Podcast appearances and mentions of Daphne Koller

Daphne Koller is an AI pioneer, MacArthur fellow, member of the National Academy of sciences and the founder and CEO of drug discovery and development company insitro. She'll talk about how attitudes surrounding AI have evolved in her multi-decade career and what's ahead - including how technology is reshaping drug discovery, paving the way for more targeted treatments for the patients who can benefit most. But maximizing AI-powered innovation will depend on better investments in data aggregation, quality and collection and navigating hype cycles that can distract from real impact.  This academic-turned-entrepreneur will also share how founding insitro (and a previous company, Coursera) helped her expand her leadership and management skills, all while driving home the importance of shaping a company culture. At insitro, this focus building a culture that works for unique needs led to a special ‘helix' inspired-structure that helps discovery biologists, automation engineers and others in the company's cross-functional teams keep communication flowing, problem solve, and prevent the siloes that can hold true innovation back.  Transcript: Insitro: Top Ten Technologies of 2024:

Startup Project Podcast: Building AI Agents for Knowledge Workers with Lutra AI Jiquan Ngiam joins Nataraj to discuss the future of AI, from the rise of deep learning to the potential of AI agents for knowledge workers. They delve into [Guest Name]'s experiences working with Andrew Ng at Coursera and Google Brain, where he witnessed the power of scaling up compute and data in pushing the boundaries of AI. Timestamps: * **0:00 - Introduction:** Nataraj welcomes [Guest Name] to the show and introduces his impressive background. * **2:28 - Working with Andrew Ng:** [Guest Name] shares his experience working with Andrew Ng, emphasizing Ng's foresight and focus on scaling up neural networks. * **6:15 - The Importance of Data and Compute:** [Guest Name] highlights how data and compute became key drivers in the success of AI, using the example of AlexNet's breakthrough in 2012. * **12:25 - Democratizing Education with Coursera:** [Guest Name] discusses the early days of Coursera and the team's vision for democratizing access to education, especially in fields like machine learning. * **17:55 - Google Brain and the Rise of Transformers:** [Guest Name] reflects on his time at Google Brain, where he witnessed the emergence of transformers and their potential for generalizing across modalities. * **21:24 - The Limits of Scaling:** [Guest Name] questions the future of AI scaling, suggesting that we may be approaching a point of diminishing returns due to data limitations and the difficulty of creating truly effective synthetic data. * **28:13 - The Need for Data on Physical Tasks:** [Guest Name] proposes a bold idea: collecting real-world data on mundane tasks to train AI agents for robotics and other applications that require replicating human behavior. * **34:23 - Lutrei.ai: AI Agents for Knowledge Work:** [Guest Name] introduces Lutrei.ai, an AI agent designed to assist knowledge workers with tasks like research, data manipulation, and automation. * **42:49 - Different Approaches to AI Agents:** [Guest Name] compares Lutrei's approach to building AI agents with other common methods, highlighting the importance of separating data and logic for reliable and scalable solutions. * **45:38 - Choosing the Right Models:** [Guest Name] discusses the diverse landscape of AI models and how Lutrei leverages different models for different tasks, from small models for summarization to larger models for reasoning and planning. * **52:04 - AI Code Generation: Cursor vs. GitHub Copilot:** [Guest Name] shares his experience using Cursor, a code generation tool, and compares it to GitHub Copilot, highlighting the potential for AI to empower average developers. * **1:00:16 - The Future of AI Code Generation:** [Guest Name] predicts that AI code generation capabilities will become ubiquitous, and the key innovations will be in user experience and interaction design. * **1:05:43 - Consuming Information:** [Guest Name] shares his favorite sources of information, including podcasts, books, and news outlets. * **1:08:44 - Mentorship and Learning:** [Guest Name] reflects on the key mentors in his career, including Andrew Ng, Daphne Koller, and John Chen. * **1:12:34 - Advice for Early Career Professionals:** [Guest Name] advises young professionals to be voracious learners and prioritize gaining diverse experiences early in their careers. * **1:16:21 - The Motivation Behind Lutrei:** [Guest Name] explains his passion for pushing the boundaries of AI while simultaneously making it accessible and impactful for a wider audience. * **1:18:33 - Closing Thoughts:** Nataraj thanks [Guest Name] for sharing his insights and expresses his excitement for the future of Lutrei.ai. **Don't miss this episode to learn more about the exciting things happening in gen AI and how it's poised to revolutionize the way we work!**

How can scientists leverage AI and machine learning to more effectively research, develop, and deliver new drugs? This week, Reid and Aria talk drug development with renowned computer scientist and executive Dr. Daphne Koller, whose company, insitro, uses machine learning to improve the quality and speed of drug discovery. She addresses several ways AI and ML are already being used to redefine diseases and create better therapeutic interventions. Plus, she shares her experience transitioning from academia to industry.  Read the transcript of this episode here: https://www.possible.fm/podcasts/dkoller For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  Topics: 00:01: Cold open 00:43: Hellos and intros 3:20: Early involvement in AI  5:00: Overview of drug discovery and its evolution over the last five years  7:50: AI and machine learning impact on biology and drug development  9:09: Pi aside defining AlphaFold  11:37: Areas of acceleration and opportunity in AI and drug discovery  17:21: Synthetic data  21:21: AI implications on therapeutic hypotheses  23:54: Personalized vs. precision medicine  27:04: Closing the data feedback loop  30:31: Exciting announcements  34:24: Privacy and data 36:41: GPT-4 story  38:48: Stuart Russell's comments on the applications of Koller's thesis  43:36: AI as a moving target 47:24: How can academia and industry positively impact society and humanity 51:58: insitro's culture  54:18: - Rapid-fire questions Select mentions:  Not the End of the World: How We Can Be the First Generation to build a Sustainable Planet by Hannah Ritchie Stuart Russell 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. Whether it's Inflection's Pi, OpenAI's ChatGPT or other AI tools, each episode will use AI to enhance and advance our discussion about what humanity could possibly get right if we leverage technology—and our collective effort—effectively.

Artificial intelligence has the potential to massively improve human health: from developing new drugs to providing more accurate diagnoses and helping people who live with severe disabilities. But AI also has the potential, if used wrongly or governed badly, to make life worse for people dealing with health problems. In this episode, we hear from people on the front lines of the technology. This episode was first published on 29 May, 2024. Radio Davos will resume new weekly podcasts from September 2024. Speakers: Victor Pineda, president and founder of the Alexandra Reeve Givens , CEO, Chris Mansi, CEO, Daphne Koller, founder and CEO of Links: Centre for the Fourth Industrial Revolution: AI Governance Alliance: Centre for Health and Healthcare: Related podcasts: Check out all our podcasts on : - - : - : - : Join the :

Artificial intelligence has the potential to massively improve human health: from developing new drugs to providing more accurate diagnoses and helping people who live with severe disabilities. But AI also has the potential, if used wrongly or governed badly, to make life worse for people dealing with health problems. In this episode, we hear from people on the front lines of the technology. This episode was first published on 29 May, 2024. Radio Davos will resume new weekly podcasts from September 2024. Speakers: Victor Pineda, president and founder of the Victor Pineda Foundation/World ENABLED Alexandra Reeve Givens , CEO, Center for Democracy and Technology Chris Mansi, CEO, Viz.ai Daphne Koller, founder and CEO of Insitro Links: Centre for the Fourth Industrial Revolution: https://centres.weforum.org/centre-for-the-fourth-industrial-revolution/home AI Governance Alliance: https://initiatives.weforum.org/ai-governance-alliance/home Centre for Health and Healthcare: https://centres.weforum.org/centre-for-health-and-healthcare/ Related podcasts: AI: Is 2024 the year that governance catches up with the tech? What's next for generative AI? Three pioneers on their Eureka moments Quality over quantity: why the time has come for 'value based health care' Special Meeting 2024: Bridging the Health Gap Special Meeting 2024: AI Powered Industries Check out all our podcasts on wef.ch/podcasts: YouTube: - https://www.youtube.com/@wef/podcasts Radio Davos - subscribe: https://pod.link/1504682164 Meet the Leader - subscribe: https://pod.link/1534915560 Agenda Dialogues - subscribe: https://pod.link/1574956552 Join the World Economic Forum Podcast Club: https://www.facebook.com/groups/wefpodcastclub

