Dan Knaub, The Video Company Humpback Whales Are Approaching People to Blow Rings. What Are They Trying to Say? June 13, 2025 NatureSocial Issues Grace Ebert After the “orca uprising” captivated anti-capitalists around the world in 2023, scientists are intrigued by another form of marine mammal communication. A study released this month by the SETI Institute and the University of California at Davis dives into a newly documented phenomenon of humpback whales blowing bubble rings while interacting with humans. In contrast to the orcas’ aggressive behavior, researchers say the humpbacks appear to be friendly, relaxed, and even curious. Bubbles aren’t new to these aquatic giants, which typically release various shapes when corraling prey and courting mates. This study follows 12 distinct incidents involving 11 whales producing 39 rings, most of which have approached boats near Hawaii, the Dominican Republic, Mo’orea, and the U.S. Atlantic coast on their own. The impact of this research reaches far beyond the oceans, though. Deciphering these non-verbal messages could aid in potential extraterrestrial communication, as they can help to “develop filters that aid in parsing cosmic signals for signs of extraterrestrial life,” a statement says. “Because of current limitations on technology, an important assumption of the search for extraterrestrial intelligence is that extraterrestrial intelligence and life will be interested in making contact and so target human receivers,” said Dr. Laurance Doyle, a SETI Institute scientist who co-wrote the paper. “This important assumption is certainly supported by the independent evolution of curious behavior in humpback whales.” (via PetaPixel) A composite image of at least one bubble ring from each interaction Previous articleNext article

There’s a Sphere-alike in Redrock, MindsEye’s open-world version of Las Vegas. It’s pretty much a straight copy of the original: a huge soap bubble, half sunk into the desert floor, with its surface turned into a gigantic TV. Occasionally you’ll pull up near the Sphere while driving an electric vehicle made by Silva, the megacorp that controls this world. You’ll sometimes come to a stop just as an advert for an identical Silva EV plays out on the huge curved screen overhead. The doubling effect can be slightly vertigo-inducing.At these moments, I truly get what MindsEye is trying to do. You’re stuck in the ultimate company town, where oligarchs and other crooks run everything, and there’s no hope of escaping the ecosystem they’ve built. MindsEye gets this all across through a chance encounter, and in a way that’s both light of touch and clever. The rest of the game tends towards the heavy-handed and silly, but it’s nice to glimpse a few instances where everything clicks.With its Spheres and omnipresent EVs, MindsEye looks and sounds like the future. It’s concerned with AI and tech bros and the insidious creep of a corporate dystopia. You play as an amnesiac former-soldier who must work out the precise damage that technology has done to his humanity, while shooting people and robots and drones. And alongside the campaign itself, MindsEye also has a suite of tools for making your own game or levels and publishing them for fellow players. All of this has come from a studio founded by Leslie Benzies, whose production credits include the likes of GTA 5.AI overlords … MindsEye. Photograph: IOI PartnersWhat’s weird, then, is that MindsEye generally plays like the past. Put a finger to the air and the wind is blowing from somewhere around 2012. At heart, this is a roughly hewn cover shooter with an open world that you only really experience when you’re driving between missions. Its topical concerns mainly exist to justify double-crosses and car chases and shootouts, and to explain why you head into battle with a personal drone that can open doors for you and stun nearby enemies.It can be an uncanny experience, drifting back through the years to a time when many third-person games still featured unskippable cut-scenes and cover that could be awkward to unstick yourself from. I should add that there are plenty of reports at the moment of crashes and technical glitches and characters turning up without their faces in place. Playing on a relatively old PC, aside from one crash and a few amusing bugs, I’ve been mostly fine. I’ve just been playing a game that feels equally elderly.This is sometimes less of a criticism than it sounds. There is a definite pleasure to be had in