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

News Climate This paint ‘sweats’ to keep your house cool A new cement-based paint cools buildings by combining sunlight reflection and water evaporation A new cooling paint reflects sunlight and mimics sweat to reduce air-conditioning use. Marie LaFauci/Getty Images By Larissa G. Capella June 13, 2025 at 11:00 am A cool house without air conditioning may soon be possible. Scientists in Singapore have developed a new type of paint that reflects sunlight and cools surfaces by slowly evaporating water. Unlike other commercially available cooling paints, which are designed to repel water to protect the underlying material, the new one even works in hot, humid places, offering a low-energy way to stay cool, researchers report June 5 in Science. “The key is passive cooling,” which requires no energy input, says material scientist Li Hong In other words, it works without using electricity or mechanical systems. Right now, radiative cooling is the most common type of passive cooling used in materials, including certain paints. It works by reflecting sunlight and radiating heat from a surface such as walls or roofs, into the sky. But in humid places like Singapore, water vapor in the air traps heat near the surface, which prevents it from escaping into the atmosphere and keeps the surfaces warm. In response, Hong and two other material scientists from Nanyang Technological University developed a cement-based paint that combines three cooling strategies: radiative cooling, evaporative cooling, which our skin uses, and solar reflection. In the study, the scientists painted three small houses: one with regular white paint, one with commercial cooling paint that uses only radiative cooling and one with their new formula. After two years of sun and rain in Singapore, the first two paints had turned yellow. But “our paint was still white,” says coauthor Jipeng Fei. Unlike other colors, white helps materials maintain their high reflectivity and cooling performance. Sign up for our newsletter We summarize the week's scientific breakthroughs every Thursday.

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]

Great star corals in the grip of disease have been saved with probiotics — beneficial bacteria that attack or displace invading pathogens or possibly trigger immune responses to them. What’s causing this deadly disease remains unidentified. But researchers at the Smithsonian Marine Station in Fort Pierce, Fla., were able to successfully halt progression of the disease’s symptoms, the team reports June 5 in Frontiers in Marine Science. The condition is called stony coral tissue loss disease and is characterized by white lesions that lead to the loss of polyps — tiny soft-bodied organisms similar to sea anemones — blanketing coral. Eventually, nothing but the white coral skeleton is left behind. The disease emerged in Florida in 2014 and has spread rampantly throughout the Florida Keys and the Caribbean. A great star coral (M. cavernosa) colony is infected with stony coral tissue loss disease on the coral reef in Fort Lauderdale. The lesion, where the white band of tissue occurs, typically moves across the coral, killing coral tissue along the way. Kelly Pitts/Smithsonian Researchers suspect that the disease is bacterial in nature. Antibiotic treatments can offer a quick fix, but these drugs do not prevent reinfection and carry the risk of the mysterious pathogen building resistance against them. So, in late 2020, the Smithsonian group tried for a more sustainable solution, giving probiotics to 30 infected great star coral colonies. The helpful microbes came from corals tested in the lab that showed resistance to the disease. “We noticed that one of the coral fragments would not get infected … so one of the first things we did was try to culture the microbes that are on this coral,” says microbiologist Blake Ushijima, who developed the probiotic used in the team’s experiment. “These microbes produce antibacterial compounds … and one had a high level of activity against bacteria from diseased corals,” acting as a “pro” biotic, by somehow neutralizing pathogens. The identified microbe, a bacterium called McH1-7, became the active ingredient in a paste delivered by divers to several infected colonies. They covered these colonies with plastic bags to immerse them in the probiotic solution, injecting the paste into the bags using a syringe. They also applied the paste directly to other colonies, slathering lesions caused by the disease. A probiotic paste of McH1-7 is applied to the disease lesion of a great star coral (M. cavernosa) colony infected with stony coral tissue loss disease. The paste was then smoothed flat with a gloved hand so that all apparently infected tissue was covered by the lesion-specific treatment.Kelly Pitts/Smithsonian For two and a half years, the team monitored the corals’ health. The probiotics slowed or stopped the disease from spreading in all eight colonies treated inside bags. On average, the disease’s ugly advance was held to only 7 percent of tissue, compared with an aggressive 30 percent on untreated colonies. The paste put directly on the coral had no beneficial effect. The results are encouraging, but coauthor Valerie Paul cautions against declaring the probiotic a cure. She doubts the practicality of swimming around with heavily weighted plastic bags and putting them on corals. And, she points out, the study was limited to one species of coral, when the disease plagues over 30. Sponsor Message Still, Ushijima considers the study a proof of concept. “The idea of coral probiotics has been thrown around for decades, but no one has directly shown their effects on disease in the wild,” he says. “I think it’s very exciting because it’s actually opening the door to a new field.”

