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Image by Andrew BrodheadResearchers have developed a novel device that literally spins away the clots that block blood flow to the brain and cause strokes.As Stanford explains in a blurb, the novel milli-spinner device may be able to save the lives of patients who experience "ischemic stroke" from brain stem clotting.Traditional clot removal, a process known as thrombectomy, generally uses a catheter that either vacuums up the blood blockage or uses a wire mesh to ensnare it — a procedure that's as rough and imprecise as it sounds. Conventional thrombectomy has a very low efficacy rate because of this imprecision, and the procedure can result in pieces of the clot breaking off and moving to more difficult-to-reach regions.Thrombectomy via milli-spinner also enters the brain with a catheter, but instead of using a normal vacuum device, it employs a spinning tube outfitted with fins and slits that can suck up the clot much more meticulously.Stanford neuroimaging expert Jeremy Heit, who also coauthored a new paper about the device in the journal Nature, explained in the school's press release that the efficacy of the milli-spinner is "unbelievable.""For most cases, we’re more than doubling the efficacy of current technology, and for the toughest clots — which we’re only removing about 11 percent of the time with current devices — we’re getting the artery open on the first try 90 percent of the time," Heit said. "This is a sea-change technology that will drastically improve our ability to help people."Renee Zhao, the senior author of the Nature paper who teaches mechanical engineering at Stanford and creates what she calls "millirobots," said that conventional thrombectomies just aren't cutting it."With existing technology, there’s no way to reduce the size of the clot," Zhao said. "They rely on deforming and rupturing the clot to remove it.""What’s unique about the milli-spinner is that it applies compression and shear forces to shrink the entire clot," she continued, "dramatically reducing the volume without causing rupture."Indeed, as the team discovered, the device can cut and vacuum up to five percent of its original size."It works so well, for a wide range of clot compositions and sizes," Zhao said. "Even for tough... clots, which are impossible to treat with current technologies, our milli-spinner can treat them using this simple yet powerful mechanics concept to densify the fibrin network and shrink the clot."Though its main experimental use case is brain clot removal, Zhao is excited about its other uses, too."We’re exploring other biomedical applications for the milli-spinner design, and even possibilities beyond medicine," the engineer said. "There are some very exciting opportunities ahead."More on brains: The Microplastics in Your Brain May Be Causing Mental Health IssuesShare This Article

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]

Al Hajar Mountains in OmanL_B_Photography/Shutters​tock A section of Earth’s mantle beneath Oman appears to be unusually warm, in what researchers say may be the first known “ghost plume” – a column of hot rock emanating from the lower mantle without apparent volcanic activity on the surface. Mantle plumes are mysterious upwellings of molten rock believed to transmit heat from the core-mantle boundary to the Earth’s surface, far from the edges of tectonic plates. There are a dozen or so examples thought to occur underneath the middle of continental plates – for instance, beneath Yellowstone and the East African rift. “But these are all cases where you do have surface volcanism,” says Simone Pilia at the King Fahd University of Petroleum and Minerals in Saudi Arabia. Oman has no such volcanic clues. Pilia first came to suspect there was a plume beneath Oman “serendipitously” after he began analysing new seismic data from the region. He observed the velocity of waves generated by distant earthquakes slowed down in a cylindrical area beneath eastern Oman, indicating the rocks there were less rigid than the surrounding material due to high temperatures. Other independent seismic measurements showed key boundaries where minerals deep in the Earth change phases in a way consistent with a hot plume. These measurements suggest the plume extends more than 660 kilometres below the surface. The presence of a plume could also explain why the region has continued to rise in elevation long after tectonic compression – a geological process where the Earth’s crust is squeezed together – stopped. It also fits with models of what could have caused a shift in the movement of the Indian tectonic plate. “The more we gathered evidence, the more we were convinced that it is a plume,” says Pilia, who named the geologic feature the “Dani plume” after his son. Unmissable news about our planet delivered straight to your inbox every month. Sign up