Podcasts about google research

In this podcast episode, Dr. Jonathan H. Westover talks with Dr. Ishan Shivanand about dealing with workplace stress and burnout. Dr. Ishan Shivanand is bringing forth what is unknown about yoga. An acclaimed mental health researcher and professor, Dr. Shivanand is the founder of “Yoga of Immortals,” an evidence-based mental resilience meditation program for holistic well- being. The program has been clinically proven to reverse anxiety (75%), depression (72%), and insomnia (82%), and improve overall quality of life (77%) among test participants within four to eight weeks of regular practice. His YOI program has received commendations from U.S. Congress, the White House Office of National Drug Control Policy (ONDCP) and more. Dr. Ishan has expertise in working with people in high-stress environments and has been requested to teach YOI modalities at prestigious institutions, including M.D. Anderson Cancer Center, the Mayo Clinic, LinkedIn, Google Research etc.  Check out all of the podcasts in the HCI Podcast Network!

This week, Paul and Mike return with a rapid-fire breakdown. From major AI companies' bold policy recommendations to the AI Action Plan to Altman's teaser of a new creative writing model that blurs the line between human and machine—there's a lot to unpack. Plus: Google's AI infrastructure bets, Claude's web search rollout, and a new study showing how AI is transforming team dynamics and boosting productivity inside companies.  Access the show notes and show links here This episode is presented by Goldcast.  Goldcast is a B2B video content platform that helps marketing teams easily produce, repurpose, and distribute video content. We use Goldcast for our virtual Summits, and one of the standout features for us is their AI-powered Content Lab. If you're running virtual events and want to maximize your content effortlessly, check out Goldcast.  Learn more at goldcast.io. This episode is also presented by our Scaling AI webinar series.  Register now to learn the framework Paul Roetzer has taught to thousands of corporate, education, and government leaders. Learn more at ScalingAI.com and click on “Register for our upcoming webinar” Timestamps: 00:05:01 — NY Times Writer “Feeling the AGI” 00:15:00 — AI Action Plan Proposals 00:24:13 — Sam Altman Teases New Creative Writing Model 00:30:21 — Claude Gets Web Search 00:31:59 — AI's Impact on Google Search 00:36:35 — Anthropic's Strong Start to the Year 00:40:19 — It Turns Out That Gemini Can Remove Image Watermarks 00:44:32 — Google Research on New Way to Scale AI 00:48:42 — New Research Shows How GenAI Changes Performance in Corporate Work 00:57:18 — The Time Horizon of Tasks AI Can Handle Is Doubling Fast 01:05:14 — Apple Comes Clean on Siri AI Delays 01:08:51 — OpenAI Agents May Threaten Consumer Apps 01:14:03 — Powering the AI Revolution 01:17:44 — Google Deep Research Tips 01:21:14 — Other Product and Funding Updates Visit our website Receive our weekly newsletter Join our community: Slack LinkedIn Twitter Instagram Facebook Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy

Die Themen in den Wissensnachrichten:+++ Mäuse leisten erste Hilfe +++ Süßstoff Aspartam verengt Blutgefäße +++ Google will Forschung mit KI voranbringen +++ **********Weiterführende Quellen zu dieser Folge:Hörtipp: Update Erde - deine News zu Klima, Mensch und NaturReviving-like prosocial behavior in response to unconscious or dead conspecifics in rodents, Science, 20.2.25A neural basis for prosocial behavior toward unresponsive individuals, Science 20.2.2025Sweetener aspartame aggravates atherosclerosis through insulin-triggered inflammation, Cell Metabolism, 19.2.25Accelerating scientific breakthroughs with an AI co-scientist, Google Research, 19.2.25Alle Quellen findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: TikTok auf&ab , TikTok wie_geht und Instagram .

In this episode of Create Like The Greats, we dive into why publishing research can be a game-changer, especially for those in AI, marketing, and SEO. The host uncovers how leveraging research-driven content can generate buzz, backlinks, and unmatched visibility for brands. Using examples from industry leaders like Google DeepMind and NVIDIA, this episode provides actionable steps to create, repurpose, and distribute impactful research. If you're ready to learn how research can transform your marketing strategies and build authority in your field, this is a must-listen! Key Takeaways and Insights: 1. The Rise of AI and Its Impact Across Industries (02:00) AI is influencing professions beyond marketing, including healthcare, education, and law. Google Trends data highlights an 8X increase in searches for AI research over the last four years. Online communities like Reddit's r/Futurology and Artificial Intelligence have seen significant growth since 2022. 2. Why Research-Driven Content Matters (10:42) Major companies like Google and NVIDIA are publishing groundbreaking AI research to generate links, press mentions, and authority. Google's AI research hubs (Google Research and DeepMind) showcase the power of free, accessible industry data. Example: Google DeepMind has journal articles with over 500-2,000 referring domains, driving backlinks, traffic, and credibility. 3. Different Types of Research-Driven Content Proprietary Research: Conduct in-depth studies with your team or academics to develop unique insights. Repurposing Data: Don't let research sit idle in PDFs—turn it into blog posts, LinkedIn infographics, social media videos, and more. Curated Research: Take existing industry studies or journals and reframe them for broader audiences in your niche. 4. Case Study: Unlocking Engagement Through Simplified Research Example: A blog post combining coffee productivity studies with up-tempo music research led to a viral response. Translating complex journals into relatable concepts amplifies visibility and audience resonance. 5. The SEO and Brand Visibility Advantages Research publications draw backlinks from reputable sources (e.g., Microsoft, universities) and are frequently cited in blogs and press. Example: DeepMind's journals generate massive referral traffic due to their accessibility and simplicity. Insights from Stanford's AI Index: The number of AI research articles tripled from 2010 to 2022. 6. Pro Tips for Distributing Research Findings Distribute research summaries via subreddit communities, LinkedIn posts, and news outlets. Convert large datasets into visual aids like charts, infographics, and videos to maximize distribution potential. Leverage PR strategies by pitching your findings to publications and blogs for additional reach. Actionable Advice for Creating Research-Driven Content Create Research: Invest in proprietary studies, user surveys, or experiments in your niche. Repurpose Content: Tailor findings into blog posts, social media visuals, and bite-sized infographics for different platforms. Collaborate: Partner with academics or industry experts to co-create compelling research studies. Translate Research: Simplify complex academic papers into relatable and engaging content for non-technical audiences. Pitch Your Study: Reach out to journalists, bloggers, and influencers who may amplify and share your findings. Resources and Links Mentioned:  Google Research – Explore Google's latest AI publications and studies. DeepMind Research – Access over 183 AI-focused journal articles. Stanford AI Index 2024 – Annual report on AI trends and insights. Tool Mentioned: Distribution.ai – Automate your content distribution processes. Related Subreddits: r/Futurology r/ArtificialIntelligence —

Send us a textYossi Matias, Vice President of Google and Head of Google Research, leads groundbreaking efforts in foundational machine learning, quantum computing, and AI for societal impact in education, health, and climate. A world-renowned AI expert, Yossi has pioneered conversational AI, driven Google Search innovations, and launched transformative initiatives like AI for Social Good and Google for Startups Accelerator. His work focuses on leveraging AI to address global challenges and improve lives on a global scale.Obum Ekeke, Head of Education Partnerships at Google DeepMind, champions equitable access to AI education and fosters diversity in the tech industry. An OBE recipient for his contributions to computing and inclusion, Obum has led initiatives that have reached millions of learners worldwide, including founding Google Educator Groups in over 60 countries. His mission is to prepare students and educators for the future by making AI knowledge accessible and impactful for all.

Martin GonzalezMartin is the creator of Google's Effective Founders Project, a global research program that uses people analytics to uncover what makes the best startup founders succeed and shares their success formula with the world.Martin is the creator of Google's Effective Founders Project, a global research program that uses people analytics to uncover what makes the best startup founders succeed and shares their success formula with the world.He has run leadership courses for thousands of funded tech startup founders across more than seventy countries in the Americas, Asia, Africa, and Europe. He is a frequent lecturer on entrepreneurship, organization design, and people analytics at Stanford, Wharton, and INSEAD.Martin is a principal of organization and talent development at Google. He works with Google's senior leaders to shape team culture, develop their people, and expand their leadership, so they can build cool things that matter. In his ten years there, he's worked with leaders across Google Research, DeepMind, Technology & Society, Responsible AI, Pixel, Fitbit, YouTube, Search, Maps, Android, and Chrome, to name a few.In 2023, The Aspen Institute recognized him as a First Movers Fellow, honoring his pioneering work at Google. The following year, in 2024, he received the Thinkers50 Radar Award, a prestigious recognition that identifies up-and-coming thinkers whose ideas are predicted to make an important impact on management thinking in the future.Prior to Google, he was a management consultant with the Boston Consulting Group and a product manager at Johnson & Johnson.Martin has studied organizational psychology and behavioral science at Columbia University and the London School of Economics. He's a serial immigrant, having lived and worked in New York, Jakarta, Singapore, Taipei, and Manila, where he is originally from. Today he lives in the San Francisco Bay Area with his wife, Bea, and three kids: Noelle, Jaime, and Andrea.