A swirling sea of pink, where fluffy tufts float majestically upward, while elsewhere violet plumes rain down from above. This is the Sun as seen in groundbreaking new images — and they're unlike anything you've ever laid eyes on.As detailed in a new study published in the journal Nature Astronomy, scientists have leveraged new coronal adaptive optics tech to bypass the blurriness caused by the turbulence of the Earth's atmosphere, a time-old obstacle that's frustrated astronomers' attempts to see features on our home star at a resolution better than 620 miles. Now, they've gotten it down to just under 40 miles — a light year sized leap.The result is some of the clearest images to date of the fine structures that make up the Sun's formidable corona, the outermost layer of its atmosphere known for its unbelievable temperatures and violent, unpredictable outbursts.The authors are optimistic that their blur-bypassing techniques will be a game-changer."These are by far the most detailed observations of this kind, showing features not previously observed, and it's not quite clear what they are," coauthor Vasyl Yurchyshyn, a research professor at the New Jersey Institute of Technology's Center for Terrestrial Research (CSTR), said in a statement about the work."It is super exciting to build an instrument that shows us the Sun like never before," echoed lead author Dirk Schmidt, an adaptive optics scientist at the US National Solar Observatory.Stretching for millions of miles into space, the corona is the staging ground for the Sun's violent outbursts, which range from solar storms, to solar flares, to coronal mass ejections. One reason scientists are interested in these phenomena is because they continue to batter our own planet's atmosphere, playing a significant role in the Earth's climate and wreaking havoc on our electronics. Then, at a reach totally beyond our very limited human purview, is the corona's mighty solar wind, which sweeps across the entire solar system, shielding it from cosmic rays.But astronomers are still trying to understand how these solar phenomena occur. One abiding mystery is why the corona can reach temperatures in the millions of degrees Fahrenheit, when the Sun's surface it sits thousands of miles above is no more than a relatively cool 10,000 degrees. The conundrum even has a name: the coronal heating problem.The level of detailed captured in the latest images, taken with an adaptive optics system installed on the Goode Solar Telescope at the CSTR, could be transformative in probing these mysteries.One type of feature the unprecedented resolution revealed were solar prominences, which are large, flashy structures that protrude from the sun's surface, found in twisty shapes like arches or loops. A spectacular video shows a solar prominence swirling like a tortured water spout as it's whipped around by the sun's magnetic field.Most awe-inspiring of all are the examples of what's known as coronal rain. Appearing like waterfalls suspended in midair, the phenomenon is caused as plasma cools and condenses into huge globs before crashing down to the sun's surface. These were imaged at a scale smaller than 100 kilometers, or about 62 miles. In solar terms, that's pinpoint accuracy."With coronal adaptive optics now in operation, this marks the beginning of a new era in solar physics, promising many more discoveries in the years and decades to come," said coauthor  Philip R. Goode at the CSTR in a statement.More on our solar system: Scientists Detect Mysterious Object in Deep Solar SystemShare This Article

Comparison of a megalodon tooth and a great white shark tooth, not associated with the study. (Image Credit: Mark_Kostich/Shutterstock) NewsletterSign up for our email newsletter for the latest science newsMegalodon teeth have always been key to understanding the ancient marine predator. Fossilized teeth are all that remain to prove the existence of these massive sharks, and the name megalodon is from the Greek for “big tooth.”A new study, published in Earth and Planetary Science Letters, highlights the importance of the megalodon’s human-hand-sized teeth once again. Thanks to extracting and analyzing the traces of zinc left in the fossilized teeth, researchers now know that the megalodon’s diet was much broader than scientists once believed.“Megalodon was by all means flexible enough to feed on marine mammals and large fish, from the top of the food pyramid as well as lower levels – depending on availability,” said Jeremy McCormack from the Department of Geosciences at Goethe University, in a press release.What Did the Megalodon Eat?Clocking in at 78 feet in length and weighing about twice as much as a semi truck, the megalodon was a big fish with a big appetite. It is suggested that a member of the Otodus shark family would require about 100,000 kilocalories per day to survive. Due to this extreme number, scientists have often assumed that the megalodon’s main source of calories came from whales.This new study suggests that whales were not the only item on the megalodon’s daily menu and that these sharks were actually quite adaptable when it came to their food. The research team analyzed 18-million-year-old giant teeth that came from two fossil deposits in Sigmaringen and Passau. What they were looking for was the presence of zinc-66 and zinc-64, two isotopes commonly ingested with food. Typically, the higher up in a food pyramid an animal is, the lower the presence of zinc. As they are oftentimes at the top of the food chain, species such as Otodus megalodon and Otodus chubutensis have a low ratio of zinc-66 to zinc-64 compared to species lower on the food chain.