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Dr. Paul Hanona and Dr. Arturo Loaiza-Bonilla discuss how to safely and smartly integrate AI into the clinical workflow and tap its potential to improve patient-centered care, drug development, and access to clinical trials. TRANSCRIPT Dr. Paul Hanona: Hello, I'm Dr. Paul Hanona, your guest host of the ASCO Daily News Podcast today. I am a medical oncologist as well as a content creator @DoctorDiscover, and I'm delighted to be joined today by Dr. Arturo Loaiza-Bonilla, the chief of hematology and oncology at St. Luke's University Health Network. Dr. Bonilla is also the co-founder and chief medical officer at Massive Bio, an AI-driven platform that matches patients with clinical trials and novel therapies. Dr. Loaiza-Bonilla will share his unique perspective on the potential of artificial intelligence to advance precision oncology, especially through clinical trials and research, and other key advancements in AI that are transforming the oncology field. Our full disclosures are available in the transcript of the episode. Dr. Bonilla, it's great to be speaking with you today. Thanks for being here. Dr. Arturo Loaiza-Bonilla: Oh, thank you so much, Dr. Hanona. Paul, it's always great to have a conversation. Looking forward to a great one today. Dr. Paul Hanona: Absolutely. Let's just jump right into it. Let's talk about the way that we see AI being embedded in our clinical workflow as oncologists. What are some practical ways to use AI? Dr. Arturo Loaiza-Bonilla: To me, responsible AI integration in oncology is one of those that's focused on one principle to me, which is clinical purpose is first, instead of the algorithm or whatever technology we're going to be using. If we look at the best models in the world, they're really irrelevant unless we really solve a real day-to-day challenge, either when we're talking to patients in the clinic or in the infusion chair or making decision support. Currently, what I'm doing the most is focusing on solutions that are saving us time to be more productive and spend more time with our patients. So, for example, we're using ambient AI for appropriate documentation in real time with our patients. We're leveraging certain tools to assess for potential admission or readmission of patients who have certain conditions as well. And it's all about combining the listening of physicians like ourselves who are end users, those who create those algorithms, data scientists, and patient advocates, and even regulators, before they even write any single line of code. I felt that on my own, you know, entrepreneurial aspects, but I think it's an ethos that we should all follow. And I think that AI shouldn't be just bolted on later. We always have to look at workflows and try to look, for example, at clinical trial matching, which is something I'm very passionate about. We need to make sure that first, it's easier to access for patients, that oncologists like myself can go into the interface and be able to pull the data in real time when you really need it, and you don't get all this fatigue alerts. To me, that's the responsible way of doing so. Those are like the opportunities, right? So, the challenge is how we can make this happen in a meaningful way – we're just not reacting to like a black box suggestion or something that we have no idea why it came up to be. So, in terms of success – and I can tell you probably two stories of things that we know we're seeing successful – we all work closely with radiation oncologists, right? So, there are now these tools, for example, of automated contouring in radiation oncology, and some of these solutions were brought up in different meetings, including the last ASCO meeting. But overall, we know that transformer-based segmentation tools; transformer is just the specific architecture of the machine learning algorithm that has been able to dramatically reduce the time for colleagues to spend allotting targets for radiation oncology. So, comparing the target versus the normal tissue, which sometimes it takes many hours, now we can optimize things over 60%, sometimes even in minutes. So, this is not just responsible, but it's also an efficiency win, it's a precision win, and we're using it to adapt even mid-course in response to tumor shrinkage. Another success that I think is relevant is, for example, on the clinical trial matching side. We've been working on that and, you know, I don't want to preach to the choir here, but having the ability for us to structure data in real time using these tools, being able to extract information on biomarkers, and then show that multi-agentic AI is superior to what we call zero-shot or just throwing it into ChatGPT or any other algorithm, but using the same tools but just fine-tuned to the point that we can be very efficient and actually reliable to the level of almost like a research coordinator, is not just theory. Now, it can change lives because we can get patients enrolled in clinical trials and be activated in different places wherever the patient may be. I know it's like a long answer on that, but, you know, as we talk about responsible AI, that's important. And in terms of what keeps me up at night on this: data drift and biases, right? So, imaging protocols, all these things change, the lab switch between different vendors, or a patient has issues with new emerging data points. And health systems serve vastly different populations. So, if our models are trained in one context and deployed in another, then the output can be really inaccurate. So, the idea is to become a collaborative approach where we can use federated learning and patient-centricity so we can be much more efficient in developing those models that account for all the populations, and any retraining that is used based on data can be diverse enough that it represents all of us and we can be treated in a very good, appropriate way. So, if a clinician doesn't understand why a recommendation is made, as you probably know, you probably don't trust it, and we shouldn't expect them to. So, I think this is the next wave of the future. We need to make sure that we account for all those things. Dr. Paul Hanona: Absolutely. And even the part about the clinical trials, I want to dive a little bit more into in a few questions. I just kind of wanted to make a quick comment. Like you said, some of the prevalent things that I see are the ambient scribes. It seems like that's really taken off in the last year, and it seems like it's improving at a pretty dramatic speed as well. I wonder how quickly that'll get adopted by the majority of physicians or practitioners in general throughout the country. And you also mentioned things with AI tools regarding helping regulators move things quicker, even the radiation oncologist, helping them in their workflow with contouring and what else they might have to do. And again, the clinical trials thing will be quite interesting to get into. The first question I had subsequent to that is just more so when you have large datasets. And this pertains to two things: the paper that you published recently regarding different ways to use AI in the space of oncology referred to drug development, the way that we look at how we design drugs, specifically anticancer drugs, is pretty cumbersome. The steps that you have to take to design something, to make sure that one chemical will fit into the right chemical or the structure of the molecule, that takes a lot of time to tinker with. What are your thoughts on AI tools to help accelerate drug development? Dr. Arturo Loaiza-Bonilla: Yes, that's the Holy Grail and something that I feel we should dedicate as much time and effort as possible because it relies on multimodality. It cannot be solved by just looking at patient histories. It cannot be solved by just looking at the tissue alone. It's combining all these different datasets and being able to understand the microenvironment, the patient condition and prior treatments, and how dynamic changes that we do through interventions and also exposome – the things that happen outside of the patient's own control – can be leveraged to determine like what's the best next step in terms of drugs. So, the ones that we heard the news the most is, for example, the Nobel Prize-winning [for Chemistry awarded to Demis Hassabis and John Jumper for] AlphaFold, an AI system that predicts protein structures right? So, we solved this very interesting concept of protein folding where, in the past, it would take the history of the known universe, basically – what's called the Levinthal's paradox – to be able to just predict on amino acid structure alone or the sequence alone, the way that three-dimensionally the proteins will fold. So, with that problem being solved and the Nobel Prize being won, the next step is, “Okay, now we know how this protein is there and just by sequence, how can we really understand any new drug that can be used as a candidate and leverage all the data that has been done for many years of testing against a specific protein or a specific gene or knockouts and what not?” So, this is the future of oncology and where we're probably seeing a lot of investments on that. The key challenge here is mostly working on the side of not just looking at pathology, but leveraging this digital pathology with whole slide imaging and identifying the microenvironment of that specific tissue. There's a number of efforts currently being done. One isn't just H&E, like hematoxylin and eosin, slides alone, but with whole imaging, now we can use expression profiles, spatial transcriptomics, and gene whole exome sequencing in the same space and use this transformer technology in a multimodality approach that we know already the slide or the pathology, but can we use that to understand, like, if I knock out this gene, how is the microenvironment going to change to see if an immunotherapy may work better, right? If we can make a microenvironment more reactive towards a cytotoxic T cell profile, for example. So, that is the way where we're really seeing the field moving forward, using multimodality for drug discovery. So, the FDA now seems to be very eager to support those initiatives, so that's of course welcome. And now the key thing is the investment to do this in a meaningful way so we can see those candidates that we're seeing from different companies now being leveraged for rare disease, for things that are going to be almost impossible to collect enough data, and make it efficient by using these algorithms that sometimes, just with multiple masking – basically, what they do is they mask all the features and force the algorithm to find solutions based on the specific inputs or prompts we're doing. So, I'm very excited about that, and I think we're going to be seeing that in the future. Dr. Paul Hanona: So, essentially, in a nutshell, we're saying we have the cancer, which is maybe a dandelion in a field of grass, and we want to see the grass that's surrounding the dandelion, which is the pathology slides. The problem is, to the human eye, it's almost impossible to look at every single piece of grass that's surrounding the dandelion. And so, with tools like AI, we can greatly accelerate our study of the microenvironment or the grass that's surrounding the dandelion and better tailor therapy, come up with therapy. Otherwise, like you said, to truly generate a drug, this would take years and years. We just don't have the throughput to get to answers like that unless we have something like AI to help us. Dr. Arturo Loaiza-Bonilla: Correct. Dr. Paul Hanona: And then, clinical trials. Now, this is an interesting conversation because if you ever look up our national guidelines as oncologists, there's always a mention of, if treatment fails, consider clinical trials. Or in the really aggressive cancers, sometimes you might just start out with clinical trials. You don't even give the standard first-line therapy because of how ineffective it is. There are a few issues with clinical trials that people might not be aware of, but the fact that the majority of patients who should be on clinical trials are never given the chance to be on clinical trials, whether that's because of proximity, right, they might live somewhere that's far from the institution, or for whatever reason, they don't qualify for the clinical trial, they don't meet the strict inclusion criteria.  But a reason you mentioned early on is that it's simply impossible for someone to be aware of every single clinical trial that's out there. And then even if you are aware of those clinical trials, to actually find the sites and put in the time could take hours. And so, how is AI going to revolutionize that? Because in my mind, it's not that we're inventing a new tool. Clinical trials have always been available. We just can't access them. So, if we have a tool that helps with access, wouldn't that be huge? Dr. Arturo Loaiza-Bonilla: Correct. And that has been one of my passions. And for those who know me and follow me and we've spoke about it in different settings, that's something that I think we can solve. This other paradox, which is the clinical trial enrollment paradox, right? We have tens of thousands of clinical trials available with millions of patients eager to learn about trials, but we don't enroll enough and many trials close to accrual because of lack of enrollment. It is completely paradoxical and it's because of that misalignment because patients don't know where to go for trials and sites don't know what patients they can help because they haven't reached their doors yet. So, the solution has to be patient-centric, right? We have to put the patient at the center of the equation. And that was precisely what we had been discussing during the ASCO meeting. There was an ASCO Education Session where we talked about digital prescreening hubs, where we, in a patient-centric manner, the same way we look for Uber, Instacart, any solution that you may think of that you want something that can be leveraged in real time, we can use these real-world data streams from the patient directly, from hospitals, from pathology labs, from genomics companies, to continuously screen patients who can match to the inclusion/exclusion criteria of unique trials. So, when the patient walks into the clinic, the system already knows if there's a trial and alerts the site proactively. The patient can actually also do decentralization. So, there's a number of decentralized clinical trial solutions that are using what I call the “click and mortar” approach, which is basically the patient is checking digitally and then goes to the site to activate. We can also have the click and mortar in the bidirectional way where the patient is engaged in person and then you give the solution like the ones that are being offered on things that we're doing at Massive Bio and beyond, which is having the patient to access all that information and then they make decisions and enroll when the time is right.  As I mentioned earlier, there is this concept drift where clinical trials open and close, the patient line of therapy changes, new approvals come in and out, and sites may not be available at a given time but may be later. So, having that real-time alerts using tools that are able already to extract data from summarization that we already have in different settings and doing this natural language ingestion, we can not only solve this issue with manual chart review, which is extremely cumbersome and takes forever and takes to a lot of one-time assessments with very high screen failures, to a real-time dynamic approach where the patient, as they get closer to that eligibility criteria, they get engaged. And those tools can be built to activate trials, audit trials, and make them better and accessible to patients. And something that we know is, for example, 91%-plus of Americans live close to either a pharmacy or an imaging center. So, imagine that we can potentially activate certain of those trials in those locations. So, there's a number of pharmacies, special pharmacies, Walgreens, and sometimes CVS trying to do some of those efforts. So, I think the sky's the limit in terms of us working together. And we've been talking with corporate groups, they're all interested in those efforts as well, to getting patients digitally enabled and then activate the same way we activate the NCTN network of the corporate groups, that are almost just-in-time. You can activate a trial the patient is eligible for and we get all these breakthroughs from the NIH and NCI, just activate it in my site within a week or so, as long as we have the understanding of the protocol. So, using clinical trial matching in a digitally enabled way and then activate in that same fashion, but not only for NCTN studies, but all the studies that we have available will be the key of the future through those prescreening hubs. So, I think now we're at this very important time where collaboration is the important part and having this silo-breaking approach with interoperability where we can leverage data from any data source and from any electronic medical records and whatnot is going to be essential for us to move forward because now we have the tools to do so with our phones, with our interests, and with the multiple clinical trials that are coming into the pipelines. Dr. Paul Hanona: I just want to point out that the way you described the process involves several variables that practitioners often don't think about. We don't realize the 15 steps that are happening in the background. But just as a clarifier, how much time is it taking now to get one patient enrolled on a clinical trial? Is it on the order of maybe 5 to 10 hours for one patient by the time the manual chart review happens, by the time the matching happens, the calls go out, the sign-up, all this? And how much time do you think a tool that could match those trials quicker and get you enrolled quicker could save? Would it be maybe an hour instead of 15 hours? What's your thought process on that? Dr. Arturo Loaiza-Bonilla: Yeah, exactly. So one is the matching, the other one is the enrollment, which, as you mentioned, is very important. So, it can take, from, as you said, probably between 4 days to sometimes 30 days. Sometimes that's how long it takes for all the things to be parsed out in terms of logistics and things that could be done now agentically. So, we can use agents to solve those different steps that may take multiple individuals. We can just do it as a supply chain approach where all those different steps can be done by a single agent in a simultaneous fashion and then we can get things much faster. With an AI-based solution using these frontier models and multi-agentic AI – and we presented some of this data in ASCO as well – you can do 5,000 patients in an hour, right? So, just enrolling is going to be between an hour and maximum enrollment, it could be 7 days for those 5,000 patients if it was done at scale in a multi-level approach where we have all the trials available. Dr. Paul Hanona: No, definitely a very exciting aspect of our future as oncologists. It's one thing to have really neat, novel mechanisms of treatment, but what good is it if we can't actually get it to people who need it? I'm very much looking for the future of that.  One of the last questions I want to ask you is another prevalent way that people use AI is just simply looking up questions, right? So, traditionally, the workflow for oncologists is maybe going on national guidelines and looking up the stage of the cancer and seeing what treatments are available and then referencing the papers and looking at who was included, who wasn't included, the side effects to be aware of, and sort of coming up with a decision as to how to treat a cancer patient. But now, just in the last few years, we've had several tools become available that make getting questions easier, make getting answers easier, whether that's something like OpenAI's tools or Perplexity or Doximity or OpenEvidence or even ASCO has a Guidelines Assistant as well that is drawing from their own guidelines as to how to treat different cancers. Do you see these replacing traditional sources? Do you see them saving us a lot more time so that we can be more productive in clinic? What do you think is the role that they're going to play with patient care? Dr. Arturo Loaiza-Bonilla: Such a relevant question, particularly at this time, because these AI-enabled query tools, they're coming left and right and becoming increasingly common in our daily workflows and things that we're doing. So, traditionally, when we go and we look for national guidelines, we try to understand the context ourselves and then we make treatment decisions accordingly. But that is a lot of a process that now AI is helping us to solve. So, at face value, it seems like an efficiency win, but in many cases, I personally evaluate platforms as the chief of hem/onc at St. Luke's and also having led the digital engagement things through Massive Bio and trying to put things together, I can tell you this: not all tools are created equal. In cancer care, each data point can mean the difference between cure and progression, so we cannot really take a lot of shortcuts in this case or have unverified output. So, the tools are helpful, but it has to be grounded in truth, in trusted data sources, and they need to be continuously updated with, like, ASCO and NCCN and others. So, the reason why the ASCO Guidelines Assistant, for instance, works is because it builds on all these recommendations, is assessed by end users like ourselves. So, that kind of verification is critical, right? We're entering a phase where even the source material may be AI-generated. So, the role of human expert validation is really actually more important, not less important. You know, generalist LLMs, even when fine-tuned, they may not be enough. You can pull a few API calls from PubMed, etc., but what we need now is specialized, context-aware, agentic tools that can interpret multimodal and real-time clinical inputs. So, something that we are continuing to check on and very relevant to have entities and bodies like ASCO looking into this so they can help us to be really efficient and really help our patients. Dr. Paul Hanona: Dr. Bonilla, what do you want to leave the listener with in terms of the future direction of AI, things that we should be cautious about, and things that we should be optimistic about? Dr. Arturo Loaiza-Bonilla: Looking 5 years ahead, I think there's enormous promise. As you know, I'm an AI enthusiast, but always, there's a few priorities that I think – 3 of them, I think – we need to tackle head-on. First is algorithmic equity. So, most AI tools today are trained on data from academic medical centers but not necessarily from community practices or underrepresented populations, particularly when you're looking at radiology, pathology, and what not. So, those blind spots, they need to be filled, and we can eliminate a lot of disparities in cancer care. So, those frameworks to incentivize while keeping the data sharing using federated models and things that we can optimize is key. The second one is the governance on the lifecycle. So, you know, AI is not really static. So, unlike a drug that is approved and it just, you know, works always, AI changes. So, we need to make sure that we have tools that are able to retrain and recall when things degrade or models drift. So, we need to use up-to-date AI for clinical practice, so we are going to be in constant revalidation and make it really easy to do. And lastly, the human-AI interface. You know, clinicians don't need more noise or we don't need more black boxes. We need decision support that is clear, that we can interpret, and that is actionable. “Why are you using this? Why did we choose this drug? Why this dose? Why now?” So, all these things are going to help us and that allows us to trace evidence with a single click. So, I always call it back to the Moravec's paradox where we say, you know, evolution gave us so much energy to discern in the sensory-neural and dexterity. That's what we're going to be taking care of patients. We can use AI to really be a force to help us to be better clinicians and not to really replace us. So, if we get this right and we decide for transparency with trust, inclusion, etc., it will never replace any of our work, which is so important, as much as we want, we can actually take care of patients and be personalized, timely, and equitable. So, all those things are what get me excited every single day about these conversations on AI. Dr. Paul Hanona: All great thoughts, Dr. Bonilla. I'm very excited to see how this field evolves. I'm excited to see how oncologists really come to this field. I think with technology, there's always a bit of a lag in adopting it, but I think if we jump on board and grow with it, we can do amazing things for the field of oncology in general. Thank you for the advancements that you've made in your own career in the field of AI and oncology and just ultimately with the hopeful outcomes of improving patient care, especially cancer patients. Dr. Arturo Loaiza-Bonilla: Thank you so much, Dr. Hanona. Dr. Paul Hanona: Thanks to our listeners for your time today. If you value the insights that you hear on ASCO Daily News Podcast, please take a moment to rate, review, and subscribe wherever you get your podcasts. Disclaimer: The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement. More on today's speakers:    Dr. Arturo Loaiza-Bonilla @DrBonillaOnc Dr. Paul Hanona @DoctorDiscover on YouTube Follow ASCO on social media:      @ASCO on Twitter      ASCO on Facebook      ASCO on LinkedIn    ASCO on BlueSky Disclosures: Paul Hanona: No relationships to disclose. Dr. Arturo-Loaiza-Bonilla: Leadership: Massive Bio Stock & Other Ownership Interests: Massive Bio Consulting or Advisory Role: Massive Bio, Bayer, PSI, BrightInsight, CardinalHealth, Pfizer, AstraZeneca, Medscape Speakers' Bureau: Guardant Health, Ipsen, AstraZeneca/Daiichi Sankyo, Natera