simple run-and-gun missions where you shoot very similar looking people over and over again and pick a path between waypoints. The shooting often feels good, and while it’s a bit of a swizz to have to drive to and from each mission, the cars have a nice fishtaily looseness to them that can, at times, invoke the Valium-tinged glory of the Driver games. (The airborne craft are less fun because they have less character.)Driving between missions … MindsEye. Photograph: Build A Rocket Boy/IOI PartnersAnd for a game that has thought a lot about the point at which AI takes over, the in-game AI around me wasn’t in danger of taking over anything. When I handed over control of my car to the game while tailing an enemy, having been told I should try not to be spotted, the game made sure our bumpers kissed at every intersection. The streets of this particular open world are filled with amusingly unskilled AI drivers. I’d frequently arrive at traffic lights to be greeted by a recent pile-up, so delighted by the off-screen collisions that had scattered road cones and Dumpsters across my path that I almost always stopped to investigate.I even enjoyed the plot’s hokeyness, which features lines such as: “Your DNA has been altered since we last met!” Has it, though? Even so, I became increasingly aware that clever people had spent a good chunk of their working lives making this game. I don’t think they intended to cast me as what is in essence a Deliveroo bullet courier for an off-brand Elon Musk. Or to drop me into an open world that feels thin not because it lacks mission icons and fishing mini-games, but because it’s devoid of convincing human detail.I suspect the problem may actually be a thematically resonant one: a reckless kind of ambition. When I dropped into the level editor I found a tool that’s astonishingly rich and complex, but which also requires a lot of time and effort if you want to make anything really special in it. This is for the mega-fans, surely, the point-one percent. It must have taken serious time to build, and to do all that alongside a campaign (one that tries, at least, to vary things now and then with stealth, trailing and sniper sections) is the kind of endeavour that requires a real megacorp behind it.MindsEye is an oddity. For all its failings, I rarely disliked playing it, and yet it’s also difficult to sincerely recommend. Its ideas, its moment-to-moment action and narrative are so thinly conceived that it barely exists. And yet: I’m kind of happy that it does. MindsEye is out now; £54.99

As President Donald Trump kicked off a birthday military parade on the streets of Washington, DC, what’s estimated as roughly 2,000 events were held across the US and beyond — protesting Trump and Elon Musk’s evisceration of government services, an unprecedented crackdown by Immigration and Customs Enforcement (ICE), and countless other actions from the administration in its first five months. Held under the title “No Kings” (with, as you’ll see, one conspicuous exception), they’re the latest in several mass protests, following April’s Hands Off events and a wave of Tesla Takedown demonstrations in March.As The Verge’s Tina Nguyen went to downtown DC, we also sent reporters to No Kings demonstrations spanning the country, plus a “No Tyrants” event in the UK. How would they unfold after promises of “very heavy force” against protesters in the capital, after the deployment of thousands of military troops in a move a judge has bluntly called illegal, and after promises to “liberate” the city of Los Angeles from its “burdensome leadership” by local elected officials? What about the overnight killing of a Minnesota Democratic state representative and her husband, and the shooting of a Democratic state senator and his wife?The answer, at the events we attended, was fairly calmly — even against a backdrop of chaos.Downtown Los Angeles, CaliforniaAn inflatable baby Donald Trump, dressed in a diaper, hovered over throngs of people rallying outside of Los Angeles City Hall. Demonstrators outnumbered clumps of California National Guard members in fatigues posted up along sidewalks. “Go home to your families, we don’t need you in our streets,” one young person wearing a long braid down her back tells them while marching past. “Trump come catch these hands foo!” the back of her sign reads. I can’t see what the front says, but I can tell there’s an empty bag of Cheetos pasted to it.The big baby joins the march, floating through the streets of Downtown LA over demonstrators. A flatbed truck rolls ahead of it, the band — maybe LA’s own Ozomatli? — singing “We don’t like Trump” to the tune of “We Want The Funk.” Ducking inside Grand Central Market from the march, I talk to Puck and Twinkle Toes — two demonstrators in line for the public restrooms. Twinkle Toes tells me she’s part of an activist clown collective called Imp and Circumstance, wearing pink and white clown makeup and a striped pink and white bow wrapped around a loose hair bun atop her head. She’s here exercising her right to free speech, she says. Demonstrators in Los Angeles marched alongside an inflatable Donald Trump baby dressed in a diaper.