• Komires: Matali Physics 6.9 Released

    We are pleased to announce the release of Matali Physics 6.9, the next significant step on the way to the seventh major version of the environment. Matali Physics 6.9 introduces a number of improvements and fixes to Matali Physics Core, Matali Render and Matali Games modules, presents physics-driven, completely dynamic light sources, real-time object scaling with destruction, lighting model simulating global illuminationin some aspects, comprehensive support for Wayland on Linux, and more.

    Posted by komires on Jun 3rd, 2025
    What is Matali Physics?
    Matali Physics is an advanced, modern, multi-platform, high-performance 3d physics environment intended for games, VR, AR, physics-based simulations and robotics. Matali Physics consists of the advanced 3d physics engine Matali Physics Core and other physics-driven modules that all together provide comprehensive simulation of physical phenomena and physics-based modeling of both real and imaginary objects.
    What's new in version 6.9?

    Physics-driven, completely dynamic light sources. The introduced solution allows for processing hundreds of movable, long-range and shadow-casting light sources, where with each source can be assigned logic that controls its behavior, changes light parameters, volumetric effects parameters and others;
    Real-time object scaling with destruction. All groups of physics objects and groups of physics objects with constraints may be subject to destruction process during real-time scaling, allowing group members to break off at different sizes;
    Lighting model simulating global illuminationin some aspects. Based on own research and development work, processed in real time, ready for dynamic scenes, fast on mobile devices, not based on lightmaps, light probes, baked lights, etc.;
    Comprehensive support for Wayland on Linux. The latest version allows Matali Physics SDK users to create advanced, high-performance, physics-based, Vulkan-based games for modern Linux distributions where Wayland is the main display server protocol;
    Other improvements and fixes which complete list is available on the History webpage.

    What platforms does Matali Physics support?

    Android
    Android TV
    *BSD
    iOS
    iPadOS
    LinuxmacOS
    Steam Deck
    tvOS
    UWPWindowsWhat are the benefits of using Matali Physics?

    Physics simulation, graphics, sound and music integrated into one total multimedia solution where creating complex interactions and behaviors is common and relatively easy
    Composed of dedicated modules that do not require additional licences and fees
    Supports fully dynamic and destructible scenes
    Supports physics-based behavioral animations
    Supports physical AI, object motion and state change control
    Supports physics-based GUI
    Supports physics-based particle effects
    Supports multi-scene physics simulation and scene combining
    Supports physics-based photo mode
    Supports physics-driven sound
    Supports physics-driven music
    Supports debug visualization
    Fully serializable and deserializable
    Available for all major mobile, desktop and TV platforms
    New features on request
    Dedicated technical support
    Regular updates and fixes

    If you have questions related to the latest version and the use of Matali Physics environment as a game creation solution, please do not hesitate to contact us.
    #komires #matali #physics #released
    Komires: Matali Physics 6.9 Released
    We are pleased to announce the release of Matali Physics 6.9, the next significant step on the way to the seventh major version of the environment. Matali Physics 6.9 introduces a number of improvements and fixes to Matali Physics Core, Matali Render and Matali Games modules, presents physics-driven, completely dynamic light sources, real-time object scaling with destruction, lighting model simulating global illuminationin some aspects, comprehensive support for Wayland on Linux, and more. Posted by komires on Jun 3rd, 2025 What is Matali Physics? Matali Physics is an advanced, modern, multi-platform, high-performance 3d physics environment intended for games, VR, AR, physics-based simulations and robotics. Matali Physics consists of the advanced 3d physics engine Matali Physics Core and other physics-driven modules that all together provide comprehensive simulation of physical phenomena and physics-based modeling of both real and imaginary objects. What's new in version 6.9? Physics-driven, completely dynamic light sources. The introduced solution allows for processing hundreds of movable, long-range and shadow-casting light sources, where with each source can be assigned logic that controls its behavior, changes light parameters, volumetric effects parameters and others; Real-time object scaling with destruction. All groups of physics objects and groups of physics objects with constraints may be subject to destruction process during real-time scaling, allowing group members to break off at different sizes; Lighting model simulating global illuminationin some aspects. Based on own research and development work, processed in real time, ready for dynamic scenes, fast on mobile devices, not based on lightmaps, light probes, baked lights, etc.; Comprehensive support for Wayland on Linux. The latest version allows Matali Physics SDK users to create advanced, high-performance, physics-based, Vulkan-based games for modern Linux distributions where Wayland is the main display server protocol; Other improvements and fixes which complete list is available on the History webpage. What platforms does Matali Physics support? Android Android TV *BSD iOS iPadOS LinuxmacOS Steam Deck tvOS UWPWindowsWhat are the benefits of using Matali Physics? Physics simulation, graphics, sound and music integrated into one total multimedia solution where creating complex interactions and behaviors is common and relatively easy Composed of dedicated modules that do not require additional licences and fees Supports fully dynamic and destructible scenes Supports physics-based behavioral animations Supports physical AI, object motion and state change control Supports physics-based GUI Supports physics-based particle effects Supports multi-scene physics simulation and scene combining Supports physics-based photo mode Supports physics-driven sound Supports physics-driven music Supports debug visualization Fully serializable and deserializable Available for all major mobile, desktop and TV platforms New features on request Dedicated technical support Regular updates and fixes If you have questions related to the latest version and the use of Matali Physics environment as a game creation solution, please do not hesitate to contact us. #komires #matali #physics #released
    WWW.INDIEDB.COM
    Komires: Matali Physics 6.9 Released
    We are pleased to announce the release of Matali Physics 6.9, the next significant step on the way to the seventh major version of the environment. Matali Physics 6.9 introduces a number of improvements and fixes to Matali Physics Core, Matali Render and Matali Games modules, presents physics-driven, completely dynamic light sources, real-time object scaling with destruction, lighting model simulating global illumination (GI) in some aspects, comprehensive support for Wayland on Linux, and more. Posted by komires on Jun 3rd, 2025 What is Matali Physics? Matali Physics is an advanced, modern, multi-platform, high-performance 3d physics environment intended for games, VR, AR, physics-based simulations and robotics. Matali Physics consists of the advanced 3d physics engine Matali Physics Core and other physics-driven modules that all together provide comprehensive simulation of physical phenomena and physics-based modeling of both real and imaginary objects. What's new in version 6.9? Physics-driven, completely dynamic light sources. The introduced solution allows for processing hundreds of movable, long-range and shadow-casting light sources, where with each source can be assigned logic that controls its behavior, changes light parameters, volumetric effects parameters and others; Real-time object scaling with destruction. All groups of physics objects and groups of physics objects with constraints may be subject to destruction process during real-time scaling, allowing group members to break off at different sizes; Lighting model simulating global illumination (GI) in some aspects. Based on own research and development work, processed in real time, ready for dynamic scenes, fast on mobile devices, not based on lightmaps, light probes, baked lights, etc.; Comprehensive support for Wayland on Linux. The latest version allows Matali Physics SDK users to create advanced, high-performance, physics-based, Vulkan-based games for modern Linux distributions where Wayland is the main display server protocol; Other improvements and fixes which complete list is available on the History webpage. What platforms does Matali Physics support? Android Android TV *BSD iOS iPadOS Linux (distributions) macOS Steam Deck tvOS UWP (Desktop, Xbox Series X/S) Windows (Classic, GDK, Handheld consoles) What are the benefits of using Matali Physics? Physics simulation, graphics, sound and music integrated into one total multimedia solution where creating complex interactions and behaviors is common and relatively easy Composed of dedicated modules that do not require additional licences and fees Supports fully dynamic and destructible scenes Supports physics-based behavioral animations Supports physical AI, object motion and state change control Supports physics-based GUI Supports physics-based particle effects Supports multi-scene physics simulation and scene combining Supports physics-based photo mode Supports physics-driven sound Supports physics-driven music Supports debug visualization Fully serializable and deserializable Available for all major mobile, desktop and TV platforms New features on request Dedicated technical support Regular updates and fixes If you have questions related to the latest version and the use of Matali Physics environment as a game creation solution, please do not hesitate to contact us.
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  • How AI is reshaping the future of healthcare and medical research