to newsletter “It’s plausible” that a plume indeed exists there, says Saskia Goes at Imperial College London, adding the study is “thorough”. However, she points out narrow plumes are notoriously difficult to detect. If it does exist, however, the presence of a “ghost plume” contained within the mantle by the relatively thick rocky layer beneath Oman would suggest there are others, says Pilia. “We’re convinced that the Dani plume is not alone.” If there are many other hidden plumes, it could mean more heat from the core is flowing directly through the mantle via plumes, rather than through slower convection, says Goes. “It has implications, potentially, for the evolution of the Earth if we get a different estimate of how much heat comes out of the mantle.” Journal referenceEarth and Planetary Science Letters DOI: 10.1016/j.epsl.2025.119467 Topics:

What’s happening with Boba Fett? The last time we saw the legendary Star Wars character was at the end of his own show, The Book of Boba Fett, in February 2022. Yes, the Disney+ spin-off series was divisive, with some Star Wars fans feeling it went too far in softening the iconic villain's character. But that can’t be it for Boba Fett, can it?Over three years later, it feels like The Book of Boba Fett Season 2 is stuck in a galaxy far, far away. Lucasfilm has given no indication that the show will return, with next year’s The Mandalorian & Grogu movie perhaps the best chance of a live-action reprisal. Will there be a The Mandalorian Season 4? Lucasfilm has yet to say, but if it does happen, perhaps Boba Fett would pop up there.In truth, the future of Boba Fett and Temuera Morrison, the actor who plays him, in the Star Wars franchise remains uncertain. And based on recent comments from Morrison himself, there is little reason to deviate from that position.PlaySpeaking in an interview with Collider to promote his new film, Ka Whawhai Tonu (In The Fire of War), the 64-year-old New Zealander said he felt "sad" not to have reprised the role since the end of The Book of Boba Fett.“Where [sic] The Book of Boba Fett Season 2? Where the hell is Season 2?" Morrison said. "I know they're doing Ahsoka Season 2. I'm going, 'Ah, where's my Season 2?'"According to Collider, Morrison revealed he actually pitched Lucasfilm on Boba Fett appearing in Ahsoka Season 2 ("can I be Rex and take his helmet off, please?"), pointing out that he plays not just Boba Fett but all the clones based upon the character.He also bumped into Star Wars stewards Dave Filoni and Jon Favreau recently, and reminded them Boba Fett still exists. Apparently “they kind of said, 'Well, well,' they didn't want to say too much, put it that way. There was a few whispers of — they didn't want to say too much — but they just left it at that."That exchange left Morrison feeling like "I've been preserved for a later date, and I'm going to be tastier." He compared his feeling following the encounter to going to grandma’s house and seeing “that preservative jar of peaches up on the shelf.”Every Upcoming Star Wars Movie and TV Show“That's what I think,” he continued. “I'm one of those peaches, and I've been put up on the shelf. I've been preserved for a later date, and I'm going to be tastier.”Morrison could be playing coy, knowing full well he’s coming back to Star Wars in some form. After all, Rosario Dawson had no idea Mark Hamill was returning as Luke Skywalker in The Mandalorian until he walked on set. Lucasfilm has a history of holding its cards close to its chest.But it does sound to me like nothing is in the works for Boba Fett, unfortunately, and indeed last year Morrison offered a reason for why that might be the case. Speaking at the From Clone Troopers to Bounty Hunters panel at Fan Expo Chicago, Morrison cited The Book of Boba Fett's poor reception as the reason for the once beloved character not returning in Star Wars: The Mandalorian & Grogu. He added that Star Wars owner Disney hadn’t asked him to appear in the incoming film or a second season of The Book of Boba Fett.The show, which told the story of Boba Fett as he escaped from the Sarlacc Pit and acted as a miniature season of The Mandalorian, was among the worst received Star Wars shows. "This show's reception does seem to have impacted the future of the character in the franchise," Morrison said at the time. That was in August 2024. Has something changed in the year since? I'm not sure.Morrison originally played Jango Fett in Star Wars: Episode 2 – Attack of the Clones but years later rejoined Star Wars as Jango's son and clone Boba Fett. He's made clear his desire to return to Star Wars, saying he wants a chunk of The Mandalorian's time just as The Mandalorian led an episode of his show.Photo by Jun Sato/WireImage.Wesley is Director, News at IGN. Find him on Twitter at @wyp100. You can reach Wesley at wesley_yinpoole@ign.com or confidentially at wyp100@proton.me.