When I think of digital biology, I think of Patrick Hsu—he's the prototype, a rarified talent in both life and computer science, who recently led the team that discovered bridge RNAs, what may be considered CRISPR 3.0 for genome editing, and is building new generative A.I. models for life science. You might call them LLLMs-large language of life models. He is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley.Above is a brief snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.Here's the transcript with links to the audio and external links to relevant papers and things we discussed.Eric Topol (00:06):Well hello, it's Eric Topol with Ground Truths and I'm really delighted to have with me today Patrick Hsu. Patrick is a co-founder and core investigator at the Arc Institute and he is also on the faculty at the University of California Berkeley. And he has been lighting things up in the world of genome editing and AI and we have a lot to talk about. So welcome, Patrick.Patrick Hsu (00:29):Thanks so much. I'm looking forward to it. Appreciate you having me on, Eric.The Arc InstituteEric Topol (00:33):Well, the first thing I'd like to get into, because you're into so many important things, but one that stands out of course is this Arc Institute with Patrick Collison who I guess if you can tell us a bit about how you two young guys got to meet and developed something that's really quite unique that I think brings together investigators at Stanford, UCSF, and Berkeley. Is that right? So maybe you can give us the skinny about you and Patrick and how all this got going.Patrick Hsu (01:05):Yeah, sure. That sounds great. So we started Arc with Patrick C and with Silvana Konermann, a longtime colleague and chemistry faculty at Stanford about three years ago now, though we've been physically operational just over two years and we're an independent research institute working at the interface of biomedical science and machine learning. And we have a few different aspects of our model, but our overall mission is to understand and treat complex human diseases. And we have three pillars to our model. We have this PI driven side of the house where we centrally fund our investigators so that they don't have to write grants and work on their very best ideas. We have a technical staff side of the house more like you'd see in a frontier AI lab or in biotech industry where we have professional teams of R&D scientists working cross-functionally on higher level organizational wide goals that we call our institute initiatives.(02:05):One focused on Alzheimer's disease experimentally and one that we call a virtual cell initiative to simulate human biology with AI foundation models. And our third pillar over time is to have things not just end up as academic papers, but really get things out into the real world as products or as medicines that can actually help patients on the translational side. And so, we thought that some really important scientific programs could be unlocked by enabling new organizational models and we are experimenting at the institutional scale with how we can better organize and incentivize and support scientists to reach these long-term capability breakthroughs.Patrick, Patrick and SilvanaEric Topol (02:52):So the two Patrick's. How did you, one Patrick I guess is a multi-billionaire from Stripe and then there's you who I suspect maybe not quite as wealthy as the other Patrick, how did you guys come together to do this extraordinary thing?Patrick Hsu (03:08):Yeah, no, science is certainly expensive. I met Patrick originally through Silvana actually. They actually met, so funny trivia, all three Arc founders did high school science together. Patrick and Silvana originally met in the European version of the European Young Scientist competition in high school. And Silvana and I met during our PhDs in her case at MIT and I was at Harvard, but we met at the Broad Institute sort of also a collaborative Harvard, MIT and Harvard hospitals Institute based in Kendall Square. And so, we sort of in various pairwise combinations known each other for decades and worked together for decades and have all collectively been really excited about science and technology and its potential to accelerate societal progress. Yet we also felt in our own ways that despite a lot of the tremendous progress, the structures in which we do this work, fund it, incentivize it and roll it out into the real world, seems like it's really possible that we'll undershoot that potential. And if you take 15 years ago, we didn't have the modern transformer that launched the current AI revolution, CRISPR technology, single-cell, mRNA technology or broadly addressable LNPs. That's a tremendous amount of technologies have developed in the next 15 years. We think there's a real unique opportunity for new institutes in the 2020s to take advantage of all of these breakthroughs and the new ones that are coming to continue to accelerate biological progress but do so in a way that's fast and flexible and really focused.Eric Topol (04:58):Yeah, I did want to talk with you a bit. First of all before I get to the next related topic, I get a kick out of you saying you've worked or known each other for decades because I think you're only in your early thirties. Is that right?Patrick Hsu (05:14):I was lucky to get an early start. I first started doing research at the local university when I was 14 actually, and I was homeschooled actually until college. And so, one of the funny things that you got to do when you're homeschooled is well, you could do whatever you want. And in my case that was work in the lab. And so, I actually worked basically full time as an intern volunteer, cut my teeth in single cell patch clamp, molecular biology, protein biochemistry, two photon and focal imaging and kind of spiraled from there. I loved the lab, I loved doing bench work. It was much more exciting to me than programming computers, which was what I was doing at the time. And I think these sort of two loves have kind of brought me and us to where we are today.Eric Topol (06:07):Before you got to Berkeley and Arc, I know you were at Broad Institute, but did you also pick up formal training in computer science and AI or is that something that was just part of the flow?Patrick Hsu (06:24):So I grew up coding. I used to work through problems sets before dinner growing up. And so, it's just something that you kind of learn natively just like learning French or Mandarin.New Models of Funding Life ScienceEric Topol (06:42):That's what I figured. Okay. Now this model of Arc Institute came along in a kind of similar timeframe as the Arena BioWorks in Boston, where some of the faculty left to go to Arena like my friend Stuart Schreiber and many others. And then of course Priscilla and Mark formed the Chan Zuckerberg Institute and its biohub and its support. So can you contrast for one, these three different models because they're both very different than of course the traditional NIH pathway, how Arc is similar or different to the others, and obviously the goal here is accelerating things that are going to really make a difference.Patrick Hsu (07:26):Yeah, the first thing I would say is zooming out. There have been lots of efforts to experiment with how we do science, the practice of science itself. And in fact, I've recently been reading this book, the Demon Under the Microscope about the history of infectious disease, and it talks about how in the 1910s through the 1930s, these German industrial dye manufacturing companies like Bayer and BASF actually launched what became essentially an early model for industrial scale science, where they were trying to develop Prontosil, Salvarsan and some of these early anti-infectives that targeted streptococcus. And these were some of the major breakthroughs that led to huge medical advances on tackling infectious disease compared to the more academic university bound model. So these trends of industrial versus academic labs and different structures to optimize breakthroughs and applications has been a through current throughout international science for the last century.(08:38):And so, the way that we do research today, and that's some of our core tenets at Arc is basically it hasn't always been this way. It doesn't need to necessarily be this way. And so, I think organizational experiments should really matter. And so, there's CZI, Altos, Arena, Calico, a variety of other organizational experiments and similarly we had MRC and Bell Labs and Xerox PARCS, NIBRT, GNF, Google Research, and so on. And so, I think there are lots of different ways that you can organize folks. I think at a high level you can think about ways that you can play with for-profit versus nonprofit structures. Whether you want to be a completely independent organization or if you want to be partnered with universities. If you want to be doing application driven science or really blue sky curiosity driven work. And I think also thinking through internally the types of expertise that you bring together.(09:42):You can think of it like a cancer institute maybe as a very vertically integrated model. You have folks working on all kinds of different areas surrounding oncology or immunotherapy and you might call that the Tower of Babel model. The other way that folks have built institutes, you might call the lily pad model where you have coverage of as many areas of biomedical research as possible. Places like the Whitehead or Salk, it will be very broad. You'll have planned epigenetics, folks looking at RNA structural biology, people studying yeast cell cycle, folks doing in vivo melanoma models. It's very broad and I think what we try to do at Arc is think about a model that you might liken more to overlapping Viking shields where there's sort of five core areas that we're deeply investing in, in genetics and genomics, computation, neuroscience, immunology and chemical biology. Now we really think of these as five areas that are maybe the minimal critical mass that you would need to make a dent on something as complicated as complex human diseases. It's certainly not the only thing that you need, but we needed a critical mass of investigators working at least in these areas.Eric Topol (11:05):Well, yeah, and they really converge on where the hottest advances are being made these days. Now can you work at Arc Institute without being one of these three universities or is it really that you maintain your faculty and your part of this other entity?Patrick Hsu (11:24):So we have a few elements to even just the academic side of the house. We have our core investigators. I'm one of them, where we have dually appointed faculty who retain their latter rank or tenured appointment in their home department, but their labs are physically cited at the Arc headquarters where we built out a lab in Stanford Research Park in Palo Alto. And so, folks move their labs there. They continue to train graduate students based on whatever graduate programs they're formally affiliated with through their university affiliation. And so, we have nearly 40 PhD students across our labs that are training on site every day.(12:03):So in addition to our core investigators, we also have what we call our innovation investigators, which is more of a grant program to faculty at our partner universities. They receive unrestricted funding from us to seed a new project or accelerate an existing area in their group and their labs stay at their home campus and they just get that funding to augment their work. The third way is our technical staff model where folks basically just come work at Arc and many of them also are establishing their own research groups focusing on technology R&D areas. And so, we have five of those technology centers working in molecular engineering, multi-omics, complex cellular models, in vivo models, and in machine learning.Discovery of Bridge RNAsEric Topol (12:54):Yeah, that's a great structure. In fact, just a few months ago, Patrick Collison, the other Patrick came to Stanford HAI where I'm on the board and you've summarized it really well and it's very different than the other models and other entities, companies included that you mentioned. It's really very impressive. Now speaking of impressive on June 26, this past few months ago, which incidentally is coincident with the draft genome in the year 2000, the human sequence. You and your colleagues, perhaps the most impressive jump in terms of an Arc Institute contribution published two papers back-to-back in Nature about bridge RNA: [Bridge RNAs direct programmable recombination of target and donor DNA] and [Structural mechanism of bridge RNA-guided recombination.] And before I get you to describe this breakthrough in genome editing, some would call it genome editing 3.0 or CRISPR 3.0, whatever. But what we have today in the clinic with the approval of CRISPR 1.0 for sickle cell and thalassemia is actually quite crude. I think most people will know it's just a double stranded DNA cleavage with all sorts of issues about repair and it's not very precise. And so, CRISPR 2.0 is supposed to be represented by David Liu's contributions and his efforts at Broad like prime and base editing and then comes yours. So maybe you can tell us about it and how it is has to be viewed as quite an important advance.Patrick Hsu (14:39):The first thing I would say before CRISPR, is that we had RNA interference. And so, even before this modern genome editing revolution with programmable CRISPRs, we had this technology that had a lot of the core selling points as well. Any target will now become druggable to us. We simply need to reprogram a guide RNA and we can get genetic access to things that are intracellular. And I think both the discovery of RNA interference by Craig Mello and Andy Fire or the invention or discovery of programmable CRISPR technologies, both depend on the same fundamental biological mechanism. These non-coding guide RNAs that are essentially a short RNA search string that you can easily reprogram to retarget a desired enzyme function, and natively both RNAi and CRISPR are molecular scissors. Their RNA or DNA nucleases that can be reprogrammed to different regions of the genome or the transcriptome to make a cut.(15:48):And as bioengineers, we have come up with all kinds of creative ways to leverage the ability to make site specific cuts to do all kinds of incredible things including genome editing or beyond transcriptional up or down regulation, molecular imaging and so on and so forth. And so, the first thing that we started thinking about in our lab was, why would mother nature have stopped only RNAi and CRISPR? There probably are lots of other non-coding RNAs out there that might be able to be programmable and if they did exist, they probably also do more complicated and interesting things than just guide a molecular scissors. So that was sort of the first core kind of intuition that we had. The second intuition that we had on the technology side, I was just wearing my biology hat, I'll put on my technology hat, is the thing that we call genome editing today hardly involves the genome.(16:50):It's really you're making a cut to change an individual base or an individual gene or locus. So really you're doing small scale single locus editing, so you might call it gene level or locus level cuts. And what you really want to be able to do is do things at the genome scale at 100 kb, a megabase at the chromosome scale. And I think that's where I think the field will inevitably go if you follow the technology curves of longer and longer range gene sequencing, longer and longer range gene synthesis, and then longer and longer range gene editing. And so, what would that look like? And we started thinking, could there be essentially recombination technologies that allow you to do cut and paste in a single step. Now, the reason for that is the way that we do gene editing today involves a cut and then a multi-step process of cellular DNA repair that resolves the cut to make the exertion or the error prone deletion or the modification that ends up happening.(17:59):And so, it's very complicated and whether that's nucleases or base or prime editing, you're all generally limited to the small-scale single locus changes. However, there are natural mechanisms that have solved this cut and paste problem, right? There are these viruses or bacterial versions of viruses known as phage that have generally been trying to exert their multi kilobase genomes into bacterial hosts and specialize throughout billions of years. So our core thought was, well, if there are these new non-coding RNAs, what kind of functions would we be excited about? Can we look in these mobile genetic elements, these so-called jumping genes for new mechanisms? They're incredibly widespread. Transposons are thought to be some of the most diverse enzyme mechanisms found in nature. And so, we started computationally by asking ourselves a very simple question. If a mobile element inserts itself into foreign DNA and it's able to somehow be programmable, presumably the inside or something encoded in the inside of the element is predictive of some sequence on the outside of the element.(19:15):And so, that was the core insight we took, and we thought let's look across the boundaries of many different mobile genetic elements and we zoomed in on a particular sub family of these MGE known as insertion sequence (IS) elements which are the most autonomous minimal transposons. Normally transposons have all kinds of genes that they use to hitchhike around the genomic galaxy and endow the bacterial host with some fitness advantage like some ability to metabolize some copper and some host or some metal. And these IS elements have only the enzymes that they need to jump around. And if you identify the boundaries of these using modern computational methods, this is actually a really non-trivial problem. But if you solve that problem to figure out with nucleotide resolution where the element boundaries end and then you look for the open reading frame of the transposases enzyme inside of this element, you'll find that it's not just that coding sequence.(20:19):There are also these non-coding flanks inside of the element boundaries. And when we looked across the non-coding, the entire IS family tree, there are hundreds of these different types of elements. We found that this particular family IS110, had the longest non-coding ends of all IS elements. And we started doing experiments in the lab to try to figure out how these work. And what we found was that these elements are cut and paste elements, so they excise themselves into a circular form and paste themselves back in into a target site linearly. But the circularization of this element brings together two distal ends together, which brings together a -35 and a -10 box that create and reconstitute a canonical bacterial transcriptional promoter. This essentially is like plugging a plug into an electrical socket in the wall and it jacks up transcription. Now you would think this transcription would turn on the transposase enzyme so it can jump around more but it transcribes a non-coding RNA out of this non-coding end.(21:30):We're like, holy crap, are these RNAs actually involved in regulating the transposon? Now the boring answer would be, oh, it regulates the expression. It's like an antisense regulate or something. The exciting answer would be, oh, it's a new type of guide RNA and you found an RNA guided integrase. So we started zooming in bound dramatically on this and we undertook a covariation analysis where we were able to show that this cryptic non-coding RNA has a totally novel guide RNA structure, totally distinct from RNAi or CRISPR guide RNAs. And it had a target site that covaried with the target site of the element. And so we're like, oh wow, this could be a programmable transposase. The second thing that we found was even more surprising, there was a second region of complementarity in that same RNA that recognized the donor sequence, which is the circularized element itself. And so, this was the first example of a bispecific guide RNA, and also the first example of RNA guided self-recognition by a mobile genetic element.Eric Topol (22:39):It's pretty extraordinary because basically you did a systematic assessment of jumping genes or transposons and you found that they contain things that previously were not at all recognized. And then you have a way to program these to edit, change the genome without having to do any cuts or nicks, right?Patrick Hsu (23:05):Yeah. So what we showed in a test tube is when we took this, so-called bridge RNA, which we named because it bridges the target and donor together along with the recombinase enzyme. So the two component system, those are the only two things that you need. They're able to cut and paste DNA and recombine them in a test tube without any DNA repair, meaning that it's independent of cellular DNA repair and it does strand nicking, exchange, junction resolution and religation all in a single mechanism. So that's when we got super excited about its potential applications as bioengineering tool.Eric Topol (23:46):Yeah, it's pretty extraordinary. And have you already gone into in vivo assessment?Patrick Hsu (23:54):Yes, in our initial set of papers, what we showed is that these are programmable and functional or recombinases in a test tube and in bacterial cells. And by reprogramming the target and donor the right way, you can use these enzymes not just for insertion, but also for flipping and cutting out DNA. And so, we actually have in a single mechanism the ability to do bridge editing, if you will, for universal DNA recombination, insertion, excision or inversion, similar to what folks have been doing for decades with Cre recombinase, but with fully programmable recognition sequences. The work that we're doing now in the lab as you can imagine is to adapt these into robust tools for mammalian genome editing, including of course, human genomes. We're excited about this, we're making good progress. The CRISPR has had thousands of labs over the last 10, 15 years working on it to make these therapeutic level potency and selectivity. We're going to work and follow that same blueprint for getting bridge systems to get to that level of performance, but we're on the path and we're very optimistic for the future.Exemplar of Digital BiologyEric Topol (25:13):Yeah, I think it's quite extraordinary and it's a whole different look to what we've been seeing in the CRISPR era for over the past decade and how that's been advancing and getting more specific and less need for repair and being able to be more versatile. But this takes it to yet another dimension. Now, this brings me to the field that when I think of this term digital biology, I think of you and now our mutual acquaintance, Jensen Huang, who everybody knows now. Back some months ago, he wrote and said at a conference, “Where do I think the next amazing revolution is going to come? And this is going to be flat out one of the biggest ones ever. There's no question that digital biology is going to be it. For the first time in human history, biology has the opportunity to be engineering, not science.” So can you critique Jensen? Is he right? And tell us how you conceive the field of digital biology.Patrick Hsu (26:20):If you look at gene therapy today, the core concepts are actually remarkably simple. They're elegant. Of course, you're missing a broken gene, you need to put it back. And that can be curative. Very simple, powerful concept. However, for complex diseases where you don't have just a single gene that goes wrong, in many cases we actually have no idea what to do. And in fact, when you're trying to put in DNA, that's over more than a gene scale. We kind of very quickly run out of ideas. Is it a CAR and a cytokine, a CAR and a cytokine and another thing? And then we're kind of out of ideas. And so, we started thinking in the lab, how can we actually design genomes where it's not just let's reduce the genome into individual Lego blocks, iGem style with promoters and different genes that we just sort of shuffle the Lego blocks around, but actually use AI to design genome sequences.(27:29):So to do that, we thought we would have to first of all, train a model that can learn and decode the foreign language of biology and use that in order to design sequences. And so, we sort of have been training DNA foundation models and virtual cell models at Arc, sort of a major effort of ours where the first thing that we tried was to take a variance of transformer architecture that's used to train ChatGPT from OpenAI, but instead apply this to study the next DNA token, right? Now, the interesting thing about next token prediction in English is that you can actually learn a surprising amount of information by just predicting the next word. You can learn world knowledge is the capital of Azerbaijan, is it Baku or is it London, right? Or if you're walking around in the kitchen, then the next text is, I then left the kitchen or the bathroom, right?(28:33):Now you're learning about spatial reasoning, and so you can also learn translation obviously. And so similarly, I think predicting the next token or the next base and DNA can lead you to learn about molecular biochemistry, is the next amino acid residue, hydrophobic or hydrophilic. And it can teach you about the mechanics of some catalytic binding pocket or something. You can learn about a disease mutation. Is the next base, the sick linked base or the wild type base and so on and so forth. And what we found was that at massive scale, DNA foundation models learn about molecular function, not just at the DNA level, but also at the RNA and the protein. And indeed, we could use these to design molecular systems like CRISPR-Cas systems, where you have a protein and the guide RNA. It could also design new DNA transposons, and we could design sequences that look plausibly like real genomes, where we generate a megabase a million bases of continuous genome sequence. And it really looks and feels like it could be a blurry picture of something that you would actually sequence. This has been a wonderful collaboration with Brian Hie, a PI at Stanford and an Arc investigator, and we're really excited about what we've seen in this work because it promises the better performance with even more scale. And so, simply by scaling up these models, by adding in more compute, more training data or more powerful models, they're going to get sharper and sharper.New A.I. Models in Life ScienceEric Topol (30:25):Yeah. Well, this whole use of large language models for the language of life, whether it's the genome proteins and on and on, actually RNA and even cells has really taken root. And of course, this is really one of the foundations of that field of digital biology, which brings together generative AI, AI tools and trying to push forward our understanding in biology. And also, obviously what's been emphasized in drug discovery, perhaps it's been emphasized even too much because we still have a lot to learn about biology, but that gets me to these models. Like today, AlphaProteo was announced by DeepMind, as we all know, AlphaFold 1, 2, now 3. They were kind of precursors of being able to predict proteins from amino acid 3D structure. And that kind of took the field by a little bit like ChatGPT for life science, but now it's a new model all the time. So you've been working on various models and Arc Institute, how do you see this unfolding? Are we just going to have every aspect of the language of life being approached in all the different interactions? And this is going to help us get to a much more deep level of understanding.Patrick Hsu (31:56):I'll say two things. The first is a lot of models that you just described are what I would call task specific models. A model for de novo design of a binder, a model for protein structure prediction. And there are other models for protein fitness or for RNA structure prediction, et cetera, et cetera. And I think what we're going to move towards are more unifying models where there's different classes of models at different levels of scale. So we will have these atomic level models for looking at generative chemistry or ligand docking. We have models that can unify genomes and their molecules, and then we have models that can unify cells and tissues. And so, for example, if you took an H&E stain of some liver, there are folks building models where you can then predict what the single cell spatial transcriptome will look like of that model. And that's obviously operating at a very different level of abstraction than a de novo protein binder. But in the long run, all of these are going to get, I think unified. I think the reason why this is possible is that biology, unlike physics, actually has this unifying theory of evolution that runs across all of its length scales from atomic, molecular, cellular, organismal to entire ecosystem. And the promise of these models is no short then to make biology a predictive discipline.Patrick Hsu (33:37):In physics, the experimentalists win the big prizes for the theorists when they measure gravitational waves or whatever. But in biology, we're very practical people. You do something three times and do a T-test. And I think my prediction is we can actually gauge the success of these LLMs or whatever in biology by how much we respect theory in this field.The A.I. ScientistEric Topol (34:05):Yeah. Well, that's a really interesting perspective, an important perspective because the proliferation of models, which we're going to get into not just doing the things that you described, but also being able to be “pseudo” scientists, the so-called AI scientist. Maybe you could comment about that concept because that's been the idea that everything from the question that could be asked to the hypothesis and the experiment design and the analysis of data and then the feedback. So what is the role of the scientists, that seems to have been overplayed? And maybe you can put that in context.Patrick Hsu (34:48):So yeah, right now there's a lot of excitement that we can use AI agents not just to do software enterprise workflows, but to be a research assistant. And then over time, itself an autonomous research scientist that can read the literature, come up with an idea, maybe run a bunch of robots in the lab or do a bunch of computational analyses and then potentially even analyze data, conclude what is going on and actually write an entire paper. Now, I think the vision of this is compelling in the long term. I think the question is really about timescale. If you break down the scientific method into its constituent parts, like hypothesis generation, doing an experiment, analyzing experiment and iterating, we're clearly going to use AI of some kind at every single step of this cycle. I think different steps will require different levels of maturity. The way that I would liken this is just wet lab automation, folks have dreamed about having pipetting robots that just do their western blots and do their cell culture for them for generations.(36:01):But of course, today they don't actually really feel fundamentally different from the same ones that we had in the 90s, let's say. Right? And so, obviously they're getting better, but it seems to me one of the trends I'm very bullish about is the explosion of humanoid robots and robot foundation models that have a world model and a sense of physics and proportionate space loaded onto them. Within five years, we're going to have home robots that can fold your clothes, that can organize your kitchen and do all of this while you're sleeping, so you wake up to a clean home every day.Eric Topol (36:40):It's not going to be just Roomba anymore. There's going to be a lot more, but it isn't just the hardware, it's also the agents playing in software, right?Patrick Hsu (36:50):It's the integrated loop of the hardware and the software where the ability to make the same machine generally intelligent will make it adaptable to a broad array of tasks. Now, what I'm excited about is those generally intelligent humanoid robots coming into the lab, where instead of creating a centrifuge or a new type of pipetter that's optimized for your Beckman or Hamilton device, instead you just have robot arms that you snap onto the edge of the bench and then they just work alongside you. And I do think that's coming, although it'll take a lot of hardware and software and computer vision engineering to make that possible.A Sense of HumorEric Topol (37:32):Yeah, and I think also going back to originating the question, there still is quite a debate about the creativity and the lack of any simulation of AGI, whatever that means anymore. And so, the human in the loop part of this is obviously I think it's still of critical nature. Now, the other thing I learned about you is you have a great sense of humor, which is really important by the way. And recently, which is great that you're active on X or Twitter because that's one way we get to see what you're thinking on a day-to-day basis. But I think you put out a poll which was really quite provocative , and it was about, here's what it said, “do more people in the world *truly* understand transformers or health insurance?” And interestingly, you got 49% for transformers at 51% for health insurance. Can you tell us what you're thinking when you put that poll together? Because obviously a lot of people don't understand either of these.Patrick Hsu (38:44):I think the core question is, there are different ways of looking at the world, some of which are very bottom up and some of which are very top down. And one of the very surprising things about transformers is they're taking something that is in principle, an incredibly simple task, which is if you have a string of text, what is the next letter? And somehow at massive, massive scale, you can unlock something that looks an awful lot like reasoning, and you've got these emergent behaviors. Now the bottoms up theory of just the linear algebra that's going on in these models couldn't possibly really help us predict that we have these emerging capabilities. And I think similarly in healthcare, there's a literal set of parts that are operating in some complex way that at massive scale becomes this incredibly confusing and dynamic system for how we can actually incentivize how we make medicines, how we actually take care of people, and how we actually pay for any of this from an economic point of view. And so, I think it was, in some sense if transformers can actually be an explainable by just linear algebra equations, maybe there will be a way to decompose the seemingly incredibly confusing world of healthcare in order to actually build a better way forward.Computing Power and the GPU Arms RaceEric Topol (40:12):Yeah. Well that's great. Now the other thing I wanted to ask you about, we open source and the arms race of GPUs and this whole kind of idea is you touched on the need for coalescing a lot of these tools to exploit the synergy. But we have an issue because many academic labs like here at Scripps Research and so many others, including as I learned even at Stanford, have limited access to GPUs. So computing power of large language models is a problem. And then the models that exist today that can be adopted like Llama or others, and they're somewhat limited. And then we also have a movement towards trying to make things more open source, like for example, recently OpenCRISPR with Profluent Bio that is basically trying to use AI for CRISPR guides. And so, how do you deal with this arms race, computing power, open source, proprietary models that are not easily accessible without a lot of resources?Patrick Hsu (41:30):So the first thing I would say is, we are in the academic science sphere really unprepared for the level of resources that are required for doing this type of cutting edge computational work. There are top Stanford computer science professors or computational researchers who have a single GPU in their office, and that's actually what their whole lab runs off of.(41:58):The UC Berkeley campus, the grid runs on something like 12 megawatts of power and how are they going to build an on-premises GPU clusters, like a central question that can scale across the entire needs? And these are two of the top computer science universities in the world. And so, I think one of our kind of core beliefs at Arc is, as science both experimentally and computationally has gotten incredibly complex, not just in terms of conceptually, but also just the actual infrastructure and machines and know-how that you need to do things. We actually need to essentially support this. So we have a private GPU cloud that we use to train our models, and we have access to significantly large clusters for large burst kind of train outs as necessary. And I think infrastructurally for running genomics experiments or doing scalable brain organoid screens, right, we're also building out the infrastructure to support that experimentally.Eric Topol (43:01):Yeah, no, I think this is one of the advantages of the new model like the Arc Institute because not many centers have that type of plasticity with access to computing power when needed. So that's where a brilliant mind you and the Arc Institute together makes for a formidable recipe for future advances and of course building on the ones you've already accomplished.The Primacy of Human TalentPatrick Hsu (43:35):I would just say, my main skill, if I have one, is to recruit really, really smart people. And so, everything that you're seeing and hearing about is the work of unbelievable colleagues who are curious, passionate, and incredible scientists.Eric Topol (43:53):But it also takes the person who can judge those who are in that category set as a role model. And you're certainly doing that. I guess just in closing, I mean, it's just such a delight to get to meet you here and kind of get your thoughts on what is the hottest thing in life science without question, which brings together the fields of AI and what's going on, not just obviously in genome editing, but this digital biology era that we're still in the early phases of, I mean, I think you could say that it's just going to continue to accelerate the exponential curve. We're still kind of on the bottom of that, I would imagine where we're headed. Any other things that you want to bring up that I haven't touched on that will round out this conversation?Patrick Hsu (44:50):I mean, I think it's very early days here at Arc.Patrick Hsu (44:53):When we founded Arc, we asked ourselves, how do we measure success? We don't have customers or revenue in the way that a typical startup does. And we felt sort of three things. The first was research institutes live and die by their talent. Can we actually hire incredible people when we make offers to people we want to come, do they come? The second was, when those folks do come to Arc, do they feel like they're able to work on important research programs that they couldn't do sort of at their prior university or company? And then longer term, the third thing was, and there's just no shortcut around this, you need to do important work. And I think we've been really excited that there are early signs that we're able to do all three of these things, and we're still, again, just following the same scaling laws that we're seeing in natural language and vision, but for the domain of biology. And so, we're excited about what's ahead and think if there are folks who are interested in learning more about Arc, just shoot me an email or DM.Eric Topol (46:07):Yeah, well I would just say, congratulations on what you've already achieved. I know you're going to keep rocking it because you already have in a short time. And for anybody who doesn't know about Arc Institute and your work and your team, I hope this is going to be putting them on notice actually what can be accomplished outside of the usual NIH funded model, which is kind of a risk-free zone where you basically have to have your results nailed down before you send in your proposal frequently, and it doesn't do great things for young people. Really, I think you actually qualify in that demographic where it's hard for them to break in for getting NIH grants and also for this type of work that you're doing. So we'll look for the next bridge beyond bridge RNAs of your just fantastic efforts. So Patrick, thanks so much for joining us today, and we'll be checking back with you and following all the great work that you'll be doing in the times ahead.Patrick Hsu (47:14):Thanks so much, Eric. It was such a pleasure to be here today. Appreciate the opportunity.*******************Thanks for listening, reading or watching!The Ground Truths newsletters and podcasts are all free, open-access, without ads.Please share this post/podcast with your friends and network if you found it informative!Voluntary paid subscriptions all go to support Scripps Research. Many thanks for that—they greatly help fund our summer internship programs.Thanks to my producer Jessica Nguyen and Sinjun Balabanoff for audio and video support at Scripps Research.Note: you can select preferences to receive emails about newsletters, podcasts, or all I don't want to bother you with an email for content that you're not interested in. Get full access to Ground Truths at erictopol.substack.com/subscribe