Hat Tip to this week's creators: @leopoldasch, @JoeSlater87, @GaryMarcus, @ulonnaya, @alex, @ttunguz, @mmasnick, @dannyrimer, @imdavidpierce, @asafitch, @ylecun, @nxthompson, @kaifulee, @DaphneKoller, @AndrewYNg, @aidangomez, @Kyle_L_Wiggers, @waynema, @QianerLiu, @nicnewman, @nmasc_, @steph_palazzolo, @nofilmschoolContents* Editorial: * Essays of the Week* Situational Awareness: The Decade Ahead* ChatGPT is b******t* AGI by 2027?* Ilya Sutskever, OpenAI's former chief scientist, launches new AI company* The Series A Crunch Is No Joke* The Series A Crunch or the Seedpocalypse of 2024 * The Surgeon General Is Wrong. Social Media Doesn't Need Warning Labels* Video of the Week* Danny Rimer on 20VC - (Must See)* AI of the Week* Anthropic has a fast new AI model — and a clever new way to interact with chatbots* Nvidia's Ascent to Most Valuable Company Has Echoes of Dot-Com Boom* The Expanding Universe of Generative Models* DeepMind's new AI generates soundtracks and dialogue for videos* News Of the Week* Apple Suspends Work on Next Vision Pro, Focused on Releasing Cheaper Model in Late 2025* Is the news industry ready for another pivot to video?* Cerebras, an Nvidia Challenger, Files for IPO Confidentially* Startup of the Week* Final Cut Camera and iPad Multicam are Truly Revolutionary* X of the Week* Leopold AschenbrennerEditorialI had not heard of Leopold Aschenbrenner until yesterday. I was meeting with Faraj Aalaei (a SignalRank board member) and my colleague Rob Hodgkinson when they began to talk about “Situational Awareness,” his essay on the future of AGI, and its likely speed of emergence.So I had to read it, and it is this week's essay of the week. He starts his 165-page epic with:Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them.So, Leopold is not humble. He finds himself “among” the few people with situational awareness.As a person prone to bigging up myself, I am not one to prematurely judge somebody's view of self. So, I read all 165 pages.He makes one point. The growth of AI capability is accelerating. More is being done at a lower cost, and the trend will continue to be super-intelligence by 2027. At that point, billions of skilled bots will solve problems at a rate we cannot imagine. And they will work together, with little human input, to do so.His case is developed using linear progression from current developments. According to Leopold, all you have to believe in is straight lines.He also has a secondary narrative related to safety, particularly the safety of models and their weightings (how they achieve their results).By safety, he does not mean the models will do bad things. He means that third parties, namely China, can steal the weightings and reproduce the results. He focuses on the poor security surrounding models as the problem. And he deems governments unaware of the dangers.Although German-born, he argues in favor of the US-led effort to see AGI as a weapon to defeat China and threatens dire consequences if it does not. He sees the “free world” as in danger unless it stops others from gaining the sophistication he predicts in the time he predicts.At that point, I felt I was reading a manifesto for World War Three.But as I see it, the smartest people in the space have converged on a different perspective, a third way, one I will dub AGI Realism. The core tenets are simple:* Superintelligence is a matter of national security. We are rapidly building machines smarter than the smartest humans. This is not another cool Silicon Valley boom; this isn't some random community of coders writing an innocent open source software package; this isn't fun and games. Superintelligence is going to be wild; it will be the most powerful weapon mankind has ever built. And for any of us involved, it'll be the most important thing we ever do. * America must lead. The torch of liberty will not survive Xi getting AGI first. (And, realistically, American leadership is the only path to safe AGI, too.) That means we can't simply “pause”; it means we need to rapidly scale up US power production to build the AGI clusters in the US. But it also means amateur startup security delivering the nuclear secrets to the CCP won't cut it anymore, and it means the core AGI infrastructure must be controlled by America, not some dictator in the Middle East. American AI labs must put the national interest first. * We need to not screw it up. Recognizing the power of superintelligence also means recognizing its peril. There are very real safety risks; very real risks this all goes awry—whether it be because mankind uses the destructive power brought forth for our mutual annihilation, or because, yes, the alien species we're summoning is one we cannot yet fully control. These are manageable—but improvising won't cut it. Navigating these perils will require good people bringing a level of seriousness to the table that has not yet been offered. As the acceleration intensifies, I only expect the discourse to get more shrill. But my greatest hope is that there will be those who feel the weight of what is coming, and take it as a solemn call to duty.I persisted in reading it, and I think you should, too—not for the war-mongering element but for the core acceleration thesis.My two cents: Leopold underestimates AI's impact in the long run and overestimates it in the short term, but he is directionally correct.Anthropic released v3.5 of Claude.ai today. It is far faster than the impressive 3.0 version (released a few months ago) and costs a fraction to train and run. it is also more capable. It accepts text and images and has a new feature that allows it to run code, edit documents, and preview designs called ‘Artifacts.'Claude 3.5 Opus is probably not far away.Situational Awareness projects trends like this into the near future, and his views are extrapolated from that perspective.Contrast that paper with “ChatGPT is B******t,” a paper coming out of Glasgow University in the UK. The three authors contest the accusation that ChatGPT hallucinates or lies. They claim that because it is a probabilistic word finder, it spouts b******t. It can be right, and it can be wrong, but it does not know the difference. It's a bullshitter.Hilariously, they define three types of BS:B******t (general)Any utterance produced where a speaker has indifference towards the truth of the utterance.Hard b******tB******t produced with the intention to mislead the audience about the utterer's agenda.Soft b******tB******t produced without the intention to mislead the hearer regarding the utterer's agenda.They then conclude:With this distinction in hand, we're now in a position to consider a worry of the following sort: Is ChatGPT hard b**********g, soft b**********g, or neither? We will argue, first, that ChatGPT, and other LLMs, are clearly soft b**********g. However, the question of whether these chatbots are hard b**********g is a trickier one, and depends on a number of complex questions concerning whether ChatGPT can be ascribed intentions.This is closer to Gary Marcus's point of view in his ‘AGI by 2027?' response to Leopold. It is also below.I think the reality is somewhere between Leopold and Marcus. AI is capable of surprising things, given that it is only a probabilistic word-finder. And its ability to do so is becoming cheaper and faster. The number of times it is useful easily outweighs, for me, the times it is not. Most importantly, AI agents will work together to improve each other and learn faster.However, Gary Marcus is right that reasoning and other essential decision-making characteristics are not logically derived from an LLM approach to knowledge. So, without additional or perhaps different elements, there will be limits to where it can go. Gary probably underestimates what CAN be achieved with LLMs (indeed, who would have thought they could do what they already do). And Leopold probably overestimates the lack of a ceiling in what they will do and how fast that will happen.It will be fascinating to watch. I, for one, have no idea what to expect except the unexpected. OpenAI Founder Illya Sutskever weighed in, too, with a new AI startup called Safe Superintelligence Inc. (SSI). The most important word here is superintelligence, the same word Leopold used. The next phase is focused on higher-than-human intelligence, which can be reproduced billions of times to create scaled Superintelligence.The Expanding Universe of Generative Models piece below places smart people in the room to discuss these developments. Yann LeCun, Nicholas Thompson, Kai-Fu Lee, Daphne Koller, Andrew Ng, and Aidan Gomez are participants. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

Artificial intelligence has the potential to massively improve human health: from developing new drugs to providing more accurate diagnoses and helping people who live with severe disabilities. But AI also has the potential, if used wrongly or governed badly, to make life worse for people dealing with health problems. In this episode, we hear from people on the front lines of the technology. Speakers: Victor Pineda, president and founder of the Alexandra Reeve Givens , CEO, Chris Mansi, CEO, Daphne Koller, founder and CEO of Links: Centre for the Fourth Industrial Revolution: AI Governance Alliance: Centre for Health and Healthcare: Related podcasts: Check out all our podcasts on : - - : - : - : Join the :

Artificial intelligence has the potential to massively improve human health: from developing new drugs to providing more accurate diagnoses and helping people who live with severe disabilities. But AI also has the potential, if used wrongly or governed badly, to make life worse for people dealing with health problems. In this episode, we hear from people on the front lines of the technology. Speakers: Victor Pineda, president and founder of the Victor Pineda Foundation/World ENABLED Alexandra Reeve Givens , CEO, Center for Democracy and Technology Chris Mansi, CEO, Viz.ai Daphne Koller, founder and CEO of Insitro Links: Centre for the Fourth Industrial Revolution: https://centres.weforum.org/centre-for-the-fourth-industrial-revolution/home AI Governance Alliance: https://initiatives.weforum.org/ai-governance-alliance/home Centre for Health and Healthcare: https://centres.weforum.org/centre-for-health-and-healthcare/ Related podcasts: AI: Is 2024 the year that governance catches up with the tech? What's next for generative AI? Three pioneers on their Eureka moments Quality over quantity: why the time has come for 'value based health care' Special Meeting 2024: Bridging the Health Gap Special Meeting 2024: AI Powered Industries Check out all our podcasts on wef.ch/podcasts: YouTube: - https://www.youtube.com/@wef/podcasts Radio Davos - subscribe: https://pod.link/1504682164 Meet the Leader - subscribe: https://pod.link/1534915560 Agenda Dialogues - subscribe: https://pod.link/1574956552 Join the World Economic Forum Podcast Club: https://www.facebook.com/groups/wefpodcastclub