To this day, Jaws remains the best example of Steven Spielberg‘s genius as a filmmaker. He somehow took a middling pulp novel about a killer shark and turned it into a thrilling adventure about masculinity and economic desperation. And to the surprise of no one, the massive success of Jaws spawned a lot of knockoffs, a glut of movies about animals terrorizing communities. None of these reach the majesty of Jaws, of course. But here’s the thing—none of them had to be Jaws. Sure, it’s nice that Spielberg’s film has impeccably designed set pieces and compelling characters, but that’s not the main reason people go to animal attack movies. We really just want to watch people get attacked. And eaten. With such standards duly lowered, let’s take a look at the best animal attack movies that came out in the past half-century since Jaws first scared us out of the water. Of course this list doesn’t cover every movie inspired by Jaws ( for example Godzilla Minus One, which devotes its middle act to a wonderful Jaws riff), and some can argue that these movies were less inspired by Jaws than other nature revolts features, such as Alfred Hitchcock‘s The Birds. But every one of these flicks owes a debt to Jaws, either in inspiration or simply getting people interested in movies about animals eating people. Those warning aside, lets make like drunken revelers on Amity Island and dive right in! 20. Sharknado (2013) Sharknado almost doesn’t belong on this list because it’s less a movie and more of a meme, a precursor to Vines and TikTok trends. Yes, many fantastic movies have been made off of an incredibly high concept and a painfully low budget. Heck, that approach made Roger Corman’s career. But Sharknado‘s high concept—a tornado sweeps over the ocean and launches ravenous sharks into the mainland—comes with a self-satisfied smirk. Somehow, Sharknado managed to capture the imagination of the public, making it popular enough to launch five sequels. At the time, viewers defended it as a so bad it’s good-style movie like The Room. But today Sharknado‘s obvious attempts to be wacky are just bad, making the franchise one more embarrassing trend, ready to be forgotten. 19. Orca (1977) For a long time, Orca had a reputation for being the most obvious Jaws ripoff, and with good reason—Italian producer Dino De Laurentiis, who would go on to support Flash Gordon, Manhunter, and truly launch David Lynch‘s career with Blue Velvet, wanted his own version of the Spielberg hit. On paper he had all the right ingredients, including a great cast with Richard Harris and Charlotte Rampling, and another oceanic threat, this time a killer whale. Orca boasts some impressive underwater cinematography, something that even Jaws largely lacks. But that’s the one thing Orca does better than Jaws. Everything else—character-building, suspense and scare scenes, basic plotting and storytelling—is done in such a haphazard manner that Orca plays more like an early mockbuster from the Asylum production company (makers of Sharknado) than it does a product from a future Hollywood player. 18. Tentacles (1977) Another Italian cheapie riding off the success of Jaws, Tentacles at least manages to be fun in its ineptitude. A giant octopus feature, Tentacles is directed by Ovidio G. Assonitis, a man whose greatest claim to fame is that he annoyed first-time director James Cameron so much on Piranha II: The Spawning that he activated the future legend’s infamous refusal to compromise with studios and producers. Tentacles somehow has a pretty impressive cast, including John Huston, Shelly Winters, and Henry Fonda all picking up paychecks. None of them really do any hard work in Tentacles, but there’s something fun about watching these greats shake the the octopus limbs that are supposed to be attacking them, as if they’re in an Ed Wood picture. 17. Kingdom of the Spiders (1977) Spielberg famously couldn’t get his mechanical shark to work, a happy accident that he overcame with incredibly tense scenes that merely suggested the monster’s presence. For his arachnids on the forgotten movie Kingdom of the Spiders, director John “Bud” Cardos has an even more formative tool to make up for the lack of effects magic: William Shatner. Shatner plays Rack Hansen, a veterinarian who discovers that the overuse of pesticides has killed off smaller insects and forced the tarantula population to seek larger prey, including humans. These types of ecological messages are common among creature features of the late ’70s, and they usually clang with hollow self-righteousness. But in Kingdom of the Spiders, Shatner delivers his lines with such blown out conviction that we enjoy his bluster, even if we don’t quite buy it. 16. The Meg (2018) The idea of Jason Statham fighting a giant prehistoric shark is an