“To navigate proof, we must reach into a thicket of errors and biases. We must confront monsters and embrace uncertainty, balancing — and rebalancing —our beliefs. We must seek out every useful fragment of data, gather every relevant tool, searching wider and climbing further. Finding the good foundations among the bad. Dodging dogma and falsehoods. Questioning. Measuring. Triangulating. Convincing. Then perhaps, just perhaps, we'll reach the truth in time.”—Adam KucharskiMy conversation with Professor Kucharski on what constitutes certainty and proof in science (and other domains), with emphasis on many of the learnings from Covid. Given the politicization of science and A.I.'s deepfakes and power for blurring of truth, it's hard to think of a topic more important right now.Audio file (Ground Truths can also be downloaded on Apple Podcasts and Spotify)Eric Topol (00:06):Hello, it's Eric Topol from Ground Truths and I am really delighted to welcome Adam Kucharski, who is the author of a new book, Proof: The Art and Science of Certainty. He's a distinguished mathematician, by the way, the first mathematician we've had on Ground Truths and a person who I had the real privilege of getting to know a bit through the Covid pandemic. So welcome, Adam.Adam Kucharski (00:28):Thanks for having me.Eric Topol (00:30):Yeah, I mean, I think just to let everybody know, you're a Professor at London School of Hygiene and Tropical Medicine and also noteworthy you won the Adams Prize, which is one of the most impressive recognitions in the field of mathematics. This is the book, it's a winner, Proof and there's so much to talk about. So Adam, maybe what I'd start off is the quote in the book that captivates in the beginning, “life is full of situations that can reveal remarkably large gaps in our understanding of what is true and why it's true. This is a book about those gaps.” So what was the motivation when you undertook this very big endeavor?Adam Kucharski (01:17):I think a lot of it comes to the work I do at my day job where we have to deal with a lot of evidence under pressure, particularly if you work in outbreaks or emerging health concerns. And often it really pushes the limits, our methodology and how we converge on what's true subject to potential revision in the future. I think particularly having a background in math's, I think you kind of grow up with this idea that you can get to these concrete, almost immovable truths and then even just looking through the history, realizing that often isn't the case, that there's these kind of very human dynamics that play out around them. And it's something I think that everyone in science can reflect on that sometimes what convinces us doesn't convince other people, and particularly when you have that kind of urgency of time pressure, working out how to navigate that.Eric Topol (02:05):Yeah. Well, I mean I think these times of course have really gotten us to appreciate, particularly during Covid, the importance of understanding uncertainty. And I think one of the ways that we can dispel what people assume they know is the famous Monty Hall, which you get into a bit in the book. So I think everybody here is familiar with that show, Let's Make a Deal and maybe you can just take us through what happens with one of the doors are unveiled and how that changes the mathematics.Adam Kucharski (02:50):Yeah, sure. So I think it is a problem that's been around for a while and it's based on this game show. So you've got three doors that are closed. Behind two of the doors there is a goat and behind one of the doors is a luxury car. So obviously, you want to win the car. The host asks you to pick a door, so you point to one, maybe door number two, then the host who knows what's behind the doors opens another door to reveal a goat and then ask you, do you want to change your mind? Do you want to switch doors? And a lot of the, I think intuition people have, and certainly when I first came across this problem many years ago is well, you've got two doors left, right? You've picked one, there's another one, it's 50-50. And even some quite well-respected mathematicians.Adam Kucharski (03:27):People like Paul Erdős who was really published more papers than almost anyone else, that was their initial gut reaction. But if you work through all of the combinations, if you pick this door and then the host does this, and you switch or not switch and work through all of those options. You actually double your chances if you switch versus sticking with the door. So something that's counterintuitive, but I think one of the things that really struck me and even over the years trying to explain it is convincing myself of the answer, which was when I first came across it as a teenager, I did quite quickly is very different to convincing someone else. And even actually Paul Erdős, one of his colleagues showed him what I call proof by exhaustion. So go through every combination and that didn't really convince him. So then he started to simulate and said, well, let's do a computer simulation of the game a hundred thousand times. And again, switching was this optimal strategy, but Erdős wasn't really convinced because I accept that this is the case, but I'm not really satisfied with it. And I think that encapsulates for a lot of people, their experience of proof and evidence. It's a fact and you have to take it as given, but there's actually quite a big bridge often to really understanding why it's true and feeling convinced by it.Eric Topol (04:41):Yeah, I think it's a fabulous example because I think everyone would naturally assume it's 50-50 and it isn't. And I think that gets us to the topic at hand. What I love, there's many things I love about this book. One is that you don't just get into science and medicine, but you cut across all the domains, law, mathematics, AI. So it's a very comprehensive sweep of everything about proof and truth, and it couldn't come at a better time as we'll get into. Maybe just starting off with math, the term I love mathematical monsters. Can you tell us a little bit more about that?Adam Kucharski (05:25):Yeah, this was a fascinating situation that emerged in the late 19th century where a lot of math's, certainly in Europe had been derived from geometry because a lot of the ancient Greek influence on how we shaped things and then Newton and his work on rates of change and calculus, it was really the natural world that provided a lot of inspiration, these kind of tangible objects, tangible movements. And as mathematicians started to build out the theory around rates of change and how we tackle these kinds of situations, they sometimes took that intuition a bit too seriously. And there was some theorems that they said were intuitively obvious, some of these French mathematicians. And so, one for example is this idea of you how things change smoothly over time and how you do those calculations. But what happened was some mathematicians came along and showed that when you have things that can be infinitely small, that intuition didn't necessarily hold in the same way.Adam Kucharski (06:26):And they came up with these examples that broke a lot of these theorems and a lot of the establishments at the time called these things monsters. They called them these aberrations against common sense and this idea that if Newton had known about them, he never would've done all of his discovery because they're just nuisances and we just need to get rid of them. And there's this real tension at the core of mathematics in the late 1800s where some people just wanted to disregard this and say, look, it works for most of the time, that's good enough. And then others really weren't happy with this quite vague logic. They wanted to put it on much sturdier ground. And what was remarkable actually is if you trace this then into the 20th century, a lot of these monsters and these particularly in some cases functions which could almost move constantly, this constant motion rather than our intuitive concept of movement as something that's smooth, if you drop an apple, it accelerates at a very smooth rate, would become foundational in our understanding of things like probability, Einstein's work on atomic theory. A lot of these concepts where geometry breaks down would be really important in relativity. So actually, these things that we thought were monsters actually were all around us all the time, and science couldn't advance without them. So I think it's just this remarkable example of this tension within a field that supposedly concrete and the things that were going to be shunned actually turn out to be quite important.Eric Topol (07:53):It's great how you convey how nature isn't so neat and tidy and things like Brownian motion, understanding that, I mean, just so many things that I think fit into that general category. In the legal, we won't get into too much because that's not so much the audience of Ground Truths, but the classic things about innocent and until proven guilty and proof beyond reasonable doubt, I mean these are obviously really important parts of that overall sense of proof and truth. We're going to get into one thing I'm fascinated about related to that subsequently and then in science. So before we get into the different types of proof, obviously the pandemic is still fresh in our minds and we're an endemic with Covid now, and there are so many things we got wrong along the way of uncertainty and didn't convey that science isn't always evolving search for what is the truth. There's plenty no shortage of uncertainty at any moment. So can you recap some of the, you did so much work during the pandemic and obviously some of it's in the book. What were some of the major things that you took out of proof and truth from the pandemic?Adam Kucharski (09:14):I think it was almost this story of two hearts because on the one hand, science was the thing that got us where we are today. The reason that so much normality could resume and so much risk was reduced was development of vaccines and the understanding of treatments and the understanding of variants as they came to their characteristics. So it was kind of this amazing opportunity to see this happen faster than it ever happened in history. And I think ever in science, it certainly shifted a lot of my thinking about what's possible and even how we should think about these kinds of problems. But also on the other hand, I think where people might have been more familiar with seeing science progress a bit more slowly and reach consensus around some of these health issues, having that emerge very rapidly can present challenges even we found with some of the work we did on Alpha and then the Delta variants, and it was the early quantification of these.Adam Kucharski (10:08):So really the big question is, is this thing more transmissible? Because at the time countries were thinking about control measures, thinking about relaxing things, and you've got this just enormous social economic health decision-making based around essentially is it a lot more spreadable or is it not? And you only had these fragments of evidence. So I think for me, that was really an illustration of the sharp end. And I think what we ended up doing with some of those was rather than arguing over a precise number, something like Delta, instead we kind of looked at, well, what's the range that matters? So in the sense of arguing over whether it's 40% or 50% or 30% more transmissible is perhaps less important than being, it's substantially more transmissible and it's going to start going up. Is it going to go up extremely fast or just very fast?Adam Kucharski (10:59):That's still a very useful conclusion. I think what often created some of the more challenges, I think the things that on reflection people looking back pick up on are where there was probably overstated certainty. We saw that around some of the airborne spread, for example, stated as a fact by in some cases some organizations, I think in some situations as well, governments had a constraint and presented it as scientific. So the UK, for example, would say testing isn't useful. And what was happening at the time was there wasn't enough tests. So it was more a case of they can't test at that volume. But I think blowing between what the science was saying and what the decision-making, and I think also one thing we found in the UK was we made a lot of the epidemiological evidence available. I think that was really, I think something that was important.Adam Kucharski (11:51):I found it a lot easier to communicate if talking to the media to be able to say, look, this is the paper that's out, this is what it means, this is the evidence. I always found it quite uncomfortable having to communicate things where you knew there were reports behind the scenes, but you couldn't actually articulate. But I think what that did is it created this impression that particularly epidemiology was driving the decision-making a lot more than it perhaps was in reality because so much of that was being made public and a lot more of the evidence around education or economics was being done behind the scenes. I think that created this kind of asymmetry in public perception about how that was feeding in. And so, I think there was always that, and it happens, it is really hard as well as a scientist when you've got journalists asking you how to run the country to work out those steps of am I describing the evidence behind what we're seeing? Am I describing the evidence about different interventions or am I proposing to some extent my value system on what we do? And I think all of that in very intense times can be very easy to get blurred together in public communication. I think we saw a few examples of that where things were being the follow the science on policy type angle where actually once you get into what you're prioritizing within a society, quite rightly, you've got other things beyond just the epidemiology driving that.Eric Topol (13:09):Yeah, I mean that term that you just use follow the science is such an important term because it tells us about the dynamic aspect. It isn't just a snapshot, it's constantly being revised. But during the pandemic we had things like the six-foot rule that was never supported by data, but yet still today, if I walk around my hospital and there's still the footprints of the six-foot rule and not paying attention to the fact that this was airborne and took years before some of these things were accepted. The flatten the curve stuff with lockdowns, which I never was supportive of that, but perhaps at the worst point, the idea that hospitals would get overrun was an issue, but it got carried away with school shutdowns for prolonged periods and in some parts of the world, especially very stringent lockdowns. But anyway, we learned a lot.Eric Topol (14:10):But perhaps one of the greatest lessons is that people's expectations about science is that it's absolute and somehow you have this truth that's not there. I mean, it's getting revised. It's kind of on the job training, it's on this case on the pandemic revision. But very interesting. And that gets us to, I think the next topic, which I think is a fundamental part of the book distributed throughout the book, which is the different types of proof in biomedicine and of course across all these domains. And so, you take us through things like randomized trials, p-values, 95 percent confidence intervals, counterfactuals, causation and correlation, peer review, the works, which is great because a lot of people have misconceptions of these things. So for example, randomized trials, which is the temple of the randomized trials, they're not as great as a lot of people think, yes, they can help us establish cause and effect, but they're skewed because of the people who come into the trial. So they may not at all be a representative sample. What are your thoughts about over deference to randomized trials?Adam Kucharski (15:31):Yeah, I think that the story of how we rank evidence in medicines a fascinating one. I mean even just how long it took for people to think about these elements of randomization. Fundamentally, what we're trying to do when we have evidence here in medicine or science is prevent ourselves from confusing randomness for a signal. I mean, that's fundamentally, we don't want to mistake something, we think it's going on and it's not. And the challenge, particularly with any intervention is you only get to see one version of reality. You can't give someone a drug, follow them, rewind history, not give them the drug and then follow them again. So one of the things that essentially randomization allows us to do is, if you have two groups, one that's been randomized, one that hasn't on average, the difference in outcomes between those groups is going to be down to the treatment effect.Adam Kucharski (16:20):So it doesn't necessarily mean in reality that'd be the case, but on average that's the expectation that you'd have. And it's kind of interesting actually that the first modern randomized control trial (RCT) in medicine in 1947, this is for TB and streptomycin. The randomization element actually, it wasn't so much statistical as behavioral, that if you have people coming to hospital, you could to some extent just say, we'll just alternate. We're not going to randomize. We're just going to first patient we'll say is a control, second patient a treatment. But what they found in a lot of previous studies was doctors have bias. Maybe that patient looks a little bit ill or that one maybe is on borderline for eligibility. And often you got these quite striking imbalances when you allowed it for human judgment. So it was really about shielding against those behavioral elements. But I think there's a few situations, it's a really powerful tool for a lot of these questions, but as you mentioned, one is this issue of you have the population you study on and then perhaps in reality how that translates elsewhere.Adam Kucharski (17:17):And we see, I mean things like flu vaccines are a good example, which are very dependent on immunity and evolution and what goes on in different populations. Sometimes you've had a result on a vaccine in one place and then the effectiveness doesn't translate in the same way to somewhere else. I think the other really important thing to bear in mind is, as I said, it's the averaging that you're getting an average effect between two different groups. And I think we see certainly a lot of development around things like personalized medicine where actually you're much more interested in the outcome for the individual. And so, what a trial can give you evidence is on average across a group, this is the effect that I can expect this intervention to have. But we've now seen more of the emergence things like N=1 studies where you can actually over the same individual, particularly for chronic conditions, look at those kind of interventions.Adam Kucharski (18:05):And also there's just these extreme examples where you're ethically not going to run a trial, there's never been a trial of whether it's a good idea to have intensive care units in hospitals or there's a lot of these kind of historical treatments which are just so overwhelmingly effective that we're not going to run trial. So almost this hierarchy over time, you can see it getting shifted because actually you do have these situations where other forms of evidence can get you either closer to what you need or just more feasibly an answer where it's just not ethical or practical to do an RCT.Eric Topol (18:37):And that brings us to the natural experiments I just wrote about recently, the one with shingles, which there's two big natural experiments to suggest that shingles vaccine might reduce the risk of Alzheimer's, an added benefit beyond the shingles that was not anticipated. Your thoughts about natural experiments, because here you're getting a much different type of population assessment, again, not at the individual level, but not necessarily restricted by some potentially skewed enrollment criteria.Adam Kucharski (19:14):I think this is as emerged as a really valuable tool. It's kind of interesting, in the book you're talking to economists like Josh Angrist, that a lot of these ideas emerge in epidemiology, but I think were really then taken up by economists, particularly as they wanted to add more credibility to a lot of these policy questions. And ultimately, it comes down to this issue that for a lot of problems, we can't necessarily intervene and randomize, but there might be a situation that's done it to some extent for us, so the classic example is the Vietnam draft where it was kind of random birthdays with drawn out of lottery. And so, there's been a lot of studies subsequently about the effect of serving in the military on different subsequent lifetime outcomes because broadly those people have been randomized. It was for a different reason. But you've got that element of randomization driving that.Adam Kucharski (20:02):And so again, with some of the recent shingles data and other studies, you might have a situation for example, where there's been an intervention that's somewhat arbitrary in terms of time. It's a cutoff on a birth date, for example. And under certain assumptions you could think, well, actually there's no real reason for the person on this day and this day to be fundamentally different. I mean, perhaps there might be effects of cohorts if it's school years or this sort of thing. But generally, this isn't the same as having people who are very, very different ages and very different characteristics. It's just nature, or in this case, just a policy intervention for a different reason has given you that randomization, which allows you or pseudo randomization, which allows you to then look at something about the effect of an intervention that you wouldn't as reliably if you were just digging into the data of yes, no who's received a vaccine.Eric Topol (20:52):Yeah, no, I think it's really valuable. And now I think increasingly given priority, if you can find these natural experiments and they're not always so abundant to use to extrapolate from, but when they are, they're phenomenal. The causation correlation is so big. The issue there, I mean Judea Pearl's, the Book of Why, and you give so many great examples throughout the book in Proof. I wonder if you could comment that on that a bit more because this is where associations are confused somehow or other with a direct effect. And we unfortunately make these jumps all too frequently. Perhaps it's the most common problem that's occurring in the way we interpret medical research data.Adam Kucharski (21:52):Yeah, I think it's an issue that I think a lot of people get drilled into in their training just because a correlation between things doesn't mean that that thing causes this thing. But it really struck me as I talked to people, researching the book, in practice in research, there's actually a bit more to it in how it's played out. So first of all, if there's a correlation between things, it doesn't tell you much generally that's useful for intervention. If two things are correlated, it doesn't mean that changing that thing's going to have an effect on that thing. There might be something that's influencing both of them. If you have more ice cream sales, it will lead to more heat stroke cases. It doesn't mean that changing ice cream sales is going to have that effect, but it does allow you to make predictions potentially because if you can identify consistent patterns, you can say, okay, if this thing going up, I'm going to make a prediction that this thing's going up.Adam Kucharski (22:37):So one thing I found quite striking, actually talking to research in different fields is how many fields choose to focus on prediction because it kind of avoids having to deal with this cause and effect problem. And even in fields like psychology, it was kind of interesting that there's a lot of focus on predicting things like relationship outcomes, but actually for people, you don't want a prediction about your relationship. You want to know, well, how can I do something about it? You don't just want someone to sell you your relationship's going to go downhill. So there's almost part of the challenge is people just got stuck on prediction because it's an easier field of work, whereas actually some of those problems will involve intervention. I think the other thing that really stood out for me is in epidemiology and a lot of other fields, rightly, people are very cautious to not get that mixed up.Adam Kucharski (23:24):They don't want to mix up correlations or associations with causation, but you've kind of got this weird situation where a lot of papers go out of their way to not use causal language and say it's an association, it's just an association. It's just an association. You can't say anything about causality. And then the end of the paper, they'll say, well, we should think about introducing more of this thing or restricting this thing. So really the whole paper and its purpose is framed around a causal intervention, but it's extremely careful throughout the paper to not frame it as a causal claim. So I think we almost by skirting that too much, we actually avoid the problems that people sometimes care about. And I think a lot of the nice work that's been going on in causal inference is trying to get people to confront this more head on rather than say, okay, you can just stay in this prediction world and that's fine. And then just later maybe make a policy suggestion off the back of it.Eric Topol (24:20):Yeah, I think this is cause and effect is a very alluring concept to support proof as you so nicely go through in the book. But of course, one of the things that we use to help us is the biological mechanism. So here you have, let's say for example, you're trying to get a new drug approved by the Food and Drug Administration (FDA), and the request is, well, we want two trials, randomized trials, independent. We want to have p-values that are significant, and we want to know the biological mechanism ideally with the dose response of the drug. But there are many drugs as you review that have no biological mechanism established. And even when the tobacco problems were mounting, the actual mechanism of how tobacco use caused cancer wasn't known. So how important is the biological mechanism, especially now that we're well into the AI world where explainability is demanded. And so, we don't know the mechanism, but we also don't know the mechanism and lots of things in medicine too, like anesthetics and even things as simple as aspirin, how it works and many others. So how do we deal with this quest for the biological mechanism?Adam Kucharski (25:42):I think that's a really good point. It shows almost a lot of the transition I think we're going through currently. I think particularly for things like smoking cancer where it's very hard to run a trial. You can't make people randomly take up smoking. Having those additional pieces of evidence, whether it's an analogy with a similar carcinogen, whether it's a biological mechanism, can help almost give you more supports for that argument that there's a cause and effect going on. But I think what I found quite striking, and I realized actually that it's something that had kind of bothered me a bit and I'd be interested to hear whether it bothers you, but with the emergence of AI, it's almost a bit of the loss of scientific satisfaction. I think you grow up with learning about how the world works and why this is doing what it's doing.Adam Kucharski (26:26):And I talked for example of some of the people involved with AlphaFold and some of the subsequent work in installing those predictions about structures. And they'd almost made peace with it, which I found interesting because I think they started off being a bit uncomfortable with like, yeah, you've got these remarkable AI models making these predictions, but we don't understand still biologically what's happening here. But I think they're just settled in saying, well, biology is really complex on some of these problems, and if we can have a tool that can give us this extremely valuable information, maybe that's okay. And it was just interesting that they'd really kind of gone through that kind process, which I think a lot of people are still grappling with and that almost that discomfort of using AI and what's going to convince you that that's a useful reliable prediction whether it's something like predicting protein folding or getting in a self-driving car. What's the evidence you need to convince you that's reliable?Eric Topol (27:26):Yeah, no, I'm so glad you brought that up because when Demis Hassabis and John Jumper won the Nobel Prize, the point I made was maybe there should be an asterisk with AI because they don't know how it works. I mean, they had all the rich data from the protein data bank, and they got the transformer model to do it for 200 million protein structure prediction, but they still to this day don't fully understand how the model really was working. So it reinforces what you're just saying. And of course, it cuts across so many types of AI. It's just that we tend to hold different standards in medicine not realizing that there's lots of lack of explainability for routine medical treatments today. Now one of the things that I found fascinating in your book, because there's different levels of proof, different types of proof, but solid logical systems.Eric Topol (28:26):And on page 60 of the book, especially pertinent to the US right now, there is a bit about Kurt Gödel and what he did there was he basically, there was a question about dictatorship in the US could it ever occur? And Gödel says, “oh, yes, I can prove it.” And he's using the constitution itself to prove it, which I found fascinating because of course we're seeing that emerge right now. Can you give us a little bit more about this, because this is fascinating about the Fifth Amendment, and I mean I never thought that the Constitution would allow for a dictatorship to emerge.Adam Kucharski (29:23):And this was a fascinating story, Kurt Gödel who is one of the greatest logical minds of the 20th century and did a lot of work, particularly in the early 20th century around system of rules, particularly things like mathematics and whether they can ever be really fully satisfying. So particularly in mathematics, he showed that there were this problem that is very hard to have a set of rules for something like arithmetic that was both complete and covered every situation, but also had no contradictions. And I think a lot of countries, if you go back, things like Napoleonic code and these attempts to almost write down every possible legal situation that could be imaginable, always just ascended into either they needed amendments or they had contradictions. I think Gödel's work really summed it up, and there's a story, this is in the late forties when he had his citizenship interview and Einstein and Oskar Morgenstern went along as witnesses for him.Adam Kucharski (30:17):And it's always told as kind of a lighthearted story as this logical mind, this academic just saying something silly in front of the judge. And actually, to my own admission, I've in the past given talks and mentioned it in this slightly kind of lighthearted way, but for the book I got talking to a few people who'd taken it more seriously. I realized actually he's this extremely logically focused mind at the time, and maybe there should have been something more to it. And people who have kind of dug more into possibilities was saying, well, what could he have spotted that bothered him? And a lot of his work that he did about consistency in mass was around particularly self-referential statements. So if I say this sentence is false, it's self-referential and if it is false, then it's true, but if it's true, then it's false and you get this kind of weird self-referential contradictions.Adam Kucharski (31:13):And so, one of the theories about Gödel was that in the Constitution, it wasn't that there was a kind of rule for someone can become a dictator, but rather people can use the mechanisms within the Constitution to make it easier to make further amendments. And he kind of downward cycle of amendment that he had seen happening in Europe and the run up to the war, and again, because this is never fully documented exactly what he thought, but it's one of the theories that it wouldn't just be outright that it would just be this cycle process of weakening and weakening and weakening and making it easier to add. And actually, when I wrote that, it was all the earlier bits of the book that I drafted, I did sort of debate whether including it I thought, is this actually just a bit in the weeds of American history? And here we are. Yeah, it's remarkable.Eric Topol (32:00):Yeah, yeah. No, I mean I found, it struck me when I was reading this because here back in 1947, there was somebody predicting that this could happen based on some, if you want to call it loopholes if you will, or the ability to change things, even though you would've thought otherwise that there wasn't any possible capability for that to happen. Now, one of the things I thought was a bit contradictory is two parts here. One is from Angus Deaton, he wrote, “Gold standard thinking is magical thinking.” And then the other is what you basically are concluding in many respects. “To navigate proof, we must reach into a thicket of errors and biases. We must confront monsters and embrace uncertainty, balancing — and rebalancing —our beliefs. We must seek out every useful fragment of data, gather every relevant tool, searching wider and climbing further. Finding the good foundations among the bad. Dodging dogma and falsehoods. Questioning. Measuring. Triangulating. Convincing. Then perhaps, just perhaps, we'll reach the truth in time.” So here you have on the one hand your search for the truth, proof, which I think that little paragraph says it all. In many respects, it sums up somewhat to the work that you review here and on the other you have this Nobel laureate saying, you don't have to go to extremes here. The enemy of good is perfect, perhaps. I mean, how do you reconcile this sense that you shouldn't go so far? Don't search for absolute perfection of proof.Adam Kucharski (33:58):Yeah, I think that encapsulates a lot of what the book is about, is that search for certainty and how far do you have to go. I think one of the things, there's a lot of interesting discussion, some fascinating papers around at what point do you use these studies? What are their flaws? But I think one of the things that does stand out is across fields, across science, medicine, even if you going to cover law, AI, having these kind of cookie cutter, this is the definitive way of doing it. And if you just follow this simple rule, if you do your p-value, you'll get there and you'll be fine. And I think that's where a lot of the danger is. And I think that's what we've seen over time. Certain science people chasing certain targets and all the behaviors that come around that or in certain situations disregarding valuable evidence because you've got this kind of gold standard and nothing else will do.Adam Kucharski (34:56):And I think particularly in a crisis, it's very dangerous to have that because you might have a low level of evidence that demands a certain action and you almost bias yourself towards inaction if you have these kind of very simple thresholds. So I think for me, across all of these stories and across the whole book, I mean William Gosset who did a lot of pioneering work on statistical experiments at Guinness in the early 20th century, he had this nice question he sort of framed is, how much do we lose? And if we're thinking about the problems, there's always more studies we can do, there's always more confidence we can have, but whether it's a patient we want to treat or crisis we need to deal with, we need to work out actually getting that level of proof that's really appropriate for where we are currently.Eric Topol (35:49):I think exceptionally important that there's this kind of spectrum or continuum in following science and search for truth and that distinction, I think really nails it. Now, one of the things that's unique in the book is you don't just go through all the different types of how you would get to proof, but you also talk about how the evidence is acted on. And for example, you quote, “they spent a lot of time misinforming themselves.” This is the whole idea of taking data and torturing it or using it, dredging it however way you want to support either conspiracy theories or alternative facts. Basically, manipulating sometimes even emasculating what evidence and data we have. And one of the sentences, or I guess this is from Sir Francis Bacon, “truth is a daughter of time”, but the added part is not authority. So here we have our president here that repeats things that are wrong, fabricated or wrong, and he keeps repeating to the point that people believe it's true. But on the other hand, you could say truth is a daughter of time because you like to not accept any truth immediately. You like to see it get replicated and further supported, backed up. So in that one sentence, truth is a daughter of time not authority, there's the whole ball of wax here. Can you take us through that? Because I just think that people don't understand that truth being tested over time, but also manipulated by its repetition. This is a part of the big problem that we live in right now.Adam Kucharski (37:51):And I think it's something that writing the book and actually just reflecting on it subsequently has made me think about a lot in just how people approach these kinds of problems. I think that there's an idea that conspiracy theorists are just lazy and have maybe just fallen for a random thing, but talking to people, you really think about these things a lot more in the field. And actually, the more I've ended up engaging with people who believe things that are just outright unevidenced around vaccines, around health issues, they often have this mountain of papers and data to hand and a lot of it, often they will be peer reviewed papers. It won't necessarily be supporting the point that they think it's supports.Adam Kucharski (38:35):But it's not something that you can just say everything you're saying is false, that there's actually often a lot of things that have been put together and it's just that leap to that conclusion. I think you also see a lot of scientific language borrowed. So I gave a talker early this year and it got posted on YouTube. It had conspiracy theories it, and there was a lot of conspiracy theory supporters who piled in the comments and one of the points they made is skepticism is good. It's the kind of law society, take no one's word for it, you need this. We are the ones that are kind of doing science and people who just assume that science is settled are in the wrong. And again, you also mentioned that repetition. There's this phenomenon, it's the illusory truth problem that if you repeatedly tell someone someone's something's false, it'll increase their belief in it even if it's something quite outrageous.Adam Kucharski (39:27):And that mimics that scientific repetition because people kind of say, okay, well if I've heard it again and again, it's almost like if you tweak these as mini experiments, I'm just accumulating evidence that this thing is true. So it made me think a lot about how you've got essentially a lot of mimicry of the scientific method, amount of data and how you present it and this kind of skepticism being good, but I think a lot of it comes down to as well as just looking at theological flaws, but also ability to be wrong in not actually seeking out things that confirm. I think all of us, it's something that I've certainly tried to do a lot working on emergencies, and one of the scientific advisory groups that I worked on almost it became a catchphrase whenever someone presented something, they finished by saying, tell me why I'm wrong.Adam Kucharski (40:14):And if you've got a variant that's more transmissible, I don't want to be right about that really. And it is something that is quite hard to do and I found it is particularly for something that's quite high pressure, trying to get a policymaker or someone to write even just non-publicly by themselves, write down what you think's going to happen or write down what would convince you that you are wrong about something. I think particularly on contentious issues where someone's got perhaps a lot of public persona wrapped up in something that's really hard to do, but I think it's those kind of elements that distinguish between getting sucked into a conspiracy theory and really seeking out evidence that supports it and trying to just get your theory stronger and stronger and actually seeking out things that might overturn your belief about the world. And it's often those things that we don't want overturned. I think those are the views that we all have politically or in other ways, and that's often where the problems lie.Eric Topol (41:11):Yeah, I think this is perhaps one of, if not the most essential part here is that to try to deal with the different views. We have biases as you emphasized throughout, but if you can use these different types of proof to have a sound discussion, conversation, refutation whereby you don't summarily dismiss another view which may be skewed and maybe spurious or just absolutely wrong, maybe fabricated whatever, but did you can engage and say, here's why these are my proof points, or this is why there's some extent of certainty you can have regarding this view of the data. I think this is so fundamental because unfortunately as we saw during the pandemic, the strident minority, which were the anti-science, anti-vaxxers, they were summarily dismissed as being kooks and adopting conspiracy theories without the right engagement and the right debates. And I think this might've helped along the way, no less the fact that a lot of scientists didn't really want to engage in the first place and adopt this methodical proof that you've advocated in the book so many different ways to support a hypothesis or an assertion. Now, we've covered a lot here, Adam. Have I missed some central parts of the book and the effort because it's really quite extraordinary. I know it's your third book, but it's certainly a standout and it certainly it's a standout not just for your books, but books on this topic.Adam Kucharski (43:13):Thanks. And it's much appreciated. It was not an easy book to write. I think at times, I kind of wondered if I should have taken on the topic and I think a core thing, your last point speaks to that. I think a core thing is that gap often between what convinces us and what convinces someone else. I think it's often very tempting as a scientist to say the evidence is clear or the science has proved this. But even on something like the vaccines, you do get the loud minority who perhaps think they're putting microchips in people and outlandish views, but you actually get a lot more people who might just have some skepticism of pharmaceutical companies or they might have, my wife was pregnant actually at the time during Covid and we waited up because there wasn't much data on pregnancy and the vaccine. And I think it's just finding what is convincing. Is it having more studies from other countries? Is it understanding more about the biology? Is it understanding how you evaluate some of those safety signals? And I think that's just really important to not just think what convinces us and it's going to be obvious to other people, but actually think where are they coming from? Because ultimately having proof isn't that good unless it leads to the action that can make lives better.Eric Topol (44:24):Yeah. Well, look, you've inculcated my mind with this book, Adam, called Proof. Anytime I think of the word proof, I'm going to be thinking about you. So thank you. Thanks for taking the time to have a conversation about your book, your work, and I know we're going to count on you for the astute mathematics and analysis of outbreaks in the future, which we will see unfortunately. We are seeing now, in fact already in this country with measles and whatnot. So thank you and we'll continue to follow your great work.**************************************Thanks for listening, watching or reading this Ground Truths podcast/post.If you found this interesting please share it!That makes the work involved in putting these together especially worthwhile.I'm also appreciative for your subscribing to Ground Truths. All content —its newsletters, analyses, and podcasts—is free, open-access. I'm fortunate to get help from my producer Jessica Nguyen and Sinjun Balabanoff for audio/video tech support to pull these podcasts together for Scripps Research.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don't hesitate to post comments and give me feedback. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years.A bit of an update on SUPER AGERSMy book has been selected as a Next Big Idea Club winner for Season 26 by Adam Grant, Malcolm Gladwell, Susan Cain, and Daniel Pink. This club has spotlighted the most groundbreaking nonfiction books for over a decade. As a winning title, my book will be shipped to thousands of thoughtful readers like you, featured alongside a reading guide, a "Book Bite," Next Big Idea Podcast episode as well as a live virtual Q&A with me in the club's vibrant online community. If you're interested in joining the club, here's a promo code SEASON26 for 20% off at the website. SUPER AGERS reached #3 for all books on Amazon this week. This was in part related to the segment on the book on the TODAY SHOW which you can see here. Also at Amazon there is a remarkable sale on the hardcover book for $10.l0 at the moment for up to 4 copies. Not sure how long it will last or what prompted it.The journalist Paul von Zielbauer has a Substack “Aging With Strength” and did an extensive interview with me on the biology of aging and how we can prevent the major age-related diseases. Here's the link. Get full access to Ground Truths at erictopol.substack.com/subscribe