    Transcript       
    PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”          
    This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.   
    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?    
    In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.  The book passage I read at the top is from “Chapter 10: The Big Black Bag.” 
    In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.   
    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open. 
    As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.  
    Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home. 
    Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.     
    Here’s my conversation with Bill Gates and Sébastien Bubeck. 
    LEE: Bill, welcome. 
    BILL GATES: Thank you. 
    LEE: Seb … 
    SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here. 
    LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening? 
    And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?  
    GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines. 
    And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.  
    And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weaknessthat, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning. 
    LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that? 
    GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, … 
    LEE: Right.  
    GATES: … that is a bit weird.  
    LEE: Yeah. 
    GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training. 
    LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. 
    BUBECK: Yes.  
    LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSRto join and start investigating this thing seriously. And the first person I pulled in was you. 
    BUBECK: Yeah. 
    LEE: And so what were your first encounters? Because I actually don’t remember what happened then. 
    BUBECK: Oh, I remember it very well.My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3. 
    I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1. 
    So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair.And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts. 
    So this was really, to me, the first moment where I saw some understanding in those models.  
    LEE: So this was, just to get the timing right, that was before I pulled you into the tent. 
    BUBECK: That was before. That was like a year before. 
    LEE: Right.  
    BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4. 
    So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.  
    So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x. 
    And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?  
    LEE: Yeah.
    BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.  
    LEE:One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine. 
    And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.  
    And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.  
    I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book. 
    But the main purpose of this conversation isn’t to reminisce aboutor indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements. 
    But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today? 
    You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.  
    Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork? 
    GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.  
    It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision. 
    But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view. 
    LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients.Does that make sense to you? 
    BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong? 
    Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.  
    Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them. 
    And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT. And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.  
    Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way. 
    It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine. 
    LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all? 
    GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that. 
    The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa,
    So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.  
    LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking? 
    GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.  
    The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.  
    LEE: Right.  
    GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.  
    LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication. 
    BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE, for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI. 
    It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for. 
    LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes. 
    I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?  
    That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential.What’s up with that? 
    BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back thatversion of GPT-4o, so now we don’t have the sycophant version out there. 
    Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF, where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad. 
    But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model. 
    So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model. 
    LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and … 
    BUBECK: It’s a very difficult, very difficult balance. 
    LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models? 
    GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there. 
    Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?  
    Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there.
    LEE: Yeah.
    GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake. 
    LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on. 
    BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGIthat kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything. 
    That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects.So it’s … I think it’s an important example to have in mind. 
    LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two? 
    BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it. 
    LEE: So we have about three hours of stuff to talk about, but our time is actually running low.
    BUBECK: Yes, yes, yes.  
    LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now? 
    GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.  
    The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities. 
    And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period. 
    LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers? 
    GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them. 
    LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.  
    I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why. 
    BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and seeproduced what you wanted. So I absolutely agree with that.  
    And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini. So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.  
    LEE: Yeah. 
    BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.  
    Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not. 
    Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision. 
    LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist … 
    BUBECK: Yeah.
    LEE: … or an endocrinologist might not.
    BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know.
    LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today? 
    BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later. 
    And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …  
    LEE: Will AI prescribe your medicines? Write your prescriptions? 
    BUBECK: I think yes. I think yes. 
    LEE: OK. Bill? 
    GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate?
    And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelectedjust on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries. 
    You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that. 
    LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.  
    I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  
    GATES: Yeah. Thanks, you guys. 
    BUBECK: Thank you, Peter. Thanks, Bill. 
    LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.   
    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.  
    And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.  
    One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.  
    HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings. 
    You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.  
    If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  
    I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.  
    Until next time.  
    #how #reshaping #future #healthcare #medical
    How AI is reshaping the future of healthcare and medical research
    Transcript        PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”           This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.  The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.      Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weaknessthat, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent.  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSRto join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well.My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair.And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE:One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce aboutor indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients.Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT. And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE, for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential.What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back thatversion of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF, where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGIthat kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects.So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and seeproduced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini. So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelectedjust on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.   GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.   I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   #how #reshaping #future #healthcare #medical
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    How AI is reshaping the future of healthcare and medical research
    Transcript [MUSIC]      [BOOK PASSAGE]   PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”   [END OF BOOK PASSAGE]     [THEME MUSIC]     This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.   [THEME MUSIC FADES] The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.    [TRANSITION MUSIC]   Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weakness [LAUGHTER] that, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. [LAUGHS]  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSR [Microsoft Research] to join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well. [LAUGHS] My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair. [LAUGHTER] And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE: [LAUGHS] One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce about [LAUGHS] or indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients. [LAUGHTER] Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT (opens in new tab). And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE [United States Medical Licensing Examination], for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential. [LAUGHTER] What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back that [LAUGHS] version of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF [reinforcement learning from human feedback], where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGI [artificial general intelligence] that kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects. [LAUGHTER] So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and see [if you have] produced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab). So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelected [LAUGHTER] just on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  [TRANSITION MUSIC]  GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  [THEME MUSIC]  I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   [MUSIC FADES]
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  • The “online monkey torture video” arrests just keep coming

    monkey abuse

    The “online monkey torture video” arrests just keep coming

    Authorities continue the slow crackdown.

    Nate Anderson



    Jun 14, 2025 7:00 am

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    Today's monkey torture videos are the products of a digitally connected world. People who enjoy watching baby animals probed, snipped, and mutilated in horrible ways often have difficulty finding local collaborators, but online communities like "million tears"—now thankfully shuttered—can help them forge connections.
    Once they do meet other like-minded souls, communication takes place through chat apps like Telegram and Signal, often using encryption.
    Money is pooled through various phone apps, then sent to videographers in countries where wages are low and monkeys are plentiful.There, monkeys are tortured by a local subcontractor—sometimes a child—working to Western specs. Smartphone video of the torture is sent back to the commissioning sadists, who share it with more viewers using the same online communities in which they met.
    The unfortunate pattern was again on display this week in an indictment the US government unsealed against several more Americans said to have commissioned these videos. The accused used online handles like "Bitchy" and "DemonSwordSoulCrusher," and they hail from all over: Tennessee, North Carolina, Ohio, Pennsylvania, and Massachusetts.
    They relied on an Indonesian videographer to create the content, which was surprisingly affordable—it cost a mere to commission video of a "burning hot screwdriver" being shoved into a baby monkey's orifice. After the money was transferred, the requested video was shot and shared through a "phone-based messaging program," but the Americans were deeply disappointed in its quality. Instead of full-on impalement, the videographer had heated a screwdriver on a burner and merely touched it against the monkey a few times.
    "So lame," one of the Americans allegedly complained to another. "Live and learn," was the response.