Today, we're going to do some something a little different. We're gonna do some business quotations, and I'll probably throw in a little commentary as we go. I found these quite inspiring many times, so I look at them frequently. Screw The Commute Podcast Show Notes Episode 887 How To Automate Your Business - https://screwthecommute.com/automatefree/ Internet Marketing Training Center - https://imtcva.org/ Higher Education Webinar – https://screwthecommute.com/webinars See Tom's Stuff – https://linktr.ee/antionandassociates 00:23 Tom's introduction to Business Quotations 01:11 Charles Schwab, Alice Walker, Wayne Gretzky, Chinese Proverb, Daphne Koller 03:13 Walt Disney, Sara Blakely, Zig Ziglar, Beatrice Dixon, Jim Rohn 06:36 Barbara Corcoran, Winston Churchill, Tory Burch, Henry Ford, Vera Wang 08:12 Richard Branson, Arianna Huffington, Bruce Lee, Madame C.J. Walker, Ginni Rometty 10:27 Mario Andretti, Giving 110% Entrepreneurial Resources Mentioned in This Podcast Higher Education Webinar - https://screwthecommute.com/webinars Screw The Commute - https://screwthecommute.com/ Screw The Commute Podcast App - https://screwthecommute.com/app/ College Ripoff Quiz - https://imtcva.org/quiz Know a young person for our Youth Episode Series? Send an email to Tom! - orders@antion.com Have a Roku box? Find Tom's Public Speaking Channel there! - https://channelstore.roku.com/details/267358/the-public-speaking-channel How To Automate Your Business - https://screwthecommute.com/automatefree/ Internet Marketing Retreat and Joint Venture Program - https://greatinternetmarketingtraining.com/ KickStartCart - http://www.kickstartcart.com/ Copywriting901 - https://copywriting901.com/ Become a Great Podcast Guest - https://screwthecommute.com/greatpodcastguest Training - https://screwthecommute.com/training Disabilities Page - https://imtcva.org/disabilities/ Tom's Patreon Page - https://screwthecommute.com/patreon/ Tom on TikTok - https://tiktok.com/@digitalmultimillionaire/ Email Tom: Tom@ScrewTheCommute.com Internet Marketing Training Center - https://imtcva.org/ Related Episodes Long Home Pages - https://screwthecommute.com/886/ More Entrepreneurial Resources for Home Based Business, Lifestyle Business, Passive Income, Professional Speaking and Online Business I discovered a great new headline / subject line / subheading generator that will actually analyze which headlines and subject lines are best for your market. I negotiated a deal with the developer of this revolutionary and inexpensive software. Oh, and it's good on Mac and PC. Go here: http://jvz1.com/c/41743/183906 The Wordpress Ecourse. Learn how to Make World Class Websites for $20 or less. https://screwthecommute.com/wordpressecourse/ Join our Private Facebook Group! One week trial for only a buck and then $37 a month, or save a ton with one payment of $297 for a year. Click the image to see all the details and sign up or go to https://www.greatinternetmarketing.com/screwthecommute/ After you sign up, check your email for instructions on getting in the group.

In this episode of the AI Grand Rounds podcast, Dr. Daphne Koller charts her professional trajectory, tracing her early fascination with computers to her influential role in AI and health care. Initially intrigued by the capacity of computers for decision-making based on theoretical principles, Koller witnessed her niche area — once considered peripheral to AI — grow to dominate the field. Her curiosity led her from abstract theory to practical machine learning applications and eventually to the complex world of biomedicine. Throughout the podcast, Koller shares her shift from pure computer science to the integration of machine learning in biological and medical research. She explains the unique challenges of applying AI to biology, distinguishing it from more deterministic fields, and how these complexities feed into her work at insitro, where she is leveraging AI throughout the drug discovery and development process, from disease understanding to therapeutic application and monitoring. She advocates for the democratizing potential of AI, underscoring its capacity to enable broader participation in scientific inquiry and problem-solving.   Transcript.