Martin Gonzalez is the creator of Google's Effective Founders Project, a global research program that uses people analytics to uncover what makes the best startup founders succeed and shares their success formula with the world. He has run leadership courses for thousands of tech startup founders across seventy countries in the Americas, Asia, Africa, and Europe. He is a frequent lecturer on entrepreneurship, organization design, and people analytics at Stanford, Wharton, and INSEAD. He is also the author of the bestselling book, The Bonfire Moment: Bring Your Team Together to Solve the Hardest Issues Startups Face. Martin is a principal of organization and leadership development at Google. He works with Google's senior leaders to shape team culture, develop their people, and expand their leadership, so they can build cool things that matter. In his ten years there, he's worked with leaders across Google Research, DeepMind, Technology & Society, Responsible AI, Pixel, Fitbit, YouTube, Search, Maps, Android, and Chrome, to name a few.  In 2023, The Aspen Institute recognized him as a First Movers Fellow, honoring his pioneering work at Google. In 2024, he was featured on the Thinkers50 Radar List, a prestigious recognition dubbed the "Oscars of management thinking" by the Financial Times, highlighting emerging thinkers expected to significantly influence future management thinking. Before Google, he was a management consultant with the Boston Consulting Group and a product manager at Johnson & Johnson.  Martin has studied organizational psychology and behavioral science at Columbia University and the London School of Economics. He's a serial immigrant, having lived and worked in New York, Jakarta, Singapore, Taipei, and Manila, where he is originally from. Today, he lives in the San Francisco Bay Area with his wife, Bea, and three kids: Noelle, Jaime, and Andrea.A Quote From This Episode"Startups have a people problem."Resources Mentioned in This EpisodeWebsite/Book - The Bonfire Moment Book - Creative Construction by Gary PisanoAbout The International Leadership Association (ILA)The ILA was created in 1999 to bring together professionals interested in studying, practicing, and teaching leadership. Register for ILA's 26th Global Conference in Chicago, IL - November 7-10, 2024.About  Scott J. AllenWebsiteWeekly Newsletter: The Leader's EdgeBlogMy Approach to HostingThe views of my guests do not constitute "truth." Nor do they reflect my personal views in some instances. However, they are views to consider, and I hope they help you clarify your perspective. Nothing can replace your reflection, research, and exploration of the topic.