“Sea bream, which fed on mussels, snails, and crustaceans, formed the lowest level of the food chain we studied,” said McCormack in the press release. “Smaller shark species such as requiem sharks and ancestors of today’s cetaceans, dolphins, and whales, were next. Larger sharks, such as sand tiger sharks, were further up the food pyramid, and at the top were giant sharks like Araloselachus cuspidatus and the Otodus sharks, which include megalodon.”Surprisingly, the zinc levels in the megalodon teeth weren’t always that different from the zinc levels in species lower down the food chain. This result means that the commonly held scientific belief that megalodons focused their attention on eating large marine mammals may be incorrect. Instead, McCormack refers to the megalodon as an “ecologically versatile generalist” that adapted to environmental and regional constraints that changed the availability and variety of their prey.A New Method in Teeth TestingUsing the zinc content of fossilized teeth is a relatively new method of analysis, and the research team working on the megalodon couldn’t be happier with their results. The methods used in this study have not only been used for prehistoric shark and whale species but also modern-day shark species, and have even been used on herbivorous prehistoric rhinoceroses.Overall, these new methods have begun to rewrite the history of megalodon’s eating habits and may help to explain more about why these giants of the food chain went extinct. “[Determining zinc isotope ratios] gives us important insights into how the marine communities have changed over geologic time, but more importantly the fact that even ‘supercarnivores’ are not immune to extinction,” said Kenshu Shimada, a paleobiologist at DePaul University and a coauthor of this study, in the press release.Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:Earth and Planetary Science Letters. Miocene marine vertebrate trophic ecology reveals megatooth sharks as opportunistic supercarnivoresAs the marketing coordinator at Discover Magazine, Stephanie Edwards interacts with readers across Discover's social media channels and writes digital content. Offline, she is a contract lecturer in English & Cultural Studies at Lakehead University, teaching courses on everything from professional communication to Taylor Swift, and received her graduate degrees in the same department from McMaster University. You can find more of her science writing in Lab Manager and her short fiction in anthologies and literary magazine across the horror genre.1 free article leftWant More? Get unlimited access for as low as $1.99/monthSubscribeAlready a subscriber?Register or Log In1 free articleSubscribeWant more?Keep reading for as low as $1.99!SubscribeAlready a subscriber?Register or Log In

A team of astronomers observed a confused exoplanet orbiting its two parent stars in a highly unusual way.As New Scientist reports, the planet, which was first discovered in 2004, is located in a system called Nu Octantis 72 light-years away, and is twice the size of Jupiter. After it was spotted, some physicists thought its mere existence was impossible due to its extremely close proximity to its twin stars.But according to a new paper published in the journal Nature, an international team of researchers is proposing a wild new theory to explain how the planet could exist while also having such an extremely tight orbit.They propose that one of the stars and the planet orbit the second star in two opposite directions. In other words, the planet is retrograde, or orbiting the star in reverse."The existence of this planet has been controversial, because there were no observational precedents and we expect planets to form in prograde orbit if they form at the same time as the stars," coauthor and University of Hong Kong professor Man Hoi Lee told IFLScience.To make matters even more unusual, the researchers propose that the planet's orbit is sandwiched between the two stars, forcing it to thread the needle during each orbit.It's an erratic dance that highlights how much there's still to learn about the complex orbital mechanics of multi-star systems."It invites scientists to consider a wider range of star and planet scenarios regarding both formation and evolution," University of Texas at Arlington professor Manfred Cuntz, who was not involved in the research, told New Scientist.One of the system's stars is a white dwarf, indicating it's nearing the end of its life cycle and making Nu Octantis an even more exotic outlier. The scientists estimate that the system was formed 2.9 billion years ago.However, the planet came to be much later. The researchers propose that it either used to orbit both stars, and changed to its unusual trajectory after one of the stars turned into a white dwarf, or it accreted its considerable mass from said white dwarf.But more research is needed before they can develop a more accurate picture of how the planet evolved."Observations of other planets in tight binary systems with late-stage or post-main- sequence stellar components will provide additional clues for us to better understand the formation and dynamical evolution of planetary systems," the team wrote in its paper.The researchers are already excited to get a closer glimpse of a similar binary star system, such as HD 59686, which also hosts an enormous gas giant with six times the mass of Jupiter.More on binary star systems: Alpha Centauri Sending Stream of Objects Into Our Solar System, Scientists ProposeShare This Article