idea so awesome, it’s shocking that his character from Spy didn’t already pitch it. And The Meg certainly does deliver when Statham’s character does commit to battle with the creature in the movie’s climax. The problem is that moment of absurd heroism comes only after a lot of long sappy nonsense. Join our mailing list Get the best of Den of Geek delivered right to your inbox! It’s hard to figure out who is to blame for The Meg‘s failure. Director Jon Turteltaub hails from well-remembered Disney classics Cool Runnings and National Treasure. But too often he forgets how to pace an adventure film and gives into his most saccharine instincts here. One of the many Chinese/Hollywood co-produced blockbusters of the 2010s, The Meg also suffers from trying to innocuously please too wide an audience. Whatever the source, The Meg only fleetingly delivers on the promise of big time peril, wasting too much time on thin character beats. 15. Lake Placid (1999) I know already some people reading this are taking exception to Lake Placid‘s low ranking, complaining that this list isn’t showing enough respect to what they consider a zippy, irreverent take on a creature feature, one written by Ally McBeal creator David E. Kelley and co-starring Betty White. To those people, I can only say, “Please rewatch Lake Placid and then consider its ranking.” Lake Placid certainly has its fun moments, helped along by White as a kindly grandmother who keeps feeding a giant croc, Bill Pullman as a dumbfounded simple sheriff, and Oliver Platt as a rich adventurer. Their various one-liners are a pleasure to remember. But within the context of a movie stuffed with late ’90s irony, the constant snark gets tiresome, sapping out all the fun of a killer crocodile film. 14. Open Water (2003) Like Sharknado, Open Water had its fans for a few years but has fallen in most moviegoers’ esteem. Unlike Sharknado, Open Water is a real movie, just one that can’t sustain its premise for its entire runtime. Writer and director Chris Kentis draws inspiration from a real-life story about a husband and wife who were accidentally abandoned in the middle of the ocean by their scuba excursion group. The same thing happens to the movie’s Susan Watkins (Blanchard Ryan) and Daniel Travis (Daniel Kintner), who respond to their predicament by airing out their relationship grievances, even as sharks start to surround them. Kentis commits to the reality of the couple’s bleak situation, which sets Open Water apart from the thrill-a-minute movies that mostly make up this list. But even with some shocking set pieces, Open Water feels too much like being stuck in car with a couple who hates each other and not enough like a shark attack thriller. 13. Eaten Alive (1976) Spielberg’s artful execution of Jaws led many of the filmmakers who followed to attempt some semblance of character development and prestige, even if done without enthusiasm (see: Orca). Not so with Tobe Hooper, who followed up the genre-defining The Texas Chainsaw Massacre with Eaten Alive. Then again, Hooper draws just as much from Psycho as he does Jaws. Neville Brand plays Judd, the proprietor of a sleazy hotel on the bayou where slimy yokels do horrible things to one another. Amity Island, this is not. But when one of the visitors annoy Judd, he feeds them to the pet croc kept in the back. Eaten Alive is a nasty bit of work, but like most of Hooper’s oeuvre, it’s a lot of fun. 12. Prophecy (1979) Directed by John Frankenheimer of The Manchurian Candidate and Grand Prix fame, Prophecy is easily the best of the more high-minded animal attack movies that followed Jaws. This landlocked film, written by David Seltzer, stars Robert Foxworth as Dr. Robert Verne, a veterinarian hired by the EPA to investigate bear attacks against loggers on a mountain in Maine. Along with his wife Maggie (Talia Shire), Verne finds himself thrown into a conflict between the mining company and the local Indigenous population who resist them. Prophecy drips with an American hippy mentality that reads as pretty conservative today (“your body, your choice” one of Maggie’s friends tells her… to urge her against getting an abortion), making its depictions of Native people, including the leader played by Italian American actor Armand Assante, pretty embarrassing. But there is a mutant bear on the loose and Frankenheimer knows how to stage an exciting sequence, which makes Prophecy a worthwhile watch. 