George Church is the godfather of modern synthetic biology and has been involved with basically every major biotech breakthrough in the last few decades.Professor Church thinks that these improvements (e.g., orders of magnitude decrease in sequencing & synthesis costs, precise gene editing tools like CRISPR, AlphaFold-type AIs, & the ability to conduct massively parallel multiplex experiments) have put us on the verge of some massive payoffs: de-aging, de-extinction, biobots that combine the best of human and natural engineering, and (unfortunately) weaponized mirror life.Watch on YouTube; listen on Apple Podcasts or Spotify.Sponsors* WorkOS Radar ensures your product is ready for AI agents. Radar is an anti-fraud solution that categorizes different types of automated traffic, blocking harmful bots while allowing helpful agents. Future-proof your roadmap today at workos.com/radar.* Scale is building the infrastructure for smarter, safer AI. In addition to their Data Foundry, they recently released Scale Evaluation, a tool that diagnoses model limitations. Learn how Scale can help you push the frontier at scale.com/dwarkesh.* Gemini 2.5 Pro was invaluable during our prep for this episode: it perfectly explained complex biology and helped us understand the most important papers. Gemini's recently improved structure and style also made using it surprisingly enjoyable. Start building with it today at https://aistudio.google.comTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps(0:00:00) – Aging solved by 2050(0:07:37) – Finding the master switch for any trait(0:19:50) – Weaponized mirror life(0:30:40) – Why hasn't sequencing/synthesis led to biotech revolution?(0:50:26) – Impact of AGI on biology research progress(1:00:35) – Biobots that use the best of biological and human engineering(1:05:09) – Odds of life in universe(1:09:57) – Is DNA the ultimate data storage?(1:13:55) – Curing rare diseases with genetic counseling(1:22:23) – NIH & NSF budget cuts(1:25:26) – How one lab spawned 100 biotech companies Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Today's guest is Annabel Romero, Specialist Leader focusing on AI for Drug Discovery at Deloitte and a structural biologist by training. Deloitte is a global consulting firm known for its work in digital transformation, data strategy, and AI adoption across regulated industries. Annabel joins Emerj Editorial Director Matthew DeMello to explore how AI systems are being designed to think more like scientists—particularly in protein modeling and life sciences research. She shares how tools like AlphaFold and large language models are accelerating drug targeting, predicting allergen cross-reactivity, and translating learnings from human biology to agricultural innovation. This episode is sponsored by Deloitte. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast!