Transcript Eric Topol (00:06):Well, hello, this is Eric Topol with Ground Truths and I am absolutely thrilled to welcome Daphne Koller, the founder and CEO of insitro, and a person who I've been wanting to meet for some time. Finally, we converged so welcome, Daphne.Daphne Koller (00:21):Thank you Eric. And it's a pleasure to finally meet you as well.Eric Topol (00:24):Yeah, I mean you have been rocking everybody over the years with elected to the National Academy of Engineering and Science and right at the interface of life science and computer science and in my view, there's hardly anyone I can imagine who's doing so much at that interface. I wanted to first start with your meeting in Davos last month because I kind of figured we start broad AI rather than starting to get into what you're doing these days. And you had a really interesting panel [←transcript] with Yann LeCun, Andrew Ng and Kai-Fu Lee and others, and I wanted to get your impression about that and also kind of the general sense. I mean AI is just moving it at speed, that is just crazy stuff. What were your thoughts about that panel just last month, where are we?Video link for the WEF PanelDaphne Koller (01:25):I think we've been living on an exponential curve for multiple decades and the thing about exponential curves is they are very misleading things. In the early stages people basically take the line between whatever we were last year, and this year and they interpolate linearly, and they say, God, things are moving so slowly. Then as the exponential curve starts to pick up, it becomes more and more evident that things are moving faster, but it's still people interpolate linearly and it's only when things really hit that inflection point that people realize that even with the linear interpolation where we'll be next year is just mind blowing. And if you realize that you're on that exponential curve where we will be next year is just totally unanticipatable. I think what we started to discuss in that panel was, are we in fact on an exponential curve? What are the rate limiting factors that may or may not enable that curve to continue specifically availability of data and what it would take to make that curve available in areas outside of the speech, whatever natural language, large language models that exist today and go far beyond that, which is what you would need to have these be applicable to areas such as biology and medicine.Daphne Koller (02:47):And so that was kind of the message to my mind from the panel.Eric Topol (02:53):And there was some differences in opinion, of course Yann can be a little strong and I think it was good to see that you're challenging on some things and how there is this “world view” of AI and how, I guess where we go from here. As you mentioned in the area of life science, there already had been before large language models hit stride, so much progress particularly in imaging cells, subcellular, I mean rare cells, I mean just stuff that was just without any labeling, without fluorescein, just amazing stuff. And then now it's gone into another level. So as we get into that, just before I do that, I want to ask you about this convergence story. Jensen Huang, I'm sure you heard his quote about biology as the opportunity to be engineering, not science. I'm sure if I understand, not science, but what about this convergence? Because it is quite extraordinary to see two fields coming together moving at such high velocity."Biology has the opportunity to be engineering not science. When something becomes engineering not science it becomes...exponentially improving, it can compound on the benefits of previous years." -Jensen Huang, NVIDIA.Daphne Koller (04:08):So, a quote that I will replace Jensen's or will propose a replacement for Jensen's quote, which is one that many people have articulated, is that math is to physics as machine learning is to biology. It is a mathematical foundation that allows you to take something that up until that point had been kind of mysterious and fuzzy and almost magical and create a formal foundation for it. Now physics, especially Newtonian physics, is simple enough that math is the right foundation to capture what goes on in a lot of physics. Biology as an evolved natural system is so complex that you can't articulate a mathematical model for that de novo. You need to actually let the data speak and then let machine learning find the patterns in those data and really help us create a predictability, if you will, for biological systems that you can start to ask what if questions, what would happen if we perturb the system in this way?The ConvergenceDaphne Koller (05:17):How would it react? We're nowhere close to being able to answer those questions reliably today, but as you feed a machine learning system more and more data, hopefully it'll become capable of making those predictions. And in order to do that, and this is where it comes to this convergence of these two disciplines, the fodder, the foundation for all of machine learning is having enough data to feed the beast. The miracle of the convergence that we're seeing is that over the last 10, 15 years, maybe 20 years in biology, we've been on a similar, albeit somewhat slower exponential curve of data generation in biology where we are turning it into a quantitative discipline from something that is entirely observational qualitative, which is where it started, to something that becomes much more quantitative and broad based in how we measure biology. And so those measurements, the tools that life scientists and bioengineers have developed that allow us to measure biological systems is what produces that fodder, that energy that you can then feed into the machine learning models so that they can start making predictions.Eric Topol (06:32):Yeah, well I think the number of layers of data no less what's in these layers is quite extraordinary. So some years ago when all the single cell sequencing was started, I said, well, that's kind of academic interest and now the field of spatial omics has exploded. And I wonder how you see the feeding the beast here. It's at every level. It's not just the cell level subcellular and single cell nuclei sequencing single cell epigenomics, and then you go all the way to these other layers of data. I know you plug into the human patient side as well as it could be images, it could be past slides, it could be the outcomes and treatments and on and on and on. I mean, so when you think about multimodal AI, has anybody really done that yet?Daphne Koller (07:30):I think that there are certainly beginnings of multimodal AI and we have started to see some of the benefits of the convergence of say, imaging and omics. And I will give an example from some of the work that we've recently distributed on a preprint server work that we did at insitro, which took imaging data from standard histopathology slides, H&E slides and aligned them with simple bulk RNA-Seq taken from those same tumor samples. And what we find is that by training models that translate from one to the other, specifically from the imaging to the omics, you're able to, for a fairly large fraction of genes, make very accurate predictions of gene expression levels by looking at the histopath images alone. And in fact, because many of the predictions are made at the tile level, not at the entire slide level, even though the omics was captured in bulk, you're able to spatially resolve the signal and get kind of like a pseudo spatial biology just by making predictions from the H&E image into these omic modalities.Multimodal A.I. and Life ScienceDaphne Koller (08:44):So there are I think beginnings of multimodality, but in order to get to multimodality, you really need to train on at least some data where the two modalities are simultaneously. And so at this point, I think the rate limiting factor is more a matter of data acquisition for training the models. It is for building the models themselves. And so that's where I think things like spatial biology, which I think like you are very excited about, are one of the places where we can really start to capture these paired modalities and get to some of those multimodal capabilities.Eric Topol (09:23):Yeah, I wanted to ask you because I mean spatial temporal is so perfect. It is two modes, and you have as the preprint you refer to and you see things like electronic health records in genomics, electronic health records in medical images. The most we've done is getting two modes of data together. And the question is as this data starts to really accrue, do we need new models to work with it or do you actually foresee that that is not a limiting step?Daphne Koller (09:57):So I think currently data availability is the most significant rate limiting step. The nice thing about modern day machine learning is that it really is structured as a set of building blocks that you can start to put together in different ways for different situations. And so, do we have the exact right models available to us today for these multimodal systems? Probably not, but do we have the right building blocks that if we creatively put them together from what has already been deployed in other settings? Probably, yes. So of course there's still a model exploration to be done and a lot of creativity in how these building blocks should be put together, but I think we have the tools available to solve these problems. What we really need is first I think a really significant data acquisition effort. And the other thing that we need, which is also something that has been a priority for us at insitro, is the right mix of people to be put together so that you can, because what happens is if you take a bunch of even extremely talented and sophisticated machine learning scientists and say, solve a biological problem, here's a dataset, they don't know what questions to ask and oftentimes end up asking questions that might be kind of interesting from machine learning perspective, but don't really answer fundamental biology questions.Daphne Koller (11:16):And conversely, you can take biologists and say, hey, what would you have machine learning do? And they will tell you, well, in our work we do A to B to C to D, and B to C is kind of painful, like counting nuclei is really painful, so can we have the machine do that for us? And it's kind of like that. Yeah, but that's boring. So what you get if you put them in a room together and actually get to the point where they communicate with each other effectively, is that not only do you get better solutions, you get better problems. I think that's really the crux of making progress here besides data is the culture and the people.A.I. and Drug DiscoveryEric Topol (11:54):Well, I'm sure you've assembled that at insitro knowing you, and I mean people tend to forget it's about the people, it's not about the models or even the data when you have all that. Now you've been onto drug discovery paths, there's at least 20 drugs that are AI driven that are in the clinic in phase one or two at some point. Obviously these are not only ones that you've been working on, but do you see this whole field now going into high gear because of this? Or is that the fact that there's all these AI companies partnering with big pharma? Is it a lot of nice agreements that are drawn up with multimillion dollar milestones or is this real?Daphne Koller (12:47):So there's a number of different layers to your question. First of all, let me start by saying that I find the notion of AI driven drugs to be a bit of a weird concept because over time most drugs will have some element of AI in them. I mean, even some of the earlier work used data science in many cases. So where do you draw the boundary? I mean, we're not going to be in a world anytime soon where AI starts out with, oh, I need to work on ALS and at the end there is a clinical trial design ready to be submitted to the FDA without anything, any human intervention in the middle. So, it's always going to be an interplay between a machine and a human with over time more and more capabilities I think being taken on by the machine, but I think inevitably a partnership for a long time to come.Daphne Koller (13:41):But coming to the second part of your question, is this real? Every big pharma has gotten to the point today that they realize they need some of that AI thing that's going around. The level of sophistication of how they incorporate that and their willingness to make some of the hard decisions of, well, if we're going to be doing this with AI, it means we shouldn't be doing it the old way anymore and we need to make a big dramatic internal shift that I think depends very much on the specific company. And some companies have more willingness to take those very big steps than others, so will some companies be able to make the adjustment? Probably. Will all of them? Probably not. I would say however, that in this new world there is also room for companies to emerge that are, if you will, AI native.Daphne Koller (14:39):And we've seen that in every technological revolution that the native companies that were born in the new age move faster, incorporate the technology much more deeply into every aspect of their work, and they end up being dominant players if not the dominant player in that new world. And you could look at the internet revolution and think back to Google did not emerge from the yellow pages. Netflix did not emerge from blockbuster, Amazon did not emerge from Walmart so some of those incumbents did make the adjustment and are still around, some did not and are no longer around. And I think the same thing will happen with drug discovery and development where there will be a new crop of leading companies to I think maybe together with some of the incumbents that we're able to make the adjustment.Eric Topol (15:36):Yeah, I think your point there is essential, and another part of this story is that a lot of people don't realize there's so many nodes of ways that AI can facilitate this whole process. I mean from the elemental data mining that identified Baricitinib for Covid and now being used even for many other indications, repurposing that to how to simulate for clinical trials and everything in between. Now, what seems like because of your incredible knack and this convergence, I mean your middle name is like convergence really, you are working at the level of really in my view, this unique aspect of bringing cells and all the other layers of data together to amp things up. Is that a fair assessment of where insitro in your efforts are directed?Three BucketsDaphne Koller (16:38):So first of all, maybe it's useful to kind of create the high level map and the simplest version I've heard is where you divide the process into three major buckets. One is what you think of as biology discovery, which is the discovery of new therapeutic hypotheses. Basically, if you modulate this target in this group of humans, you will end up affecting this clinical outcome. That's the first third. The middle third is, okay, well now we need to turn that hypothesis into an actual molecule that does that. So basically generating molecules. And then finally there's the enablement and acceleration of the clinical development process, which is the final third. Most companies in the AI space have really focused in on that middle third because it is well-defined, you know when you've succeeded if someone gives you a target and what's called a target product profile (TPP) at the end of whatever, two, three years, whether you've been able to create a molecule that achieves the appropriate properties of selectivity and solubility and all those other things. The first third is where a lot of the mistakes currently happen in drug discovery and development. Most drugs that go into the clinic don't fail because we didn't have the right molecule. I mean that happens, but it's not the most common failure mode. The most common failure mode is that the target was just a wrong target for this disease in this patient population.Daphne Koller (18:09):So the real focus of us, the core of who we are as a company is on that early third of let's make sure we're going after the right clinical hypotheses. Now with that, obviously we need to make molecules and some of those molecules we make in-house, and obviously we use machine learning to do that as well. And then the last third is we discover that if you have the right therapeutic hypothesis, which includes which is the right patient population, that can also accelerate and enable your clinical trials, so we end up doing some of that as well. But the core of what we believe is the failure mode of drug discovery and what it's going to take to move it to the next level is the articulation of therapeutic hypotheses that actually translate into clinical outcome. And so in order to do that, we've put together, to your point about convergence, two very distinct types of data.Daphne Koller (19:04):One is data that we print in our own internal data factory where we have this incredible set of capabilities that uses stem cells and CRISPR and microscopy and single cell measurements and spatial biology and all that to generate massive amounts of in-house data. And then because ultimately you care not about curing cells, you care about curing people, you also need to bring in the clinical data. And again, here also we look at multiple high content data modalities, imaging and omics, and of course human genetics, which is one of the few sources of ground truth for causality that is available in medicine and really bring all those different data modalities across these two different scales together to come up with what we believe are truly high quality therapeutic hypotheses that we then advance into the clinic.AlphaFold2, the ExemplarEric Topol (19:56):Yeah, no, I think that's an extraordinary approach. It's a bold, ambitious one, but at least it is getting to the root of what is needed. One of the things you mentioned of course, is the coming up with molecules, and I wanted to get your comments about the AlphaFold2 world and the ability to not just design proteins now of course that are not extant proteins, but it isn't just proteins, it could be antibodies, it could be peptides and small molecules. How much does that contribute to your perspective?Daphne Koller (20:37):So first of all, let me say that I consider the AlphaFold story across its incarnations to be one of the best examples of the hypothesis that we set out trying to achieve or trying to prove, which is if you feed a machine learning model enough data, it will learn to do amazing things. And the space of protein folding is one of those areas where there has been enough data in biology that is the sequence to structure mapping is something that over the years, because it's so consistent across different cells, across different species even, we have a lot of data of sequence to structure, which is what enabled AlphaFold to be successful. Now since then, of course, they've taken it to a whole new level. I think what we are currently able to do with protein-based therapeutics is entirely sort of a consequence of that line of development. Whether that same line of development is also going to unlock other therapeutic modalities such as small molecules where the amount of data is unfortunately much less abundant and often locked away in the bowels of big pharma companies that are not eager to share.Daphne Koller (21:57):I think that question remains. I have not yet seen that same level of performance in de novo design of small molecule therapeutics because of the data availability limitations. Now people have a lot of creative ideas about that. We use DNA encoded libraries as a way of generating data at scale for small molecules. Others have used other approaches including active learning and pre-training and all sorts of approaches like that. We're still waiting, I think for a truly convincing demonstration that you can get to that same level of de novo design in small molecules as you can in protein therapeutics. Now as to how that affects us, I'm so excited about this development because our focus, as I mentioned, is the discovery of novel therapeutic hypotheses. You then need to turn those therapeutic hypotheses into actual molecules that do the work. We know we're not going to be the expert in every single therapeutic modality from small molecules to macro cycles, to the proteins to mRNA, siRNA, there's so many of those that you need to have therapeutic modality experts in each of those modalities that can then as you discover a target that you want to modulate, you can basically go and ask what is the right partner to help turn this into an actual therapeutic intervention?Daphne Koller (23:28):And we've already had some conversations with some modality partners as we like to call them that help us take some of our hypotheses and turn it into molecules. They often are very hungry for new targets because they oftentimes kind of like, okay, here's the three or four or whatever, five low hanging fruits that our technology uniquely unlocks. But then once you get past those well validated targets like, okay, what's next? Am I just going to go read a bunch of papers and hope for the best? And so oftentimes they're looking for new hypotheses and we're looking for partners to make molecules. It's a great partnership.Can We Slow the Aging Process?Eric Topol (24:07):Oh yeah, no question about that. Now, we've seen in recent times some leaps in drugs that were worked on for decades, like the GLP-1s for obesity, which are having effects potentially well beyond obesity didn't require any AI, but just slogging away at it for decades. And you previously were at Calico, which is trying to deal with aging. Do you think that we're going to see drug interventions that are going to slow the aging process because of this unique time of this exponential point we are in where we're a computer and science and digital biology come together?Daphne Koller (24:52):So I think the GLP-1s are an incredible achievement. And I would point out, I know you said and incorrectly that it didn't use any AI, but they did actually use an understanding of human genetics. And I think human genetics and the genotype phenotype statistical associations that they revealed is in some ways the biological precursor to AI it is a way of leveraging very large amounts of data, admittedly using simpler statistical tools, but still to discover in a data-driven way, novel therapeutic hypothesis. So I consider the work that we do to be a progeny of the kind of work that statistical geneticists have done. And of course a lot of heavy lifting needed to be done after that in order to make a drug that actually worked and kudos to the leaders in that space. In terms of the modulation of aging, I mean aging is a process of decline over time, and the rate of that decline is definitely something that is modifiable.Daphne Koller (26:07):And we all know that external factors such as lifestyle, diet, exercise, even exposure to sun or smoking, accelerates the aging process. And you could easily imagine, as we've seen in the GLP-1s that a therapeutic intervention can change that trajectory. So will we be able to using therapeutic interventions, increase health span so that we live healthy longer? I think the answer to that is undoubtedly, yes. And we've seen that consistently with therapeutic interventions, not even just the GLP-1s, but going backwards, I mean even statins and earlier things. Will we be able to increase the maximum life span so that people habitually live past 120, 150? I don't know. I don't know that anybody knows the answer to that question. I personally would be quite happy with increasing my health span so that at the age of 80, I'm still able to actively go hiking and scuba diving at 90 and 100 and that would be a pretty good place to start.Eric Topol (27:25):Well, I'm with you on that, but I just want to ask though, because the drugs we have today that are highly effective, I mean statins is a good example. They work at a particular level of the body. They don't have across the board modulation of effect. And I guess what I was asking is, do you foresee we will have some way to do that across all systems? I mean, that is getting to, now that we have so many different ways to intervene on the process, is there a way that you envision in the future that we'll be able to here, I'm not talking about in expanding lifespan, I'm talking about promoting health, whether it's the immune system or whether it's through mitochondria and mTOR, caloric, I mean all these different things you think that's conceivable or is that just, I mean companies like Calico and others have been chasing this. What do you think?Daphne Koller (28:30):Again, I think it's a thing that is hard to predict. I mean, we know that different organ systems age at different rates, and is there a single bio even in a single individual, and it's been well established that you can test brain age versus muscle health versus cardiovascular, and they can be quite different in the same individual, so is there a single hub? No, that governs all forms of aging. I don't know if that's true. I think it's oftentimes different. We know protein folding has an effect, you know DNA damage has an effect. That's why our skin ages because it's exposed to sun. Is there going to be a single switch that reverts it all back? Certainly some companies are pursuing that single bullet approach. I personally would probably say that based on the biology that I've seen, there's at least as much potential in trying to find ways to slow the decline in a way that specific to say as we discussed the immune system or correcting protein, misfolding dysfunction or things like that. And I'm not dismissing there is a single magic switch, but let's just say I think we should be exploring multiple alternatives.Eric Topol (29:58):Yeah, no, I like your reasoning. I think it's actually like everything else you said here. It makes a lot of sense. The logic is hard to argue with. Well, I think what you're doing there at insitro is remarkable and it seems to be quite distinct from other strategies, and that's not at all surprising knowing your background and your aspiration.Daphne Koller (30:27):Never like to follow the crowd. It's boring.Eric Topol (30:30):Right, and I do know you left an aging directed company effort at Calico to do what you're doing. So that must have been an opening for you that you saw was much more diverse perhaps, or maybe I'm mistaken that Calico is not really age specific in its goals.Daphne Koller (30:49):So what inspired me to go found insitro was the realization that we are making medicines today in a way that is not that different from the way in which we were making medicines 20 or 30 years ago in terms of the process by which we go from a, here's what I want to work on to here's a drug is a very much an artisanal one-off each one of them is a snowflake. There is very little commonality and sharing of insights and infrastructure across those efforts except in relatively limited tool-based ways. And I wanted to change that. I wanted to take the tools of engineering and data and machine learning and build a very different approach of going from a problem definition to a therapeutic intervention. And it didn't make sense to build that within a company that's focused on any single biology, not just aging because it is such a broad-based foundation.Daphne Koller (31:58):And I will tell you that I think we are on the path to building the thing that I set out to build. And as one example of that, I will use the work that we've recently done in metabolic disease where based on the foundations that we've built using both the clinical machine learning work and the cellular machine learning work, we were able to go from a problem articulation of this is the indication that we want to work on to a proof of concept in a translatable animal model in one year. That is pretty unusual. Admittedly, this is with an SiRNA tool compound. Nice thing about things that are liver directed is that it's not that difficult of a path to go from an SiRNA tool compound to an actual SiRNA drug. And so hopefully that's a fairly linear journey from there even, which is great.Daphne Koller (32:51):But the fact that we were able to go from problem articulation to a proof of concept in a translatable animal model in one year, that is unusual. And we're starting to see that now across our other therapeutic areas. It takes a long time to build a platform because you're basically building a foundation. It's like, okay, where's the fruit of all of that? I mean, you're building and building and building and nothing comes out for a while because you're building so much of the infrastructure. But once you've built it, you turn the crank and stuff starts to come out, you turn the crank again, and it works faster and better than the previous time. And so the essence of what we've built and what has turned into the tagline for the company is what we call pipeline through platform, which is we're building a pipeline of therapeutic interventions that comes off of a platform. And that's rare in biopharma, the only platform companies that really have emerged by and larger therapeutic modality platforms, things like Moderna and Alnylam, which have gotten really good at a particular modality and that's awesome. We're building a discovery platform and that is a fairly unusual thing.Eric Topol (34:02):Right. Well, I have no doubt you'll be discovering a lot of important things. That one sounds like it could be a big impact on NASH.Daphne Koller (34:14):Yeah, we hope so.Eric Topol (34:14):A big unmet need that's not going to be fixed by what we have today. So Daphne, it's really a joy to talk with you and palpable enthusiasm for where the field is going as one of its real leaders and we'll be cheering for you. I hope we'll reconnect in the times ahead to get another progress report because you're definitely rocking it there and you've got a lot of great ideas for how to change the life science medical world of the future.Daphne Koller (34:48):Thank you so much. It's a pleasure to meet you, and it's a long and difficult journey, but I think we're on the right path, so looking forward to seeing that all that pan out.Eric Topol (34:58):You made a compelling case in a short visit, so thank you.Daphne Koller (35:02):Thank you so much.Thanks for your subscription and listening/reading these posts.All content on Ground Truths—newsletter analyses and podcasts—is free.Voluntary paid subscriptions all go to support Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