“The more people that are out here, the more we know that this is not okay. That we don’t want an autocrat. We want democracy,” Puck tells me, adding that the Pride March in Hollywood last weekend was “nothing but love and sunshine” despite protests and burning driverless cars making headlines in downtown. “The news tries to make you think all of LA is rioting. It’s not.” Puck says.Back out on the streets, a young man quickly writes “Fuck ICE” on a black wall with white spray paint before a group of older demonstrators wearing floppy hats shushes him away — warning him that tagging will only attract more law enforcement.Further along, another older man with tufts of white hair sticking out under his Lakers cap walks stiffly and slowly along under the summer sun. A Mexican flag draped across his shoulders, he crosses Hope Street. A young man wearing a Nike cap makes his way over to ask if he wants water; the old man accepts a bottle and keeps walking without stopping. The march has looped around downtown, and is coming to an end back at City Hall. As I make my way to my bus stop, a line of police vehicles — sirens blasting — whizzes past me, back toward the crowd still gathering around City Hall.The Los Angeles Police Department issued a dispersal order for parts of downtown Los Angeles later in the afternoon, citing people “throwing rocks, bricks, bottles and other objects.” Law enforcement reportedly cleared crowds using gas, and the LAPD authorized the use of “less lethal” force.— Justine CalmaPortland, OregonFour different “No Kings” protests in the greater Portland area on Saturday drew massive crowds of tens of thousands across the city. Various activists, government officials, and representatives for politicians spoke at the rallies, which also featured music and live performances. (One advertised free drag shows.) Protesters of all ages came with dogs, strollers, flags, banners, and hand-made signs. At the downtown waterfront, some tourist boats appeared to still be departing, but the bike rental stand (which also sells ice cream) was closed for the day with a hand-lettered explanation reading “No crowns, no thrones, no kings” and “Americans against oligarchy.” Women appearing to be organizers passed out free American flags; many attendees came with their own American flags modified to fly upside down. Most protesters brought signs expressing a wide range of sentiments on the theme of “No Kings.” Some signs were surprisingly verbose (“If the founders wanted a unitary executive (a king) we’d all still be British”) while others were more succinct (“Sic semper tyrannis”). Others opted for simple images, such as a picture of a crown crossed out, or — less frequently — a guillotine. Image: Sarah JeongThe waterfront park area was filled with people from the shoreline to the curb of the nearest street, where protesters held up signs to passing cars that honked in approval. The honking of a passing fire truck sent the crowd into an uproarious cheer. Portland is about a thousand miles from the border with Mexico, but the flag of its distant neighbor nation has emerged as protest iconography in solidarity with Los Angeles. The rainbow pride flag was flown as often as the Mexican flag. Military veterans were scattered throughout the crowd, some identifying themselves as having seen action in conflicts spanning from Vietnam to Afghanistan. Emanuel, an Air Force veteran, told me that he had turned out in defense of the constitution and due process, saying, “Nobody has any rights if one person doesn’t have any rights.” Image: Sarah JeongAnger was directed at ICE and the mass deportations all throughout the day, in signage, in chants, and in rally speeches. The previous night, about 150 people protested at a local ICE facility — coincidentally located by the Tesla dealership — a mile south of downtown, near a highway exit. The ICE facility protests, which have been continuous for some days, have been steadily building up. A couple of “No Kings” signs were present on Friday. (The following day, a handful of “Chinga la migra” signs would show up at the “No Kings” protests). Demonstrators stood on the curb urging passing cars to “Honk if you hate fascists,” successfully eliciting car horns every few seconds, including some from a pristine white Tesla. Federal law enforcement in camo and helmets, their faces obscured, maced and shot at protesters with pepper balls, targeting them through the gates and sniping at them from the rooftop of the building. A handful of protesters — many wearing gas masks and respirators — formed phalanx formations in the driveway, wielding umbrellas and handmade shields. On Saturday, a speaker at one of the “No Kings” rallies advertised the occupation of the ICE facility, saying, “We’re a sanctuary city.” The crowd — replete with American flags both upside down and right side up — cheered. — Sarah JeongNew Port Richey, FloridaNearly every intersection on Pasco County’s State Road 54 looks the same: a cross-section of strip malls, each anchored by a Walmart or Target or Publix, surrounded by a mix of restaurants, nail salons, and gas stations. It’s not an environment that is particularly conducive to protests, but hundreds of people turned out in humid, 90-plus degree weather anyway. The overall size of the crowd is hard to determine, but it’s larger than I — and other attendees — anticipated, given the local demographics. (Trump won 61 percent of the vote in Pasco County in 2024.) New Port Richey, FL. Image: Gaby Del ValleEveryone is on the sidewalk; an organizer with a megaphone tells people to use crosswalks if they’re going to attempt to brave the six-lane highway. Two days earlier, Governor Ron DeSantis said Floridians could legally run over protesters on the street if they feel “threatened.” New Port Richey, FL. Image: Gaby Del ValleSo far, most drivers seem friendly. There are lots of supportive honks. One woman rolls down her window and thanks the protesters. “I love you! I wish I could be with you, but I have to work today!” she yells as she drives away. Not everyone is amenable. A man in a MAGA hat marches through the crowd waving a “thin green line” flag and yelling “long live the king!” as people in the crowd call him a traitor. A pickup truck drives by blasting “Ice Ice Baby,” waving another pro-law enforcement flag. The protesters have flags, too: American flags large and small, some upside down; Mexican; Ukrainian; Palestinian; Canadian; different configurations of pride and trans flags. Their signs, like their flags, illustrate their diverse reasons for attending: opposition to Trump’s “big beautiful” funding bill, DOGE’s budget cuts, and ICE arrests; support for immigrants, government workers, and Palestinians. One woman wears an inflatable chicken suit. Her friend pulls an effigy of Trump — dressed to look both like an eighteenth-century monarch, a taco, and a chicken — alongside her.New Port Richey, FL. Image: Gaby Del ValleMost of the demonstrators are on the older side, but there are people of all ages in attendance. “I thought it was going to be maybe 20 people with a couple of signs,” Abby, 24, says, adding that she’s pleasantly surprised at both the turnout and the fact that most of the protesters are of retirement age. Abe, 20, tells me this is his first protest. Holding a sign that says “ICE = GESTAPO,” he tells me he came out to support a friend who is Mexican. Three teenagers walk by with signs expressing support for immigrants: “While Trump destroys America, we built it.” “Trump: 3 felonies. My parents: 0.” As I drive away, I notice nine counter-protesters off to the side, around the corner from the main event. They wave their own flags, but the demonstrators seemingly pay them no mind.— Gaby Del ValleHistoric Filipinotown, Los AngelesWearing a camo baseball cap — “Desert Storm Veteran” emblazoned on the front — Joe Arciaga greets a crowd of about 100 people in Los Angeles’ Historic Filipinotown around 9:00AM.