11. Piranha 3D (2010) Piranha 3D begins with a denim-wearing fisherman named Matt, played by Richard Dreyfuss no less, falling into the water and immediately getting devoured by the titular flesh-eaters. This weird nod to Matt Hooper and Jaws instead of Joe Dante’s Piranha, the movie Piranha 3D is supposed to be remaking, is just one of the many oddities at play yhere. Screenwriters Pete Goldfinger and Josh Stolberg have some of the wacky energy and social satire of the original film, but director Alexandre Aja, a veteran of the French Extreme movement, includes so much nastiness in Piranha 3D that we’re not sure if we want to laugh or throw up. Still, there’s no denying the power of Piranha 3D‘s set pieces, including a shocking sequence in which the titular beasties attack an MTV/Girls Gone Wild Spring Break party and chaos ensues. Furthermore, Piranha 3D benefits from a strong cast, which includes Elizabeth Shue, Adam Scott, and Ving Rhames. 10. Anaconda (1997) With its many scenes involving an animal attacking a ragtag group on a boat, Anaconda clearly owes a debt to Jaws. However, with its corny characters and shoddy late ’90s CGI, Anaconda feels today less like a Jaws knockoff and more like a forerunner to Sharknado and the boom of lazy Syfy and Redbox horror movies that followed. Whatever its influences and legacy, there’s no denying that Anaconda is, itself, a pretty fun movie. Giant snakes make for good movie monsters, and the special effects have become dated in a way that feels charming. Moreover, Anaconda boasts a enjoyably unlikely cast, including Eric Stoltz as a scientist, Owen Wilson and Ice Cube as members of a documentary crew, and Jon Voight as what might be the most unhinged character of his career, second only to his crossbow enthusiast from Megalopolis. 9. The Shallows (2016) The Shallows isn’t the highest-ranking shark attack movie on this list but it’s definitely the most frightening shark attack thriller since Jaws. That’s high praise, indeed, but The Shallows benefits from a lean and mean premise and clear direction by Jaume Collet-Serra, who has made some solid modern thrillers. The Shallows focuses almost entirely on med student Nancy Adams (Blake Lively), who gets caught far from shore after the tide comes in and is hunted by a shark. A lot of the pleasure of The Shallows comes from seeing how Collet-Serra and screenwriter Anthony Jaswinski avoid the problems that plague many of the movies on this list. Adams is an incredibly competent character, and we pull for her even after the mistake that leaves her stranded. Moreover, The Shallows perfectly balances thrill sequences with character moments, making for one of the more well-rounded creature features of the past decade. 8. Razorback (1984) Jaws, of course, has a fantastic opening scene, a thrilling sequence in which the shark kills a drunken skinny dipper. Of the movies on this list, only Razorback comes close to matching the original’s power, and it does so because director Russell Mulcahy, who would make Highlander next, goes for glossy absurdity. In the Razorback‘s first three minutes, a hulking wild boar smashes through the rural home of an elderly man in the Australian outback, carrying away his young grandson. Over the sounds of a synth score, the old man stumbles away from his now-burning house, screaming up into the sky. Sadly, the rest of Razorback cannot top that moment. Mulcahy directs the picture with lots of glossy style, while retaining the grit of the Australian New Wave movement. But budget restrictions keep the titular beast from really looking as cool as one would hope, and the movie’s loud, crazy tone can’t rely on Jaws-like power of suggestion. 7. Crawl (2019) Alexandre Aja’s second movie on this list earns its high rank precisely because it does away with the tonal inconsistencies that plagued Piranha 3D and leans into what the French filmmaker does so well: slicked down and mean horror. Set in the middle of a Florida hurricane, Crawl stars Kaya Scodelario as competitive swimmer Haley and always-welcome character actor Barry Pepper as her father Dave, who get trapped in a flooding basement that’s menaced by alligators. Yet as grimy as Crawl can get, Aja also executes the strong character work in the script by Michael Rasmussen and Shawn Rasmussen. Dave and Haley are real people, not just gator-bait, making their peril feel all the more real, and their triumphs all the sweeter. 