Professor Hannah Fry is joined by Jeff Dean, one of the most legendary figures in computer science and chief scientist of Google DeepMind and Google Research. Jeff was instrumental to the field in the late 1990s, writing the code that transformed Google from a small startup into the multinational company it is today. Hannah and Jeff discuss it all - from the early days of Google and neural networks, to the long term potential of multi-modal models like Gemini.Thanks to everyone who made this possible, including but not limited to: Presenter: Professor Hannah FrySeries Producer: Dan HardoonEditor: Rami Tzabar, TellTale Studios Commissioner & Producer: Emma YousifMusic composition: Eleni Shaw Camera Director and Video Editor: Tommy BruceAudio Engineer: Perry RogantinVideo Studio Production: Nicholas DukeVideo Editor: Bilal MerhiVideo Production Design: James BartonVisual Identity and Design: Eleanor TomlinsonCommissioned by Google DeepMind Want to share feedback? Why not leave a review on your favorite streaming platform? Have a suggestion for a guest that we should have on next? Leave us a comment on YouTube and stay tuned for future episodes.

News includes the upcoming signed installers for Livebook and Elixir on Windows, the release of Telemetry v1.3 with improved documentation, LiveView Native 0.3.0's announcement ahead of ElixirConf, Google Research introducing an alternative SQL syntax with a pipe, a Livebook leveraging LLMs and FFMPEG for media conversion, legal updates on the US non-compete agreements ban, and potential antitrust actions against Google, and more! Show Notes online - http://podcast.thinkingelixir.com/218 (http://podcast.thinkingelixir.com/218) Elixir Community News - https://x.com/josevalim/status/1825954736094457943 (https://x.com/josevalim/status/1825954736094457943?utm_source=thinkingelixir&utm_medium=shownotes) – The next versions of Livebook and Elixir will have signed installers on Windows, thanks to the Erlang Ecosystem Foundation and Wojtek Mach. - https://x.com/wojtekmach/status/1826521109476344035 (https://x.com/wojtekmach/status/1826521109476344035?utm_source=thinkingelixir&utm_medium=shownotes) – Wojtek Mach discusses the challenges of packaging Livebook into a .msix for the Windows Store and asks for contributions from those familiar with the process. - https://hexdocs.pm/telemetry/1.3.0/readme.html (https://hexdocs.pm/telemetry/1.3.0/readme.html?utm_source=thinkingelixir&utm_medium=shownotes) – Telemetry v1.3 is out with improved documentation, rewritten to ExDoc from Erlang edoc, thanks to contributions from Wojtek Mach and Andrea Leopardi. OTP 27 is required. - https://x.com/bcardarella/status/1826266402631889091 (https://x.com/bcardarella/status/1826266402631889091?utm_source=thinkingelixir&utm_medium=shownotes) – LiveView Native 0.3.0 is now released with the official announcement at ElixirConf. Blog posts, tutorials to follow. - https://x.com/bcardarella/status/1826279303623082421 (https://x.com/bcardarella/status/1826279303623082421?utm_source=thinkingelixir&utm_medium=shownotes) – Additional details about the LiveView Native 0.3.0 release. - https://twitter.com/simonw/status/1827482890680332386 (https://twitter.com/simonw/status/1827482890680332386?utm_source=thinkingelixir&utm_medium=shownotes) – Google Research released a paper on an alternative SQL syntax with a pipe, similar to Ecto querying syntax. - https://simonwillison.net/2024/Aug/24/pipe-syntax-in-sql/ (https://simonwillison.net/2024/Aug/24/pipe-syntax-in-sql/?utm_source=thinkingelixir&utm_medium=shownotes) – More details on the new SQL syntax introduced by Google for ZetaSQL. - https://twitter.com/ac_alejos/status/1794105872680972458 (https://twitter.com/ac_alejos/status/1794105872680972458?utm_source=thinkingelixir&utm_medium=shownotes) – A Livebook that uses LLMs and FFMPEG to simplify the process of converting videos or audio by suggesting the right flags and switches. - https://github.com/acalejos/CinEx (https://github.com/acalejos/CinEx?utm_source=thinkingelixir&utm_medium=shownotes) – Detailed information on using LLMs within Livebook for conversion tasks. - https://www.reuters.com/legal/us-judge-strikes-down-biden-administration-ban-worker-noncompete-agreements-2024-08-20/ (https://www.reuters.com/legal/us-judge-strikes-down-biden-administration-ban-worker-noncompete-agreements-2024-08-20/?utm_source=thinkingelixir&utm_medium=shownotes) – A US Judge struck down the FTC's ban on non-compete agreements, stating the FTC lacks legal authority and the ban is too wide-reaching. - https://www.nytimes.com/2024/08/13/technology/google-monopoly-antitrust-justice-department.html (https://www.nytimes.com/2024/08/13/technology/google-monopoly-antitrust-justice-department.html?utm_source=thinkingelixir&utm_medium=shownotes) – The US government is considering ordering Google to be broken up following antitrust allegations. - https://www.macrumors.com/2024/08/22/apple-eu-default-app-update/ (https://www.macrumors.com/2024/08/22/apple-eu-default-app-update/?utm_source=thinkingelixir&utm_medium=shownotes) – Apple might allow EU residents to delete apps currently blocked from removal, addressing app store issues in the EU. - Living in a time when industry rules are being challenged creates opportunities for new businesses and markets, as highlighted by ongoing legal issues with major tech companies like Google and Apple. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - @ThinkingElixir (https://twitter.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen - @brainlid (https://twitter.com/brainlid) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel - @bernheisel (https://twitter.com/bernheisel) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

In April, Google DeepMind published a paper that boasts 57 authors, including experts from a range of disciplines in different parts of Google, including DeepMind, Jigsaw, and Google Research, as well as researchers from academic institutions such as Oxford, University College London, Delft University of Technology, University of Edinburgh, and a think tank at Georgetown, the Center for Security and Emerging Technology. The paper speculates about the ethical and societal risks posed by the types of AI assistants Google and other tech firms want to build, which the authors say are “likely to have a profound impact on our individual and collective lives.” Justin Hendrix the chance to speak to two of the papers authors about some of these issues:Shannon Vallor, a professor of AI and data ethics at the University of Edinburgh and director of the Center for Technomoral Futures in the Edinburgh Futures Institute; andIason Gabriel, a research scientist at Google DeepMind in its ethics research team.