“Good morning everyone, are you ready for some beautiful trouble?” Arciaga says into the megaphone, an American flag bandana wrapped around his wrist. The faces of Filipino labor leaders Philip Vera Cruz and Larry Itliong, who organized farm workers alongside Cesar Chavez, peer over his shoulders from a mural that lines the length of Unidad Park where Arciaga and a group called Lakas Collective helped organize this neighborhood No Kings rally. “I’m a Desert Storm veteran, and I’m a father of three and a grandfather of three, and I want to work for a future where democracy is upheld, due process, civil rights, the preservation of the rule of law — That’s all I want. I’m not a billionaire, I’m just a regular Joe, right?”, he tells The Verge.Joe Arciaga speaks to people at a rally in Historic Filipinotown, Los Angeles. Image: Justine Calma“I am mad as hell,” he says, when I ask him about the Army 250th anniversary parade Donald Trump has organized in Washington, DC coinciding with the president’s birthday. “The guy does not deserve to be honored, he’s a draft dodger, right?” Arciaga says. He’s “livid” that the President and DOGE have fired veterans working for federal agencies and slashed VA staff.Arciaga organizes the crowd into two lines that file out of the park to stand along Beverly Blvd., one of the main drags through LA. Arciaga has deputized a handful of attendees with security or medical experience with whistles to serve as “marshals” tasked with flagging and de-escalating any potentially risky situation that might arise. Johneric Concordia, one of the co-founders of the popular The Park’s Finest barbecue joint in the neighborhood, is MCing out on Beverly Blvd. He and Arciaga direct people onto the sidewalks and off the asphalt as honking cars zip by. In between chants of “No hate! No fear! Immigrants are welcome here!” and rap songs from LA artist Bambu that Concordia plays from a speaker, Concordia hypes up the organizers. “Who’s cool? Joe’s cool?” He spits into the microphone connected to his speaker. “Who’s streets? Our streets!” the crowd cheers. An hour later, a man sitting at a red light in a black Prius rolls down his window. “Go home!” he yells from the intersection. “Take your Mexican flag and go home!”The crowd mostly ignores him. One attendee on the corner holds up his “No Kings” sign to the Prius without turning his head to look at him. A few minutes later, a jogger in a blue t-shirt raises his fist as he passes the crowd. “Fuck yeah guys,” he says to cheers.By 10AM, the neighborhood event is coming to a close. Demonstrators start to trickle away, some fanning out to other rallies planned across LA today. Concordia is heading out too, microphone and speaker still in hand, “If you’re headed to downtown, watch out for suspicious crew cuts!” — Justine CalmaSan Francisco, California1/10Most of the crowd trickled out after 2pm, which was the scheduled end time of the protest, but hundreds stayed in the area. Image: Vjeran PavicLondon, UKLondon’s protest was a little different than most: it was almost entirely bereft of “No Kings” signs, thanks to the fact that about two miles away much larger crowds were gathered to celebrate the official birthday of one King Charles III. “We don’t have anything against King Charles,” Alyssa, a member of organizers Indivisible London, told me. And so, “out of respect for our host country as immigrants,” they instead set up shop in front of the US embassy with a tweaked message: “No kings, no crowns” became “no tyrants, no clowns.” London, UK. Image: Dominic PrestonOf the hundreds gathered, not everyone got the memo, with a few painted signs decrying kings and crowns regardless, and one brave Brit brandishing a bit of cardboard with a simple message: “Our king is better than yours!”London, UK. Image: Dominic PrestonStill, most of the crowd were on board, with red noses, clown suits, and Pennywise masks dotted throughout, plus costumes ranging from tacos to Roman emperors. “I think tyrants is the better word, and that’s why I dressed up as Caesar, because he was the original,” says Anna, a Long Island native who’s lived in London for three years. “Nobody likes a tyrant. Nobody. And they don’t do well, historically, but they destroy a lot.”For 90 minutes or so the crowd — predominantly American, judging by the accents around me — leaned into the circus theme. Speakers shared the stage with performers, from a comic singalong of anti-Trump protest songs to a protracted pantomime in which a woman in a banana costume exhorted the crowd to pelt a Donald Trump impersonator with fresh peels. London, UK. Image: Dominic PrestonDuring a break in festivities, Alyssa told the crowd, “The most threatening sound to an oligarch is laughter.”