6. Piranha (1978) Piranha is the only entry on this list to get a seal of approval from Stephen Spielberg himself, who not only praised the movie, even as Universal Pictures planned to sue the production, but also got director Joe Dante to later helm Gremlins. It’s not hard to see why Piranha charmed Spielberg, a man who loves wacky comedy. Dante’s Looney Tunes approach is on full display in some of the movie’s best set pieces. But Piranha is special because it also comes from legendary screenwriter John Sayles, who infuses the story with social satire and cynicism that somehow blends with Dante’s approach. The result is a film about piranha developed by the U.S. military to kill the Vietnamese getting unleashed into an American river and making their way to a children’s summer camp, a horrifying idea that Dante turns into good clean fun. 5. Slugs (1988) If we’re talking about well-made movies, then Slugs belongs way below any of the movies on this list, somewhere around the killer earthworm picture Squirm. But if we’re thinking about pure enjoyable spectacle, it’s hard to top Slugs, a movie about, yes, flesh-eating slugs. Yes, it’s very funny to think about people getting terrorized by creatures that are famous for moving very, very slowly. But Spanish director Juan Piquer Simón, perhaps best known for his equally bugnuts giallo Pieces (1982), pays as little attention to realism as he does to good taste. Slugs is filled with insane and ghastly sequences of killer slugs ending up in unlikely places, swarming the floor of someone’s bedroom or inside a fancy restaurant, and then devouring people, one methodical bite at a time. 4. Deep Blue Sea (1999) When it comes to goofy ’90s CGI action, it’s hard to top Deep Blue Sea, directed by Renny Harlin and featuring sharks with genetically enhanced brains. Deep Blue Sea doesn’t have a strong sense of pacing, it lacks any sort of believable character development, and the effects looked terrible even in 1999. But it’s also the only movie on this list that features LL Cool J as a cool chef who recites a violent version of the 23rd Psalm and almost gets cooked alive in an oven by a genius-level shark. It’s scenes like the oven sequence that makes Deep Blue Sea such a delight, despite its many, many flaws. The movie tries to do the most at every turn, whether that’s clearly reediting the movie in postproduction so that LL Cool J’s chef becomes a central character, stealing the spotlight form intended star Saffron Burrows, or a ridiculous Samuel L. Jackson monologue with a delightfully unexpected climax. 3. Alligator (1980) In many ways, Alligator feels like screenwriter John Sayles’ rejoinder to Piranha. If Joe Dante sanded down Piranha‘s sharp edges with his goofy humor, then Alligator is so filled with mean-spiritedness that no director could dilute it. Not that Lewis Teague, a solid action helmer who we’ll talk about again shortly, would do that. Alligator transports the old adage about gators in the sewers from New York to Chicago where the titular beast, the subject of experiments to increase its size, begins preying on the innocent. And on the not so innocent. Alligator shows no respect for the good or the bad, and the film is filled with scenes of people getting devoured, whether it’s a young boy who becomes a snack during a birthday party prank or an elderly mafioso who tries to abandon his family during the gator’s rampage. 2. Grizzly (1976) Grizzly stands as the greatest of the movies obviously ripping off Jaws precisely because it understands its limitations. It takes what it can from Spielberg’s masterpiece, including the general premise of an animal hunting in a tourist location, and ignores what it can’t pull off, namely three-dimensional characters. This clear-eyed understanding of everyone’s abilities makes Grizzly a lean, mean, and satisfying thriller. Directed by blaxploitation vet William Girdler and written by Harvey Flaxman and David Sheldon, Grizzly stars ’70s low-budget king Christopher George as a park ranger investigating unusually vicious bear attacks on campers. That’s not the richest concept in the world, but Girdler and co. execute their ideas with such precision, and George plays his character with just the right amount of machismo, that Grizzly manages to deliver on everything you want from an animal attack. 1. Cujo (1983) To some modern readers, it might seem absurd to put Cujo on a list of Jaws knockoffs. After all, Stephen King is a franchise unto himself and he certainly doesn’t need another movie’s success to get a greenlight for any of his projects. But you have to remember that Cujo came out in 1983 and was just the third of his works to get adapted theatrically, which makes its Jaws connection more valid. After all, the main section of the film—in which mom (Dee Wallace) and her son Tad (Danny Pintauro) are trapped in their car and menaced by the titular St. Bernard—replicates the isolation on Quint’s fishing vessel, the Orca, better than any other film on this list. However, it’s not just director Lewis Teague’s ability to create tension that puts Cujo at the top. Writers Don Carlos Dunaway and Lauren Currier key into the complicated familial dynamics of King’s story, giving the characters surprising depth. It’s no wonder that Spielberg would cast Wallace as another overwhelmed mom for E.T. The Extraterrestrial the very next year, proving that he still has a soft spot for animal attack movies—even if none of them came close to matching the power of Jaws.