”I really love the notion of contributing something to physics.” — Chemistry laureate John Jumper has always been passionate about science and understanding the world. With the AI tool AlphaFold, he and his co-laureate Demis Hassabis have provided a possibility to predict protein structures. In this podcast conversation, Jumper speaks about the excitement of seeing how AI can help us more in the future.Jumper also shares his scientific journey and how he ended up working with AlphaFold. He describes a special memory from the 2018 CASP conference where AlphaFold was presented for the first time. Another life-changing moment was the announcement of the Nobel Prize in Chemistry in October 2024 – Jumper tells us how his life has changed since then. Through their lives and work, failures and successes – get to know the individuals who have been awarded the Nobel Prize on the Nobel Prize Conversations podcast. Find it on Acast, or wherever you listen to pods. https://linktr.ee/NobelPrizeConversations© Nobel Prize Outreach. Hosted on Acast. See acast.com/privacy for more information.

Can AI help us model biology down to the molecular level? Neil deGrasse Tyson, Chuck Nice, and Gary O'Reilly learn about Nobel-prize-winning Alphafold, the protein folding problem, and how solving it could end disease with AI researcher, Max Jaderberg. NOTE: StarTalk+ Patrons can listen to this entire episode commercial-free here: https://startalkmedia.com/show/curing-all-disease-with-ai-with-max-jaderberg/Thanks to our Patrons Riley r, pesketti, Lindsay Vanlerberg, Andreas, Silvia Valentine, Brazen Rigsby, Marc, Lyda Swanston, Kevin Henry, Roberto Reyes, Cadexn, Cassandra Shanklin, Stan Adamson, Will Slade, Zach VanderGraaff, Tom Spalango, Laticia Edmonds, jason scott, Jigar Gada, Robert Jensen, Matt D., TOL, Thomas McDaniel, Sr., Ryan Ramsey, truthmind, Aaron TInker, George Assaf, Dante Ruzinok, Jonathan Ford, Just Ernst, David Eli Janes, Tamil, Sarah, Earnest Lee, Craig Hanson, Rob, Be Love, Brandon Wilson, TJ Kellysawyer, Bodhi Animations, Dave P., Christina Williams, Ivaylo Vartigorov, Roy Mitsuoka (@surflightroy), John Brendel, Moises Zorrilla, deborah shaw, Jim Muoio, Tahj Ward, Phil, Alex, Brian D. Smith, Nate Barmore, John J Lopez, Raphael Velazquez Cruz, Catboi Air, Jelly Mint, Audie Cruz for supporting us this week. Subscribe to SiriusXM Podcasts+ to listen to new episodes of StarTalk Radio ad-free and a whole week early.Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus.

Think AI is hitting a wall? Nope. This is just the start. Actually, we're at the first chapter. Here's what that means, and how you can move your company ahead. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the conversationUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Generative AI's current phaseMeta's in-house AI chips developmentOpenAI's new developer toolsDay zero of AI and future prospectsReinforcement learning advancementsEmergent reasoning capabilities in AIBusiness implications of AI advancementsAI in healthcare and scienceTimestamps:00:00 Day Zero of AI03:31 AI Tools Enhance Customization & Access09:02 Reinforcement Learning Enhances AI Reasoning11:27 Agentic AI: The Future of Tasks15:59 Tech Potential vs. Everyday Utilization18:48 AI Models Offer Broad Benefits23:15 "Generative AI: Optimism and Oversight"27:08 Generative AI vs. Domain-Specific AI29:24 Superhuman AI: Next FrontierKeywords:Generative AI, Fortune 100 leaders, chat GBT, Microsoft Copilot, enterprise companies, day zero of AI, livestream podcast, free daily newsletter, leveraging AI, capital expenditures, Meta AI chips, Nvidia, Taiwan's TSMC, AI infrastructure investments, Amazon, Google, Microsoft, OpenAI, responses API, agents SDK, legal research, customer support, deep research, agentic AI, supervised learning, reinforcement learning, language models, health care, computational biology, AlphaFold, protein folding prediction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

In Part 2 of Tom's wide-ranging conversation with Andy Weir, Andy explores how AI will transform material science, medicine, biotechnology, and possibly even human evolution itself. From AI-designed drugs and custom gene editing to the ethical dilemmas of “designer babies” and the future of cosmetic self-alteration, Andy contemplates what these advances could mean for human identity, equality, and society's deepest values. The episode then hurtles into the far future, weighing the prospects of artificial superintelligence, AI alignment, and the ultimate “tool or agent” debate. Tom and Andy touch on open versus closed source AI, existential risk, and what humanity's historical track record tells us about technology. SHOWNOTES 22:08 AI's leap in material science, biotech, and AlphaFold's revolution28:49 Hardware bottlenecks and the coming “AI card” revolution32:09 Efficiency breakthroughs, compression, and training paradigm shifts36:10 How new materials could propel us to low Earth orbit38:39 AI-designed proteins: The promise and danger within biology39:47 The ethics of designer babies: Health, intelligence, and consent46:38 The coming age of “cosmetic ethnicity” and identity fluidity47:29 Body hacking: Social and economic consequences, from eating to politics48:32 Why society will push—and resist—genetic modifications49:34 The looming “intelligence arms race” between humans and AI50:15 Why Andy doubts the need to compete with AI; the “bulldozer analogy”57:15 Caution and optimism: Why Andy expects a post-scarcity AI future58:10 Why “control” is likely to stay with humans—unless we hand it over1:01:04 Open source debate, narrative control, and algorithmic bias1:28:00 What excites Andy: Self-driving cars and societal revolution1:33:57 Andy on writing, his approach to AI, and what's next for his books1:35:29 Where to follow Andy Weir FOLLOW ANDY WEIR:Twitter/X: @andyweirauthorFacebook: Andy Weir CHECK OUT OUR SPONSORS ButcherBox: Ready to level up your meals? Go to ⁠https://ButcherBox.com/impact⁠ to get $20 off your first box and FREE bacon for life with the Bilyeu Box! Vital Proteins: Get 20% off by going to ⁠https://www.vitalproteins.com⁠ and entering promo code IMPACT at check out Netsuite: Download the CFO's Guide to AI and Machine Learning at ⁠https://NetSuite.com/THEORY⁠ iTrust Capital: Use code IMPACTGO when you sign up and fund your account to get a $100 bonus at ⁠https://www.itrustcapital.com/tombilyeu⁠  Mint Mobile: If you like your money, Mint Mobile is for you. Shop plans at ⁠https://mintmobile.com/impact.⁠  DISCLAIMER: Upfront payment of $45 for 3-month 5 gigabyte plan required (equivalent to $15/mo.). New customer offer for first 3 months only, then full-price plan options available. Taxes & fees extra. See MINT MOBILE for details. What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business:⁠ join me here at ZERO TO FOUNDER⁠ SCALING a business:⁠ see if you qualify here.⁠ Get my battle-tested strategies and insights delivered weekly to your inbox:⁠ sign up here.⁠ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast,⁠ Tom Bilyeu's Mindset Playbook⁠ —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** LISTEN TO IMPACT THEORY AD FREE + BONUS EPISODES on APPLE PODCASTS:⁠ apple.co/impacttheory⁠ ********************************************************************** FOLLOW TOM: Instagram:⁠ https://www.instagram.com/tombilyeu/⁠ Tik Tok:⁠ https://www.tiktok.com/@tombilyeu?lang=en⁠ Twitter:⁠ https://twitter.com/tombilyeu⁠ YouTube:⁠ https://www.youtube.com/@TomBilyeu⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Delve into the intersection of privacy, ethics, and technology with Dr. Rand Hindi, CEO of Zama. We discuss Dr. Hindi's early fascination with encryption, the future of homomorphic encryption, and its profound implications for various sectors including healthcare and advertising.Hindi explains advancements in bioinformatics, his vision of ubiquitous computing, and how individualized medicine can benefit from encrypted data.He also shares his personal experiments with diet and fitness, promising advancements in medtech, and the future outlook on transhumanism.00:00 Introduction to Privacy and Ethics00:07 Meet Dr. Rand Hindi: Deep Tech Entrepreneur00:53 The Personal Connection to Encryption03:26 Homomorphic Encryption Explained05:34 Advancements and Applications of Homomorphic Encryption07:22 The Future of Ubiquitous Computing09:36 AlphaFold and the Future of Biology12:12 Homomorphic Encryption in Healthcare14:47 Personal Experiments in Weight Loss & Fitness19:41 The Role of AI in Job Automation21:53 Ethereum and the Future of Blockchain24:17 The Potential of Precision Psychedelics26:55 Transhumanism and Extending Human Lifespan34:43 Geoengineering and Climate Change37:32 Final Thoughts and Farewell Get full access to Evolving with Nita Jain at www.nitajain.com/subscribe

The race to harness AI for scientific discovery may be the most consequential technological competition of this time—yet it's happening largely out of public view. While many AI headlines focus on chatbots writing essays and tech giants battling over billion-dollar models, a quiet revolution is brewing in America's laboratories.AI systems like AlphaFold (which recently won a Nobel Prize for protein structure prediction) are solving scientific problems that stumped humans for decades. A bipartisan coalition in Congress is now championing what they call the "American Science Acceleration Project" or ASAP—an audacious plan to make U.S. scientific research "ten times faster by 2030" through strategic deployment of AI. But as federal science funding faces pressure and international competition heats up, can America build the AI-powered scientific infrastructure we need? Will the benefits reach beyond elite coastal institutions to communities nationwide? And how do we ensure that as AI transforms scientific discovery, it creates opportunities instead of new divides?Joining us is Austin Carson, Founder and President of SeedAI, a nonprofit dedicated to expanding AI access and opportunity across America. Before launching SeedAI, Carson led government affairs at NVIDIA and served as Legislative Director for Rep. Michael McCaul. He's been deep in AI policy since 2016—ancient history in this rapidly evolving field—and recently organized the first-ever generative AI red-teaming event at DEF CON, collaborating with the White House to engage hundreds of college students in identifying AI vulnerabilities.

After pioneering reinforcement learning breakthroughs at DeepMind with Capture the Flag and AlphaStar, Max Jaderberg aims to revolutionize drug discovery with AI as Chief AI Officer of Isomorphic Labs, which was spun out of DeepMind. He discusses how AlphaFold 3's diffusion-based architecture enables unprecedented understanding of molecular interactions, and why we're approaching a "Move 37 moment" in AI-powered drug design where models will surpass human intuition. Max shares his vision for general AI models that can solve all diseases, and the importance of developing agents that can learn to search through the whole potential design space. Hosted by Stephanie Zhan, Sequoia capital Mentioned in this episode:  Playing Atari with Deep Reinforcement Learning: Seminal 2013 paper on Reinforcement Learning  Capture the Flag: 2019 DeepMind paper on the emergence of cooperative agents AlphaStar: 2019 DeepMind paper on attaining grandmaster level in StarCraft II using multi-agent RL AlphaFold Server: Web interface for AlphaFold 3 model for non-commercial academic use

This week on "Waking Up With AI," Katherine Forrest and Anna Gressel explore the intersection of AI and biology, focusing on how DeepMind's AlphaFold has revolutionized protein folding prediction by enabling scientists to better understand protein structures and interactions. ## Learn More About Paul, Weiss's Artificial Intelligence Practice: https://www.paulweiss.com/practices/litigation/artificial-intelligence

Demis Hassabis, CEO of Google DeepMind, sparked excitement with his 60 Minutes interview, outlining AI's potential to end all diseases within a decade. Drawing parallels to AlphaFold's revolutionary protein folding solution, Hassabis envisions AI drastically accelerating drug discovery, compressing timelines from years and billions to mere months by rapidly analyzing vast datasets. He highlights DeepMind's AI's astonishing discovery of millions of new materials, far surpassing traditional research, showcasing AI's power to "blaze through solutions." We delve into this ambitious vision, considering its feasibility and comparing it to futuristic scenarios, while also exploring AI's growing impact in cybersecurity, fraud prevention, and diagnostics.Beyond healthcare, we touch upon Will Manidis's intriguing observations on unexpected "miracle cures" linked to LLMs and a humorous take from Sam Altman on ChatGPT etiquette. We also spotlight a compelling custom ChatGPT prompt shared by @andrewchen (https://x.com/andrewchen/status/1914168705228882105). Join us for a thought-provoking discussion on the transformative power of AI and its potential to revolutionize our future.Mentioned: @GoogleDeepMind @demishassabis @WillManidis @andrewchen

How can AI help us understand and master deeply complex systems—from the game Go, which has 10 to the power 170 possible positions a player could pursue, or proteins, which, on average, can fold in 10 to the power 300 possible ways? This week, Reid and Aria are joined by Demis Hassabis. Demis is a British artificial intelligence researcher, co-founder, and CEO of the AI company, DeepMind. Under his leadership, DeepMind developed Alpha Go, the first AI to defeat a human world champion in Go and later created AlphaFold, which solved the 50-year-old protein folding problem. He's considered one of the most influential figures in AI. Demis, Reid, and Aria discuss game theory, medicine, multimodality, and the nature of innovation and creativity. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  Listen to more from Possible here. Learn more about your ad choices. Visit podcastchoices.com/adchoices

How can AI help us understand and master deeply complex systems—from the game Go, which has 10 to the power 170 possible positions a player could pursue, or proteins, which, on average, can fold in 10 to the power 300 possible ways? This week, Reid and Aria are joined by Demis Hassabis. Demis is a British artificial intelligence researcher, co-founder, and CEO of the AI company, DeepMind. Under his leadership, DeepMind developed Alpha Go, the first AI to defeat a human world champion in Go and later created AlphaFold, which solved the 50-year-old protein folding problem. He's considered one of the most influential figures in AI. Demis, Reid, and Aria discuss game theory, medicine, multimodality, and the nature of innovation and creativity. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  Select mentions:  Hitchhiker's Guide to the Galaxy by Douglas Adams AlphaGo documentary: https://www.youtube.com/watch?v=WXuK6gekU1Y Nash equilibrium & US mathematician John Forbes Nash Homo Ludens by Johan Huizinga Veo 2, an advanced, AI-powered video creation platform from Google DeepMind The Culture series by Iain Banks Hartmut Neven, German-American computer scientist Topics: 3:11 - Hellos and intros 5:20 - Brute force vs. self-learning systems 8:24 - How a learning approach helped develop new AI systems 11:29 - AlphaGo's Move 37 16:16 - What will the next Move 37 be? 19:42 - What makes an AI that can play the video game StarCraft impressive 22:32 - The importance of the act of play 26:24 - Data and synthetic data 28:33 - Midroll ad 28:39 - Is it important to have AI embedded in the world? 33:44 - The trade-off between thinking time and output quality 36:03 - Computer languages designed for AI 40:22 - The future of multimodality  43:27 - AI and geographic diversity  48:24 - AlphaFold and the future of medicine 51:18 - Rapid-fire Questions Possible is an award-winning podcast that sketches out the brightest version of the future—and what it will take to get there. Most of all, it asks: what if, in the future, everything breaks humanity's way? Tune in for grounded and speculative takes on how technology—and, in particular, AI—is inspiring change and transforming the future. Hosted by Reid Hoffman and Aria Finger, each episode features an interview with an ambitious builder or deep thinker on a topic, from art to geopolitics and from healthcare to education. These conversations also showcase another kind of guest: AI. Each episode seeks to enhance and advance our discussion about what humanity could possibly get right if we leverage technology—and our collective effort—effectively.

In this episode of Crazy Wisdom, Stewart Alsop speaks with German Jurado about the strange loop between computation and biology, the emergence of reasoning in AI models, and what it means to "stand on the shoulders" of evolutionary systems. They talk about CRISPR not just as a gene-editing tool, but as a memory architecture encoded in bacterial immunity; they question whether LLMs are reasoning or just mimicking it; and they explore how scientists navigate the unknown with a kind of embodied intuition. For more about German's work, you can connect with him through email at germanjurado7@gmail.com.Check out this GPT we trained on the conversation!Timestamps00:00 - Stewart introduces German Jurado and opens with a reflection on how biology intersects with multiple disciplines—physics, chemistry, computation.05:00 - They explore the nature of life's interaction with matter, touching on how biology is about the interface between organic systems and the material world.10:00 - German explains how bioinformatics emerged to handle the complexity of modern biology, especially in genomics, and how it spans structural biology, systems biology, and more.15:00 - Introduction of AI into the scientific process—how models are being used in drug discovery and to represent biological processes with increasing fidelity.20:00 - Stewart and German talk about using LLMs like GPT to read and interpret dense scientific literature, changing the pace and style of research.25:00 - The conversation turns to societal implications—how these tools might influence institutions, and the decentralization of expertise.30:00 - Competitive dynamics between AI labs, the scaling of context windows, and speculation on where the frontier is heading.35:00 - Stewart reflects on English as the dominant language of science and the implications for access and translation of knowledge.40:00 - Historical thread: they discuss the Republic of Letters, how the structure of knowledge-sharing has evolved, and what AI might do to that structure.45:00 - Wrap-up thoughts on reasoning, intuition, and the idea of scientists as co-evolving participants in both natural and artificial systems.50:00 - Final reflections and thank-yous, German shares where to find more of his thinking, and Stewart closes the loop on the conversation.Key InsightsCRISPR as a memory system – Rather than viewing CRISPR solely as a gene-editing tool, German Jurado frames it as a memory architecture—an evolved mechanism through which bacteria store fragments of viral DNA as a kind of immune memory. This perspective shifts CRISPR into a broader conceptual space, where memory is not just cognitive but deeply biological.AI models as pattern recognizers, not yet reasoners – While large language models can mimic reasoning impressively, Jurado suggests they primarily excel at statistical pattern matching. The distinction between reasoning and simulation becomes central, raising the question: are these systems truly thinking, or just very good at appearing to?The loop between computation and biology – One of the core themes is the strange feedback loop where biology inspires computational models (like neural networks), and those models in turn are used to probe and understand biological systems. It's a recursive relationship that's accelerating scientific insight but also complicating our definitions of intelligence and understanding.Scientific discovery as embodied and intuitive – Jurado highlights that real science often begins in the gut, in a kind of embodied intuition before it becomes formalized. This challenges the myth of science as purely rational or step-by-step and instead suggests that hunches, sensory experience, and emotional resonance play a crucial role.Proteins as computational objects – Proteins aren't just biochemical entities—they're shaped by information. Their structure, function, and folding dynamics can be seen as computations, and tools like AlphaFold are beginning to unpack that informational complexity in ways that blur the line between physics and code.Human alignment is messier than AI alignment – While AI alignment gets a lot of attention, Jurado points out that human alignment—between scientists, institutions, and across cultures—is historically chaotic. This reframes the AI alignment debate in a broader evolutionary and historical context, questioning whether we're holding machines to stricter standards than ourselves.Standing on the shoulders of evolutionary processes – Evolution is not just a backdrop but an active epistemic force. Jurado sees scientists as participants in a much older system of experimentation and iteration—evolution itself. In this view, we're not just designing models; we're being shaped by them, in a co-evolution of tools and understanding.