Generative AI is advancing exponentially. What is happening at the frontier of research and application and how are novel techniques and approaches changing the risks and opportunities linked to frontier, generative AI models? This is the full audio from a session at the World Economic Forum's Annual Meeting 2024. Speakers: Yann LeCun, Silver Professor of Data Science, Computer Science, Neural Science and Electrical Engineering, New York University Nicholas Thompson, Chief Executive Officer, The Atlantic Kai-Fu Lee, Founder, 01.AI Pte. Ltd. Daphne Koller, Founder and Chief Executive Officer, Insitro Inc Andrew Ng, Founder, DeepLearning.AI, LLC Aidan Gomez, Co-Founder and Chief Executive Officer, Cohere Inc. Watch the session here: https://www.weforum.org/events/world-economic-forum-annual-meeting-2024/sessions/the-expanding-universe-of-generative-models Follow all the action from Davos at wef.ch/wef24 and across social media using the hashtag #WEF24. Check out all our podcasts on wef.ch/podcasts: YouTube: https://www.youtube.com/@wef Radio Davos - subscribe: https://pod.link/1504682164 Meet the Leader - subscribe: https://pod.link/1534915560 Agenda Dialogues - subscribe: https://pod.link/1574956552 World Economic Forum Book Club Podcast - subscribe: https://pod.link/1599305768 Join the World Economic Forum Podcast Club: https://www.facebook.com/groups/wefpodcastclub https://www.weforum.org/podcasts/radio-davos/episodes/