    So the group tried again. "Million tears" had been booted by its host, but the group reconstituted on another platform and renamed itself "the trail of trillion tears." They reached out to another Indonesian videographer and asked for a more graphic version of the same video. But this version, more sadistic than the last, still didn't satisfy. As one of the Americans allegedly said to another, "honey that's not what you asked for. Thats the village idiot version. But I'm talking with someone about getting a good voto do it."
    Arrests continue
    In 2021, someone leaked communications from the "million tears" group to animals rights organizations like Lady Freethinker and Action for Primates, which handed it over to authorities. Still, it took several years to arrest and prosecute the torture group's leaders.
    In 2024, one of these leaders—Ronald Bedra of Ohio—pled guilty to commissioning the videos and to mailing "a thumb drive containing 64 videos of monkey torture to a co-conspirator in Wisconsin." His mother, in a sentencing letter to the judge, said that her son must "have been undergoing some mental crisis when he decided to create the website." As a boy, he had loved all of the family pets, she said, even providing a funeral for a fish.
    Bedra was sentenced late last year to 54 months in prison. According to letters from family members, he has also lost his job, his wife, and his kids.
    In April 2025, two more alleged co-conspirators were indicted and subsequently arrested; their cases were unsealed only this week. Two other co-conspirators from this group still appear to be uncharged.
    In May 2025, 11 other Americans were indicted for their participation in monkey torture groups, though they appear to come from a different network. This group allegedly "paid a minor in Indonesia to commit the requested acts on camera."
    As for the Indonesian side of this equation, arrests have been happening there, too. Following complaints from animal rights groups, police in Indonesia have arrested multiple videographers over the last two years.

    Nate Anderson
    Deputy Editor

    Nate Anderson
    Deputy Editor

    Nate is the deputy editor at Ars Technica. His most recent book is In Emergency, Break Glass: What Nietzsche Can Teach Us About Joyful Living in a Tech-Saturated World, which is much funnier than it sounds.

    34 Comments
    #online #monkey #torture #video #arrests
    The “online monkey torture video” arrests just keep coming
    monkey abuse The “online monkey torture video” arrests just keep coming Authorities continue the slow crackdown. Nate Anderson – Jun 14, 2025 7:00 am | 34 Credit: Getty Images Credit: Getty Images Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more Today's monkey torture videos are the products of a digitally connected world. People who enjoy watching baby animals probed, snipped, and mutilated in horrible ways often have difficulty finding local collaborators, but online communities like "million tears"—now thankfully shuttered—can help them forge connections. Once they do meet other like-minded souls, communication takes place through chat apps like Telegram and Signal, often using encryption. Money is pooled through various phone apps, then sent to videographers in countries where wages are low and monkeys are plentiful.There, monkeys are tortured by a local subcontractor—sometimes a child—working to Western specs. Smartphone video of the torture is sent back to the commissioning sadists, who share it with more viewers using the same online communities in which they met. The unfortunate pattern was again on display this week in an indictment the US government unsealed against several more Americans said to have commissioned these videos. The accused used online handles like "Bitchy" and "DemonSwordSoulCrusher," and they hail from all over: Tennessee, North Carolina, Ohio, Pennsylvania, and Massachusetts. They relied on an Indonesian videographer to create the content, which was surprisingly affordable—it cost a mere to commission video of a "burning hot screwdriver" being shoved into a baby monkey's orifice. After the money was transferred, the requested video was shot and shared through a "phone-based messaging program," but the Americans were deeply disappointed in its quality. Instead of full-on impalement, the videographer had heated a screwdriver on a burner and merely touched it against the monkey a few times. "So lame," one of the Americans allegedly complained to another. "Live and learn," was the response. So the group tried again. "Million tears" had been booted by its host, but the group reconstituted on another platform and renamed itself "the trail of trillion tears." They reached out to another Indonesian videographer and asked for a more graphic version of the same video. But this version, more sadistic than the last, still didn't satisfy. As one of the Americans allegedly said to another, "honey that's not what you asked for. Thats the village idiot version. But I'm talking with someone about getting a good voto do it." Arrests continue In 2021, someone leaked communications from the "million tears" group to animals rights organizations like Lady Freethinker and Action for Primates, which handed it over to authorities. Still, it took several years to arrest and prosecute the torture group's leaders. In 2024, one of these leaders—Ronald Bedra of Ohio—pled guilty to commissioning the videos and to mailing "a thumb drive containing 64 videos of monkey torture to a co-conspirator in Wisconsin." His mother, in a sentencing letter to the judge, said that her son must "have been undergoing some mental crisis when he decided to create the website." As a boy, he had loved all of the family pets, she said, even providing a funeral for a fish. Bedra was sentenced late last year to 54 months in prison. According to letters from family members, he has also lost his job, his wife, and his kids. In April 2025, two more alleged co-conspirators were indicted and subsequently arrested; their cases were unsealed only this week. Two other co-conspirators from this group still appear to be uncharged. In May 2025, 11 other Americans were indicted for their participation in monkey torture groups, though they appear to come from a different network. This group allegedly "paid a minor in Indonesia to commit the requested acts on camera." As for the Indonesian side of this equation, arrests have been happening there, too. Following complaints from animal rights groups, police in Indonesia have arrested multiple videographers over the last two years. Nate Anderson Deputy Editor Nate Anderson Deputy Editor Nate is the deputy editor at Ars Technica. His most recent book is In Emergency, Break Glass: What Nietzsche Can Teach Us About Joyful Living in a Tech-Saturated World, which is much funnier than it sounds. 34 Comments #online #monkey #torture #video #arrests
    ARSTECHNICA.COM
    The “online monkey torture video” arrests just keep coming
    monkey abuse The “online monkey torture video” arrests just keep coming Authorities continue the slow crackdown. Nate Anderson – Jun 14, 2025 7:00 am | 34 Credit: Getty Images Credit: Getty Images Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more Today's monkey torture videos are the products of a digitally connected world. People who enjoy watching baby animals probed, snipped, and mutilated in horrible ways often have difficulty finding local collaborators, but online communities like "million tears"—now thankfully shuttered—can help them forge connections. Once they do meet other like-minded souls, communication takes place through chat apps like Telegram and Signal, often using encryption. Money is pooled through various phone apps, then sent to videographers in countries where wages are low and monkeys are plentiful. (The cases I have seen usually involve Indonesia; read my feature from last year to learn more about how these groups work.) There, monkeys are tortured by a local subcontractor—sometimes a child—working to Western specs. Smartphone video of the torture is sent back to the commissioning sadists, who share it with more viewers using the same online communities in which they met. The unfortunate pattern was again on display this week in an indictment the US government unsealed against several more Americans said to have commissioned these videos. The accused used online handles like "Bitchy" and "DemonSwordSoulCrusher," and they hail from all over: Tennessee, North Carolina, Ohio, Pennsylvania, and Massachusetts. They relied on an Indonesian videographer to create the content, which was surprisingly affordable—it cost a mere $40 to commission video of a "burning hot screwdriver" being shoved into a baby monkey's orifice. After the money was transferred, the requested video was shot and shared through a "phone-based messaging program," but the Americans were deeply disappointed in its quality. Instead of full-on impalement, the videographer had heated a screwdriver on a burner and merely touched it against the monkey a few times. "So lame," one of the Americans allegedly complained to another. "Live and learn," was the response. So the group tried again. "Million tears" had been booted by its host, but the group reconstituted on another platform and renamed itself "the trail of trillion tears." They reached out to another Indonesian videographer and asked for a more graphic version of the same video. But this version, more sadistic than the last, still didn't satisfy. As one of the Americans allegedly said to another, "honey that's not what you asked for. Thats the village idiot version. But I'm talking with someone about getting a good vo [videographer] to do it." Arrests continue In 2021, someone leaked communications from the "million tears" group to animals rights organizations like Lady Freethinker and Action for Primates, which handed it over to authorities. Still, it took several years to arrest and prosecute the torture group's leaders. In 2024, one of these leaders—Ronald Bedra of Ohio—pled guilty to commissioning the videos and to mailing "a thumb drive containing 64 videos of monkey torture to a co-conspirator in Wisconsin." His mother, in a sentencing letter to the judge, said that her son must "have been undergoing some mental crisis when he decided to create the website." As a boy, he had loved all of the family pets, she said, even providing a funeral for a fish. Bedra was sentenced late last year to 54 months in prison. According to letters from family members, he has also lost his job, his wife, and his kids. In April 2025, two more alleged co-conspirators were indicted and subsequently arrested; their cases were unsealed only this week. Two other co-conspirators from this group still appear to be uncharged. In May 2025, 11 other Americans were indicted for their participation in monkey torture groups, though they appear to come from a different network. This group allegedly "paid a minor in Indonesia to commit the requested acts on camera." As for the Indonesian side of this equation, arrests have been happening there, too. Following complaints from animal rights groups, police in Indonesia have arrested multiple videographers over the last two years. Nate Anderson Deputy Editor Nate Anderson Deputy Editor Nate is the deputy editor at Ars Technica. His most recent book is In Emergency, Break Glass: What Nietzsche Can Teach Us About Joyful Living in a Tech-Saturated World, which is much funnier than it sounds. 34 Comments
    0 Comentários 0 Compartilhamentos
  • The Download: gambling with humanity’s future, and the FDA under Trump