A 3D Map of the Human Brain has been created by a collaboration between Harvard researchers and Google Research analysing 1,400 terabytes of data.

Chris Guess, Lead Technologist at the Duke Reporters' Lab at Duke University Learn more about your ad choices. Visit megaphone.fm/adchoices

Aimar Bretos entrevista a Pilar Manchón, directora senior de estrategia de investigación de Google Research.

Aimar Bretos entrevista a Pilar Manchón, directora senior de estrategia de investigación de Google Research.

Google researcher Blaise Agüera y Arcas spends his work days developing artificial intelligence models and his free time conducting surveys for fun. He tells Steve how he designed an algorithm for the U.S. Navy at 14, how he discovered the truth about printing-press pioneer Johannes Gutenberg, and when A.I. first blew his mind. SOURCE:Blaise Agüera y Arcas, fellow at Google Research. RESOURCES:Who Are We Now?, by Blaise Agüera y Arcas (2023)."Artificial General Intelligence Is Already Here," by Blaise Agüera y Arcas and Peter Norvig (Noema Magazine, 2023)."Transformer: A Novel Neural Network Architecture for Language Understanding," by Jakob Uszkoreit (Google Research Blog, 2017)."Communication-Efficient Learning of Deep Networks from Decentralized Data," by H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas (arXiv, 2016)."How PhotoSynth Can Connect the World's Images," by Blaise Agüera y Arcas (TED Talk, 2007)."Has History Been Too Generous to Gutenberg?" by Dinitia Smith (The New York Times, 2001). EXTRAS:"'My God, This Is a Transformative Power,'" by People I (Mostly) Admire (2023)."How to Think About A.I.," series by Freakonomics Radio (2023)."Satya Nadella's Intelligence Is Not Artificial," by Freakonomics Radio (2023)."Yul Kwon (Part 2): 'Hey, Do You Have Any Bright Ideas?'" by People I (Mostly) Admire (2021)."Yul Kwon: 'Don't Try to Change Yourself All at Once,'" by People I (Mostly) Admire (2021).

In this episode of ACM ByteCast, Rashmi Mohan hosts 2021 ACM Fellow Edward Y. Chang, an Adjunct Professor in the Department of Computer Science at Stanford University. Prior to this role, he was a Director of Google Research and President of HTC Healthcare, among other roles. He is the Founder and CTO of Ally.ai, an organization making groundbreaking moves in the field using Generative AI technologies in various applications, most notably healthcare, sales planning, and corporate finance. He's an accomplished author of multiple books and highly cited papers whose many awards and recognitions include the Google Innovation Award, IEEE Fellow, Tricorder XPRIZE, and the Presidential Award of Taiwan. Edward also also credited as the inventor of the digital video recorder (DVR), which replaced the traditional tape-based VCR in 1999 and introduced interactive features for streaming videos. Edward, who was born in Taipei, discusses his career, from studying Operations Research at UC Berkeley to graduate work at Stanford University, where his classmates included the co-founders of Google and where his PhD dissertation focused on on a video streaming network that became DVR. Later, at Google, he worked on developing the data-centric approach to machine learning, and led development of parallel versions of commonly used ML algorithms that could handle large datasets, with the goal of improving the ML infrastructure accuracy to power Google's multiple functions. He also shares his work at HTC in Taipei, which focused on healthcare projects, such as using VR technology to scan a patient's brain; as well as his current interest, studying AI and consciousness. He talks about the challenges he's currently facing in developing bleeding edge technologies at Ally.ai and addresses a fundamental question about the role of human in a future AI landscape.

“Google” Research…How do we know it is true or right? What do we need to ask or learn so we can analyse all the information we receive for ourselves?

Join us for another exciting episode of Roofing Road Trips® as Megan Ellsworth visits with Jeffrey Steuben, Cool Roofing Rating Council (CRRC) executive director, and Stuart Ruis, CRRC board chair, for a preview of what to expect at their 2024 Annual Meeting taking place June 5, 2024, at the Palms Casino in Las Vegas, Nevada. Jeffrey and Stuart will share information about the Annual Meeting guest presenters from Google Research, The Western Coatings Technology Center at Cal Poly in San Luis Obispo, and the City of Phoenix, along with more details about what to expect. This one-day event is for CRRC members and non-members who want to obtain timely, accurate and valuable information regarding the latest innovations, trends and discussions relevant to reflective building materials. Get the inside scoop here!   Learn more at RoofersCoffeeShop.com!    Are you a contractor looking for resources? Become an R-Club Member today! https://www.rooferscoffeeshop.com/rcs-club-sign-up    Follow Us!   https://www.instagram.com/rooferscoffeeshop/?hl=en  https://www.facebook.com/rooferscoffeeshop/  https://www.linkedin.com/company/rooferscoffeeshop-com  https://www.tiktok.com/@rooferscoffeeshop    #RoofersCoffeeShop #CRRC #RoofingProfessionals #RoofingContractors #RoofingIndustry   

Groq's AI hardware breakthroughs with LPU architecture achieving speeds of 500 tokens per second. Japan's $67 billion investment to become a global chip powerhouse and insulate its economy from growing US-China tensions. Neural Network Diffusion paper demonstrating that diffusion models can generate high-performing neural network parameters. VideoPrism paper from Google Research achieving state-of-the-art performance on 30 out of 33 video understanding benchmarks. Contact:  sergi@earkind.com Timestamps: 00:34 Introduction 01:47 Groq Goes Viral with Crazy Fast AI Inference 03:01 Japan Bets $67 Billion to Become a Global Chip Powerhouse Once Again 04:54 My benchmark for large language models 06:01 Fake sponsor 07:54 Neural Network Diffusion 09:19 Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models 11:16 VideoPrism: A Foundational Visual Encoder for Video Understanding 12:42 Outro

Si de nombreux outils de génération d'images utilisant l'intelligence artificielle ont récemment vu le jour et connaissent souvent un grand succès, les outils similaires pour la vidéo sont un peu plus rares et beaucoup moins convaincants. C'est donc pour répondre à cette problématique qu'une équipe de chercheurs, dont certains travaillent pour le compte de Google Research, s'est attelée à mettre au point Lumiere, un nouveau modèle d'IA pour la génération vidéo. Plutôt que d'assembler des séquences d'images individuelles, Lumière forme des vidéos entières en un seul processus, gérant simultanément le placement des objets et leur mouvement. Je cite les propos des chercheurs qui ont travaillé sur cette IA, "l'architecture Space-Time U-Net génère toute la durée de la vidéo en une seule fois, par le biais d'un seul passage dans le modèle. Cela contraste avec les modèles vidéo existants qui synthétisent des images clés distantes suivies d'une super-résolution temporelle" fin de citation. Dans le détail, Lumiere peut générer 80 images à une fréquence de 16 images par seconde, ce qui correspond à une séquence finale de 5 secondes. On est bien loin d'un long métrage, mais cette durée est conforme à la plupart des solutions existantes actuellement. Pour info, la résolution pour Lumiere est 1 024 x 1 024 pixels. Si ces caractéristiques sont intéressantes, reste à savoir comment générer ces vidéos. Et bien vous pouvez simplement soumettre un prompt, une phrase descriptive à Lumiere. Comme pour les générateurs d'images traditionnels, il s'agit d'une simple description de ce que vous souhaitez comme rendu. L'IA peut également générer la vidéo à partir d'une image que vous lui soumettez. Et troisième possibilité enfin, ce modèle génère non seulement des vidéos, mais aussi, plus rarement, peut éditer et animer des vidéos existantes ou remplir des zones spécifiques ! Attention, Lumière est encore un projet de recherche et ne peut pas être testé pour le moment. Pour plus d'info sur le sujet, le lien de l'article présentant Lumiere par Google Research est dans le description de cet épisode. Lumiere par Google Research : https://arxiv.org/pdf/2401.12945.pdf Learn more about your ad choices. Visit megaphone.fm/adchoices

Si de nombreux outils de génération d'images utilisant l'intelligence artificielle ont récemment vu le jour et connaissent souvent un grand succès, les outils similaires pour la vidéo sont un peu plus rares et beaucoup moins convaincants. C'est donc pour répondre à cette problématique qu'une équipe de chercheurs, dont certains travaillent pour le compte de Google Research, s'est attelée à mettre au point Lumiere, un nouveau modèle d'IA pour la génération vidéo.Plutôt que d'assembler des séquences d'images individuelles, Lumière forme des vidéos entières en un seul processus, gérant simultanément le placement des objets et leur mouvement. Je cite les propos des chercheurs qui ont travaillé sur cette IA, "l'architecture Space-Time U-Net génère toute la durée de la vidéo en une seule fois, par le biais d'un seul passage dans le modèle. Cela contraste avec les modèles vidéo existants qui synthétisent des images clés distantes suivies d'une super-résolution temporelle" fin de citation. Dans le détail, Lumiere peut générer 80 images à une fréquence de 16 images par seconde, ce qui correspond à une séquence finale de 5 secondes. On est bien loin d'un long métrage, mais cette durée est conforme à la plupart des solutions existantes actuellement. Pour info, la résolution pour Lumiere est 1 024 x 1 024 pixels.Si ces caractéristiques sont intéressantes, reste à savoir comment générer ces vidéos. Et bien vous pouvez simplement soumettre un prompt, une phrase descriptive à Lumiere. Comme pour les générateurs d'images traditionnels, il s'agit d'une simple description de ce que vous souhaitez comme rendu. L'IA peut également générer la vidéo à partir d'une image que vous lui soumettez. Et troisième possibilité enfin, ce modèle génère non seulement des vidéos, mais aussi, plus rarement, peut éditer et animer des vidéos existantes ou remplir des zones spécifiques ! Attention, Lumière est encore un projet de recherche et ne peut pas être testé pour le moment. Pour plus d'info sur le sujet, le lien de l'article présentant Lumiere par Google Research est dans le description de cet épisode.Lumiere par Google Research : https://arxiv.org/pdf/2401.12945.pdf Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

On this episode of The AI Moment, we discuss two emerging Gen AI trends: Microsoft Copilot's AI revenue potential and LLM research. We also celebrate our latest Adults In the Generative AI Rumpus Room.  The discussion covers: With the most used enterprise software and operating system in the world, Microsoft placed a significant bet on AI with the introduction of Copilot to enterprise users in September 2023. Now Microsoft has unleashed Copilot, making it available to nearly every 365 user. What will the impact be? Is Microsoft poised to generate material revenues from AI in 2024?  LLMs are evolving at lightning speed, in part due to a copious amount of academic research and what it means for the market. More Adults in the Generative AI Rumpus Room: Non-profit Fairly Trained, Google Research.  