We're looking back on 2023 and sharing a handful of our favorite conversations. Last year was full of insightful conversations that shaped the way we think about the most innovative movements in the AI space. Want to hear more? Check out the full episodes here: What is Digital Life? with OpenAI Co-Founder & Chief Scientist Ilya Sutskever  How AI can help small businesses with Former Square CEO Alyssa Henry Will Everyone Have a Personal AI? With Mustafa Suleyman, Founder of DeepMind and Inflection How will AI bring us the future of medicine? With Daphne Koller from Insitro The case for AI optimism with Reid Hoffman from Inflection AI Your AI Friends Have Awoken, With Noam Shazeer Mistral 7B and the Open Source Revolution With Arthur Mensch, CEO Mistral AI The Computing Platform Underlying AI with Jensen Huang, Founder and CEO NVIDIA Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @reidhoffman l @alyssahhenry l @ilyasut l @mustafasuleyman l @DaphneKoller l @arthurmensch l @MrJensenHuang Show Notes:  (0:00) Introduction (0:27) Ilya Sutskever on the governance structure of OpenAI (3:11) Alyssa Henry on how AI can small business owners (5:25) Mustafa Suleyman on defining intelligence (8:53) Reid Hoffman's advice for co-working with AI (11:47) Daphne Koller on probabilistic graphical models (13:15) Noam Shazeer on the possibilities of LLMs (14:27) Arthur Mensch on keeping AI open (17:19) Jensen Huang on how Nvidia decides what to work on

Bio Eats World is now Raising Health!Vijay Pande, founding partner of Bio + Health, is joined by Daphne Koller, Andrew Ng, Aviv Regev, and Jakob Uszkoreit.Vijay leads us on a reflective journey through the monumental achievements in AI from the 1980s to today, with a focus on the progress in healthcare and life sciences. This episode is drawn from the episodes we recorded in 2023 with Daphne Koller, Andrew Ng, Aviv Regev, and Jakob Uszkoreit, which are linked below.This blend of expert commentary and firsthand insights explores the burgeoning impact of AI on healthcare innovation and how it's reshaping the future of health.AI and Actionable Insights for Drug Development with Daphne KollerNavigating the Future of AI with Andrew NgWhen Quantity Becomes Quality with Aviv RegevUsing AI to Take Bio Farther with Jakob Uszkoreit

Today's episode continues our coverage from a16z's recent AI Revolution event. You'll hear a16z Bio & Health GP Vijay Pande speak with Daphne Koller about the fascinating convergence of machine learning and genomics – two industries that have benefitted decades of investment and progress – which are now colliding head on.Daphne is a prominent innovator at this intersection, as a long-time professor in computer science at Stanford and co-founder of Coursera, who has decided to step back into the arena with her company Insitro. In fact, Insitro is a blend of in silico and in virto!If you'd like to access all the talks from AI Revolution in full, visit a16z.com/airevolution. Resources:Find Daphne on Twitter: https://twitter.com/DaphneKollerFind Vijay on Twitter: https://twitter.com/vijaypandeFind Insitro on Twitter: https://twitter.com/insitro Stay Updated: Find a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

This episode is live from our recent AI Revolutionaries (AIR) conference. In this episode, Daphne Koller, founder and CEO at insitro and AI expert, chats with Vijay Pande of Bio + Health.You can check out the full conversation with video and transcript at https://a16z.com/digital-biology/. 

[0:59] Why life sciences?[3:42] AI in the life sciences[7:20] LLM for cells[11:55] Engineering disease and drug discovery[13:51] Bits vs. atoms[17:55] The opportunity aheadThis conversation is part of our AI Revolution series, recorded August 2023 at a live event in San Francisco. The series features some of the most impactful builders in the field of AI discussing and debating where we are, where we're going, and the big open questions in AI. Find more content from our AI Revolution series on www.a16z.com/AIRevolution.

Industry veteran and serial entrepreneur Daphne Koller tells In Vivo about how her company, Insitro, is unleashing the potential of computational biology and machine learning to discover new drug compounds.

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Jacob sits down with PathAI Ceo Dr. Andy Beck to discuss the state of AI adoption in diagnosis, why Path acquired their own lab, pathologists' jobs in the future and nailing GTM to reach a ~$1B valuation.  00:00 intro 01:01 pathology and AI 13:30 nailing go-to-market strategy 19:47 how pathology labs can go digital 25:36 regulatory frameworks and roadblocks 33:05 do improvements in foundation models impact PathAI? 36:26 standardizing diagnosis 40:05 how will the job of a pathologist change going forward? 42:35 NASH 47:30 working in Daphne Koller's lab With your co-hosts: @jasoncwarner - Former CTO GitHub, VP Eng Heroku & Canonical @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @jordan_segall - Partner at Redpoint

Jacob sits down with PathAI Ceo Dr. Andy Beck to discuss the state of AI adoption in diagnosis, why Path acquired their own lab, pathologists' jobs in the future and nailing GTM to reach a ~$1B valuation.  00:00 intro 01:01 pathology and AI 13:30 nailing go-to-market strategy 19:47 how pathology labs can go digital 25:36 regulatory frameworks and roadblocks 33:05 do improvements in foundation models impact PathAI? 36:26 standardizing diagnoses 40:05 how will the job of a pathologist change going forward? 42:35 NASH 47:30 working in Daphne Koller's lab

Jacob sits down with Insitro CEO Daphne Koller to discuss founding Coursera, where and how ML can drive the most impact in drug development, and if foundation models can transform core drug discovery work and edtech.  (00:00) - intro (00:54) - Daphne's journey (09:18) - AI and biology discovery (10:59) - insitro vs. traditional pharma (20:04) - phenotyping patients (26:01) - early mistakes (29:51) - the future of data (35:33) - partnering with larger pharma companies (38:17) - impact of LLMs on biopharma (44:04) - over-hyped/under-hyped  With your co-hosts: @jasoncwarner - Former CTO GitHub, VP Eng Heroku & Canonical @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @jacobeffron - Partner at Redpoint, Former PM Flatiron Health

Jacob sits down with Insitro CEO Daphne Koller to discuss founding Coursera, where and how ML can drive the most impact in drug development, and if foundation models can transform core drug discovery work and edtech. 00:00 intro00:59 Daphne's journey09:02 AI and biology discovery10:42 insitro vs. traditional pharma19:47 phenotyping patients25:44 early mistakes29:34 the future of data35:16 partnering with larger pharma companies38:00 impact of LLMs on biopharma43:47 over-hyped/under-hyped

In this episode, Daphne Koller, founder and CEO of insitro—as well as the co-founder of Coursera, a MacArthur Award winner, and a former professor in the department of computer science at Stanford University—chats with a16z Bio + Health founding partner Vijay Pande. Together, they talk about Daphne's career journey, how Daphne thinks about the last few decades of progress in AI, and how insitro leverages artificial intelligence and machine learning to explore biology through new models of discovery.