    This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.Tech billionaires are making a risky bet with humanity’s future

    Sam Altman, Jeff Bezos, Elon Musk, and others may have slightly different goals, but their grand visions for the next decade and beyond are remarkably similar.They include aligning AI with the interests of humanity; creating an artificial superintelligence that will solve all the world’s most pressing problems; merging with that superintelligence to achieve immortality; establishing a permanent, self-­sustaining colony on Mars; and, ultimately, spreading out across the cosmos.Three features play a central role with powering these visions, says Adam Becker, a science writer and astrophysicist: an unshakable certainty that technology can solve any problem, a belief in the necessity of perpetual growth, and a quasi-religious obsession with transcending our physical and biological limits.In his timely new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, Becker reveals how these fantastical visions conceal a darker agenda. Read the full story.

    —Bryan Gardiner

    This story is from the next print edition of MIT Technology Review, which explores power—who has it, and who wants it. It’s set to go live on Wednesday June 25, so subscribe & save 25% to read it and get a copy of the issue when it lands!

    Here’s what food and drug regulation might look like under the Trump administration

    Earlier this week, two new leaders of the US Food and Drug Administration published a list of priorities for the agency. Both Marty Makary and Vinay Prasad are controversial figures in the science community. They were generally highly respected academics until the covid pandemic, when their contrarian opinions on masking, vaccines, and lockdowns turned many of their colleagues off them.

    Given all this, along with recent mass firings of FDA employees, lots of people were pretty anxious to see what this list might include—and what we might expect the future of food and drug regulation in the US to look like. So let’s dive into the pair’s plans for new investigations, speedy approvals, and the “unleashing” of AI.

    —Jessica Hamzelou

    This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

    The must-reads

    I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

    1 NASA is investigating leaks on the ISSIt’s postponed launching private astronauts to the station while it evaluates.+ Its core component has been springing small air leaks for months.+ Meanwhile, this Chinese probe is en route to a near-Earth asteroid.2 Undocumented migrants are using social media to warn of ICE raidsThe DIY networks are anonymously reporting police presences across LA.+ Platforms’ relationships with protest activism has changed drastically. 

    3 Google’s AI Overviews is hallucinating about the fatal Air India crashIt incorrectly stated that it involved an Airbus plane, not a Boeing 787.+ Why Google’s AI Overviews gets things wrong.4 Chinese engineers are sneaking suitcases of hard drives into the countryTo covertly train advanced AI models.+ The US is cracking down on Huawei’s ability to produce chips.+ What the US-China AI race overlooks.5 The National Hurricane Center is joining forces with DeepMindIt’s the first time the center has used AI to predict nature’s worst storms.+ Here’s what we know about hurricanes and climate change.6 OpenAI is working on a product with toymaker MattelAI-powered Barbies?!+ Nothing is safe from the creep of AI, not even playtime.+ OpenAI has ambitions to reach billions of users.7 Chatbots posing as licensed therapists may be breaking the lawDigital rights organizations have filed a complaint to the FTC.+ How do you teach an AI model to give therapy?8 Major companies are abandoning their climate commitmentsBut some experts argue this may not be entirely bad.+ Google, Amazon and the problem with Big Tech’s climate claims.9 Vibe coding is shaking up software engineeringEven though AI-generated code is inherently unreliable.+ What is vibe coding, exactly?10 TikTok really loves hotdogs And who can blame it?Quote of the day

    “It kind of jams two years of work into two months.”

    —Andrew Butcher, president of the Maine Connectivity Authority, tells Ars Technica why it’s so difficult to meet the Trump administration’s new plans to increase broadband access in certain states.

    One more thing

    The surprising barrier that keeps us from building the housing we needIt’s a tough time to try and buy a home in America. From the beginning of the pandemic to early 2024, US home prices rose by 47%. In large swaths of the country, buying a home is no longer a possibility even for those with middle-class incomes. For many, that marks the end of an American dream built around owning a house. Over the same time, rents have gone up 26%.The reason for the current rise in the cost of housing is clear to most economists: a lack of supply. Simply put, we don’t build enough houses and apartments, and we haven’t for years.

    But the reality is that even if we ease the endless permitting delays and begin cutting red tape, we will still be faced with a distressing fact: The construction industry is not very efficient when it comes to building stuff. Read the full story.

    —David Rotman

    We can still have nice things

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

    If you’ve ever flown through outer space, at least while watching a documentary or a science fiction film, you’ve seen how artists turn astronomical findings into stunning visuals. But in the process of visualizing data for their latest planetarium show, a production team at New York’s American Museum of Natural History made a surprising discovery of their own: a trillion-and-a-half mile long spiral of material drifting along the edge of our solar system.