Today's guest is Dan Elton, a Staff Scientist at the National Human Genome Research Institute (NHGRI) at the National Institutes of Health (NIH). Dan returns to the program to explore how AI is advancing genetic research, from protein engineering to gene editing and risk prediction. One of the most significant breakthroughs in this space is AlphaFold, DeepMind's AI model that predicts protein structures with unprecedented accuracy. While it does not analyze genetic sequences directly, its ability to model protein folding is transforming drug development and protein engineering. Dan also discusses the potential for AI to improve polygenic risk prediction, where machine learning models are being applied to assess disease risk based on genetic markers. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Today's guest is Nishtha Jain, Head of Innovation and Digital Technology at Takeda Pharmaceuticals. Nistha returns to the program to discuss the transformative impact of AI across the pharmaceutical value chain. She and Emerj Editorial Director Matthew DeMello explore how AI is improving drug development efficiency, addressing data challenges, and overcoming regulatory hurdles. She highlights key breakthroughs like Google DeepMind's AlphaFold and AI-assisted clinical trial optimization, emphasizing the potential for faster, more cost-effective drug development. The discussion also covers the challenges of AI adoption, including data accessibility, regulatory compliance, and ethical considerations like bias in AI models. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Many in venture capital and biopharma are anointing artificial intelligence the savior of drug discovery—but what can AI actually do?In this eye-opening episode, Michael Marks sits down with Mike Nohaile, CEO of Prellis Biologics, to explore the hype versus reality in AI-enabled drug discovery. Mike details why, despite significant breakthroughs like AlphaFold and recent Nobel Prize win for computational protein design, fully AI-generated medicines still present challenges. He also discusses why we urgently need more effective medicines and details Prellis' unique system which combines laser printed human organoids and an externalized human immune system with AI, enabling the discovery of fully human antibodies. If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits.Links:Explore Prellis Biologicshttps://prellisbio.com/Understand AlphaFold, DeepMind's AI model for predicting protein structureshttps://deepmind.google/alphafoldRead about the 2024 Nobel Prize in Chemistry https://www.nobelprize.org/prizes/chemistry/2024/press-release/ 

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It could be argued that biology has always boiled down to chemistry, and that chemistry has always boiled down to physics. However, not many would deny that the fields of biology and chemistry are overlapping more than ever, with both leveraging computing methods, also more than ever. This conversation with Dr. Ramesh Jha, Technical Staff Member at Los Alamos National Laboratory (LANL), crosses biology, chemistry, and computing methods. The work of his biome team at LANL uses computational tools to inform the design of enzymes that are produced via PCR-based cloning and then expressed in microbes. They use fluorescent gene circuits in these microbes, along with flow cytometry, to screen these large libraries for advantageous gain-of-function variants. When they find an interesting mutation, they isolate it, sequence it, and produce and evaluate those biocatalytic enzymes for bioremediation, biomanufacturing, and other important applications. Ramesh makes this complex and interdisciplinary science approachable and gives hope to how it could help address problems of “forever chemicals” and other environmental and manufacturing challenges. Join us for this interesting and inspiring conversation.  Subscribe to get future episodes as they drop and if you like what you're hearing we hope you'll share a review or recommend the series to a colleague.  Visit the Invitrogen School of Molecular Biology to access helpful molecular biology resources and educational content, and please share this resource with anyone you know working in molecular biology. For Research Use Only. Not for use in diagnostic procedures.