Life-saving therapeutics continue to grow more costly to discover. At the same time, recent advances in using machine learning for the life sciences and medicine are extraordinary. Are we on the verge of a paradigm shift in biotech? This week on the podcast, a pioneer in AI, Daphne Koller, joins Sarah Guo and Elad Gil on the podcast to help us explore that question. Daphne is the CEO and founder of Insitro — a company that applies machine learning to pharma discovery and development, specifically by leveraging “induced pluripotent stem cells.” We explain Insitro's approach, why they're focused on generating their own data, why you can't cure schizophrenia in mice, and how to design a culture that supports both research and engineering. Daphne was previously a computer science professor at Stanford, and co-founder and co-CEO of edutech company Coursera. Show Links:  Insitro - About  Video: AWS re:Invent 2019 – Daphne Koller of insitro Talks About Using AWS to Transform Drug Development  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @DaphneKoller Show Notes:  [1:49] - How Daphne combined her biology and tech interests and ran a bifurcated lab at Stanford [4:34] - Why Daphne resigned an endowed chair at Stanford to build Coursera  [14:14] - How insitro approaches target identification problems and training data  [18:33] - What are pluripotent stem cells and how insitro identifies individual neurons  [24:08 ] - How insitro operates as an engine for drug discovery and partners to create the drugs themselves [26:48] - Role of regulations, clinical trials and disease progression in drug delivery  [33:19] - Building a team and workplace culture that can bridge both bio and computer sciences  [39:50] - What Daphne is paying attention to in the so-called golden age of machine learning   [43:12] - Advice for leading a startup in edtech and healthtech

In this episode of Lexman Artificial, Lexman interviews Stanford computer scientist and entrepreneur Daphne Koller. They discuss the concept of nematoda, the use of adhesives in the chest, and the art of monochromism.

Lexman Artificial interviews Daphne Koller, one of the most successful entrepreneurs in Silicon Valley. They discuss her experiences founding two startups, how she manages her bankroll, and how she goes about choosing which startups to invest in.

Daphne Koller (founder ofXero) shares her story of running the now international software company. In this episode, she talks about her struggles early on, including the time she nearly lost everything because she made a critical mistake with her company's finances. Through persistence and hard work, she was able to turn things around and now her company is thriving.

Daphne Koller, a renowned entomologist and winner of the MacArthur Fellowship, comes on the show to discuss her work on the ventral lobe of the brain in ants. She also shares some of her personal trinkets and forewing decorations.

Daphne Koller was a precociously clever child. She completed her first degree – a double major in mathematics and computer science – when she was just 17 and went on to become a distinguished Professor at Stanford University in California. But before long she'd given up this comfortable academic position to create the biggest online education platform in the world. In 2018, she founded the drug discovery company Insitro hoping to create a space where data scientists and molecular biologists could work together as equals. Daphne tells Jim Al-Khalili how a single question from her supervisor nudged her to use her considerable mathematical ability to do something useful and why she believes the time is right for artificial intelligence to discover new medicines. Producer: Anna Buckley

In the latest episode of the Lexman Artificial Podcast, Daphne Koller returns to talk all things granite arbitrage. She shares stories of her find in Israel and her observations of the international arbitrage market.

Shravan Goli has been the Chief Product Officer and Head of Consumer Revenue at Coursera since 2018. Shravan came to Coursera with over 20 years of experience of building products and leading companies. He has built products at Microsoft and Yahoo, been the CEO of Dictionary.com, and was most recently President of tech job marketplace Dice (part of public company DHI Group Inc).Coursera was launched in 2012 by two Stanford Computer Science professors, Andrew Ng and Daphne Koller, with a mission to provide universal access to world-class learning. It is now one of the largest online learning platforms in the world, with over 100 million registered learners globally.Coursera partners with over 275 of the world's top universities (Yale, University of London, Penn) and industry educators (Google, Meta, IBM) to offer courses, Specializations, projects, certificates, and degrees. Over 7,000 businesses, government entities, and campuses have used Coursera's enterprise offering to provide job-relevant online education to their employees, citizens, and students.Recommended ResourcesA New U: Faster + Cheaper Alternatives to College by Ryan Craig

A deep dive into Martian efforts to pursue sustainable development using monauls as the foundation.

Varun is the cofounder and CTO of AKASA, which develops purpose-built AI and automation solutions for the healthcare industry.Building a physics simulator for a robot helicopter as a student at Stanford helped Varun connect his interests in physics, machine learning, and AI. Check out that project here. His instructor? Andrew Ng.Along with Ng, Varun was lucky to connect with some brilliant AI folks during his time at Stanford, like Jeffrey Dean, Head of Google AI; Daphne Koller, cofounder of Coursera; and Sebastian Thrun, cofounder of Udacity.When Varun earned his PhD in computer science and AI, Koller and Thrun served as his advisors. You can read their work here.In 2017, Udacity acquired Varun's startup, CloudLabs, the company behind Terminal.  Connect with Varun on LinkedIn.Today's Lifeboat badge goes to user John Woo for their answer to the question Update the row that has the current highest (maximum) value of one field.

About this Episode: This week's guest, Daphne Koller, is the Founder and CEO of Insitro - a company shifting the paradigm of new drug discovery using predictive models. Patrick and Daphne talk about why she founded Insitro, how to create unified datasets, and the importance of being realistic about drug discovery.

About this Episode: This week's guest, Daphne Koller, is the Founder and CEO of Insitro - a company shifting the paradigm of new drug discovery using predictive models. Patrick and Daphne talk about why she founded Insitro, how to create unified datasets, and the importance of being realistic about drug discovery.

Lexman interviews Daphne about her work in the field of artificial intelligence. They discuss the challenges and benefits of working together as a team, and how to optimize their copemate's performance.

Daphne Koller joins Lexman as they discuss freeholders, Otis, and incinerations.

Google engineer Daphne Koller eloquently discusses her new book, "Versos: A Memoir." In it, Koller tells the story of her life, from her adolescence in New York to her time as a Google executive.

Daphne Koller joins Lexman to talk about her recent furlough from her job at the Antarctica Scaladiers ranch. They discuss the joys and hardships of life on the ranch, as well as the unique culture of the Antarctic.

Daphne Koller joins Lexman for a discussion about Tara, slapshots, and murders. They discuss the legality of dieldrin in the United States and how the toxin affects the body.

Daphne Koller, a successful entrepreneur and the founder of Daphne Ventures, joins Lexman for a discussion about entrepreneurship, investing and the importance of having insurance in business.

Check out Weights and Biases here! https://wandb.me/MLST Lukas Biewald is an entrepreneur living in San Francisco. He was the founder and CEO of Figure Eight an Internet company that collects training data for machine learning. In 2018, he founded Weights and Biases, a company that creates developer tools for machine learning. Recently WandB got a cash injection of 15 million dollars in its second funding round. Lukas has a bachelors and masters in mathematics and computer science respectively from Stanford university. He was a research student under the tutelage of the legendary Daphne Koller. Lukas Biewald https://twitter.com/l2k [00:00:00] Preamble [00:01:27] Intro to Lukas [00:02:46] How did Lukas build 2 sucessful startups? [00:05:49] Rebalancing games with ML [00:08:14] Elevator pitch for WandB [00:10:38] Science vs Engineering divide in ML DevOps [00:14:11] Too much focus on the minutiae? [00:18:03] Vertical information sharing in large enterprises (metrics) [00:20:37] Centralised vs Decentralised topology [00:24:02] Generalisation vs specialisation [00:28:59] Enhancing explainability [00:33:14] Should we try and understand "the machine" or is testing / behaviourism enough? [00:36:55] WandB roadmap [00:39:06] WandB / ML Ops competitor space? [00:44:10] How is WandB differentiated over Sagemaker / AzureML [00:46:02] WandB Sponsorship of ML YT channels [00:48:43] Alternatives to deep learning? [00:53:47] How to build a business like WandB Panel: Tim Scarfe Ph.D and Keith Duggar Ph.D Note we didn't get paid by Weights and Biases to conduct this interview.