    “So this is a really fun thing that happened,” says Jackie Faherty, the museum’s senior scientist.

    Last winter, Faherty and her colleagues were beneath the dome of the museum’s Hayden Planetarium, fine-tuning a scene that featured the Oort cloud, the big, thick bubble surrounding our Sun and planets that’s filled with ice and rock and other remnants from the solar system’s infancy. The Oort cloud begins far beyond Neptune, around one and a half light years from the Sun. It has never been directly observed; its existence is inferred from the behavior of long-period comets entering the inner solar system. The cloud is so expansive that the Voyager spacecraft, our most distant probes, would need another 250 years just to reach its inner boundary; to reach the other side, they would need about 30,000 years. 

    The 30-minute show, Encounters in the Milky Way, narrated by Pedro Pascal, guides audiences on a trip through the galaxy across billions of years. For a section about our nascent solar system, the writing team decided “there’s going to be a fly-by” of the Oort cloud, Faherty says. “But what does our Oort cloud look like?” 

    To find out, the museum consulted astronomers and turned to David Nesvorný, a scientist at the Southwest Research Institute in San Antonio. He provided his model of the millions of particles believed to make up the Oort cloud, based on extensive observational data.

    “Everybody said, go talk to Nesvorný. He’s got the best model,” says Faherty. And “everybody told us, ‘There’s structure in the model,’ so we were kind of set up to look for stuff,” she says. 

    The museum’s technical team began using Nesvorný’s model to simulate how the cloud evolved over time. Later, as the team projected versions of the fly-by scene into the dome, with the camera looking back at the Oort cloud, they saw a familiar shape, one that appears in galaxies, Saturn’s rings, and disks around young stars.

    “We’re flying away from the Oort cloud and out pops this spiral, a spiral shape to the outside of our solar system,” Faherty marveled. “A huge structure, millions and millions of particles.”

    She emailed Nesvorný to ask for “more particles,” with a render of the scene attached. “We noticed the spiral of course,” she wrote. “And then he writes me back: ‘what are you talking about, a spiral?’” 

    While fine-tuning a simulation of the Oort cloud, a vast expanse of ice material leftover from the birth of our Sun, the ‘Encounters in the Milky Way’ production team noticed a very clear shape: a structure made of billions of comets and shaped like a spiral-armed galaxy, seen here in a scene from the final Space ShowMore simulations ensued, this time on Pleiades, a powerful NASA supercomputer. In high-performance computer simulations spanning 4.6 billion years, starting from the Solar System’s earliest days, the researchers visualized how the initial icy and rocky ingredients of the Oort cloud began circling the Sun, in the elliptical orbits that are thought to give the cloud its rough disc shape. The simulations also incorporated the physics of the Sun’s gravitational pull, the influences from our Milky Way galaxy, and the movements of the comets themselves. 

    In each simulation, the spiral persisted.

    “No one has ever seen the Oort structure like that before,” says Faherty. Nesvorný “has a great quote about this: ‘The math was all there. We just needed the visuals.’” 

    An illustration of the Kuiper Belt and Oort Cloud in relation to our solar system.As the Oort cloud grew with the early solar system, Nesvorný and his colleagues hypothesize that the galactic tide, or the gravitational force from the Milky Way, disrupted the orbits of some comets. Although the Sun pulls these objects inward, the galaxy’s gravity appears to have twisted part of the Oort cloud outward, forming a spiral tilted roughly 30 degrees from the plane of the solar system.

    “As the galactic tide acts to decouple bodies from the scattered disk it creates a spiral structure in physical space that is roughly 15,000 astronomical units in length,” or around 1.4 trillion miles from one end to the other, the researchers write in a paper that was published in March in the Astrophysical Journal. “The spiral is long-lived and persists in the inner Oort Cloud to the present time.”

    “The physics makes sense,” says Faherty. “Scientists, we’re amazing at what we do, but it doesn’t mean we can see everything right away.”

    It helped that the team behind the space show was primed to look for something, says Carter Emmart, the museum’s director of astrovisualization and director of Encounters. Astronomers had described Nesvorný’s model as having “a structure,” which intrigued the team’s artists. “We were also looking for structure so that it wouldn’t just be sort of like a big blob,” he says. “Other models were also revealing this—but they just hadn’t been visualized.”

    The museum’s attempts to simulate nature date back to its first habitat dioramas in the early 1900s, which brought visitors to places that hadn’t yet been captured by color photos, TV, or the web. The planetarium, a night sky simulator for generations of would-be scientists and astronauts, got its start after financier Charles Hayden bought the museum its first Zeiss projector. The planetarium now boasts one of the world’s few Zeiss Mark IX systems.

    Still, these days the star projector is rarely used, Emmart says, now that fulldome laser projectors can turn the old static starfield into 3D video running at 60 frames per second. The Hayden boasts six custom-built Christie projectors, part of what the museum’s former president called “the most advanced planetarium ever attempted.”

     In about 1.3 million years, the star system Gliese 710 is set to pass directly through our Oort Cloud, an event visualized in a dramatic scene in ‘Encounters in the Milky Way.’ During its flyby, our systems will swap icy comets, flinging some out on new paths.Emmart recalls how in 1998, when he and other museum leaders were imagining the future of space shows at the Hayden—now with the help of digital projectors and computer graphics—there were questions over how much space they could try to show.

    “We’re talking about these astronomical data sets we could plot to make the galaxy and the stars,” he says. “Of course, we knew that we would have this star projector, but we really wanted to emphasize astrophysics with this dome video system. I was drawing pictures of this just to get our heads around it and noting the tip of the solar system to the Milky Way is about 60 degrees. And I said, what are we gonna do when we get outside the Milky Way?’

    “ThenNeil Degrasse Tyson “goes, ‘whoa, whoa, whoa, Carter, we have enough to do. And just plotting the Milky Way, that’s hard enough.’ And I said, ‘well, when we exit the Milky Way and we don’t see any other galaxies, that’s sort of like astronomy in 1920—we thought maybe the entire universe is just a Milky Way.'”

    “And that kind of led to a chaotic discussion about, well, what other data sets are there for this?” Emmart adds.

    The museum worked with astronomer Brent Tully, who had mapped 3500 galaxies beyond the Milky Way, in collaboration with the National Center for Super Computing Applications. “That was it,” he says, “and that seemed fantastical.”

    By the time the first planetarium show opened at the museum’s new Rose Center for Earth and Space in 2000, Tully had broadened his survey “to an amazing” 30,000 galaxies. The Sloan Digital Sky Survey followed—it’s now at data release 18—with six million galaxies.

    To build the map of the universe that underlies Encounters, the team also relied on data from the European Space Agency’s space observatory, Gaia. Launched in 2013 and powered down in March of this year, Gaia brought an unprecedented precision to our astronomical map, plotting the distance between 1.7 billion stars. To visualize and render the simulated data, Jon Parker, the museum’s lead technical director, relied on Houdini, a 3D animation tool by Toronto-based SideFX.

    The goal is immersion, “whether it’s in front of the buffalo downstairs, and seeing what those herds were like before we decimated them, to coming in this room and being teleported to space, with an accurate foundation in the science,” Emmart says. “But the art is important, because the art is the way to the soul.” 