James Gentz has seen birds aplenty on his East Texas rice-and-crawfish farm: snow geese and pintails, spoonbills and teal. The whooping crane couple, though, he found “magnificent.” These endangered, long-necked behemoths arrived in 2021 and set to building a nest amid his flooded fields. “I just loved to see them,” Gentz says. Not every farmer is thrilled to host birds. Some worry about the spread of avian flu, others are concerned that the birds will eat too much of their valuable crops. But as an unstable climate delivers too little water, careening temperatures and chaotic storms, the fates of human food production and birds are ever more linked—with the same climate anomalies that harm birds hurting agriculture too. In some places, farmer cooperation is critical to the continued existence of whooping cranes and other wetland-dependent waterbird species, close to one-third of which are experiencing declines. Numbers of waterfowl (think ducks and geese) have crashed by 20 percent since 2014, and long-legged wading shorebirds like sandpipers have suffered steep population losses. Conservation-minded biologists, nonprofits, government agencies, and farmers themselves are amping up efforts to ensure that each species survives and thrives. With federal support in the crosshairs of the Trump administration, their work is more important (and threatened) than ever. Their collaborations, be they domestic or international, are highly specific, because different regions support different kinds of agriculture—grasslands, or deep or shallow wetlands, for example, favored by different kinds of birds. Key to the efforts is making it financially worthwhile for farmers to keep—or tweak—practices to meet bird forage and habitat needs. Traditional crawfish-and-rice farms in Louisiana, as well as in Gentz’s corner of Texas, mimic natural freshwater wetlands that are being lost to saltwater intrusion from sea level rise. Rice grows in fields that are flooded to keep weeds down; fields are drained for harvest by fall. They are then re-flooded to cover crawfish burrowed in the mud; these are harvested in early spring—and the cycle begins again. That second flooding coincides with fall migration—a genetic and learned behavior that determines where birds fly and when—and it lures massive numbers of egrets, herons, bitterns, and storks that dine on the crustaceans as well as on tadpoles, fish, and insects in the water. On a biodiverse crawfish-and-rice farm, “you can see 30, 40, 50 species of birds, amphibians, reptiles, everything,” says Elijah Wojohn, a shorebird conservation biologist at nonprofit Manomet Conservation Sciences in Massachusetts. In contrast, if farmers switch to less water-intensive corn and soybean production in response to climate pressures, “you’ll see raccoons, deer, crows, that’s about it.” Wojohn often relies on word-of-mouth to hook farmers on conservation; one learned to spot whimbrel, with their large, curved bills, got “fired up” about them and told all his farmer friends. Such farmer-to-farmer dialogue is how you change things among this sometimes change-averse group, Wojohn says. In the Mississippi Delta and in California, where rice is generally grown without crustaceans, conservation organizations like Ducks Unlimited have long boosted farmers’ income and staying power by helping them get paid to flood fields in winter for hunters. This attracts overwintering ducks and geese—considered an extra “crop”—that gobble leftover rice and pond plants; the birds also help to decompose rice stalks so farmers don’t have to remove them. Ducks Unlimited’s goal is simple, says director of conservation innovation Scott Manley: Keep rice farmers farming rice. This is especially important as a changing climate makes that harder. 2024 saw a huge push, with the organization conserving 1 million acres for waterfowl. Some strategies can backfire. In Central New York, where dwindling winter ice has seen waterfowl lingering past their habitual migration times, wildlife managers and land trusts are buying less productive farmland to plant with native grasses; these give migratory fuel to ducks when not much else is growing. But there’s potential for this to produce too many birds for the land available back in their breeding areas, says Andrew Dixon, director of science and conservation at the Mohamed Bin Zayed Raptor Conservation Fund in Abu Dhabi, and coauthor of an article about the genetics of bird migration in the 2024 Annual Review of Animal Biosciences. This can damage ecosystems meant to serve them. Recently, conservation efforts spanning continents and thousands of miles have sprung up. One seeks to protect buff-breasted sandpipers. As they migrate 18,000 miles to and from the High Arctic where they nest, the birds experience extreme hunger—hyperphagia—that compels them to voraciously devour insects in