Funding for the NIH and US biomedical research is imperiled at a momentous time of progress. Exemplifying this is the work of Dr. Anna Greka, a leading physician-scientist at the Broad Institute who is devoted to unlocking the mysteries of rare diseases— that cumulatively affect 30 million Americans— and finding cures, science supported by the NIH.A clip from our conversationThe audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.Transcript with audio and external linksEric Topol (00:06):Well, hello. This is Eric Topol from Ground Truths, and I am really delighted to welcome today, Anna Greka. Anna is the president of the American Society for Clinical Investigation (ASCI) this year, a very prestigious organization, but she's also at Mass General Brigham, a nephrologist, a cell biologist, a physician-scientist, a Core Institute Member of the Broad Institute of MIT and Harvard, and serves as a member of the institute's Executive Leadership Team. So we got a lot to talk about of all these different things you do. You must be pretty darn unique, Anna, because I don't know any cell biologists, nephrologists, physician-scientist like you.Anna Greka (00:48):Oh, thank you. It's a great honor to be here and glad to chat with you, Eric.Eric Topol (00:54):Yeah. Well, I had the real pleasure to hear you speak at a November conference, the AI for Science Forum, which we'll link to your panel. Where I was in a different panel, but you spoke about your extraordinary work and it became clear that we need to get you on Ground Truths, so you can tell your story to everybody. So I thought rather than kind of going back from the past where you were in Greece and somehow migrated to Boston and all that. We're going to get to that, but you gave an amazing TED Talk and it really encapsulated one of the many phenomenal stories of your work as a molecular sleuth. So maybe if you could give us a synopsis, and of course we'll link to that so people could watch the whole talk. But I think that Mucin-1 or MUC1, as you call it, discovery is really important to kind of ground our discussion.A Mysterious Kidney Disease Unraveled Anna Greka (01:59):Oh, absolutely. Yeah, it's an interesting story. In some ways, in my TED Talk, I highlight one of the important families of this story, a family from Utah, but there's also other important families that are also part of the story. And this is also what I spoke about in London when we were together, and this is really sort of a medical mystery that initially started on the Mediterranean island of Cyprus, where it was found that there were many families in which in every generation, several members suffered and ultimately died from what at the time was a mysterious kidney disease. This was more than 30 years ago, and it was clear that there was something genetic going on, but it was impossible to identify the gene. And then even with the advent of Next-Gen sequencing, this is what's so interesting about this story, it was still hard to find the gene, which is a little surprising.Anna Greka (02:51):After we were able to sequence families and identify monogenic mutations pretty readily, this was still very resistant. And then it actually took the firepower of the Broad Institute, and it's actually from a scientific perspective, an interesting story because they had to dust off the old-fashioned Sanger sequencing in order to get this done. But they were ultimately able to identify this mutation in a VNTR region of the MUC1 gene. The Mucin-1 gene, which I call a dark corner of the human genome, it was really, it's highly repetitive, very GC-rich. So it becomes very difficult to sequence through there with Next-Gen sequencing. And so, ultimately the mutation of course was found and it's a single cytosine insertion in a stretch of cytosines that sort of causes this frameshift mutation and an early stop codon that essentially results in a neoprotein like a toxic, what I call a mangled protein that sort of accumulates inside the kidney cells.Anna Greka (03:55):And that's where my sort of adventure began. It was Eric Lander's group, who is the founding director of the Broad who discovered the mutation. And then through a conversation we had here in Boston, we sort of discovered that there was an opportunity to collaborate and so that's how I came to the Broad, and that's the beginnings of this story. I think what's fascinating about this story though, that starts in a remote Mediterranean island and then turns out to be a disease that you can find in every continent all over the world. There are probably millions of patients with kidney disease in whom we haven't recognized the existence of this mutation. What's really interesting about it though is that what we discovered is that the mangled protein that's a result of this misspelling of this mutation is ultimately captured by a family of cargo receptors, they're called the TMED cargo receptors and they end up sort of grabbing these misfolded proteins and holding onto them so tight that it's impossible for the cell to get rid of them.Anna Greka (04:55):And they become this growing heap of molecular trash, if you will, that becomes really hard to manage, and the cells ultimately die. So in the process of doing this molecular sleuthing, as I call it, we actually also identified a small molecule that actually disrupts these cargo receptors. And as I described in my TED Talk, it's a little bit like having these cargo trucks that ultimately need to go into the lysosome, the cells recycling facility. And this is exactly what this small molecule can do. And so, it was just like a remarkable story of discovery. And then I think the most exciting of all is that these cargo receptors turn out to be not only relevant to this one mangled misshapen protein, but they actually handle a completely different misshapen protein caused by a different genetic mutation in the eye, causing retinitis pigmentosa, a form of blindness, familial blindness. We're now studying familial Alzheimer's disease that's also involving these cargo receptors, and there are other mangled misshapen proteins in the liver, in the lung that we're now studying. So this becomes what I call a node, like a nodal mechanism that can be targeted for the benefit of many more patients than we had previously thought possible, which has been I think, the most satisfying part about this story of molecular sleuthing.Eric Topol (06:20):Yeah, and it's pretty extraordinary. We'll put the figure from your classic Cell paper in 2019, where you have a small molecule that targets the cargo receptor called TMED9.Anna Greka (06:34):Correct.Expanding the MissionEric Topol (06:34):And what's amazing about this, of course, is the potential to reverse this toxic protein disease. And as you say, it may have applicability well beyond this MUC1 kidney story, but rather eye disease with retinitis pigmentosa and the familial Alzheimer's and who knows what else. And what's also fascinating about this is how, as you said, there were these limited number of families with the kidney disease and then you found another one, uromodulin. So there's now, as you say, thousands of families, and that gets me to part of your sleuth work is not just hardcore science. You started an entity called the Ladders to Cures (L2C) Scientific Accelerator.Eric Topol (07:27):Maybe you can tell us about that because this is really pulling together all the forces, which includes the patient advocacy groups, and how are we going to move forward like this?Anna Greka (07:39):Absolutely. I think the goal of the Ladders to Cures Accelerator, which is a new initiative that we started at the Broad, but it really encompasses many colleagues across Boston. And now increasingly it's becoming sort of a national, we even have some international collaborations, and it's only two years that it's been in existence, so we're certainly in a growth mode. But the inspiration was really some of this molecular sleuthing work where I basically thought, well, for starters, it cannot be that there's only one molecular node, these TMED cargo receptors that we discovered there's got to be more, right? And so, there's a need to systematically go and find more nodes because obviously as anyone who works in rare genetic diseases will tell you, the problem for all of us is that we do what I call hand to hand combat. We start with the disease with one mutation, and we try to uncover the mechanism and then try to develop therapies, and that's wonderful.Anna Greka (08:33):But of course, it's slow, right? And if we consider the fact that there are 30 million patients in the United States in every state, everywhere in the country who suffer from a rare genetic disease, most of them, more than half of them are children, then we can appreciate the magnitude of the problem. Out of more than 8,000 genes that are involved in rare genetic diseases, we barely have something that looks like a therapy for maybe 500 of them. So there's a huge mismatch in the unmet need and magnitude of the problem. So the Ladders to Cures Accelerator is here to address this and to do this with the most modern tools available. And to your point, Eric, to bring patients along, not just as the recipients of whatever we discover, but also as partners in the research enterprise because it's really important to bring their perspectives and of course their partnerships in things like developing appropriate biomarkers, for example, for what we do down the road.Anna Greka (09:35):But from a fundamental scientific perspective, this is basically a project that aims to identify every opportunity for nodes, underlying all rare genetic diseases as quickly as possible. And this was one of the reasons I was there at the AI for Science Forum, because of course when one undertakes a project in which you're basically, this is what we're trying to do in the Ladders to Cures Accelerator, introduce dozens of thousands of missense and nonsense human mutations that cause genetic diseases, simultaneously introduce them into multiple human cells and then use modern scalable technology tools. Things like CRISPR screens, massively parallel CRISPR screens to try to interrogate all of these diseases in parallel, identify the nodes, and then develop of course therapeutic programs based on the discovery of these nodes. This is a massive data generation project that is much needed and in addition to the fact that it will help hopefully accelerate our approach to all rare diseases, genetic diseases. It is also a highly controlled cell perturbation dataset that will require the most modern tools in AI, not only to extract the data and understand the data of this dataset, but also because this, again, an extremely controlled, well controlled cell perturbation dataset can be used to train models, train AI models, so that in the future, and I hope this doesn't sound too futuristic, but I think that we're all aiming for that cell biologists for sure dream of this moment, I think when we can actually have in silico the opportunity to make predictions about what cell behaviors are going to look like based on a new perturbation that was not in the training set. So an experiment that hasn't yet been done on a cell, a perturbation that has not been made on a human cell, what if like a new drug, for example, or a new kind of perturbation, a new chemical perturbation, how would it affect the behavior of the cell? Can we make a predictive model for that? This doesn't exist today, but I think this is something, the cell prediction model is a big question for biology for the future. And so, I'm very energized by the opportunity to both address this problem of rare monogenic diseases that remains an unmet need and help as many patients as possible while at the same time advancing biology as much as we possibly can. So it's kind of like a win-win lifting all boats type of enterprise, hopefully.Eric Topol (12:11):Yeah. Well, there's many things to get to unpack what you've just been reviewing. So one thing for sure is that of these 8,000 monogenic diseases, they have relevance to the polygenic common diseases, of course. And then also the fact that the patient family advocates, they are great at scouring the world internet, finding more people, bringing together communities for each of these, as you point out aptly, these rare diseases cumulatively are high, very high proportion, 10% of Americans or more. So they're not so rare when you think about the overall.Anna Greka (12:52):Collectively.Help From the Virtual Cell?Eric Topol (12:53):Yeah. Now, and of course is this toxic proteinopathies, there's at least 50 of these and the point that people have been thinking until now that, oh, we found a mangled protein, but what you've zeroed in on is that, hey, you know what, it's not just a mangled protein, it's how it gets stuck in the cell and that it can't get to the lysosome to get rid of it, there's no waste system. And so, this is such fundamental work. Now that gets me to the virtual cell story, kind of what you're getting into. I just had a conversation with Charlotte Bunne and Steve Quake who published a paper in December on the virtual cell, and of course that's many years off, but of course it's a big, bold, ambitious project to be able to say, as you just summarized, if you had cells in silico and you could do perturbations in silico, and of course they were validated by actual experiments or bidirectionally the experiments, the real ones helped to validate the virtual cell, but then you could get a true acceleration of your understanding of cell biology, your field of course.Anna Greka (14:09):Exactly.Eric Topol (14:12):So what you described, is it the same as a virtual cell? Is it kind of a precursor to it? How do you conceive this because this is such a complex, I mean it's a fundamental unit of life, but it's also so much more complex than a protein or an RNA because not only all the things inside the cell, inside all these organelles and nucleus, but then there's all the outside interactions. So this is a bold challenge, right?Anna Greka (14:41):Oh my god, it's absolutely from a biologist perspective, it's the challenge of a generation for sure. We think taking humans to Mars, I mean that's an aspirational sort of big ambitious goal. I think this is the, if you will, the Mars shot for biology, being able to, whether the terminology, whether you call it a virtual cell. I like the idea of saying that to state it as a problem, the way that people who think about it from a mathematics perspective for example, would think about it. I think stating it as the cell prediction problem appeals to me because it actually forces us biologists to think about setting up the way that we would do these cell perturbation data sets, the way we would generate them to set them up to serve predictions. So for example, the way that I would think about this would be can I in the future have so much information about how cell perturbations work that I can train a model so that it can predict when I show it a picture of another cell under different conditions that it hasn't seen before, that it can still tell me, ah, this is a neuron in which you perturbed the mitochondria, for example, and now this is sort of the outcome that you would expect to see.Anna Greka (16:08):And so, to be able to have this ability to have a model that can have the ability to predict in silico what cells would look like after perturbation, I think that's sort of the way that I think about this problem. It is very far away from anything that exists today. But I think that the beginning starts, and this is one of the unique things about my institute, if I can say, we have a place where cell biologists, geneticists, mathematicians, machine learning experts, we all come together in the same place to really think and grapple with these problems. And of course we're very outward facing, interacting with scientists all across the world as well. But there's this sort of idea of bringing people into one institute where we can just think creatively about these big aspirational problems that we want to solve. I think this is one of the unique things about the ecosystem at the Broad Institute, which I'm proud to be a part of, and it is this kind of out of the box thinking that will hopefully get us to generate the kinds of data sets that will serve the needs of building these kinds of models with predictive capabilities down the road.Anna Greka (17:19):But as you astutely said, AlphaFold of course was based on the protein database existing, right? And that was a wealth of available information in which one could train models that would ultimately be predictive, as we have seen this miracle that Demi Hassabis and John Jumper have given to humanity, if you will.Anna Greka (17:42):But as Demis and John would also say, I believe is as I have discussed with them, in fact, the cell prediction problem is really a bigger problem because we do not have a protein data bank to go to right now, but we need to create it to generate these data. And so, my Ladders to Cures Accelerator is here to basically provide some part of the answer to that problem, create this kind of well-controlled database that we need for cell perturbations, while at the same time maximizing our learnings about these fully penetrant coding mutations and what their downstream sequelae would be in many different human cells. And so, in this way, I think we can both advance our knowledge about these monogenic diseases, build models, hopefully with predictive capabilities. And to your point, a lot of what we will learn about this biology, if we think that it involves 8,000 or more out of the 20,000 genes in our genome, it will of course serve our understanding of polygenic diseases ultimately as well as we go deeper into this biology and we look at the combinatorial aspects of what different mutations do to human cells. And so, it's a huge aspirational problem for a whole generation, but it's a good one to work on, I would say.Learning the Language of Life with A.I. Eric Topol (19:01):Oh, absolutely. Now I think you already mentioned something that's quite, well, two things from what you just touched on. One of course, how vital it is to have this inner or transdisciplinary capability because you do need expertise across these vital areas. But the convergence, I mean, I love your term nodal biology and the fact that there's all these diseases like you were talking about, they do converge and nodal is a good term to highlight that, but it's not. Of course, as you mentioned, we have genome editing which allows to look at lots of different genome perturbations, like the single letter change that you found in MUC1 pathogenic critical mutation. There's also the AI world which is blossoming like I've never seen. In fact, I had in Science this week about learning the language of life with AI and how there's been like 15 new foundation models, DNA, proteins, RNA, ligands, all their interactions and the beginning of the cell story too with the human cell.Eric Topol (20:14):So this is exploding. As you said, the expertise in computer science and then this whole idea that you could take these powerful tools and do as you said, which is the need to accelerate, we just can't sit around here when there's so much discovery work to be done with the scalability, even though it might take years to get to this artificial intelligence virtual cell, which I have to agree, everyone in biology would say that's the holy grail. And as you remember at our conference in London, Demi Hassabis said that's what we'd like to do now. So it has the attention of leaders in AI around the world, obviously in the science and the biomedical community like you and many others. So it is an extraordinary time where we just can't sit still with these tools that we have, right?Anna Greka (21:15):Absolutely. And I think this is going to be, you mentioned the ASCI presidency in the beginning of our call. This is going to be the president gets to give an address at the annual meeting in Chicago. This is going to be one of the points I make, no matter what field in biomedicine we're in, we live in, I believe, a golden era and we have so many tools available to us that we can really accelerate our ability to help more patients. And of course, this is our mandate, the most important stakeholders for everything that we do as physician-scientists are our patients ultimately. So I feel very hopeful for the future and our ability to use these tools and to really make good on the promise of research is a public good. And I really hope that we can advance our knowledge for the benefit of all. And this is really an exciting time, I think, to be in this field and hopefully for the younger colleagues a time to really get excited about getting in there and getting involved and asking the big questions.Career ReflectionsEric Topol (22:21):Well, you are the prototype for this and an inspiration to everyone really, I'm sure to your lab group, which you highlighted in the TED Talk and many other things that you do. Now I want to spend a little bit of time about your career. I think it's fascinating that you grew up in Greece and your father's a nephrologist and your mother's a pathologist. So you had two physicians to model, but I guess you decided to go after nephrology, which is an area in medicine that I kind of liken it to Rodney Dangerfield, he doesn't get any respect. You don't see many people that go into nephrology. But before we get to your decision to do that somehow or other you came from Greece to Harvard for your undergrad. How did you make that connect to start your college education? And then subsequently you of course you stayed in Boston, you've never left Boston, I think.Anna Greka (23:24):I never left. Yeah, this is coming into 31 years now in Boston.Anna Greka (23:29):Yeah, I started as a Harvard undergraduate and I'm now a full professor. It's kind of a long, but wonderful road. Well, actually I would credit my parents. You mentioned that my father, they're both physician-scientists. My father is now both retired, but my father is a nephrologist, and my mother is a pathologist, actually, they were both academics. And so, when we were very young, we lived in England when my parents were doing postdoctoral work. That was actually a wonderful gift that they gave me because I became bilingual. It was a very young age, and so that allowed me to have this advantage of being fluent in English. And then when we moved back to Greece where I grew up, I went to an American school. And from that time, this is actually an interesting story in itself. I'm very proud of this school.Anna Greka (24:22):It's called Anatolia, and it was founded by American missionaries from Williams College a long time ago, 150 and more years ago. But it is in Thessaloniki, Greece, which is my hometown, and it's a wonderful institution, which gave me a lot of gifts as well, preparing me for coming to college in the United States. And of course, I was a good student in high school, but what really was catalytic was that I was lucky enough to get a scholarship to go to Harvard. And that was really, you could say the catalyst that propelled me from a teenager who was dreaming about a career as a physician-scientist because I certainly was for as far back as I remember in fact. But then to make that a reality, I found myself on the Harvard campus initially for college, and then I was in the combined Harvard-MIT program for my MD PhD. And then I trained in Boston at Mass General in Brigham, and then sort of started my academic career. And that sort of brings us to today, but it is an unlikely story and one that I feel still very lucky and blessed to have had these opportunities. So for sure, it's been wonderful.Eric Topol (25:35):We're the ones lucky that you came here and set up shop and you did your productivity and discovery work and sleuthing has been incredible. But I do think it's interesting too, because when you did your PhD, it was in neuroscience.Anna Greka (25:52):Ah, yes. That's another.Eric Topol (25:54):And then you switch gears. So tell us about that?Anna Greka (25:57):This is interesting, and actually I encourage more colleagues to think about it this way. So I have always been driven by the science, and I think that it seems a little backward to some people, but I did my PhD in neuroscience because I was interested in understanding something about these ion channels that were newly discovered at the time, and they were most highly expressed in the brain. So here I was doing work in the brain in the neuroscience program at Harvard, but then once I completed my PhD and I was in the middle of my residency training actually at Mass General, I distinctly remember that there was a paper that came out that implicated the same family of ion channels that I had spent my time understanding in the brain. It turned out to be a channelopathy that causes kidney disease.Anna Greka (26:43):So that was the light bulb, and it made me realize that maybe what I really wanted to do is just follow this thread. And my scientific curiosity basically led me into studying the kidney and then it seemed practical therefore to get done with my clinical training as efficiently as possible. So I finished residency, I did nephrology training, and then there I was in the lab trying to understand the biology around this channelopathy. And that sort of led us into the early projects in my young lab. And in fact, it's interesting we didn't talk about that work, but that work in itself actually has made it all the way to phase II trials in patients. This was a paper we published in Science in 2017 and follow onto that work, there was an opportunity to build this into a real drug targeting one of these ion channels that has made it into phase II trials. And we'll see what happens next. But it's this idea of following your scientific curiosity, which I also talked about in my TED Talk, because you don't know to what wonderful places it will lead you. And quite interestingly now my lab is back into studying familial Alzheimer's and retinitis pigmentosa in the eye in brain. So I tell people, do not limit yourself to whatever someone says your field is or should be. Just follow your scientific curiosity and usually that takes you to a lot more interesting places. And so, that's certainly been a theme from my career, I would say.Eric Topol (28:14):No, I think that's perfect. Curiosity driven science is not the term. You often hear hypothesis driven or now with AI you hear more AI exploratory science. But no, that's great. Now I want to get a little back to the AI story because it's so fascinating. You use lots of different types of AI such as cellular imaging would be fusion models and drug discovery. I mean, you've had drug discovery for different pathways. You mentioned of course the ion channel and then also as we touched on with your Cell paper, the whole idea of targeting the cargo receptor with a small molecule and then things in between. You discussed this of course at the London panel, but maybe you just give us the skinny on the different ways that you incorporate AI in the state-of-the-art science that you're doing?Anna Greka (29:17):Sure, yeah, thank you. I think there are many ways in which even for quite a long time before AI became such a well-known kind of household term, if you will, the concept of machine learning in terms of image processing is something that has been around for some time. And so, this is actually a form of AI that we use in order to process millions of images. My lab has by produced probably more than 20 million images over the last few years, maybe five to six years. And so, if you can imagine it's impossible for any human to process this many images and make sense of them. So of course, we've been using machine learning that is becoming increasingly more and more sophisticated and advanced in terms of being able to do analysis of images, which is a lot of what we cell biologists do, of course.Anna Greka (30:06):And so, there's multiple different kinds of perturbations that we do to cells, whether we're using CRISPR or base editing to make, for example, genome wide or genome scale perturbations or small molecules as we have done as well in the past. These are all ways in which we are then using machine learning to read out the effects in images of cells that we're looking at. So that's one way in which machine learning is used in our daily work, of course, because we study misshape and mangled proteins and how they are recognized by these cargo receptors. We also use AlphaFold pretty much every day in my lab. And this has been catalytic for us as a tool because we really are able to accelerate our discoveries in ways that were even just three or four years ago, completely impossible. So it's been incredible to see how the young people in my lab are just so excited to use these tools and they're becoming extremely savvy in using these tools.Anna Greka (31:06):Of course, this is a new generation of scientists, and so we use AlphaFold all the time. And this also has a lot of implications of course for some of the interventions that we might think about. So where in this cargo receptor complex that we study for example, might we be able to fit a drug that would disrupt the complex and lead the cargo tracks into the lysosome for degradation, for example. So there's many ways in which AI can be used for all of these functions. So I would say that if we were to organize our thinking around it, one way to think about the use of machine learning AI is around what I would call understanding biology in cells and what in sort of more kind of drug discovery terms you would call target identification, trying to understand the things that we might want to intervene on in order to have a benefit for disease.Anna Greka (31:59):So target ID is one area in which I think machine learning and AI will have a catalytic effect as they already are. The other of course, is in the actual development of the appropriate drugs in a rational way. So rational drug design is incredibly enabled by AlphaFold and all these advances in terms of understanding protein structures and how to fit drugs into them of all different modalities and kinds. And I think an area that we are not yet harnessing in my group, but I think the Ladders to Cures Accelerator hopes to build on is really patient data. I think that there's a lot of opportunity for AI to be used to make sense of medical records for example and how we extract information that would tell us that this cohort of patients is a better cohort to enroll in your trial versus another. There are many ways in which we can make use of these tools. Not all of them are there yet, but I think it's an exciting time for being involved in this kind of work.Eric Topol (32:58):Oh, no question. Now it must be tough when you know the mechanism of these families disease and you even have a drug candidate, but that it takes so long to go from that to helping these families. And what are your thoughts about that, I mean, are you thinking also about genome editing for some of these diseases or are you thinking to go through the route of here's a small molecule, here's the tox data in animal models and here's phase I and on and on. Where do you think because when you know so much and then these people are suffering, how do you bridge that gap?Anna Greka (33:39):Yeah, I think that's an excellent question. Of course, having patients as our partners in our research is incredible as a way for us to understand the disease, to build biomarkers, but it is also exactly creating this kind of emotional conflict, if you will, because of course, to me, honesty is the best policy, if you will. And so, I'm always very honest with patients and their families. I welcome them to the lab so they can see just how long it takes to get some of these things done. Even today with all the tools that we have, of course there are certain things that are still quite slow to do. And even if you have a perfect drug that looks like it fits into the right pocket, there may still be some toxicity, there may be other setbacks. And so, I try to be very honest with patients about the road that we're on. The small molecule path for the toxic proteinopathies is on its way now.Anna Greka (34:34):It's partnered with a pharmaceutical company, so it's on its way hopefully to patients. Of course, again, this is an unpredictable road. Things can happen as you very well know, but I'm at least glad that it's sort of making its way there. But to your point, and I'm in an institute where CRISPR was discovered, and base editing and prime editing were discovered by my colleagues here. So we are in fact looking at every other modality that could help with these diseases. We have several hurdles to overcome because in contrast to the liver and the brain, the kidney for example, is not an organ in which you can easily deliver nucleic acid therapies, but we're making progress. I have a whole subgroup within the bigger group who's focusing on this. It's actually organized in a way where they're running kind of independently from the cell biology group that I run.Anna Greka (35:31):And it's headed by a person who came from industry so that she has the opportunity to really drive the project the way that it would be run milestone driven, if you will, in a way that it would be run as a therapeutics program. And we're really trying to go after all kinds of different nucleic acid therapies that would target the mutations themselves rather than the cargo receptors. And so, there's ASO and siRNA technologies and then also actual gene editing technologies that we are investigating. But I would say that some of them are closer than others. And again, to your question about patients, I tell them honestly when a project looks to be more promising, and I also tell them when a project looks to have hurdles and that it will take long and that sometimes I just don't know how long it will take before we can get there. The only thing that I can promise patients in any of our projects, whether it's Alzheimer's, blindness, kidney disease, all I can promise is that we're working the hardest we possibly can on the problem.Anna Greka (36:34):And I think that is often reassuring I have found to patients, and it's best to be honest about the fact that these things take a long time, but I do think that they find it reassuring that someone is on it essentially, and that there will be some progress as we move forward. And we've made progress in the very first discovery that came out of my lab. As I mentioned to you, we've made it all the way to phase II trials. So I have seen the trajectory be realized, and I'm eager to make it happen again and again as many times as I can within my career to help as many people as possible.The Paucity of Physician-ScientistsEric Topol (37:13):I have no doubts that you'll be doing this many times in your career. No, there's no question about it. It's extraordinary actually. There's a couple of things there I want to pick up on. Physician-scientists, as you know, are a rarefied species. And you have actually so nicely told the story about when you have a physician-scientist, you're caring for the patients that you're researching, which is, most of the time we have scientists. Nothing wrong with them of course, but you have this hinge point, which is really important because you're really hearing the stories and experiencing the patients and as you say, communicating about the likelihood of being able to come up with a treatment or the progress. What are we going to do to get more physician-scientists? Because this is a huge problem, it has been for decades, but the numbers just keep going lower and lower.Anna Greka (38:15):I think you're absolutely right. And this is again, something that in my leadership of the ASCI I have made sort of a cornerstone of our efforts. I think that it has been well-documented as a problem. I think that the pressures of modern clinical care are really antithetical to the needs of research, protected time to really be able to think and be creative and even have the funding available to be able to pursue one's program. I think those pressures are becoming so heavy for investigators that many of them kind of choose one or the other route most often the clinical route because that tends to be, of course where they can support their families better. And so, this has been kind of the conundrum in some ways that we take our best and brightest medical students who are interested in investigation, we train them and invest in them in becoming physician-scientists, but then we sort of drop them at the most vulnerable time, which is usually after one completes their clinical and scientific training.Anna Greka (39:24):And they're embarking on early phases of one's careers. It has been found to be a very vulnerable point when a lot of people are now in their mid-thirties or even late thirties perhaps with some family to take care of other burdens of adulthood, if you will. And I think what it becomes very difficult to sustain a career where one salary is very limited due to the research component. And so, I think we have to invest in our youngest people, and it is a real issue that there's no good mechanism to do that at the present time. So I was actually really hoping that there would be an opportunity with leadership at the NIH to really think about this. It's also been discussed at the level of the National Academy of Medicine where I had some role in discussing the recent report that they put out on the biomedical enterprise in the United States. And it's kind of interesting to see that there is a note made there about this issue and the fact that there needs to be, I think, more generous investment in the careers of a few select physician-scientists that we can support. So if you look at the numbers, currently out of the entire physician workforce, a physician-scientist comprised of less than 1%.Anna Greka (40:45):It's probably closer to 0.8% at this point.Eric Topol (40:46):No, it's incredible.Anna Greka (40:48):So that's really not enough, I think, to maintain the enterprise and if you will, this incredible innovation economy that the United States has had this miracle engine, if you will, in biomedicine that has been fueled in large part by physician investigators. Of course, our colleagues who are non-physician investigators are equally important partners in this journey. But we do need a few of the physician-scientists investigators I think as well, if you really think about the fact that I think 70% of people who run R&D programs in all the big pharmaceutical companies are physician-scientists. And so, we need people like us to be able to work on these big problems. And so, more investment, I think that the government, the NIH has a role to play there of course. And this is important from both an economic perspective, a competition perspective with other nations around the world who are actually heavily investing in the physician-scientist workforce.Anna Greka (41:51):And I think it's also important to do so through our smaller scale efforts at the ASCI. So one of the things that I have been involved in as a council member and now as president is the creation of an awards program for those early career investigators. So we call them the Emerging-Generation Awards, and we also have the Young Physician-Scientist Awards. And these are really to recognize people who are making that transition from being kind of a trainee and a postdoc and have finished their clinical training into becoming an independent assistant professor. And so, those are small awards, but they're kind of a symbolic tap on the shoulder, if you will, that the ASCI sees you, you're talented, stay the course. We want you to become a future member. Don't give up and please keep on fighting. I think that can take us only so far.Anna Greka (42:45):I mean, unless there's a real investment, of course still it will be hard to maintain people in the pipeline. But this is just one way in which we have tried to, these programs that the ASCI offers have been very successful over the last few years. We create a cohort of investigators who are clearly recognized by members of the ASCI is being promising young colleagues. And we give them longitudinal training as part of a cohort where they learn about how to write a grant, how to write a paper, leadership skills, how to run a lab. And they're sort of like a buddy system as well. So they know that they're in it together rather than feeling isolated and struggling to get their careers going. And so, we've seen a lot of success. One way that we measure that is conversion into an ASCI membership. And so, we're encouraged by that, and we hope that the program can continue. And of course, as president, I'm going to be fundraising for that as well, it's part of the role. But it is a really worthy cause because to your point, we have to somehow make sure that our younger colleagues stay the course that we can at least maintain, if not bolster our numbers within the scientific workforce.Eric Topol (43:57):Well, you outlined some really nice strategies and plans. It's a formidable challenge, of course. And we'd like to see billions of dollars to support this. And maybe someday we will because as you say, if we could relieve the financial concerns of people who have curiosity driven ideas.Anna Greka (44:18):Exactly.Eric Topol (44:19):We could do a lot to replenish and build a big physician-scientist workforce. Now, the last thing I want to get to, is you have great communication skills. Obviously, anybody who is listening or watching this.Eric Topol (44:36):Which is another really important part of being a scientist, no less a physician or the hybrid of the two. But I wanted to just go to the backstory because your TED Talk, which has been watched by hundreds of thousands of people, and I'm sure there's hundreds of thousands more that will watch it, but the TED organization is famous for making people come to the place a week ahead. This is Vancouver used to be in LA or Los Angeles area and making them rehearse the talk, rehearse, rehearse, rehearse, which seems crazy. You could train the people there, how to give a talk. Did you have to go through that?Anna Greka (45:21):Not really. I did rehearse once on stage before I actually delivered the talk live. And I was very encouraged by the fact that the TED folks who are of course very well calibrated, said just like that. It's great, just like that.Eric Topol (45:37):That says a lot because a lot of people that do these talks, they have to do it 10 times. So that kind of was another metric. But what I don't like about that is it just because these people almost have to memorize their talks from giving it so much and all this coaching, it comes across kind of stilted and unnatural, and you're just a natural great communicator added to all your other things.Anna Greka (46:03):I think it's interesting. Actually, I would say, if I may, that I credit, of course, I actually think that it's important, for us physician-scientists, again, science and research is a public good, and being able to communicate to the public what it is that we do, I think is kind of an obligation for the fact that we are funded by the public to do this kind of work. And so, I think that's important. And I always wanted to cultivate those communication skills for the benefit of communicating simply and clearly what it is that we do in our labs. But also, I would say as part of my story, I mentioned that I had the opportunity to attend a special school growing up in Greece, Anatolia, which was an American school. One of the interesting things about that is that there was an oratory competition.Anna Greka (46:50):I got very early exposure entering that competition. And if you won the first prize, it was in the kind of ancient Rome way, first among equals, right? And so, that was the prize. And I was lucky to have this early exposure. This is when I was 14, 15, 16 years old, that I was training to give these oratory speeches in front of an audience and sort of compete with other kids who were doing the same. I think these are just wonderful gifts that a school can give a student that have stayed with me for life. And I think that that's a wonderful, yeah, I credit that experience for a lot of my subsequent capabilities in this area.Eric Topol (47:40):Oh, that's fantastic. Well, this has been such an enjoyable conversation, Anna. Did I miss anything that we need to bring up, or do you think we have it covered?Anna Greka (47:50):Not at all. No, this was wonderful, and I thoroughly enjoyed it as well. I'm very honored seeing how many other incredible colleagues you've had on the show. It's just a great honor to be a part of this. So thank you for having me.Eric Topol (48:05):Well, you really are such a great inspiration to all of us in the biomedical community, and we'll be cheering for your continued success and thanks so much for joining today, and I look forward to the next time we get a chance to visit.Anna Greka (48:20):Absolutely. Thank you, Eric.**************************************Thanks for listening, watching or reading Ground Truths. Your subscription is greatly appreciated.If you found this podcast interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths—newsletters, analyses, and podcasts—is free, open-access.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. And such support is becoming more vital In light of current changes of funding and support for biomedical research at NIH and other US governmental agencies.Thanks to my producer Jessica Nguyen and to Sinjun Balabanoff for audio and video support at Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

Can AI compress the years long research time of a PhD into seconds? Research scientist Max Jaderberg explores how “AI analogs” simulate real-world lab work with staggering speed and scale, unlocking new insights on protein folding and drug discovery. Drawing on his experience working on Isomorphic Labs' and Google DeepMind's AlphaFold 3 — an AI model for predicting the structure of molecules — Jaderberg explains how this new technology frees up researchers' time and resources to better understand the real, messy world and tackle the next frontiers of science, medicine and more. Hosted on Acast. See acast.com/privacy for more information.