— Dominic PrestonProspect Park, Brooklyn, New YorkThe No Kings protest at Brooklyn’s Grand Army Plaza was a calmer affair. Instead of gathering under the picturesque memorial arch, protesters were largely sequestered to a corner right outside Prospect Park, with some streets blocked off by police. The weekly farmers market was in full swing, meaning people cradling bundles of rhubarb were swerving in and out of protest signs that read things like, “Hating Donald Trump is Brat” and “Is it time to get out the pitch forks?” Like during the Hands Off protest in April, New York got rain on Saturday.Prospect Park, Brooklyn. Image: Mia SatoThe area where protesters were gathered made it difficult to count the crowd, but there were hundreds — perhaps a few thousand — people that streamed in and out. At one point, some protesters began marching down the street alongside Prospect Park, while others stayed at Grand Army Plaza to chant, cheer, and hold signs up at oncoming vehicles. With its proximity to the public library, the park, and densely populated neighborhoods, the massive intersection is a high-foot traffic area. Cars blared their horns as they passed, American flags waving in the chilly afternoon breeze.Jane, a Brooklyn resident who stood on the curb opposite the protesters, said she isn’t typically someone who comes out to actions like this: before the No Kings event, she had only ever been to one protest, the Women’s March. (Jane asked that The Verge use her first name only.) Prospect Park, Brooklyn. Image: Mia Sato“I’m deeply concerned about our country,” Jane said, pausing as a long stream of trucks and cars honked continuously in support of the protesters in the background. “I think Trump is behaving as an authoritarian. We’ve seen in Russia, in Hungary, in Hong Kong, that the slide from freedom to not freedom is very fast and very quick if people do not make their voices heard,” Jane said. “I’m concerned that that’s what’s happening in the United States.” Jane also cited cuts to Medicaid and funding for academic research as well as tariffs as being “unacceptable.”Prospect Park, Brooklyn. Image: Mia SatoThe event was peaceful — there were lots of kids present — and people were in good spirits despite the rain. Protest signs ran the gamut from general anti-Trump slogans (“I trust light tampons more than this administration”) to New York City-specific causes like “Andrew Cuomo can’t read” (there is a contenious mayoral election this month). One sign read, “Fix your hearts or die,” an iconic line from the late director, David Lynch’s, Twin Peaks: The Return. And of course, amid nationwide immigration raids that have been escalated by the involvement of the federal government, ICE was top of mind: one sign simply read, “Melt ICE,” and another protester held a large “NO ICE IN NYC” sign. Though it was smaller and more contained than other events, the protest didn’t lack conviction: attendees of all ages stood in the cold rain, chanting and blowing into vuvuzela, banging the lids of pots and pans. At one point a man stood on the median on the street, leading the group in chants of “No justice, no peace.” Cars laid on the horn as they drove by.— Mia SatoAkron, OhioIt’s been raining pretty hard the last few days in Akron, OH, so much that I didn’t think there’d be a large turnout for our chapter of the No Kings protest. But I was emphatically proven wrong as the crowds I saw dwarfed the Tesla Takedown protests last month. Officially, the protest was to take place in front of the John F. Seiberling Federal Building on Main Street in Downtown Akron. But the concentration of people spilled over from that small space down Main Street and up Market Street. All told, though there were no official counts, I estimate somewhere between 500 to 900 people in this blue enclave in Northeast Ohio.The mood was exuberant, buoyed by supporters who honked their horns as they passed. The chorus of horns was nonstop, and when a sanitation truck honked as it went by, cheers got louder. The chants the crowds were singing took on a local flare. Ohio is the home of the Ohio State Buckeyes and anywhere you go, shout “O-H” and you’ll invariably get an “I-O” response. The crowds used that convention to make their own chant, “OH-IO, Donald Trump has got to go.”There was no police presence here and the crowd was very good at policing itself. Ostensibly out of concern for the incidents where people have rammed their cars into protestor crowds, the people here have taken up crossing guard duties, aiding folks who wish to cross Main or Market Streets. Toward the