short grasses where the bugs proliferate. But many stops along the birds’ round-trip route are threatened. There are water shortages affecting agriculture in Texas, where the birds forage at turf grass farms; grassland loss and degradation in Paraguay; and in Colombia, conversion of forage lands to exotic grasses and rice paddies these birds cannot use. Conservationists say it’s critical to protect habitat for “buffies” all along their route, and to ensure that the winters these small shorebirds spend around Uruguay’s coastal lagoons are a food fiesta. To that end, Manomet conservation specialist Joaquín Aldabe, in partnership with Uruguay’s agriculture ministry, has so far taught 40 local ranchers how to improve their cattle grazing practices. Rotationally moving the animals from pasture to pasture means grasses stay the right length for insects to flourish. There are no easy fixes in the North American northwest, where bird conservation is in crisis. Extreme drought is causing breeding grounds, molting spots, and migration stopover sites to vanish. It is also endangering the livelihoods of farmers, who feel the push to sell land to developers. From Southern Oregon to Central California, conservation allies have provided monetary incentives for water-strapped grain farmers to leave behind harvest debris to improve survivability for the 1 billion birds that pass through every year, and for ranchers to flood-irrigate unused pastures. One treacherous leg of the northwest migration route is the parched Klamath Basin of Oregon and California. For three recent years, “we saw no migrating birds. I mean, the peak count was zero,” says John Vradenburg, supervisory biologist of the Klamath Basin National Wildlife Refuge Complex. He and myriad private, public, and Indigenous partners are working to conjure more water for the basin’s human and avian denizens, as perennial wetlands become seasonal wetlands, seasonal wetlands transition to temporary wetlands, and temporary wetlands turn to arid lands. Taking down four power dams and one levee has stretched the Klamath River’s water across the landscape, creating new streams and connecting farm fields to long-separated wetlands. But making the most of this requires expansive thinking. Wetland restoration—now endangered by loss of funding from the current administration—would help drought-afflicted farmers by keeping water tables high. But what if farmers could also receive extra money for their businesses via eco-credits, akin to carbon credits, for the work those wetlands do to filter-clean farm runoff? And what if wetlands could function as aquaculture incubators for juvenile fish, before stocking rivers? Klamath tribes are invested in restoring endangered c’waam and koptu sucker fish, and this could help them achieve that goal. As birds’ traditional resting and nesting spots become inhospitable, a more sobering question is whether improvements can happen rapidly enough. The blistering pace of climate change gives little chance for species to genetically adapt, although some are changing their behaviors. That means that the work of conservationists to find and secure adequate, supportive farmland and rangeland as the birds seek out new routes has become a sprint against time. This story originally appeared at Knowable Magazine. Lela Nargi, Knowable Magazine Knowable Magazine explores the real-world significance of scholarly work through a journalistic lens. 0 Comments

smelly shield Penguin poop may help preserve Antarctic climate Ammonia aerosols from penguin guano likely play a part in the formation of heat-shielding clouds. Bob Berwyn, Inside Climate News – May 24, 2025 7:07 am | 4 Credit: Getty Credit: Getty Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy, and the environment. Sign up for their newsletter here. New research shows that penguin guano in Antarctica is an important source of ammonia aerosol particles that help drive the formation and persistence of low clouds, which cool the climate by reflecting some incoming sunlight back to space. The findings reinforce the growing awareness that Earth’s intricate web of life plays a significant role in shaping the planetary climate. Even at the small levels measured, the ammonia particles from the guano interact with sulfur-based aerosols from ocean algae to start a chemical chain reaction that forms billions of tiny particles that serve as nuclei for water vapor droplets. The low marine clouds that often cover big tracts of the Southern Ocean around Antarctica are a wild card in the climate system because scientists don’t fully understand how they will react to human-caused heating of the atmosphere and oceans. One recent study suggested that the big increase in the annual global temperature during 2023 and 2024 that has continued into this year was caused in part by a reduction of that cloud cover. “I’m constantly surprised at the depth of how one small change affects everything else,” said Matthew Boyer, a coauthor of the new study and an atmospheric scientist at the University of Helsinki’s Institute for Atmospheric and Earth System Research. “This really does show that there is a deep connection between ecosystem processes and the climate. And really, it’s the synergy between what’s coming from the oceans, from the sulfur-producing species, and then the ammonia coming from the