Google's Impact on Health Report 2025 reveals the company's extensive influence on global digital health, including their Nobel Prize-winning AlphaFold 2 AI system. The value of watches that detect falls and raises the alarm just got a significant upgrade with Google receiving FDA clearance for "loss of pulse detection" technology for wearables that can identify sudden cardiac arrest signs remotely. New Australian study finds social media influencers driving demand for unnecessary health tests with limited clinical evidence. Commercialisation, misinformation and the rise in health equity gaps. Reporting by Pulse+IT. Tina Purnat's BMJ Opinion Piece is a must read for those interested in the proliferation of misinformationWas Chris Longhurst right years earlier than he predicted – the US has proposed legislation that would classify AI as a "practitioner licensed by law" with prescription capabilities, raising significant ethical and regulatory questions. The UK is exploring implementation of a single digital patient record system across health and social care, promising better continuity but facing logistical challenges. Will they pull this off?Check out the Global Digital Health Partnership's digital health repository LinkOur guest of Pulse is Dr Karen DeSalvo, Chief Health Officer at Google. Part 1 of our chat covers Karen's impressive career trajectory and personal motivations, AI, and how Google is focused on getting correct health information into the hands of everybody. Follow Karen on LinkedIn LinkVisit Pulse+IT.news to subscribe to breaking digital news, weekly newsletters and a rich treasure trove of archival material. People in the know, get their news from Pulse+IT – Your leading voice in digital health news.Follow us on LinkedIn Louise | George | Pulse+ITFollow us on BlueSky Louise | George | Pulse+ITSend us your questions pulsepod@pulseit.newsProduction by Octopod Productions | Ivan Juric

Hugo Penedones é licenciado em Engenharia Informática e Computação pela Universidade do Porto e é cofundador e atualmente CTO da Inductiva.AI, uma empresa de Inteligência Artificial para a ciência e engenharia. Anteriormente, passou pela Google DeepMind, onde foi membro fundador do projeto AlphaFold, um algoritmo de previsão de estruturas de proteínas que viria a revolucionar a ciência nesta área a levar a atribuição do Prémio Nobel de Química de 2024 a Demis Hassabis e John M. Jumper (David Baker foi o 3º laureado com o Nobel). Ao longo da sua carreira, trabalhou em diversas áreas, incluindo visão por computador, pesquisa web, bioinformática e aprendizagem por reforço em instituições de investigação como o Idiap e a EPFL na Suíça. _______________ Índice: (0:00) Início (3:30) PUB (3:54) IA aplicada à Ciência | Projecto Alphafold (Google Deepmind) | Paper em que o convidado foi co-autor (14:01) Alphafold vs LLMs (ex: ChatGPT) | AlphaGo (22:20) Como num hackathon com o Hugo e dois colegas começou o Alphafold | Demis Hassabis (CEO da Deepmind) (28:31) Outras aplicações de AI na ciência: fusão nuclear, previsão do tempo (41:14) IA na engenharia de materiais: descoberta de novos materiais e o potencial dos supercondutores (46:35) IA cientista: Poderá a IA formular hipóteses científicas no futuro? | Matemática | P vs NP (57:10 ) Modelos de machine learning são caixas negras? (1:03:12) Inductiva, a startup do convidado dedicada a simulações numéricas com machine learning (1:13:47) A promessa da computação quântica Cortar de 1:14:44 a 1:16:38 (assegura pf que fica silêncio no final, antes de eu fazer a pergunta seguinte, que muda de tema) (1:16:03) Desafios da qualidade dos dados na ciência com IA | Será possível simularmos uma célula? (1:24:44) Que progressos podemos esperar da IA na ciência nos próximos 10 anos? | Alphacell ______________ Esta conversa foi editada por: João RibeiroSee omnystudio.com/listener for privacy information.

On this week's episode of No Priors, Sarah Guo is joined by leading members of the teams at Vevo Therapeutics and the Arc Institute – Nima Alidoust, CEO/Co-Founder at Vevo Therapeutics; Johnny Yu, CSO/Co-Founder at Vevo Therapeutics; Patrick Hsu, CEO/Co-Founder at Arc Institute; Dave Burke, CTO at Arc Institute; and Hani Goodarzi, Core Investigator at Arc Institute. Predicting protein structure (AlphaFold 3, Chai-1, Evo 2) was a big AI/biology breakthrough. The next big leap is modeling entire human cells—how they behave in disease, or how they respond to new therapeutics. The same way LLMs needed enormous text corpora to become truly powerful, Virtual Cell Models need massive, high-quality cellular datasets to train on. In this episode, the teams discuss the groundbreaking release of the Tahoe-100M single cell dataset, Arc Atlas, and how these advancements could transform drug discovery. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @Nalidoust | @IAmJohnnyYu | @PDHsh | @Davey_Burke | @Genophoria Download the Tahoe Dataset Show Notes: 0:00 Introduction 1:40 Significance of Tahoe-100M dataset 4:22 Where we are with virtual cell models and protein language models 10:26 Significance of perturbational data 17:39 Challenges and innovations in data collection 24:42 Open sourcing and community collaboration 33:51 Predictive ability and importance of virtual cell models 35:27 Drug discovery and virtual cell models 44:27 Platform vs. single hypothesis companies 46:05 Rise of Chinese biotechs 51:36 AI in drug discovery

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy: Cara A: -Regalo: Arranca “El Café de Ganimedes”, nuestro nuevo pódcast (5:00) -Nuevo récord en fusión, WEST logra 1337 segundos (EAST logró 1066, ref. ep497) (21:00) -La astronomía multimensajero definitiva (GW + FRB + neutrino) (26:00) -Dudas sobre el origen de la peste negra (ref. ep377) (31:00) -AlphaFold falla en proteínas metamórficas (ref. ep438) (36:00) -Escribas del Antiguo Egipto y marcadores óseos específicos asociados al riesgo ocupacional (46:00) Este episodio continúa en la Cara B. Contertulios: María Ribes, Sara Robisco, Juan Carlos Gil, Francis Villatoro, Héctor Socas. Imagen de portada realizada con Midjourney. Todos los comentarios vertidos durante la tertulia representan únicamente la opinión de quien los hace... y a veces ni eso

V epizodi 170 je bil gost dr. Boštjan Kobe, znanstvenik, ki je diplomiral iz kemije na Univerzi v Ljubljani, doktoriral v Teksasu in trenutno deluje v Avstraliji. Njegovo raziskovalno področje je strukturna biologija, s poudarkom na proteinih in imunskem odzivu. V epizodi se dotakneva naslednjih tematik: Razvoj kristalografije Uporaba strukturne biologije Izvor življenja in evolucija Etična vprašanja pri genskem manipuliranju CRISPR tehnologija in njene posledice AlphaFold in prihodnost napovedovanja strukture proteinov ============= Obvladovanje matematike je ključ do odklepanja neštetih priložnosti v življenju in karieri. Zato je Klemen Selakovič soustvaril aplikacijo Astra AI. Ta projekt uteleša vizijo sveta, kjer se noben otrok ne počuti neumnega ali nesposobnega. Kjer je znanje človeštva dostopno vsakomur. Pridruži se pri revoluciji izobraževanja s pomočjo umetne inteligence. https://astra.si/ai/  

The 2024 Nobel Prize in Chemistry was shared by three researchers who used artificial intelligence to predict the three-dimensional shapes of proteins based on their amino acid sequences. In this episode, we hear from one of them, Demis Hassabis, CEO and co-founder of Google DeepMind. His AI program, AlphaFold, curates the 3D-structures of more than 200 million naturally-occurring proteins (https://alphafold.ebi.ac.uk/). This database is available to the public for free! After a brief introduction to the topic, we hear Dr. Hassabis's Nobel lecture on how he got involved in this groundbreaking research, and how he sees AI impacting biology in the future. Here is the link to the full public-domain lecture (with slides and charts): https://www.nobelprize.org/prizes/chemistry/2024/hassabis/lecture/ ‘Bench Talk: The Week in Science' is a weekly radio program that airs on WFMP Louisville FORward Radio 106.5 FM (forwardradio.org) every Monday at 7:30 pm, Tuesday at 11:30 am, and Wednesday at 7:30 am. Visit our Facebook page for links to the articles discussed in this episode: https://www.facebook.com/pg/BenchTalkRadio/posts/?ref=page_internal

We're experimenting and would love to hear from you!In this episode of 'Discover Daily', we delve into the groundbreaking advancements in AI-driven drug discovery, highlighting DeepMind's AlphaFold 3 and significant partnerships between Isomorphic Labs and pharmaceutical giants Eli Lilly and Novartis. The episode explores how these collaborations, backed by substantial investments reaching into billions, are revolutionizing the development of small molecule therapeutics and accelerating the traditionally lengthy drug development process.We then shift to Finland's historic entry into the Artemis Accords as the 53rd nation and first signatory of 2025, marking a significant milestone in international space cooperation. This strategic move not only demonstrates Finland's commitment to peaceful space exploration but also positions the country to benefit from potential investment returns and enhanced polar region monitoring capabilities in response to increasing climate change impacts.The episode concludes with a detailed examination of a new executive order mandating the declassification of remaining files related to the assassinations of JFK, RFK, and MLK Jr. This unprecedented move towards transparency sets specific timelines for intelligence agencies to develop comprehensive release plans, potentially unveiling new insights into these pivotal moments in American history while addressing decades of public interest in these historical events.From Perplexity's Discover Feed: https://www.perplexity.ai/page/ai-developed-drugs-coming-soon-KafDx1.USaWRvWfDBgYk.g https://www.perplexity.ai/page/finland-signs-artemis-accords-SJdroKJERvqYlwwVm9z_pQhttps://www.perplexity.ai/page/trump-signs-executive-order-to-rUYlBy8tR1yBhoZc8YhyCg Perplexity is the fastest and most powerful way to search the web. Perplexity crawls the web and curates the most relevant and up-to-date sources (from academic papers to Reddit threads) to create the perfect response to any question or topic you're interested in. Take the world's knowledge with you anywhere. Available on iOS and Android Join our growing Discord community for the latest updates and exclusive content. Follow us on: Instagram Threads X (Twitter) YouTube Linkedin

Can artificial intelligence redefine the future of drug development and clinical trials?  In this episode of Tech Talks Daily, I sit down with Dave Latshaw, Ph.D., the internationally recognized AI and machine learning expert who serves as CEO of BioPhy. Founded in 2019, BioPhy focuses on using AI to revolutionize the later stages of drug development, a critical yet often overlooked segment of the pharmaceutical pipeline. Dave shares insights into BioPhy's innovative platform, which combines scientific, clinical, and regulatory insights to predict clinical trial success and steer capital allocation. At the heart of BioPhy's approach is its patent-pending AI engine, BioLogic, and generative AI solution, BioPhyRx, designed to enhance clinical trial outcomes, reduce failure rates, and accelerate the time to market for life-saving drugs. Dave also explores how BioPhy's operational assessment model prioritizes immediate ROI by addressing challenges downstream from drug discovery. In our conversation, Dave delves into the complexities of AI adoption in pharma, including the challenges of scaling AI solutions, managing high computational costs, and overcoming stakeholder fears about job displacement. Drawing from his experience at Johnson & Johnson, where his AI innovations contributed to the global rollout of the COVID-19 vaccine, Dave reflects on lessons learned and the transformative potential of AI in healthcare. As we look ahead, Dave discusses the future of AI in reducing administrative burdens on clinicians, automating regulatory compliance, and enabling groundbreaking advancements like DeepMind's AlphaFold.  How can AI transform not just how we develop drugs but also the healthcare outcomes for millions of people worldwide? Tune in to find out, and share your thoughts on the role of AI in the future of medicine.

Episode 142Happy holidays! This is one of my favorite episodes of the year — for the third time, Nathan Benaich and I did our yearly roundup of all the AI news and advancements you need to know. This includes selections from this year's State of AI Report, some early takes on o3, a few minutes LARPing as China Guys……… If you've stuck around and continue to listen, I'm really thankful you're here. I love hearing from you. You can find Nathan and Air Street Press here on Substack and on Twitter, LinkedIn, and his personal site. Check out his writing at press.airstreet.com. Find me on Twitter (or LinkedIn if you want…) for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Outline* (00:00) Intro* (01:00) o3 and model capabilities + “reasoning” capabilities* (05:30) Economics of frontier models* (09:24) Air Street's year and industry shifts: product-market fit in AI, major developments in science/biology, "vibe shifts" in defense and robotics* (16:00) Investment strategies in generative AI, how to evaluate and invest in AI companies* (19:00) Future of BioML and scientific progress: on AlphaFold 3, evaluation challenges, and the need for cross-disciplinary collaboration* (32:00) The “AGI” question and technology diffusion: Nathan's take on “AGI” and timelines, technology adoption, the gap between capabilities and real-world impact* (39:00) Differential economic impacts from AI, tech diffusion* (43:00) Market dynamics and competition* (50:00) DeepSeek and global AI innovation* (59:50) A robotics renaissance? robotics coming back into focus + advances in vision-language models and real-world applications* (1:05:00) Compute Infrastructure: NVIDIA's dominance, GPU availability, the competitive landscape in AI compute* (1:12:00) Industry consolidation: partnerships, acquisitions, regulatory concerns in AI* (1:27:00) Global AI politics and regulation: international AI governance and varying approaches* (1:35:00) The regulatory landscape* (1:43:00) 2025 predictions * (1:48:00) ClosingLinks and ResourcesFrom Air Street Press:* The State of AI Report* The State of Chinese AI* Open-endedness is all we'll need* There is no scaling wall: in discussion with Eiso Kant (Poolside)* Alchemy doesn't scale: the economics of general intelligence* Chips all the way down* The AI energy wars will get worse before they get betterOther highlights/resources:* Deepseek: The Quiet Giant Leading China's AI Race — an interview with DeepSeek CEO Liang Wenfeng via ChinaTalk, translated by Jordan Schneider, Angela Shen, Irene Zhang and others* A great position paper on open-endedness by Minqi Jiang, Tim Rocktäschel, and Ed Grefenstette — Minqi also wrote a blog post on this for us!* for China Guys only: China's AI Regulations and How They Get Made by Matt Sheehan (+ an interview I did with Matt in 2022!)* The Simple Macroeconomics of AI by Daron Acemoglu + a critique by Maxwell Tabarrok (more links in the Report)* AI Nationalism by Ian Hogarth (from 2018)* Some analysis on the EU AI Act + regulation from Lawfare Get full access to The Gradient at thegradientpub.substack.com/subscribe

Nathan welcomes back computational biochemist Amelie Schreiber for a fascinating update on AI's revolutionary impact in biology. In this episode of The Cognitive Revolution, we explore recent breakthroughs including AlphaFold3, ESM3, and new diffusion models transforming protein engineering and drug discovery. Join us for an insightful discussion about how AI is reshaping our understanding of molecular biology and making complex protein engineering tasks more accessible than ever before. Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Shopify: Shopify is the world's leading e-commerce platform, offering a market-leading checkout system and exclusive AI apps like Quikly. Nobody does selling better than Shopify. Get a $1 per month trial at https://shopify.com/cognitive SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. CHAPTERS: (00:00:00) Teaser (00:00:46) About the Episode (00:04:30) AI for Biology (00:07:14) David Baker's Impact (00:11:49) AlphaFold 3 & ESM3 (00:16:40) Protein Interaction Prediction (Part 1) (00:16:44) Sponsors: Shopify | SelectQuote (00:19:18) Protein Interaction Prediction (Part 2) (00:31:12) MSAs & Embeddings (Part 1) (00:32:32) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++ (00:34:49) MSAs & Embeddings (Part 2) (00:35:57) Beyond Structure Prediction (00:51:13) Dynamics vs. Statics (00:57:24) In-Painting & Use Cases (00:59:48) Workflow & Platforms (01:06:45) Design Process & Success Rates (01:13:23) Ambition & Task Definition (01:19:25) New Models: PepFlow & GeoAB (01:28:23) Flow Matching vs. Diffusion (01:30:42) ESM3 & Multimodality (01:37:10) Summary & Future Directions (01:45:34) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431

In this Three Breakthroughs episode of the Alumni Ventures Tech Optimist Podcast, host Mike Collins, Founder and CEO of Alumni Ventures, is joined by Senior Principal Naren Ramaswamy to explore three transformative advancements in artificial intelligence. The conversation covers the debate over AI scaling approaches, the revolutionary impact of AlphaFold 3 in molecular biology and drug discovery, and the rapid development of AI agents that promise to redefine productivity and trust. This episode offers an inspiring look at how these innovations are shaping the future of science, technology, and human collaboration.To Learn More:Alumni Ventures (AV)AV LinkedInAV Deep Tech FundTech OptimistLegal Disclosure:https://av-funds.com/tech-optimist-disclosuresCreators & Guests Mike Collins - Host Naren Ramaswamy - Guest

Can AI compress the yearslong research time of a PhD into seconds? Research scientist Max Jaderberg explores how “AI analogs” simulate real-world lab work with staggering speed and scale, unlocking new insights on protein folding and drug discovery. Drawing on his experience working on Isomorphic Labs' and Google DeepMind's AlphaFold 3 — an AI model for predicting the structure of molecules — Jaderberg explains how this new technology frees up researchers' time and resources to better understand the real, messy world and tackle the next frontiers of science, medicine and more.