“We are now in a world where there is this an abundance of data, which is only the beginning to what we're likely to be able to see in the coming years. At the same time, we have this incredible set of machine learning methods [...]. This seems to be a moment in time when those two tidal waves are about to come together in a way that offers us the opportunity to unlock some of the underlying secrets and complexities that underlie human health and human disease.” This week we are sharing for posterity the discussion that Parker Moss, our Chief Commercial Officer, hosted earlier this month at the Genomics England Research Summit. Parker's discussion was with the world renowned Daphne Koller, the founder and CEO of insitro. Parker and Daphne explored the use of AI and machine learning in drug discovery and discussed the value of multimodal analysis. They also touched on some of the challenges of causal inference and target validation with unsupervised machine learning methodologies. Parker and Daphne then discussed the recent partnership between Genomics England and insitro.

Over time, drug development has become more and more challenging.Success rates of clinical trials hover in the single digits, and the cost of developing a new treatment is greater than $2.5B.So, what can we do to make drug discovery faster, less expensive and more successful? How might advancements in machine learning and the availability of biomedical data revolutionize the drug design process?Daphne Koller is the CEO of Insitro, a company that is rethinking drug discovery using machine learning. She spent 18 years as a professor in the computer science department at Stanford before leaving to build the education platform Coursera. In 2016, Daphne returned to her passion for improving human health with machine learning, first as Chief Computing Officer at Calico Labs and then as the Founder of Insitro.On this episode of HLTH Matters, Daphne joins host Dr. Gautam Gulati to explain how her experience with her father's autoimmune condition informs her work and why we need to rethink the fundamental categorization of disease. Daphne describes how releasing machines from our preconceptions of what's important uncovers new science around the drivers of disease and serves as a critical starting point for developing new interventions. Listen in for Daphne's insight on leveraging machine learning to improve clinical trials and learn why data collection should be part of the fabric of every biopharma company.Topics CoveredDaphne's background in machine learning, biology and medical dataHow Daphne's experience with her father's autoimmune disease informs her work at InsitroWhy we need to rethink the fundamental categorization of what disease isDaphne's insight on the history of machine learning and the danger in overhyping what the technology can doThe pros and cons of using end-to-end learning to make predictionsHow releasing machines from preconceptions of what's important helps uncover new scienceWhy understanding the drivers of disease is a critical starting point for developing new interventionsWhy data collection should be part of the fabric of every biopharma companyHow Daphne's work in machine learning can be used to improve clinical trialsWhy now is the right time for a company like InsitroHow Daphne thinks about privacy and issues of informed consent Connect with Daphne KollerInsitro Connect with Dr. Gautam GulatiHLTHDr. G. on LinkedInDr. G. on Twitter ResourcesCourseraArt Levinson at CalicoDr. Hal Barron at GSKAducanumabChasing My Cure: A Doctor's Race to Turn Hope into Action by David FajgenbaumInsitro's Partnership with GileadUK Biobank Introductory Quote[5:34] “What I really wanted to build was a company that rethought drug discovery and development from the ground up, using machine learning as a foundational tool.”

Meet Daphne Koller, Ph.D.:Daphne Koller, Ph.D. is the founder and CEO of insitro, a company that aims to improve drug discovery and development through machine learning. She is also a co-founder and board member for Engageli. Previously, Dr. Koller co-founded Coursera and served as the co-CEO, President, and eventually co-Chairman. She was also the Chief Computing Officer for CalicoLabs. Dr. Koller earned her Ph.D. in Computer Science at Stanford University and taught there as a professor for 18 years. Key Insights:Dr. Koller is “bilingual” in the worlds of biomedicine and machine learning. She has had a diverse career in academia, industry, and entrepreneurism.Two Worlds. At insitro, Dr. Koller brings together the two worlds of machine learning and biomedicine. Machine learning understands the capabilities of what data can provide, and biomedicine understands the insights that can be extracted. Working together can not only solve problems, but reveal new questions. (7:27)Science as a Team Sport. Dr. Koller contrasts academia's focus on the individual researcher with industry's focus on organizational growth and teamwork. She emphasizes the importance of releasing one's ego and creating a whole that is larger than the sum of the parts. (22:16)The Right Team. It is important for founders to build the right team around them. The executive leadership needs to have the ability to strategize and shape the vision, as well as be equipped with industry expertise. From day one, be deliberate about culture and creating alignment. (25:03)This episode is hosted by Suchi Saria, Ph.D. She is a member of the Advisory Council for Day Zero and is the founder and CEO of Bayesian Health. She is also an Associate Professor of computer science, statistics, and health policy, and the Director of the Machine Learning and Healthcare Lab at Johns Hopkins University.Relevant Links:Learn more about insitroTake one of Dr. Koller's courses on CourseraFollow Dr. Koller on Twitter

In this episode, we are joined by Dr. Suchi Saria as we discuss her journey starting as an engineer applying AI solutions to healthcare including development of state of the art prediction models, doing strategy and advocacy at the highest level of Government organizations to her recent very exciting startup called Bayesian Health. Suchi Saria is the John C. Malone Associate Professor of computer science at the Whiting School of Engineering and of statistics and health policy at the Bloomberg School of Public Health. She directs the Machine Learning and Healthcare Lab and is the founding research director of the Malone Center for Engineering in Healthcare. She was invited to join the National Academy of Engineering's Frontiers of Engineering program in 2017 and, in 2018, to join the National Academy of Medicine's program for Emerging Leaders in Health and Medicine. Saria came to Johns Hopkins in 2012. Prior to that, she received her Ph.D. from Stanford University working with Daphne Koller. Saria's goal is to use sophisticated computer science and the deluge of data available in health care and other settings to individualize patient care and to save lives. Her pioneering work centers on enabling new classes of diagnostic and treatment planning tools for health care—tools that use statistical machine-learning techniques to tease out subtle information from “messy” observational datasets, and provide reliable inferences for individualizing care decisions. Saria's work has received recognition including: best paper awards at machine learning, informatics, and medical venues; a Rambus Fellowship (2004-2010), an NSF Computing Innovation Fellowship (2011); selection by IEEE Intelligent Systems to Artificial Intelligence's “10 to Watch” (2015); the DARPA Young Faculty Award (2016); and the Sloan Research Fellowship (2018). She has also been named as one of Popular Science magazine's “Brilliant 10” (2016); one of MIT's “35 Innovators Under 35” (2017); and a member of the World Economic Forum's Young Global Leaders (2018).In 2017, Saria's work was among four research contributions presented by France Córdova, Director of the National Science Foundation, to the U.S. House of Representatives” Commerce, Justice Science Appropriations Committee. More information about Dr. Saria can be found here: https://suchisaria.jhu.edu/

Daphne Koller is the CEO and founder of insitro, a machine learning-enabled drug discovery company. Previously, she was a professor of computer science at Stanford University for 18 years, co-founder and co-CEO of Coursera, and the Chief Computing Officer of Calico, an Alphabet company in the healthcare space. She received the MacArthur Foundation Fellowship in 2004. In this conversation with Stanford adjunct lecturer Ravi Belani, Koller examines the key turning points in her diverse and innovative career, and speaks about how she searched for the opportunities that would have the greatest impact on the world.

Todd Zipper, President of Wiley Education Services, welcomes Dan Avida, CEO and Co-founder of Engageli. Todd and Dan discuss how the pandemic highlighted the need for more engaging online learning. Topics Discussed: • The need for engaging synchronous tools in online education and ways to improve asynchronous learning • The shortcomings of traditional teleconferencing tools like Google Meet, Slack, and Teams • Ways to improve engagement and collaboration between instructors and students to help drive quality outcomes at scale • How to address inclusivity, equity, and diversity through platform design and functionality • The future of the personalized learning journey, machine learning, use of data, dashboards, and AI Guest Bio Dan Avida has been working in technology companies as an executive and/or board member for over three decades. Several of these companies have scaled from a small founding team to over $100M in revenues and valuations of over $1B. He started Engageli in 2020 with wife and Coursera co-founder Daphne Koller, Stanford emeritus professor Serge Plotkin, and former executive at 2U and Trilogy, Jamie Nacht Farrell. He earned his B.S. in Computer Engineering from Technion, the Israel Institute of Technology, summa cum laude.

This is the last episode of Season 1. Join us in the Fall for Season 2, and in the meantime, please take our brief survey! http://bit.ly/theaihealthpodcastDr. Daphne Koller is CEO and Founder of insitro, a machine-learning enabled drug discovery company. She has been a Stanford CS Professor, co-founder of Coursera and Engageli, one of TIME Magazine's 100 influential people, and a MacArthur Fellow. She speaks with us about how insitro uses AI and induced pluripotent stem cells to make drug discovery more efficient and successful.Pranav and Adriel first give an overview on pluripotent stem cells. The interview with Dr. Koller starts at 5:41. If you like what you hear, let a friend know, subscribe wherever you get your podcasts, and connect with us on Twitter @AIHealthPodcast.