penguins.” Climate survivors Aquatic penguins evolved from flying birds about 60 million years ago, shortly after the age of dinosaurs, and have persisted through multiple, slow, natural cycles of ice ages and warmer interglacial eras, surviving climate extremes by migrating to and from pockets of suitable habitat, called climate refugia, said Rose Foster-Dyer, a marine and polar ecologist with the University of Canterbury in New Zealand. A 2018 study that analyzed the remains of an ancient “super colony” of the birds suggests there may have been a “penguin optimum” climate window between about 4,000 and 2,000 years ago, at least for some species in some parts of Antarctica, she said. Various penguin species have adapted to different habitat niches and this will face different impacts caused by human-caused warming, she said. Foster-Dyer has recently done penguin research around the Ross Sea, and said that climate change could open more areas for land-breeding Adélie penguins, which don’t breed on ice like some other species. “There’s evidence that this whole area used to have many more colonies … which could possibly be repopulated in the future,” she said. She is also more optimistic than some scientists about the future for emperor penguins, the largest species of the group, she added. “They breed on fast ice, and there’s a lot of publications coming out about how the populations might be declining and their habitat is hugely threatened,” she said. “But they’ve lived through so many different cycles of the climate, so I think they’re more adaptable than people currently give them credit for.” In total, about 20 million breeding pairs of penguins nest in vast colonies all around the frozen continent. Some of the largest colonies, with up to 1 million breeding pairs, can cover several square miles.There aren’t any solid estimates for the total amount of guano produced by the flightless birds annually, but some studies have found that individual colonies can produce several hundred tons. Several new penguin colonies were discovered recently when their droppings were spotted in detailed satellite images. A few penguin colonies have grown recently while others appear to be shrinking, but in general, their habitat is considered threatened by warming and changing ice conditions, which affects their food supplies. The speed of human-caused warming, for which there is no precedent in paleoclimate records, may exacerbate the threat to penguins, which evolve slowly compared to many other species, Foster-Dyer said. “Everything’s changing at such a fast rate, it’s really hard to say much about anything,” she said. Recent research has shown how other types of marine life are also important to the global climate system. Nutrients from bird droppings help fertilize blooms of oxygen-producing plankton, and huge swarms of fish that live in the middle layers of the ocean cycle carbon vertically through the water, ultimately depositing it in a generally stable sediment layer on the seafloor. Tricky measurements Boyer said the new research started as a follow-up project to other studies of atmospheric chemistry in the same area, near the Argentine Marambio Base on an island along the Antarctic Peninsula. Observations by other teams suggested it could be worth specifically trying to look at ammonia, he said. Boyer and the other scientists set up specialized equipment to measure the concentration of ammonia in the air from January to March 2023. They found that, when the wind blew from the direction of a colony of about 60,000 Adélie penguins about 5 miles away, the ammonia concentration increased to as high as 13.5 parts per billion—more than 1,000 times higher than the background reading. Even after the penguins migrated from the area toward the end of February, the ammonia concentration was still more than 100 times as high as the background level. “We have one instrument that we use in the study to give us the chemistry of gases as they’re actually clustering together,” he said. “In general, ammonia in the atmosphere is not well-measured because it’s really difficult to measure, especially if you want to measure at a very high sensitivity, if you have low concentrations like in Antarctica,” he said. Penguin-scented winds The goal was to determine where the ammonia is coming from, including testing a previous hypothesis that the ocean surface could be the source, he said. But the size of the penguin colonies made them the most likely source. “It’s well known that sea birds give off ammonia. You can smell them. The birds stink,” he said. “But we didn’t know how much there was. So what we did with this study was to quantify ammonia and to quantify its impact on the cloud formation process.” The scientists had to wait until the wind blew from the penguin colony toward the research station. “If we’re lucky, the wind blows from that direction and not from the direction of the power generator,” he said. “And we were lucky enough that we had one specific event where the winds from the penguin colony persisted long enough that we were actually able to track the growth of the particles. You could be there for a year, and it might not happen.” The ammonia from the guano does not form the particles but supercharges the process that does, Boyer said. “It’s really the dimethyl sulfide from phytoplankton that gives off