end of my time at the protest, I saw an older gentleman wearing Kent State gear and holding a sign that read, “Remember another time the National Guard was called in?” His sign featured a drawing of the famous photo from the event in which four Kent State students during a protest of the Vietnam War were killed by National Guard troops. I caught up with him to ask him some questions and he told me his name was Chuck Ayers, a professional cartoonist, and was present at the shooting. Akron, OH. Image: Ash Parrish“When I saw the National Guard in front of the federal building in LA,” he told me, “It was just another flashback.”He did not tell me this at the time, but Ayers is a nationally recognized cartoonist, noted for co-creating the comic strip Crankshaft. He’s lived in Ohio his entire life and of course, drew that sign himself. As he was telling me about how seeing news of the National Guard being deployed in LA, I could see him strain to hold back his emotions. He said it still hurts to see this 55 years later, but that he was heartened to see so many people standing here in community and solidarity. He also said that given his pain and trauma he almost didn’t come. When I asked why he showed up when it so obviously causes him pain he said simply, “Because I have to.”— Ash ParrishOneonta, New YorkOn a northward drive to Oneonta — population roughly 15,000, the largest city in New York’s mainly rural Otsego County — one of the most prominent landmarks is a sprawling barn splashed in huge, painted block letters with TRUMP 2024. (The final digits have been faithfully updated every election since 2016.) It’s Trump country, but not uniformly Trumpy country, as evidenced by what I estimated as a hundreds-strong crowd gathered in a field just below Main Street that came together with a friendly county-fair atmosphere. Kids sat on their parents’ shoulders; American flags fluttered next to signs with slogans like SHADE NEVER MADE ANYONE LESS GAY, and attendees grumbled persistently about the event’s feeble sound system, set up on the bed of a pickup truck. It was the kind of conspicuously patriotic, far-from-urban protest that the Trump administration has all but insisted doesn’t exist.Image: Adi RobertsonBeyond a general condemnation of Trump, protest signs repped the same issues being denounced across the country. The wars in Gaza and Ukraine made an appearance, as did Elon Musk and Tesla. A couple of people called out funding cuts for organizations like NPR, one neatly lettered sign reminded us that WEATHER FORECASTING SAVES LIVES, another warned “Keep your nasty little hands off Social Security,” and a lot — unsurprisingly, given the past week’s events — attacked mass deportations and ICE. An attendee who identified himself as Bill, standing behind a placard that blocked most of him from sight, laid out his anger at the administration’s gutting of the Environmental Protection Agency. “I think if it was not for protests, there would be no change,” he told me.The event itself, supported by a coalition including the local chapter of Indivisible, highlighted topics like reproductive justice and LGBTQ rights alongside issues for groups often stereotyped as Republican blocs — there was a speech about Department of Veterans Affairs cuts and a representative from the local Office for the Aging (whose words were mostly lost to the sound system’s whims). Rules for a march around the modest downtown were laid out: no blocking pedestrians or vehicles, and for the sake of families doing weekend shopping, watch the language. “Fuck!” one person yelled indistinctly from the audience. “No, no,” the event’s emcee chided gently. The philosophy, as she put it, was one of persuasion. “We want to build the resistance, not make people angry at us.”Image: Adi RobertsonBut even in a place that will almost certainly never see a National Guard deployment or the ire of a Truth Social post, the Trump administration’s brutal deportation program had just hit close to home. Only hours before the protest commenced, ICE agents were recorded handcuffing a man and removing him in an unmarked black car — detaining what was reportedly a legal resident seeking asylum from Venezuela. The mayor of Oneonta, Mark Drnek, relayed the news to the crowd. “ICE! We see you!” boomed Drnek from the truckbed. “We recognize you for what you are, and we understand, and we reject your vile purpose.”The crowd cheered furiously. The stars and stripes waved.- Adi RobertsonSee More: Policy