    The museum, he adds, is “a testament to wonder. And I think wonder is a gateway to inspiration, and inspiration is a gateway to motivation.”

    Three-D visuals aren’t just powerful tools for communicating science, but increasingly crucial for science itself. Software like OpenSpace, an open source simulation tool developed by the museum, along with the growing availability of high-performance computing, are making it easier to build highly detailed visuals of ever larger and more complex collections of data.

    “Anytime we look, literally, from a different angle at catalogs of astronomical positions, simulations, or exploring the phase space of a complex data set, there is great potential to discover something new,” says Brian R. Kent, an astronomer and director of science communications at National Radio Astronomy Observatory. “There is also a wealth of astronomics tatical data in archives that can be reanalyzed in new ways, leading to new discoveries.”

    As the instruments grow in size and sophistication, so does the data, and the challenge of understanding it. Like all scientists, astronomers are facing a deluge of data, ranging from gamma rays and X-rays to ultraviolet, optical, infrared, and radio bands.

    Our Oort cloud, a shell of icy bodies that surrounds the solar system and extends one-and-a-half light years in every direction, is shown in this scene from ‘Encounters in the Milky Way’ along with the Oort clouds of neighboring stars. The more massive the star, the larger its Oort cloud“New facilities like the Next Generation Very Large Array here at NRAO or the Vera Rubin Observatory and LSST survey project will generate large volumes of data, so astronomers have to get creative with how to analyze it,” says Kent. 

    More data—and new instruments—will also be needed to prove the spiral itself is actually there: there’s still no known way to even observe the Oort cloud. 

    Instead, the paper notes, the structure will have to be measured from “detection of a large number of objects” in the radius of the inner Oort cloud or from “thermal emission from small particles in the Oort spiral.” 

    The Vera C. Rubin Observatory, a powerful, U.S.-funded telescope that recently began operation in Chile, could possibly observe individual icy bodies within the cloud. But researchers expect the telescope will likely discover only dozens of these objects, maybe hundreds, not enough to meaningfully visualize any shapes in the Oort cloud. 