New Year's Resolutions: We're Doing It Wrong This month, Kathy and Jyl take a deep dive into their New Year's Resolutions. Just kidding, no diving necessary as we learned something about each other: Neither of us approaches the New Year with an unachievable punch list that will put us under unnecessary pressure. You shouldn't either. Why? Check out this research from the Google: Research suggests that only 9% of Americans who make resolutions complete them.  Research shows that 23% of people quit their resolution by the end of the first week, 43% quit by the end of January, and all but that dwindling 9% quit. Why? Many resolutions are created based on tradition rather than need. "Need" is much more motivating and that need may not come until a random date in the future like July 23rd. Wait until then. Obstacles. For many, obstacles are a dream killer when it comes to resolutions. A missed day at the gym due to snow, a sale on Ben & Jerry's and suddenly, wait, what was your resolution? Size matters. Many create resolutions that are simply too big. Life updates should be done in small chunks so that one might feel several hints of success over a longer period rather than continue eyeing some unlikely win. Accountability. Who needs it? Resolutions are for those still full of hope and energy and the ability to stick to something. Not that we're not, but, well... Thoughts from Jyl: I realized that New Year's resolutions were yet another way I could spend months beating myself up for not reaching success. My current approach is tiered with a few goals that are slam dunks, a couple that are challenging, and one stretch goal, such as writing a book (or in this 2024's case, publishing an audiobook). Thoughts from Kathy: I put a twist on the traditional resolution this year by digging into things I want to rid myself of in 2024. This was a group activity with friends and, after we wrote down the items/people/etc that we would be happier without, we burned those lists as we said goodbye to those things that were going to drag us down. What we agreed on the most? Forgiveness. Yes, even (or especially) when it comes to resolutions. Forgive and Forget, you are under no pressure to perform. If you've made a resolution and it isn't working out, set it aside for a month and see if it's still important. No? Then leave it behind. You didn't need that resolution anyway. All that being said? Of course, we did give ourselves a couple of dreams for 2024. Jyl: Publish Audiobook (stretch goal!) | Lose the riff-raff | Wear the jewelry | Clean the pantry | Career Casual Kathy: Start doing voiceovers | Focus on health but not in an obsessive way | Organize the freezer | Leave work at work This episode was not sponsored by ⁠Any Adventure or Pixie & Pan Vacations⁠ so absolutely check them out! Mentions: Any Adventure and Pixie & Pan Vacations Norwegian Bliss Alaskan Cruise (yes, you should come along) Quail Ridge Bookstore Sing When You Win by Jon Hume (Welcome to Wrexham) What to Expect When You Weren't Expecting by Jyl Barlow Can't get enough of us? Well, you're one of very few. Get to know us!  ⁠⁠Jyl Barlow⁠⁠ has all things Jyl! Also, it's pronounced, “jill.” ⁠⁠Which Way's Up⁠⁠ is Jyl's blog, home of weekly epiphanies and often overshares ⁠⁠What to Expect When You Weren't Expecting⁠⁠ is Jyl's best-selling memoir about her hilarious struggles as a (step)mother. Buy it online at Amazon, Barnes & Noble, Goodreads, and Target! ⁠⁠Kathy Crowley⁠⁠'s Thought for the Day (accessorized with a favorite timepiece and signature scent) can be found on ⁠Instagram⁠. Watch videos of all our Nonsor products on ⁠⁠YouTube⁠⁠ or⁠ ⁠TikTok⁠⁠! Wine & Whine is part of Bearlow Productions and is created Jyl Barlow and Kathy Crowley.

Today Kevin and Laura talk with Fergus Hurley. Fergus is the is the co-founder of Google's AI-powered compliance intelligence platform, Checks. We chat about Google's new AI platform, Gemini. We learn what multimodal AI is and how this is different from Bard and OpenAI. We discuss using Gen AI in the entrainment industry and the writer's strike. We chat about AI bias and even find out about Fergus's favorite Irish whiskey. Fergus spearheaded the creation of the platform by developing a product that leverages Google's sophisticated AI technology to streamline privacy compliance for some of the industry's leading digital applications such as Miniclip, Headspace, and Rovio. Fergus is an AI expert and enthusiast with an extensive background spanning over 15 years in the realms of mobile apps, and digital product development. In his prior role, he served as a zero-to-one focused product manager at Google Play, where he played a pivotal part in enhancing the Android user experience on a global scale. Preceding this, he made significant contributions to Google Research, focusing his expertise on Google Assistant and Waze Carpool.Fergus holds a Master's degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) and a Bachelor's degree in Electrical Engineering from University College Cork in Ireland.Please visit checks.google.com to learn more about Fergus' current platform.

For the last paper read of the year, Arize CPO & Co-Founder, Aparna Dhinakaran, is joined by a Dat Ngo (ML Solutions Architect) and Aman Khan (Product Manager) for an exploration of the new kids on the block: Gemini and Mixtral-8x7B. There's a lot to cover, so this week's paper read is Part I in a series about Mixtral and Gemini. In Part I, we  provide some background and context for Mixtral 8x7B from Mistral AI, a high-quality sparse mixture of experts model (SMoE) that outperforms Llama 2 70B on most benchmarks with 6x faster inference Mixtral also matches or outperforms GPT3.5 on most benchmarks. This open-source model was optimized through supervised fine-tuning and direct preference optimization. Stay tuned for Part II in January, where we'll build on this conversation in and discuss Gemini-developed by teams at DeepMind and Google Research. Link to transcript and live recording: https://arize.com/blog/a-deep-dive-into-generatives-newest-models-mistral-mixtral-8x7b/To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

AI researcher, author, and VP and Fellow at Google Research, Blaise Agüera y Arcas has contributed to research papers cited more than 20,000 times, including a seminal LaMDA paper from 2022. His new book, Who Are We Now?, explores how biology, ecology, sexuality, history, and culture have intertwined to create a dynamic “us” that's neither natural nor artificial. Blaise joins Robb and Josh for a philosophical exploration of identity, collective intelligence, and the ways AI might put us back into balance with nature. A frequent TED speaker and winner of MIT's TR35 Prize, Blaise brings the perspective of a deeply forward-thinking researcher to our ongoing conversation about AI.

With all the buzz surrounding AI,  we're missing an understanding of how recent AI advancements affect those in the global South. I talk to Rida Qadri about ways in which generative AI fails to represent those in the Global South, what the implications of these failures are, and what's needed to do better. Rida Qadri is an interdisciplinary scholar focusing on the cultural impacts of generative AI for people and communities in the global south. She is a Research Scientist at Google Research, and has a PhD in Computational Urban Science and Masters in Urban Studies from MIT.Both Rida and I are speaking in our private capacities, and neither Rida's nor my views expressed in this episode necessarily represent those of our respective employers. 

In episode 99 of The Gradient Podcast, Daniel Bashir speaks to Professor Martin Wattenberg.Professor Wattenberg is a professor at Harvard and part-time member of Google Research's People + AI Research (PAIR) initiative, which he co-founded. His work, with long-time collaborator Fernanda Viégas, focuses on making AI technology broadly accessible and reflective of human values. At Google, Professor Wattenberg, his team, and Professor Viégas have created end-user visualizations for products such as Search, YouTube, and Google Analytics. Note: Professor Wattenberg is recruiting PhD students through Harvard SEAS—info here.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:30) Prof. Wattenberg's background* (04:40) Financial journalism at SmartMoney* (05:35) Contact with the academic visualization world, IBM* (07:30) Transition into visualizing ML* (08:25) Skepticism of neural networks in the 1980s* (09:45) Work at IBM* (10:00) Multiple scales in information graphics, organization of information* (13:55) How much information should a graphic display to whom? * (17:00) Progressive disclosure of complexity in interface design* (18:45) Visualization as a rhetorical process* (20:45) Conversation Thumbnails for Large-Scale Discussions* (21:35) Evolution of conversation interfaces—Slack, etc.* (24:20) Path dependence — mutual influences between user behaviors and technology, takeaways for ML interface design* (26:30) Baby Names and Social Data Analysis — patterns of interest in baby names* (29:50) History Flow* (30:05) Why investigate editing dynamics on Wikipedia?* (32:06) Implications of editing patterns for design and governance* (33:25) The value of visualizations in this work, issues with Wikipedia editing* (34:45) Community moderation, bureaucracy* (36:20) Consensus and guidelines* (37:10) “Neutral” point of view as an organizing principle* (38:30) Takeaways* PAIR* (39:15) Tools for model understanding and “understanding” ML systems* (41:10) Intro to PAIR (at Google)* (42:00) Unpacking the word “understanding” and use cases* (43:00) Historical comparisons for AI development* (44:55) The birth of TensorFlow.js* (47:52) Democratization of ML* (48:45) Visualizing translation — uncovering and telling a story behind the findings* (52:10) Shared representations in LLMs and their facility at translation-like tasks* (53:50) TCAV* (55:30) Explainability and trust* (59:10) Writing code with LMs and metaphors for using* More recent research* (1:01:05) The System Model and the User Model: Exploring AI Dashboard Design* (1:10:05) OthelloGPT and world models, causality* (1:14:10) Dashboards and interaction design—interfaces and core capabilities* (1:18:07) Reactions to existing LLM interfaces* (1:21:30) Visualizing and Measuring the Geometry of BERT* (1:26:55) Note/Correction: The “Atlas of Meaning” Prof. Wattenberg mentions is called Context Atlas* (1:28:20) Language model tasks and internal representations/geometry* (1:29:30) LLMs as “next word predictors” — explaining systems to people* (1:31:15) The Shape of Song* (1:31:55) What does music look like? * (1:35:00) Levels of abstraction, emergent complexity in music and language models* (1:37:00) What Prof. Wattenberg hopes to see in ML and interaction design* (1:41:18) OutroLinks:* Professor Wattenberg's homepage and Twitter* Harvard SEAS application info — Professor Wattenberg is recruiting students!* Research* Earlier work* A Fuzzy Commitment Scheme* Stacked Graphs—Geometry & Aesthetics* A Multi-Scale Model of Perceptual Organization in Information Graphics* Conversation Thumbnails for Large-Scale Discussions* Baby Names and Social Data Analysis* History Flow (paper)* At Harvard and Google / PAIR* Tools for Model Understanding: Facets, SmoothGrad, Attacking discrimination with smarter ML* TensorFlow.js* Visualizing translation* TCAV* Other ML papers:* The System Model and the User Model: Exploring AI Dashboard Design (recent speculative essay)* Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task* Visualizing and Measuring the Geometry of BERT* Artwork* The Shape of Song Get full access to The Gradient at thegradientpub.substack.com/subscribe

This episode is sponsored by Celonis ,the global leader in process mining. AI has landed and enterprises are adapting. To give customers slick experiences and teams the technology to deliver. The road is long, but you're closer than you think. Your business processes run through systems. Creating data at every step. Celonis recontrusts this data to generate Process Intelligence. A common business language. So AI knows how your business flows. Across every department, every system and every process. With AI solutions powered by Celonis enterprises get faster, more accurate insights. A new level of automation potential. And a step change in productivity, performance and customer satisfaction Process Intelligence is the missing piece in the AI Enabled tech stack. Go to https:/celonis.com/eyeonai to find out more. Welcome to episode 146 of the Eye on AI podcast. In this episode, host Craig Smith sits down with Viren Jain, a leading Research Scientist at Google in Mountain View, California. Viren, at the helm of the Connectomics team, has pioneered breakthroughs in synapse-resolution brain mapping in collaboration with esteemed institutions such as HHMI, Max Planck, and Harvard.   The conversation kicks off with Jain introducing his academic journey and the evolution of connectomics – the comprehensive study of neural connections in the brain. The duo delves deep into the challenges and advancements in imaging technologies, comparing their progression to genome sequencing. Craig probes further, inquiring about shared principles across organisms, the dynamic behavior of the brain, and the role of electron microscopes in understanding neural structures.   The dialogue also touches upon Google's role in the research, Jain's collaborative ventures, and the potential future of AI and connectomics. Viren also shares his insights into neuron tracing, the significance of combining algorithm predictions, the zebra finch bird's song-learning mechanism, and the broader goal of enhancing human health and medicine.   Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Preview, Introduction and Celonis (06:45) Viren's Academic and Professional Journey (13:17) AI's Technological Progress and Challenges (22:20) Deep Dive into Connectomics (39:20) Google's Role in AI (44:16) Natural Learning vs. AI Algorithms (57:32) Brain Mapping: Present and Future (01:00:33) Brain Studies for Medical Advancement (01:06:05) Final Reflections and Celonis ad  

¿Cómo se enseña a hablar a una inteligencia artificial? ¿Cómo implementamos el lenguaje natural de nuestro uso diario en nuestra interacción con las máquinas y qué aplicaciones pueden venir derivadas de ello? Recibimos en la Nabucodonosor a un referente mundial: Pilar Manchón, Directora Senior de Estrategia de Investigación de Google Research para hablarnos sobre la investigación actual en torno a los asistentes virtuales y la IA, los estándares éticos que se están aplicando en la misma y el camino por recorrer en un futuro cada vez más cercano y que nos obliga cada vez más a estar no sólo pendientes sino también productivos a la hora de conocer y planificar los avances en inteligencia artificial. Con Don Víctor exploraremos en otro tipo de lenguajes, los lenguajes inventados o “raros” de canciones que pondrán a prueba la capacidad de comprensión de cualquier IA que se precie. Escuchar audio

In the latest episode of Tell Me Why, Jill Blickstein, VP and Chief Sustainability Officer talks about sustainability and why it's so important for our airline. We're on a path to net-zero emissions by 2050, and there is a lot to be excited about, including a recent study American participated in with Google Research and Breakthrough Energy on avoiding contrails in flight.