Our 190th episode with a summary and discussion of last week's big AI news! Hosted by Andrey Kurenkov and Jeremie Harris. Note from Andrey: this one is coming out a bit later than planned, apologies! Next one will be coming out sooner. Feel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.ai Read out our text newsletter and comment on the podcast at https://lastweekin.ai/. Sponsors: The Generator - An interdisciplinary AI lab empowering innovators from all fields to bring visionary ideas to life by harnessing the capabilities of artificial intelligence In this episode: * OpenAI's pitch for a $100 billion data center and AI strategy plan outlines infrastructure and regulatory needs, emphasizing AI's foundational role akin to electricity.  * Google's Gemini model challenges OpenAI's dominance, showing strong performance in chatbot arenas alongside generative AI advancements.  * DeepMind's AlphaFold3 gets open-sourced for academic use, while new chips from NVIDIA and Google show significant performance boosts.  * Anthropic and TSMC updates highlight strategic funding, regulation influences, and the complex dynamics of AI hardware and international policy. If you would like to become a sponsor for the newsletter, podcast, or both, please fill out this form. Timestamps + Links: (00:00:00) Intro / Banter (00:02:44) News Preview (00:03:34) Sponsor Break Tools & Apps (00:04:36) OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI (00:16:22) OpenAI Nears Launch of AI Agent Tool to Automate Tasks for Users (00:19:14) Google drops new Gemini model and it goes straight to the top of the LLM leaderboard (00:19:14)  Chinese AI startup takes aim at OpenAI's Sora with image-to-video tool launch (00:20:04) Introducing the Forge Reasoning API Beta and Nous Chat: An Evolution in LLM Inference Applications & Business (00:23:47) OpenAI Discusses AI Data Center That Could Cost $100 Billion (00:26:48) Elon Musk's massive AI data center gets unlocked — xAI gets approved for 150MW of power, enabling all 100,000 GPUs to run concurrently (00:29:34) Newest Google and Nvidia Chips Speed AI Training (00:34:45) Ex-OpenAI CTO Murati's New Team Takes Shape (00:34:45) Amazon Discussing New Multibillion-Dollar Investment in Anthropic Projects & Open Source (00:37:52) Google DeepMind open-sources AlphaFold 3, ushering in a new era for drug discovery and molecular biology (00:41:29) Near plans to build world's largest 1.4T parameter open-source AI model Research & Advancements (00:45:38) The Super Weight in Large Language Models (00:55:42) Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task (01:03:47) Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models (01:08:14) Contextualized Evaluations: Taking the Guesswork Out of Language Model Evaluations Policy & Safety (01:11:14) The Code of Practice for general-purpose AI offers a unique opportunity for the EU (01:15:38) Three Sketches of ASL-4 Safety Case Components (01:23:05) U.S Department of Commerce finalizes $6.6 billion CHIPS Act funding for TSMC Fab 21 Arizona site , TSMC cannot make 2nm chips abroad now: MOEA (01:26:21) OpenAI to present plans for U.S. AI strategy and an alliance to compete with China (01:30:42) OpenAI loses another lead safety researcher, Lilian Weng (01:33:00) Outro

Can AI compress the yearslong research time of a PhD into seconds? Research scientist Max Jaderberg explores how “AI analogs” simulate real-world lab work with staggering speed and scale, unlocking new insights on protein folding and drug discovery. Drawing on his experience working on Google DeepMind's AlphaFold 3 — an AI model for predicting the structure of molecules — Jaderberg explains how this new technology frees up researchers' time and resources to better understand the real, messy world and tackle the next frontiers of science, medicine and more.

Can AI compress the yearslong research time of a PhD into seconds? Research scientist Max Jaderberg explores how “AI analogs” simulate real-world lab work with staggering speed and scale, unlocking new insights on protein folding and drug discovery. Drawing on his experience working on Google DeepMind's AlphaFold 3 — an AI model for predicting the structure of molecules — Jaderberg explains how this new technology frees up researchers' time and resources to better understand the real, messy world and tackle the next frontiers of science, medicine and more.

Episode 34: How will the 2024 election impact AI advancements by 2025? Matt Wolfe (https://x.com/mreflow) and Nathan Lands (https://x.com/NathanLands) dive into the implications of the upcoming Trump term. In this episode, Matt and Nathan discuss the potential AI developments over the next few years, how different political outcomes could shape AI progress, and the shifting landscape in Silicon Valley. They explore the latest in AI models like OpenAI 01, the debate over AGI timelines, and how regulatory approaches might impact America's competitive edge in tech. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) This won't become political; mood is changing. (04:55) Reduce government size, invest severance in tech. (08:28) AI can reveal corruption in government spending. (10:58) AI regulation may favor big companies, hinder startups. (14:50) Sam Altman expects AGI by 2025, despite skepticism. (17:59) AGI expected around 2025-2027, training slowing. (19:56) AI models don't learn in real-time conversations. (22:48) Humans struggle to foresee technological advancements' impact. (27:53) AGI leads to ASI due to intelligence. (29:39) Optimistic about AI and future advancements. (32:20) Predicts accurately, but often wrong on timing. — Mentions: Sam Altman: https://blog.samaltman.com/ OpenAI: https://openai.com/ Dario Amodei: https://www.linkedin.com/in/dario-amodei-3934934/ Anthropic: https://www.anthropic.com/ AlphaFold: https://alphafold.ebi.ac.uk/ Dogecoin: https://dogecoin.com/ — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

Join Andy Harrison, a pioneer in computational biology, as he delves into the remarkable ways AI is revolutionizing drug discovery. Learn how tools like AlphaFold are accelerating the development of life-saving medicines by predicting protein structures with unprecedented accuracy. Discover the immense potential of AI in streamlining the pharmaceutical industry and improving patient outcomes. Learn more about your ad choices. Visit megaphone.fm/adchoices

Terminamos nuestro repaso a los premios Nobel de ciencias, como siempre, con el galardón de Química, que este año ha sido todo lo contrario de una sorpresa. Se lo han llevado tres de los candidatos más firmes: David Baker, "por diseñar nuevas proteínas mediante ordenador", y Demis Hassabis y John Jumper, "por sus métodos para predecir la estructura tridimensional de las proteínas". Jumper y Hassabis son los responsables de que exista AlphaFold, una inteligencia artificial de la que hemos hablado más de una vez en La Brújula, y que fue la primera en predecir la forma tridimensional de una proteína a partir de su secuencia de aminoácidos. Esto ha supuesto una revolución para la bioquímica, porque la secuencia de aminoácidos de las proteínas podemos "leerlas" en el ADN, y gracias a programas como éste ahora podemos pasar de "la letra" a "el objeto". Baker, por su parte, es uno de los padres de las técnicas informáticas para el estudio de proteínas, y es responsable de RoseTTAFold, el "competidor" de AlphaFold, que aunque llegó un poco más tarde también está siendo parte de esta revolución. En el programa de hoy repasamos muy rápido la relevancia de estas investigaciones, pero si queréis aprender más sobre ellas podéis volver a escuchar los capítulos s08e16 y s10e17 de este pódcast. También podéis buscar el episodio s05e10 de nuestro pódcast hermano, Aparici en Órbita. En todos ellos os hablamos de estas inteligencias artificiales en mucho más detalle. Este programa se emitió originalmente el 9 de octubre de 2024. Podéis escuchar el resto de audios de La Brújula en la app de Onda Cero y en su web, ondacero.es

What would you like to see more of? Let us know!In this episode of Discover Daily, we explore two major technological breakthroughs shaping our future. First, we discuss DeepMind's release of AlphaFold 3's source code, a significant advancement in protein structure prediction that promises to accelerate drug development by up to three years. With 76% accuracy in protein-ligand interactions and 65% accuracy in protein-DNA interactions, this open-source release marks a new era in computational biology and pharmaceutical research.We then delve into China's ambitious Solar Great Wall project, a massive renewable energy installation that stretches 400 kilometers across Inner Mongolia's desert landscape. This unprecedented initiative combines solar power generation with ecological restoration, featuring 196,000 solar panels in the Dalad Banner section alone. By 2030, the project aims to generate 180 billion kilowatt-hours annually, exceeding Beijing's current annual power consumption while simultaneously combating desertification.The dual-purpose design of the Solar Great Wall showcases how renewable energy projects can address multiple challenges simultaneously. Beyond power generation, the installation creates 50,000 new jobs, enables desert farming under the panels, and serves as a protective barrier for the Yellow River ecosystem. The project's innovative bifacial panels and AI-driven tracking systems demonstrate China's commitment to leading the global transition to sustainable energy.From Perplexity's Discover Feed:https://www.perplexity.ai/page/deepmind-releases-alphafold-co-jvNh2oy5TLyXE0SSrjYSCghttps://www.perplexity.ai/page/china-s-solar-great-wall-opa_xYm3RdO7j31AfESZuwPerplexity is the fastest and most powerful way to search the web. Perplexity crawls the web and curates the most relevant and up-to-date sources (from academic papers to Reddit threads) to create the perfect response to any question or topic you're interested in. Take the world's knowledge with you anywhere. Available on iOS and Android Join our growing Discord community for the latest updates and exclusive content. Follow us on: Instagram Threads X (Twitter) YouTube Linkedin

Send Everyday AI and Jordan a text messageYa know how in big cities there's a mix of cars and buses? It's a symbiotic symphony of private vehicles and public transportation sharing the road. Can AI be like that? Nik Marda, the Technical Lead, AI Governance at Mozilla joins us to help us build that path. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Nik questions on AI governanceUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Public AI Explained2. Public AI Implementations and Limitations3. Trustworthiness and Concerns in AI Models.4. AI Integration in Daily Life5. Funding and Investment Needs for Public AITimestamps:00:00 Public AI promises a safer, smarter future.04:00 AI needs balance: commercial and public infrastructure.07:41 Public-private AI ecosystems can symbiotically coexist.12:34 Prioritize AI safety with smaller, quality datasets.15:38 Public AI models, data, and infrastructure needed.19:36 Public AI prioritizes trust, safety, accessibility, accountability.23:51 Public AI approaches offer control and cost benefits.27:56 Diversify AI sources for improved organizational resiliency.30:12 Public AI offers open-source, mission-focused alternatives.Keywords:Public AI, Private AI, AI application development, Llama, OpenAI, Google, investment in AI, public AI initiatives, Olmo, Falcon 40b, AlphaFold 2, Mozilla's Common Voice, National AI Research Resource, FAST, India AI, AI Factories, AI vendors, AI systems, AI Trust, AI Accountability, AI Governance, Mozilla, commercial AI infrastructure, sensitive data, public-private structures, closed proprietary AI models, open-source AI models, AI integration, incentive structure, proprietary AI systems. Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

AI's Influence on Creativity, Writing, and Learning: A Deep Dive with Naomi S. Baron   Join us in this insightful episode as we explore the profound impact of artificial intelligence on writing, creativity, and education with renowned linguist and author Naomi S. Baron. Delve into key discussions from her book, 'Who Wrote This: How AI and the Lure of Efficiency Threaten Human Writing,' highlighting both the potential benefits and ethical dilemmas of AI-generated content. Discover the complexity of copyright issues in the AI era, the importance of maintaining manual skills and personal touch in professional fields, and the significance of mental challenges in fostering authentic creativity. Learn about AI breakthroughs, such as AlphaFold in medicine, and real-world experiments like Google's Notebook LLM. This episode is a must-watch for anyone interested in the evolving role of AI in our lives, the protection of human authorship, and the vital interplay between technology and the human mind.   00:00 Introduction to AI Writing Tools 00:52 Meet the Expert: Naomi S. Baron 01:28 AI's Impact on Authorship and Creativity 03:08 The Deep Dive Experiment 06:05 Legal and Ethical Concerns 14:24 The Value of Human Creativity 28:46 The Struggle and Reward of Creativity 31:48 The Creative Struggle: Is It Necessary? 32:45 Artistic Mastery: From Bach to Picasso 35:44 Innovation and Discipline: Insights from Peter Compo 36:38 The Impact of AI on Education and Skills 42:13 The Importance of Personal Voice in Writing 44:35 The Physicality of Reading and Writing 54:35 The Future of Jobs in the Age of AI 01:01:51 Concluding Thoughts and Reflections

(0:00) Bestie intros! (3:18) The science behind Hurricanes Helene and Milton (14:59) The economics of intensifying natural disasters (29:03) AlphaFold creators win Nobel Prize in Chemistry (35:17) The Jayter's Ball (38:53) Google antitrust update: DOJ is going for a breakup (53:32) VC giveback: CRV will return ~$275M of a $500M fund to LPs (1:03:44) New TikTok survey shows increased usage as a news source (1:15:26) Election update: Are polling problems causing a strategy shift for Kamala Harris? Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://allin.com/meetups https://youtube.com/@allin https://allin.com/tequila https://allin.com https://x.com/Ry_Bass/status/1844367980249178396 https://www.newsweek.com/hurricane-helene-update-economic-losses-damage-could-total-160-billion-1961240 https://www.climate.gov/news-features/blogs/enso/september-2024-enso-update-binge-watch https://www.nature.com/articles/s43247-024-01442-3 https://x.com/vkhosla/status/1844166857655533811 https://www.aoml.noaa.gov/hrd/hrd_sub/sfury.html https://www.nature.com/articles/d41586-024-03214-7 https://www.bloomberg.com/news/articles/2024-10-09/us-says-it-s-weighing-google-breakup-as-remedy-in-monopoly-case https://assets.bwbx.io/documents/users/iqjWHBFdfxIU/rtKjE02hAh_k/v0 https://x.com/AOC/status/1844034727935988155 https://www.nytimes.com/2024/10/02/technology/crv-vc-fund-returning-money.html https://www.axios.com/2023/03/03/founders-fund-slashes-vc-peter-thiel https://www.pewresearch.org/short-reads/2024/09/17/more-americans-regularly-get-news-on-tiktok-especially-young-adults https://www.pewresearch.org/data-labs/2024/10/08/who-u-s-adults-follow-on-tiktok https://www.wsj.com/world/europe/russia-pays-criminals-to-sow-mayhem-in-europe-warns-u-k-spy-chief-21ab960c https://x.com/iapolls2022/status/1844418916107341948 https://x.com/2waytvapp/status/1844803367740096811 https://x.com/DavidSacks/status/1829383729284067659

This week, Geoffrey Hinton and Dennis Hassabis won Nobel Prizes in Physics and Chemistry for their groundbreaking AI work, sparking conversations about AI's influence across scientific disciplines. The episode explores Hinton's AI safety concerns and Hassabis' work on AlphaFold, transforming protein structure prediction. An unexpected set of honors, these awards spotlight AI's growing role in advancing research far beyond traditional computer science. Concerned about being spied on? Tired of censored responses? AI Daily Brief listeners receive a 20% discount on Venice Pro. Visit ⁠⁠⁠https://venice.ai/nlw⁠⁠⁠ and enter the discount code NLWDAILYBRIEF. The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614 Subscribe to the newsletter: https://aidailybrief.beehiiv.com/ Join our Discord: https://bit.ly/aibreakdown

The Nobel Prize in chemistry went to three scientists for groundbreaking work using artificial intelligence to advance biomedical and protein research. AlphaFold uses databases of protein structures and sequences to predict and even design protein structures. It speeds up a months or years-long process to mere hours or minutes. Amna Nawaz discussed more with one of the winners, Demis Hassabis. PBS News is supported by - https://www.pbs.org/newshour/about/funders

In this episode:00:45 The biggest black hole jets ever seenAstronomers have spotted a pair of enormous jets emanating from a supermassive black hole with a combined length of 23 million light years — the biggest ever discovered. Jets are formed when matter is ionized and flung out of a black hole, creating enormous and powerful structures in space. Thought to be unstable, physicists had theorized there was a limit to how large these jets could be, but the new discovery far exceeds this, suggesting there may be more of these monstrous jets yet to be discovered.Research Article: Oei et al. 09:44 Research HighlightsThe knitted fabrics designed to protect wearers from mosquito bites, and the role that islands play in fostering language diversity.Research Highlight: Plagued by mosquitoes? Try some bite-blocking fabricsResearch Highlight: Islands are rich with languages spoken nowhere else12:26 A sustainable, one-step method for alloy productionMaking metal alloys is typically a multi-step process that creates huge amounts of emissions. Now, a team demonstrates a way to create these materials in a single step, which they hope could significantly reduce the environmental burdens associated with their production. In a lab demonstration, they use their technique to create an alloy of nickel and iron called invar — a widely-used material that has a high carbon-footprint. The team show evidence that their method can produce invar to a quality that rivals that of conventional manufacturing, and suggest their technique is scalable to create alloys at an industrial scale.Research article: Wei et al.25:29 Briefing ChatHow AI-predicted protein structures have helped chart the evolution of a group of viruses, and the neurons that cause monkeys to ‘choke' under pressure.Nature News: Where did viruses come from? AlphaFold and other AIs are finding answersNature News: Why do we crumble under pressure? Science has the answerSubscribe to the Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday. Hosted on Acast. See acast.com/privacy for more information.

We may be on the cusp of a revolution in medicine, thanks to tools like AlphaFold, the technology for Google DeepMind, which helps scientists predict and see the shapes of thousands of proteins. How does AlphaFold work, what difference is it actually making in science, and what kinds of mysteries could it unlock? Today's guest is Pushmeet Kohli. He is the head of AI for science at DeepMind. We talk about proteins, why they matter, why they're challenging, how AlphaFold could accelerate and expand the hunt for miracle drugs, and what tools like AlphaFold tell us about the mystery of the cosmos and our efforts to understand it. If you have questions, observations, or ideas for future episodes, email us at PlainEnglish@Spotify.com. Host: Derek Thompson Guest: Pushmeet Kohli Producer: Devon Baroldi Learn more about your ad choices. Visit podcastchoices.com/adchoices