    For us, here and now, the 1.4 trillion mile-long spiral will remain confined to the inside of a dark dome across the street from Central Park.
    #how #planetarium #show #discovered #spiral
    How a planetarium show discovered a spiral at the edge of our solar system
    If you’ve ever flown through outer space, at least while watching a documentary or a science fiction film, you’ve seen how artists turn astronomical findings into stunning visuals. But in the process of visualizing data for their latest planetarium show, a production team at New York’s American Museum of Natural History made a surprising discovery of their own: a trillion-and-a-half mile long spiral of material drifting along the edge of our solar system. “So this is a really fun thing that happened,” says Jackie Faherty, the museum’s senior scientist. Last winter, Faherty and her colleagues were beneath the dome of the museum’s Hayden Planetarium, fine-tuning a scene that featured the Oort cloud, the big, thick bubble surrounding our Sun and planets that’s filled with ice and rock and other remnants from the solar system’s infancy. The Oort cloud begins far beyond Neptune, around one and a half light years from the Sun. It has never been directly observed; its existence is inferred from the behavior of long-period comets entering the inner solar system. The cloud is so expansive that the Voyager spacecraft, our most distant probes, would need another 250 years just to reach its inner boundary; to reach the other side, they would need about 30,000 years.  The 30-minute show, Encounters in the Milky Way, narrated by Pedro Pascal, guides audiences on a trip through the galaxy across billions of years. For a section about our nascent solar system, the writing team decided “there’s going to be a fly-by” of the Oort cloud, Faherty says. “But what does our Oort cloud look like?”  To find out, the museum consulted astronomers and turned to David Nesvorný, a scientist at the Southwest Research Institute in San Antonio. He provided his model of the millions of particles believed to make up the Oort cloud, based on extensive observational data. “Everybody said, go talk to Nesvorný. He’s got the best model,” says Faherty. And “everybody told us, ‘There’s structure in the model,’ so we were kind of set up to look for stuff,” she says.  The museum’s technical team began using Nesvorný’s model to simulate how the cloud evolved over time. Later, as the team projected versions of the fly-by scene into the dome, with the camera looking back at the Oort cloud, they saw a familiar shape, one that appears in galaxies, Saturn’s rings, and disks around young stars. “We’re flying away from the Oort cloud and out pops this spiral, a spiral shape to the outside of our solar system,” Faherty marveled. “A huge structure, millions and millions of particles.” She emailed Nesvorný to ask for “more particles,” with a render of the scene attached. “We noticed the spiral of course,” she wrote. “And then he writes me back: ‘what are you talking about, a spiral?’”  While fine-tuning a simulation of the Oort cloud, a vast expanse of ice material leftover from the birth of our Sun, the ‘Encounters in the Milky Way’ production team noticed a very clear shape: a structure made of billions of comets and shaped like a spiral-armed galaxy, seen here in a scene from the final Space ShowMore simulations ensued, this time on Pleiades, a powerful NASA supercomputer. In high-performance computer simulations spanning 4.6 billion years, starting from the Solar System’s earliest days, the researchers visualized how the initial icy and rocky ingredients of the Oort cloud began circling the Sun, in the elliptical orbits that are thought to give the cloud its rough disc shape. The simulations also incorporated the physics of the Sun’s gravitational pull, the influences from our Milky Way galaxy, and the movements of the comets themselves.  In each simulation, the spiral persisted. “No one has ever seen the Oort structure like that before,” says Faherty. Nesvorný “has a great quote about this: ‘The math was all there. We just needed the visuals.’”  An illustration of the Kuiper Belt and Oort Cloud in relation to our solar system.As the Oort cloud grew with the early solar system, Nesvorný and his colleagues hypothesize that the galactic tide, or the gravitational force from the Milky Way, disrupted the orbits of some comets. Although the Sun pulls these objects inward, the galaxy’s gravity appears to have twisted part of the Oort cloud outward, forming a spiral tilted roughly 30 degrees from the plane of the solar system. “As the galactic tide acts to decouple bodies from the scattered disk it creates a spiral structure in physical space that is roughly 15,000 astronomical units in length,” or around 1.4 trillion miles from one end to the other, the researchers write in a paper that was published in March in the Astrophysical Journal. “The spiral is long-lived and persists in the inner Oort Cloud to the present time.” “The physics makes sense,” says Faherty. “Scientists, we’re amazing at what we do, but it doesn’t mean we can see everything right away.” It helped that the team behind the space show was primed to look for something, says Carter Emmart, the museum’s director of astrovisualization and director of Encounters. Astronomers had described Nesvorný’s model as having “a structure,” which intrigued the team’s artists. “We were also looking for structure so that it wouldn’t just be sort of like a big blob,” he says. “Other models were also revealing this—but they just hadn’t been visualized.” The museum’s attempts to simulate nature date back to its first habitat dioramas in the early 1900s, which brought visitors to places that hadn’t yet been captured by color photos, TV, or the web. The planetarium, a night sky simulator for generations of would-be scientists and astronauts, got its start after financier Charles Hayden bought the museum its first Zeiss projector. The planetarium now boasts one of the world’s few Zeiss Mark IX systems. Still, these days the star projector is rarely used, Emmart says, now that fulldome laser projectors can turn the old static starfield into 3D video running at 60 frames per second. The Hayden boasts six custom-built Christie projectors, part of what the museum’s former president called “the most advanced planetarium ever attempted.”  In about 1.3 million years, the star system Gliese 710 is set to pass directly through our Oort Cloud, an event visualized in a dramatic scene in ‘Encounters in the Milky Way.’ During its flyby, our systems will swap icy comets, flinging some out on new paths.Emmart recalls how in 1998, when he and other museum leaders were imagining the future of space shows at the Hayden—now with the help of digital projectors and computer graphics—there were questions over how much space they could try to show. “We’re talking about these astronomical data sets we could plot to make the galaxy and the stars,” he says. “Of course, we knew that we would have this star projector, but we really wanted to emphasize astrophysics with this dome video system. I was drawing pictures of this just to get our heads around it and noting the tip of the solar system to the Milky Way is about 60 degrees. And I said, what are we gonna do when we get outside the Milky Way?’ “ThenNeil Degrasse Tyson “goes, ‘whoa, whoa, whoa, Carter, we have enough to do. And just plotting the Milky Way, that’s hard enough.’ And I said, ‘well, when we exit the Milky Way and we don’t see any other galaxies, that’s sort of like astronomy in 1920—we thought maybe the entire universe is just a Milky Way.'” “And that kind of led to a chaotic discussion about, well, what other data sets are there for this?” Emmart adds. The museum worked with astronomer Brent Tully, who had mapped 3500 galaxies beyond the Milky Way, in collaboration with the National Center for Super Computing Applications. “That was it,” he says, “and that seemed fantastical.” By the time the first planetarium show opened at the museum’s new Rose Center for Earth and Space in 2000, Tully had broadened his survey “to an amazing” 30,000 galaxies. The Sloan Digital Sky Survey followed—it’s now at data release 18—with six million galaxies. To build the map of the universe that underlies Encounters, the team also relied on data from the European Space Agency’s space observatory, Gaia. Launched in 2013 and powered down in March of this year, Gaia brought an unprecedented precision to our astronomical map, plotting the distance between 1.7 billion stars. To visualize and render the simulated data, Jon Parker, the museum’s lead technical director, relied on Houdini, a 3D animation tool by Toronto-based SideFX. The goal is immersion, “whether it’s in front of the buffalo downstairs, and seeing what those herds were like before we decimated them, to coming in this room and being teleported to space, with an accurate foundation in the science,” Emmart says. “But the art is important, because the art is the way to the soul.”  The museum, he adds, is “a testament to wonder. And I think wonder is a gateway to inspiration, and inspiration is a gateway to motivation.” Three-D visuals aren’t just powerful tools for communicating science, but increasingly crucial for science itself. Software like OpenSpace, an open source simulation tool developed by the museum, along with the growing availability of high-performance computing, are making it easier to build highly detailed visuals of ever larger and more complex collections of data. “Anytime we look, literally, from a different angle at catalogs of astronomical positions, simulations, or exploring the phase space of a complex data set, there is great potential to discover something new,” says Brian R. Kent, an astronomer and director of science communications at National Radio Astronomy Observatory. “There is also a wealth of astronomics tatical data in archives that can be reanalyzed in new ways, leading to new discoveries.” As the instruments grow in size and sophistication, so does the data, and the challenge of understanding it. Like all scientists, astronomers are facing a deluge of data, ranging from gamma rays and X-rays to ultraviolet, optical, infrared, and radio bands. Our Oort cloud, a shell of icy bodies that surrounds the solar system and extends one-and-a-half light years in every direction, is shown in this scene from ‘Encounters in the Milky Way’ along with the Oort clouds of neighboring stars. The more massive the star, the larger its Oort cloud“New facilities like the Next Generation Very Large Array here at NRAO or the Vera Rubin Observatory and LSST survey project will generate large volumes of data, so astronomers have to get creative with how to analyze it,” says Kent.  More data—and new instruments—will also be needed to prove the spiral itself is actually there: there’s still no known way to even observe the Oort cloud.  Instead, the paper notes, the structure will have to be measured from “detection of a large number of objects” in the radius of the inner Oort cloud or from “thermal emission from small particles in the Oort spiral.”  The Vera C. Rubin Observatory, a powerful, U.S.-funded telescope that recently began operation in Chile, could possibly observe individual icy bodies within the cloud. But researchers expect the telescope will likely discover only dozens of these objects, maybe hundreds, not enough to meaningfully visualize any shapes in the Oort cloud.  For us, here and now, the 1.4 trillion mile-long spiral will remain confined to the inside of a dark dome across the street from Central Park. #how #planetarium #show #discovered #spiral