Abe Murray is a visionary founder, accomplished builder, and distinguished product engineering lead with a remarkable track record in the tech industry. Currently serving as a General Partner at AlleyCorp Robotics, Abe leverages his expertise to drive innovation and excellence in the field of robotics and technology. Abe was previously a product and engineering leader at Alphabet. He launched Play Books and Magazines at Android, delivered $XXXM in business while building the Verily Life Science product teams and Boston office, and shipped AI /ML/computer vision across Google while building the Google Research product team. Before Google, he built a Web 2.0 startup and worked on UAVs in the defense industry.  Abe has some shiny degrees (HBS MBA, WPI MS EE, URI BS Comp Eng) but says he learned the most when he dropped out of high school to run fishing boats and factory lines in the family aquaculture business. In his free time, Abe enjoys being a parent and partner, building all kinds of things with his kids, and staying as healthy as he's able. About VSC Ventures: For 20 years, our award-winning ⁠⁠PR agency VSC⁠⁠ has worked with innovative startups on positioning, messaging, and awareness and we are bringing that same expertise to help climate startups with storytelling and narrative building. Last year, general partners Vijay Chattha and Jay Kapoor raised a $21M fund to co-invest in the most promising startups alongside leading climate funds. Through the conversations on our show CLIMB by VSC, we're excited to share what we're doing at VSC and VSC Ventures on climate innovation with companies like ⁠⁠Ample⁠⁠, ⁠⁠Actual⁠⁠, ⁠⁠Sesame Solar⁠⁠, ⁠⁠Synop⁠⁠, ⁠⁠Vibrant Planet⁠⁠, and ⁠⁠Zume⁠⁠ among many others.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 4 things GiveDirectly got right and wrong sending cash to flood survivors, published by GiveDirectly on July 31, 2023 on The Effective Altruism Forum. For Hassana in Kogi, Nigeria, October's floods were not like years past. "All our farmlands washed away as many had not yet harvested what they planted. The flooding continued until our homes and other things were destroyed. At this point we were running helter-skelter," she said. These floods, the worst in a decade, result from predictable seasonal rains. If we can anticipate floods, we can also anticipate the action needed to help. So why does aid often take months (even up to a year) to reach people like Hassana? Traditional humanitarian processes can be slow and cumbersome, government and aid agencies often lack the capacity and money to respond, and most aid is delivered in person, an added challenge when infrastructure is damaged. Digital cash transfers can avoid these issues, and getting them to work in a disaster setting means more people will survive climate change. In the past year, with support from Google.org, GiveDirectly ran pilots to send cash remotely to flood survivors: in Nigeria, we sent funds to survivors weeks after flooding and in Mozambique, we sent funds days before predicted floods. Below, we outline what worked, what didn't, and how you can help for next time. Over 1.5B people in low and middle income countries are threatened by extreme floods. Evidence shows giving them unconditional cash during a crisis lets them meet their immediate needs and rebuild their lives. However, operating in countries with limited infrastructure during severe weather events is complicated, so we ran two pilots to test and learn (see Appendix): What went right and what went wrong Innovating in the face of climate change requires a 'no regrets' strategy, accepting a degree of uncertainty in order to act early to prevent suffering. In that spirit, we're laying out what worked and did not: ✅ Designing with community input meant our program worked better A cash program only works if recipients can easily access the money. In Nigeria, we customized our program design based on dozens of community member interviews: Use the local dialect: There are 500+ dialects spoken in Nigeria, and our interviews determined a relatively uncommon one, Egbura Koto, was most widely used in the villages we were targeting. We hired field staff who spoke Egbura Koto, which made the program easier to access and more credible to community members, with one saying, "I didn't believe the program at first when my husband told me but when I got a call from GiveDirectly and someone spoke in my language, I started believing." Promote mobile money: Only 10% of Nigerians have a mobile money account (compared to 90% of Kenya), so we planned to text recipients instructions to create one and provide a hotline for assistance. But would they struggle to set up the new technology? Our interviews found most households had at least one technologically savvy member, and younger residents often helped their older or less literate neighbors read texts, so we proceeded with our design. In the end, 94% of surveyed recipients found the mobile money cash out process "easy." Send cash promptly: Cash is most useful where markets are functioning, so should we delay sending payments until floods recede if it means more shops will be reopened? In our interviews, residents explained the nearby Lokoja and Koton-Karfe markets functioned throughout flooding and could be reached in 10 minutes by boat. We decided not to design in a delay and found the nearby markets were, in fact, open during peak flooding. ❌ We didn't send payments before severe floods In Mozambique, we attempted to pay people days ahead of severe floods based on data from Google Research's Flood Forecasting ...

Today's guest is one of the pioneers in generative AI having spent nine years at Google Research building teams that developed breakthrough technologies that led to innovations like the transformer architecture behind ChatGPT.Jad Tarifi co-founded Integral AI in 2021 after a distinguished career in AI roles as a researcher and leader. He received his PhD in Computer Science and AI from the University of Florida and did his undergrad at the University of Waterloo.Thanks to great former guest and friend of the podcast Hina Dixit from Samsung NEXT for the intro to Jad.Listen and learn: Can machines learn common sense? Do humans have common sense? Why Integral AI is providing a “base model for the world” Can machines ever learn as quickly as humans? How to improve the efficiency of LLMs with better algorithms Why the current transformer architecture is poorly designed for next word prediction How to use AI and robotics to create “magic wands” and “crystal balls” How to use AI to do “science at scale” What are the ethical implications of bots that can change the human life span How AGI is related to objective morality Jad's four tenets of a new definition of “freedom” References in this episode… Integral.ai Blake Lemoine and the “sentience” debate Podcastle, generative AI for podcasts (a technology nobody needs)

Today's guest is Alex Siegman, Machine Learning Technical Program Manager at Google AI. Google are excited about the transformational power of AI and the helpful new ways it can be applied. From research that expands what's possible, to product integrations designed to make everyday things easier, and applying AI to make a difference in the lives of those who need it most, Google are committed to responsible innovation and technologies that benefit all of humanity. Alex is an experienced leader in the AI/ML space, specializing in designing and leading AI-centric programs. He joined Goggle in April 2021 and currently partners with scientists, applied researchers, and engineering & product teams, alongside compliance, ethics and fairness orgs to develop and deploy human-centric, generative foundation models and other incubatory programs. He is also responsible to manage strategic planning, budgeting, communications, compute, and resource allocation for a 110+ person organization within Research. In the episode, Alex will talk about: An overview of the work he does with Google Research, What a typical working day for him looks like, The most rewarding aspects of his role, What the interview process was like joining Google, His advice for people who want to work at Google, Skills and qualifications needed to succeed in his team, New tech trends that excite him and What ‘s changed for him since his last episode

In this week's podcast, we talk food and AI with Google's Evan Rapoport. Over the past decade, Evan has led teams for Google Research looking at how AI could impact biodiversity and change. During our conversation, we talk about a project called Tidal, in which he and Google used AI technology like computer vision and applied it to aquaculture. We also discuss the impact of AI more broadly on the food system and how Evan thinks newer technology like generative AI might have an impact sooner than we think on the world of food.

The episode features Paige Bailey, the lead product manager for generative models at Google DeepMind. Paige's work has helped transform the way that people work and design software using the power of machine learning. Her current work is pushing the boundaries of innovation with Bard and the soon to be released Gemini. Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. Resources to learn more about Paige Baileyhttps://twitter.com/DynamicWebPaigehttps://github.com/dynamicwebpaige References from the Episode Diamond Age - Neal Stephenson - https://amzn.to/3BCwk4n Google Deepmind - https://www.deepmind.com/ Google Research - https://research.google/ Jax - https://jax.readthedocs.io/en/latest/ Jeff Dean - https://research.google/people/jeff/ Oriol Vinyals - https://research.google/people/OriolVinyals/ Roy Frostig - https://cs.stanford.edu/~rfrostig/ Matt Johnson - https://www.linkedin.com/in/matthewjamesjohnson/ Peter Hawkins - https://github.com/hawkinsp Skye Wanderman-Milne - https://www.linkedin.com/in/skye-wanderman-milne-73887b29/ Yash Katariya - https://www.linkedin.com/in/yashkatariya/ Andrej Karpathy - https://karpathy.ai/ Resources to learn more about Learning from Machine Learninghttps://www.linkedin.com/company/learning-from-machine-learninghttps://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

What if AI could revolutionize healthcare with advanced language learning models? Sarah and Elad welcome Karan Singhal, Staff Software Engineer at Google Research, who specializes in medical AI and the development of MedPaLM2. On this episode, Karan emphasizes the importance of safety in medical AI applications and how language models like MedPaLM2 have the potential to augment scientific workflows and transform the standard of care. Other topics include the best workflows for AI integration, the potential impact of AI on drug discoveries, how AI can serve as a physician's assistant, and how privacy-preserving machine learning and federated learning can protect patient data, while pushing the boundaries of medical innovation. No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode. Show Links: May 10, 2023: PaLM 2 Announcement April 13, 2023: A Responsible Path to Generative AI in Healthcare March 31, 2023: Scientific American article on Med-PaLM February 28, 2023: The Economist article on Med-PaLM KaranSinghal.com Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @thekaransinghal Show Notes: [00:22] - Google's Medical AI Development [08:57] - Medical Language Model and MedPaLM 2 Improvements [18:18] - Safety, cost/benefit decisions, drug discovery, health information, AI applications, and AI as a physician's assistant. [24:51] - Privacy Concerns - HIPAA's implications, privacy-preserving machine learning, and advances in GPT-4 and MedPOM2. [37:43] - Large Language Models in Healthcare and short/long term use.

Dr. Aparna Taneja works at Google Research in India on innovative projects driving real-world social impact. Her team collaborates with an NGO called ARMMAN with the mission to improve maternal and child health outcomes in underserved communities of India. Prior to Google she was a Post-Doc at Disney Research, Zurich, and has a PhD from the Computer Vision and Geometry Group in ETH Zurich and a Bachelor's in Computer Science from the Indian Institute of Technology, Delhi.Time stamps of the conversation00:00:46 Introductions00:01:20 Background and Interest in AI00:03:59 Satellite imaging and AI at Google00:08:30 Multi-Agent systems for social impact - part of AI for social good00:10:30 Awareness of AI benefits in non-tech fields00:13:42 Project SAHELI - improving maternal and child health using AI00:20:05 Intuition for methodology 00:22:07 Measuring impact on health00:27:42 Challenges when working with real-world data00:32:58 Problem scoping and defining research statements00:38:16 Disconnect between tech and non-tech communities while collaborating00:43:22 What motivates you, the theoretical or application side of research00:47:17 What research skills are a must when working on real-world challenges using AI00:50:33 Factors considered before doing a PhD00:54:08 Significance of Ph.D. for research roles in the industry00:58:15 Choosing industry vs Academia01:02:38 Managing personal life with a research career01:07:58 Advice to young students interested in AI on getting startedLearn more about Aparna here: https://research.google/people/106890/Research: https://scholar.google.com/citations?user=XtMi1L0AAAAJ&hl=enAbout the Host:Jay is a Ph.D. student at Arizona State University.Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahmlAbout the author: https://www.public.asu.edu/~jgshah1/

Welcome to the newest episode of The Cloud Pod podcast! Justin, Ryan and Matthew are your hosts this week as we discuss all the latest news and announcements in the world of the cloud and AI - including what's new with Google Deepmind, as well as goings on over at the Finops X Conference. Join us!  Titles we almost went with this week:

There is a whole team at Google dedicated to designing AI best practices. They are committed to making progress in the responsible development of AI and share reliable, effective user-centered research, tools, datasets, and other resources with users. Meet one of the members of the team, Christina Greer, as she shares the in's and out's of working in the field of Responsible AI and how her personal experience and values make her a key player in this space! Resources: AI Principles: https://goo.gle/3VrCpJP  Responsible AI practices: https://goo.gle/41XVeqI Guest bio: Christina Greer is a software engineer at Google Research. A veteran of a variety of efforts across the company including ads, data processing pipelines, and Google Assistant, she joined Google Research in 2018 to focus on bias and fairness in ML. Since then, she has built both teams and software to support measuring and mitigating ML models for bias, and consults with products across Google to support building safer products that work for everyone. In her spare time, Christina is a creative writer and a mom of 2 great kids.    #AI #ML

In episode 65 of The Gradient Podcast, Daniel Bashir speaks to Sewon Min.Sewon is a fifth-year PhD student in the NLP group at the University of Washington, advised by Hannaneh Hajishirzi and Luke Zettlemoyer. She is a part-time visiting researcher at Meta AI and a recipient of the JP Morgan PhD Fellowship. She has previously spent time at Google Research and Salesforce research.Have suggestions for future podcast guests (or other feedback)? Let us know here!Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:00) Origin Story* (04:20) Evolution of Sewon's interests, question-answering and practical NLP* (07:00) Methodology concerns about benchmarks* (07:30) Multi-hop reading comprehension* (09:30) Do multi-hop QA benchmarks actually measure multi-hop reasoning?* (12:00) How models can “cheat” multi-hop benchmarks* (13:15) Explicit compositionality* (16:05) Commonsense reasoning and background information* (17:30) On constructing good benchmarks* (18:40) AmbigQA and ambiguity* (22:20) Types of ambiguity* (24:20) Practical possibilities for models that can handle ambiguity* (25:45) FaVIQ and fact-checking benchmarks* (28:45) External knowledge* (29:45) Fact verification and “complete understanding of evidence”* (31:30) Do models do what we expect/intuit in reading comprehension?* (34:40) Applications for fact-checking systems* (36:40) Intro to in-context learning (ICL)* (38:55) Example of an ICL demonstration* (40:45) Rethinking the Role of Demonstrations and what matters for successful ICL* (43:00) Evidence for a Bayesian inference perspective on ICL* (45:00) ICL + gradient descent and what it means to “learn”* (47:00) MetaICL and efficient ICL* (49:30) Distance between tasks and MetaICL task transfer* (53:00) Compositional tasks for language models, compositional generalization* (55:00) The number and diversity of meta-training tasks* (58:30) MetaICL and Bayesian inference* (1:00:30) Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations* (1:02:00) The copying effect* (1:03:30) Copying effect for non-identical examples* (1:06:00) More thoughts on ICL* (1:08:00) Understanding Chain-of-Thought Prompting* (1:11:30) Bayes strikes again* (1:12:30) Intro to Sewon's text retrieval research* (1:15:30) Dense Passage Retrieval (DPR)* (1:18:40) Similarity in QA and retrieval* (1:20:00) Improvements for DPR* (1:21:50) Nonparametric Masked Language Modeling (NPM)* (1:24:30) Difficulties in training NPM and solutions* (1:26:45) Follow-on work* (1:29:00) Important fundamental limitations of language models* (1:31:30) Sewon's experience doing a PhD* (1:34:00) Research challenges suited for academics* (1:35:00) Joys and difficulties of the PhD* (1:36:30) Sewon's advice for aspiring PhDs* (1:38:30) Incentives in academia, production of knowledge* (1:41:50) OutroLinks:* Sewon's homepage and Twitter* Papers* Solving and re-thinking benchmarks* Multi-hop Reading Comprehension through Question Decomposition and Rescoring / Compositional Questions Do Not Necessitate Multi-hop Reasoning* AmbigQA: Answering Ambiguous Open-domain Questions* FaVIQ: FAct Verification from Information-seeking Questions* Language Modeling* Rethinking the Role of Demonstrations* MetaICL: Learning to Learn In Context* Towards Understanding CoT Prompting* Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations* Text representation/retrieval* Dense Passage Retrieval* Nonparametric Masked Language Modeling Get full access to The Gradient at thegradientpub.substack.com/subscribe

In this episode, Frauke sits down with neuroscientist and Google Research spinoff Osmo CEO Alex Wiltschko to discuss the exciting frontier of digitizing smell through artificial intelligence. Alex shares the groundbreaking work Osmo is doing to help the fragrance industry find new and interesting odor molecules never smelled before. He discusses where we are in terms of understanding our sense of smell vs. our other senses of sight and sound (hint: it's about where we were with vision in 1820) and the incredible potential he sees in having AI support the artistic work of perfumers, and providing them with the means to be even more creative. Alex shares how Osmo works exactly, where its potential lies beyond perfumery, and what the future holds for digital noses. This episode will leave you amazed at what possibilities lie in store for olfaction - we just have to follow our nose. Learn more about Osmo. Read the Wired article to learn more about the company. Get Frauke's free Smell To Be Well audio training Follow Frauke on Instagram @falkaromatherapy Follow Frauke on Facebook @falkaromatherapy Visit the FALK Aromatherapy website: www.falkaromatherapy.com Check out Frauke's Scent*Tattoo project: www.scenttattoo.com --- Send in a voice message: https://anchor.fm/anaromaticlife/message

Today we're joined by Vinodkumar Prabhakaran, a Senior Research Scientist at Google Research. In our conversation with Vinod, we discuss his two main areas of research, using ML, specifically NLP, to explore these social disparities, and how these same social disparities are captured and propagated within machine learning tools. We explore a few specific projects, the first using NLP to analyze interactions between police officers and community members, determining factors like level of respect or politeness and how they play out across a spectrum of community members. We also discuss his work on understanding how bias creeps into the pipeline of building ML models, whether it be from the data or the person building the model. Finally, for those working with human annotators, Vinod shares his thoughts on how to incorporate principles of fairness to help build more robust models.  The complete show notes for this episode can be found at https://twimlai.com/go/617.

Ever wondered how to get the most out of your listings and become a better agent? Making yourself a better agent and improve your listings is easier than you think. In this episode, Rob & Greg touch on the different technologies and marketing strategies that agents can use to get an edge in sales. Tune in for why Rob believes great photography is the best way and why Greg thinks it's just putting lipstick on a pig and pricing is the best strategy. UPDATE 1-26-2023   A representative of Homes.com has reached out and said that the new version of their website has rolled out and been live for the last couple months.  Though there has been no industry wide notification and they have still yet to launch any of their much touted “listing enhancement” products.   The new site features include:   Completely new tech stack with noticed speed improvements Historical AVM chart with three models + average Deeper set of property characteristics from MLS,  New design,  IDX data via Ten-X in 460+ MLS markets (no more supplemental data via ListHub) New agent search with updated agent profiles (includes transaction history) New Homes Pro dashboard for agents on the back end (full integration with Homesnap Pro), and agent-client instant messaging for collaboration   Click here to check out last week's episode with Kevin for insight on how new construction impacts the housing market. Greg's Google Research on Redfin Listen to the Industry Relations Podcast across all podcast platforms! Listen to the podcast on Apple Listen to the podcast on Stitcher   Connect with Rob and Greg:  Rob's Website Greg's Website This podcast is produced by Two Brothers Creative 2023

Andy and Dave discuss the latest in AI and autonomy news and research, including a report from Human Center AI that assesses progress (or lack thereof) of the implementation of the three pillars of America's strategy for AI innovation. The Department of Energy is offering up a total of $33M for research in leveraging AI/ML for nuclear fusion. China's Navy appears to have launched a naval mothership for aerial drones. China is also set to introduce regulation on “deepfakes,” requiring users to give consent and prohibiting the technology for fake news, among many other things. Xiamen University and other researchers publish a “multidisciplinary open peer review dataset” (MOPRD), aiming to provide ways to automate the peer review process. Google executives issue a “code red” for Google's search business over the success of OpenAI's ChatGPT. New York City schools have blocked access for students and teachers to ChatGPT unless it involves the study of the technology itself. Microsoft plans to launch a version of Bing that integrates ChatGPT to its answers. And the International Conference on Machine Learning bans authors from using AI tools like ChatGPT to write scientific papers (though still allows the use of such systems to “polish” writing). In February, an AI from DoNotPay will likely be the first to represent a defendant in court, telling the defendant what to say and when. In research, the UCLA Departments of Psychology and Statistics demonstrate that analogical reasoning can emerge from large language models such as GPT-3, showing a strong capacity for abstract pattern induction. Research from Google Research, Stanford, Chapel Hill, and DeepMind shows that certain abilities only emerge from large language models that have a certain number of parameters and a large enough dataset. And finally, John H. Miller publishes Ex Machina through the Santa Fe Institute Press, examining the topic of Coevolving Machines and the Origins of the Social Universe. https://www.cna.org/our-media/podcasts/ai-with-ai  

Illia is a Co-Founder of NEAR Protocol, a fully sharded, proof-of-stake, Layer 1 blockchain. NEAR has raised over $500 million and is widely considered one of the most promising projects in the blockchain space. Prior to NEAR, Illia managed a team of Deep Learning and Natural Language Understanding Researchers at Google Research where he worked on building TensorFlow. Follow Illia on Twitter @ilblackdragon. [2:18] - How an early interest in computer programming led Illia down a path towards artificial intelligence and blockchains [16:45] - Existing blockchain issues that led to the creation of the NEAR Protocol [19:52] - NEAR's focus on scalability and usability for long-term adoption [26:00] - Why Illia views sharding as the superior strategy to scale blockchains [34:47] - NEAR's approach to encourage adoption of its tech stack [47:20] - Governance considerations for NEAR's decentralized ecosystem [55:10] - The coming convergence of blockchain and artificial intelligence --- Support the show by checking out my sponsors: Join Levels and get personalized insights to learn about your metabolic health. Go to https://levels.link/jake. --- https://homeofjake.com

Andy and Dave discuss the latest in AI news and research, starting with a publication from the UK's National Cyber Security Centre, providing a set of security principles for developers implementing machine learning models. Gartner publishes the 2022 update to its “AI Hype Cycle,” which qualitatively plots the position of various AI efforts along the “hype cycle.” PromptBase opens its doors, promising to provide users with better “prompts” for text-to-image generators (such as DALL-E) to generate “optimal images.” Researchers explore the properties of vanadium dioxide (VO2), which demonstrates volatile memory-like behavior under certain conditions. MetaAI announces a nascent ability to decode speech from a person's brain activity, without surgery (using EEG and MEG). Unitree Robotics, a Chinese tech company, is producing its Aliengo robotic dog, which can carry up to 11 pounds and perform other actions. Researchers at the University of Geneva demonstrate that transformers can build world models with fewer samples, for example, able to generate “pixel perfect” predictions of Pong after 120 games of training. DeepMind AI demonstrates the ability to teach a team of agents to play soccer by controlling at the level of joint torques and combine it with longer-term goal-directed behavior, where the agents demonstrate jostling for the ball and other behaviors. Researchers at Urbana-Champaign and MIT demonstrate a Composable Diffusion model to tweak and improve the output of text-to-image transformers. Google Research publishes results on AudioLM, which generates “natural and coherent continuations” given short prompts. And Michael Cohen, Marcus Hutter, and Michael Osborne published a paper in AI Magazine, arguing that dire predictions about the threat of advanced AI may not have gone far enough in their warnings, offering a series of assumptions on which their arguments depend. https://www.cna.org/our-media/podcasts/ai-with-ai  

Google Research says to Open AI: “Hold my beer.” They've announced a new AI-based text-to-image generator to rival DALL-E 2. Is the shocking earnings warning from Snap a result of Apple's ATT changes or is this indicative of the broader tech slowdown? Google's street view turns 15 with some new bells and whistles. And does Apple REALLY want you to repair your own iPhone, or no?Sponsors:CreditKarma.comWork Check PodcastLinks:OpenAI: Look at our awesome image generator! Google: Hold my Shiba Inu (TechCrunch)Zoom pops 16% on first-quarter earnings beat and strong guidance (CNBC)Snap plunges 30% after CEO warns company will miss revenue and earnings estimates, slow hiring (CNBC)Google is testing a smaller, modular Street View camera system (Engadget)APPLE SHIPPED ME A 79-POUND IPHONE REPAIR KIT TO FIX A 1.1-OUNCE BATTERY (The Verge)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.