the sulfur,” he said. “The ammonia enhances the formation rate of particles. Without ammonia, sulfuric acid can form new particles, but with ammonia, it’s 1,000 times faster, and sometimes even more, so we’re talking up to four orders of magnitude faster because of the guano.” This is important in Antarctica specifically because there are not many other sources of particles, such as pollution or emissions from trees, he added. “So the strength of the source matters in terms of its climate effect over time,” he said. “And if the source changes, it’s going to change the climate effect.” It will take more research to determine if penguin guano has a net cooling effect on the climate. But in general, he said, if the particles transport out to sea and contribute to cloud formation, they will have a cooling effect. “What’s also interesting,” he said, “is if the clouds are over ice surfaces, it could actually lead to warming because the clouds are less reflective than the ice beneath.” In that case, the clouds could actually reduce the amount of heat that brighter ice would otherwise reflect away from the planet. The study did not try to measure that effect, but it could be an important subject for future research, he added. The guano effect lingers even after the birds leave the breeding areas. A month after they were gone, Boyer said ammonia levels in the air were still 1,000 times higher than the baseline. “The emission of ammonia is a temperature-dependent process, so it’s likely that once wintertime comes, the ammonia gets frozen in,” he said. “But even before the penguins come back, I would hypothesize that as the temperature warms, the guano starts to emit ammonia again. And the penguins move all around the coast, so it’s possible they’re just fertilizing an entire coast with ammonia.” Bob Berwyn, Inside Climate News 4 Comments

By Natalia Mesa Published May 23, 2025 | Comments (1) | Giant ground sloths Megalocnus rodens and Megalonyx wheatleyi at the American Museum of Natural History © Dallas Krentzel Giant sloths with razor-sharp claws and as large as Asian bull elephants once roamed the Earth, snacking on leaves at the tops of trees with a prehensile tongue. Now, scientists have figured out why they became so huge—and why these massive sloths didn’t stick around—according to a new study published in Science. Today, two sloth species dwell in Central and South America. But long ago, dozens of sloth species populated the Americas, all the way from Argentina to Canada. Like modern-day sloths, the smaller species were tree-dwelling. But the larger sloths? “They looked like grizzly bears but five times larger,” Rachel Narducci, collection manager of vertebrate paleontology at the Florida Museum of Natural History and coauthor of the study, in a statement. The larger sloths didn’t do much tree climbing, at risk of falling to their deaths. Instead, they survived by being terrifying; the largest sloths had long, sharp claws that they used to carve their own caves out of raw earth and rocks. But exactly why they got so large remained a mystery. To figure out how these sloths got so massive, researchers analyzed ancient sloth DNA and compared more than 400 fossils from natural history museums to create a sloth tree of life. The researchers traced the sloths’ origin to 35 million years ago. And, because the scientists were particularly interested in how sloths got their size, they estimated their weights by taking fossil measurements. The researchers concluded that the Earth’s past climate was a big factor. Thirty-five million years ago, the first ancestor of modern-day sloths, which lived in what is now Argentina, was roughly the size of a large dog. Sloths hardly changed in size for 20 million years, and lived on the ground. Then, during a warming period around 16 million years ago, sloths adapted by evolving smaller physiques due to their need to keep cool. Then, as Earth cooled down again—which it’s been doing on and off for the past 50 million years—sloths started to get bigger and bigger. They also started to migrate, fanning out from Argentina throughout North and South America, and even up to Alaska and Canada. These new habitats presented challenges that the sloths met, in part, by bulking up. This new size also helped them keep warm and stay safe from predators. “This would’ve allowed them to conserve energy and water and travel more efficiently across habitats with limited resources,” Narducci said. “And if you’re in an open grassland, you need protection, and being bigger provides some of that.” They reached their most massive size during the Pleistocene Ice Ages, which spanned roughly 3 million to 12,000 years ago, shortly before they disappeared. Scientists aren’t completely sure why sloths went extinct, but they do have some guesses. Early humans migrated to the Americas around 20,000 years ago. Larger ground-dwelling sloths likely became a prime, meaty target for early humans, and being on the ground became a liability. Larger sloths were the first to go, but tree sloths didn’t escape unscathed. Over time, more and more species of tree-dwelling sloths went extinct, too. Two species survived in the Caribbean until around 4,500 years ago—until humans wiped them out. Now, sloths mostly keep to Central and South America, but thankfully aren’t on the menu anymore. Daily Newsletter