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    How a planetarium show discovered a spiral at the edge of our solar system
    If you’ve ever flown through outer space, at least while watching a documentary or a science fiction film, you’ve seen how artists turn astronomical findings into stunning visuals. But in the process of visualizing data for their latest planetarium show, a production team at New York’s American Museum of Natural History made a surprising discovery of their own: a trillion-and-a-half mile long spiral of material drifting along the edge of our solar system. “So this is a really fun thing that happened,” says Jackie Faherty, the museum’s senior scientist. Last winter, Faherty and her colleagues were beneath the dome of the museum’s Hayden Planetarium, fine-tuning a scene that featured the Oort cloud, the big, thick bubble surrounding our Sun and planets that’s filled with ice and rock and other remnants from the solar system’s infancy. The Oort cloud begins far beyond Neptune, around one and a half light years from the Sun. It has never been directly observed; its existence is inferred from the behavior of long-period comets entering the inner solar system. The cloud is so expansive that the Voyager spacecraft, our most distant probes, would need another 250 years just to reach its inner boundary; to reach the other side, they would need about 30,000 years.  The 30-minute show, Encounters in the Milky Way, narrated by Pedro Pascal, guides audiences on a trip through the galaxy across billions of years. For a section about our nascent solar system, the writing team decided “there’s going to be a fly-by” of the Oort cloud, Faherty says. “But what does our Oort cloud look like?”  To find out, the museum consulted astronomers and turned to David Nesvorný, a scientist at the Southwest Research Institute in San Antonio. He provided his model of the millions of particles believed to make up the Oort cloud, based on extensive observational data. “Everybody said, go talk to Nesvorný. He’s got the best model,” says Faherty. And “everybody told us, ‘There’s structure in the model,’ so we were kind of set up to look for stuff,” she says.  The museum’s technical team began using Nesvorný’s model to simulate how the cloud evolved over time. Later, as the team projected versions of the fly-by scene into the dome, with the camera looking back at the Oort cloud, they saw a familiar shape, one that appears in galaxies, Saturn’s rings, and disks around young stars. “We’re flying away from the Oort cloud and out pops this spiral, a spiral shape to the outside of our solar system,” Faherty marveled. “A huge structure, millions and millions of particles.” She emailed Nesvorný to ask for “more particles,” with a render of the scene attached. “We noticed the spiral of course,” she wrote. “And then he writes me back: ‘what are you talking about, a spiral?’”  While fine-tuning a simulation of the Oort cloud, a vast expanse of ice material leftover from the birth of our Sun, the ‘Encounters in the Milky Way’ production team noticed a very clear shape: a structure made of billions of comets and shaped like a spiral-armed galaxy, seen here in a scene from the final Space Show (curving, dusty S-shape behind the Sun) [Image: © AMNH] More simulations ensued, this time on Pleiades, a powerful NASA supercomputer. In high-performance computer simulations spanning 4.6 billion years, starting from the Solar System’s earliest days, the researchers visualized how the initial icy and rocky ingredients of the Oort cloud began circling the Sun, in the elliptical orbits that are thought to give the cloud its rough disc shape. The simulations also incorporated the physics of the Sun’s gravitational pull, the influences from our Milky Way galaxy, and the movements of the comets themselves.  In each simulation, the spiral persisted. “No one has ever seen the Oort structure like that before,” says Faherty. Nesvorný “has a great quote about this: ‘The math was all there. We just needed the visuals.’”  An illustration of the Kuiper Belt and Oort Cloud in relation to our solar system. [Image: NASA] As the Oort cloud grew with the early solar system, Nesvorný and his colleagues hypothesize that the galactic tide, or the gravitational force from the Milky Way, disrupted the orbits of some comets. Although the Sun pulls these objects inward, the galaxy’s gravity appears to have twisted part of the Oort cloud outward, forming a spiral tilted roughly 30 degrees from the plane of the solar system. “As the galactic tide acts to decouple bodies from the scattered disk it creates a spiral structure in physical space that is roughly 15,000 astronomical units in length,” or around 1.4 trillion miles from one end to the other, the researchers write in a paper that was published in March in the Astrophysical Journal. “The spiral is long-lived and persists in the inner Oort Cloud to the present time.” “The physics makes sense,” says Faherty. “Scientists, we’re amazing at what we do, but it doesn’t mean we can see everything right away.” It helped that the team behind the space show was primed to look for something, says Carter Emmart, the museum’s director of astrovisualization and director of Encounters. Astronomers had described Nesvorný’s model as having “a structure,” which intrigued the team’s artists. “We were also looking for structure so that it wouldn’t just be sort of like a big blob,” he says. “Other models were also revealing this—but they just hadn’t been visualized.” The museum’s attempts to simulate nature date back to its first habitat dioramas in the early 1900s, which brought visitors to places that hadn’t yet been captured by color photos, TV, or the web. The planetarium, a night sky simulator for generations of would-be scientists and astronauts, got its start after financier Charles Hayden bought the museum its first Zeiss projector. The planetarium now boasts one of the world’s few Zeiss Mark IX systems. Still, these days the star projector is rarely used, Emmart says, now that fulldome laser projectors can turn the old static starfield into 3D video running at 60 frames per second. The Hayden boasts six custom-built Christie projectors, part of what the museum’s former president called “the most advanced planetarium ever attempted.”  In about 1.3 million years, the star system Gliese 710 is set to pass directly through our Oort Cloud, an event visualized in a dramatic scene in ‘Encounters in the Milky Way.’ During its flyby, our systems will swap icy comets, flinging some out on new paths. [Image: © AMNH] Emmart recalls how in 1998, when he and other museum leaders were imagining the future of space shows at the Hayden—now with the help of digital projectors and computer graphics—there were questions over how much space they could try to show. “We’re talking about these astronomical data sets we could plot to make the galaxy and the stars,” he says. “Of course, we knew that we would have this star projector, but we really wanted to emphasize astrophysics with this dome video system. I was drawing pictures of this just to get our heads around it and noting the tip of the solar system to the Milky Way is about 60 degrees. And I said, what are we gonna do when we get outside the Milky Way?’ “Then [planetarium’s director] Neil Degrasse Tyson “goes, ‘whoa, whoa, whoa, Carter, we have enough to do. And just plotting the Milky Way, that’s hard enough.’ And I said, ‘well, when we exit the Milky Way and we don’t see any other galaxies, that’s sort of like astronomy in 1920—we thought maybe the entire universe is just a Milky Way.'” “And that kind of led to a chaotic discussion about, well, what other data sets are there for this?” Emmart adds. The museum worked with astronomer Brent Tully, who had mapped 3500 galaxies beyond the Milky Way, in collaboration with the National Center for Super Computing Applications. “That was it,” he says, “and that seemed fantastical.” By the time the first planetarium show opened at the museum’s new Rose Center for Earth and Space in 2000, Tully had broadened his survey “to an amazing” 30,000 galaxies. The Sloan Digital Sky Survey followed—it’s now at data release 18—with six million galaxies. To build the map of the universe that underlies Encounters, the team also relied on data from the European Space Agency’s space observatory, Gaia. Launched in 2013 and powered down in March of this year, Gaia brought an unprecedented precision to our astronomical map, plotting the distance between 1.7 billion stars. To visualize and render the simulated data, Jon Parker, the museum’s lead technical director, relied on Houdini, a 3D animation tool by Toronto-based SideFX. The goal is immersion, “whether it’s in front of the buffalo downstairs, and seeing what those herds were like before we decimated them, to coming in this room and being teleported to space, with an accurate foundation in the science,” Emmart says. “But the art is important, because the art is the way to the soul.”  The museum, he adds, is “a testament to wonder. And I think wonder is a gateway to inspiration, and inspiration is a gateway to motivation.” Three-D visuals aren’t just powerful tools for communicating science, but increasingly crucial for science itself. Software like OpenSpace, an open source simulation tool developed by the museum, along with the growing availability of high-performance computing, are making it easier to build highly detailed visuals of ever larger and more complex collections of data. “Anytime we look, literally, from a different angle at catalogs of astronomical positions, simulations, or exploring the phase space of a complex data set, there is great potential to discover something new,” says Brian R. Kent, an astronomer and director of science communications at National Radio Astronomy Observatory. “There is also a wealth of astronomics tatical data in archives that can be reanalyzed in new ways, leading to new discoveries.” As the instruments grow in size and sophistication, so does the data, and the challenge of understanding it. Like all scientists, astronomers are facing a deluge of data, ranging from gamma rays and X-rays to ultraviolet, optical, infrared, and radio bands. Our Oort cloud (center), a shell of icy bodies that surrounds the solar system and extends one-and-a-half light years in every direction, is shown in this scene from ‘Encounters in the Milky Way’ along with the Oort clouds of neighboring stars. The more massive the star, the larger its Oort cloud [Image: © AMNH ] “New facilities like the Next Generation Very Large Array here at NRAO or the Vera Rubin Observatory and LSST survey project will generate large volumes of data, so astronomers have to get creative with how to analyze it,” says Kent.  More data—and new instruments—will also be needed to prove the spiral itself is actually there: there’s still no known way to even observe the Oort cloud.  Instead, the paper notes, the structure will have to be measured from “detection of a large number of objects” in the radius of the inner Oort cloud or from “thermal emission from small particles in the Oort spiral.”  The Vera C. Rubin Observatory, a powerful, U.S.-funded telescope that recently began operation in Chile, could possibly observe individual icy bodies within the cloud. But researchers expect the telescope will likely discover only dozens of these objects, maybe hundreds, not enough to meaningfully visualize any shapes in the Oort cloud.  For us, here and now, the 1.4 trillion mile-long spiral will remain confined to the inside of a dark dome across the street from Central Park.
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