Podcasts about chief scientist

Jim Harter, PhD discusses his book "It's The Manager" and Gallup research that finds managers and team leaders are the single biggest factor in your organization's success. Jim is Chief Scientist for Gallup's workplace management practice. He is coauthor of the New York Times bestseller "12: The Elements of Great Managing," and "Wellbeing: The Five Essential Elements," also a New York Times and Wall Street Journal bestseller.  Host, Kevin Craine Do you want to be a guest? https://Everyday-MBA.com/guest Do you want to advertise on the show? https://Everyday-MBA.com/advertise

The Arctic serves as the world's refrigerator, keeping the planet cool. But temperatures in the Arctic are rising nearly four times faster than the rest of the world and its sea ice is near a historic low. We put out a call to scientists aboard research vessels currently sailing in the Arctic Ocean, and two answered “aye.” Hear our ship-to-shore conversations with them about how warmer temperatures affect life on land and sea. Plus, a biologist who recently returned from the field describes how he inadvertently stumbled upon surprising data during his decades-long study of polar bears.  Guests: Autun Purser – Marine biologist at the Alfred- Wegener Institute and the current Chief Scientist on the Arctic research vessel, the Polarstern  Andrew Derocher – Zoologist at the University of Alberta in Canada who is leading a decades-long study on polar bears of the West Hudson Bay region, in the high Canadian North    Jonathan Nash – Oceanographer, Oregon State University, and member of the GIANT team sailing near Greenland aboard the RRS Sir David Attenborough  Descripción en español Big Picture Science is part of the Airwave Media podcast network. Please contact advertising@airwavemedia.com to inquire about advertising on Big Picture Science. You can get early access to ad-free versions of every episode by joining us on Patreon. Thanks for your support! Learn more about your ad choices. Visit megaphone.fm/adchoices

Help us expand our Muslim media project here: https://www.thinkingmuslim.com/membership Donate to our charity partner Baitulmaal here: http://btml.us/thinkingmuslimArtificial intelligence is rapidly reshaping the world, but who is shaping artificial intelligence?In this episode of The Thinking Muslim, we sit down with Waleed Kadous, one of the leading minds in AI, to explore how this technology is transforming education, politics, geopolitics, and even the future of religious authority. As governments, corporations, and global powers race to develop increasingly powerful AI systems, where do Muslims fit into this conversation?From the rise of Anthropic and the global AI race to the philosophical assumptions embedded within today's models, this discussion examines why AI is far more than just another technological breakthrough. It is a civilisational shift that Muslims cannot afford to ignore.About Waleed Kadous:Waleed Kadous is Chief Scientist at Anyscale and one of the leading voices in artificial intelligence. Previously, he held senior engineering leadership roles at Google and Uber, where he served as Engineering Strategy Lead to the CTO. With expertise spanning AI, machine learning, robotics, and large-scale engineering systems, Waleed has spent his career developing cutting-edge technologies while helping shape the future of the industry. His work sits at the intersection of technical innovation, strategy, and the societal impact of AI.Find Dr. Waleed Kadous here:X: https://x.com/waleedkOr give your one-off donation here: https://www.thinkingmuslim.com/donateSubscribe to our Dubbed ChannelsArabic: https://www.youtube.com/@ThinkingMuslimArabicFrench: https://www.youtube.com/@ThinkingMuslimFrançaisSpanish: https://www.youtube.com/@TheThinkingMuslimEspañolListen to the audio version of the podcast:Spotify: https://open.spotify.com/show/7vXiAjVFnhNI3T9Gkw636aApple Podcasts: https://podcasts.apple.com/gb/podcast/the-thinking-muslim/id1471798762Purchase our Thinking Muslim mug: https://www.thinkingmuslim.com/merchFind us on:X: https://x.com/thinking_muslimLinkedIn: https://www.linkedin.com/company/the-thinking-muslim/Facebook: https://www.facebook.com/The-Thinking-Muslim-Podcast-105790781361490Instagram: https://www.instagram.com/thinkingmuslimpodcast/Telegram: https://t.me/thinkingmuslimBlueSky: https://bsky.app/profile/thinkingmuslim.bsky.socialThreads: https://www.threads.com/@thinkingmuslimpodcastFind Muhammad Jalal here:X: https://twitter.com/jalalaynInstagram: https://www.instagram.com/jalalayns/Sign up to Muhammad Jalal's newsletter: https://jalalayn.substack.comWebsite Archive: https://www.thinkingmuslim.comDisclaimer:The views expressed in this video are those of the individual speaker(s) and do not represent the views of the host, producers, platform, or any affiliated organisation. This content is provided for lawful, informational, and analytical purposes only, and should not be taken as professional advice. Viewer discretion is advised. Hosted on Acast. See acast.com/privacy for more information.

Ameet Talwalkar, Carnegie Mellon ML professor and Chief Scientist at Datadog, joins Ben Lorica to trace time series foundation models from skepticism to Datadog's Toto V1 and V2. Subscribe to the Gradient Flow Newsletter

Friday Headlines: Bianca Jones’ father calls on Albanese to intervene over inadequate methanol poisoning charges Iran launches fresh attacks on Gulf states as US strikes ship attempting to breach blockade Second case of bird flu detected in NSW Smoking rates hit historic low across Australia Pauline Hanson and Gina Rinehart spotted at White Lotus hotel Deep Dive: Is Australia a world leader on AI? Anthony Albanese wants you to think so – he gave a speech last week in the Great Hall at The University of Sydney, where he implied that new Australian AI regulations could set a global benchmark – similar to superannuation and the social media ban. Today we’re looking at Labor’s AI plan, and whether it really is a world-leading attempt to curb the harms of AI and harness its potential. What’s right with the plan, and what’s missing? Joining The Briefing is AI expert Professor Toby Walsh, Chief Scientist of the Artificial Intelligence Institute at the University of New South Wales. Further listening from the headlines: Why smoking is ‘cool’ again Follow The Briefing: TikTok: @thebriefingpodInstagram: @thebriefingpodcast YouTube: @TheBriefingPodcastSee omnystudio.com/listener for privacy information.

It's official: mosquitoes trapped in Milford on June 29 tested positive for West Nile virus. results represent the first WNV positive mosquitoes identified in the state by the Connecticut Agricultural Experiment Station this year. We spoke with Dr. Philip Armstrong, Chief Scientist at the CAES about mosquito bite prevention and concerns.

Send us Fan MailWe know more about the surface of the Moon than we do the deep ocean.Dr. Diva Amon is a Trinidadian deep-sea biologist, National Geographic Explorer, co-founder of SpeSeas and Chief Scientist leading the historic Deep Wonders of Trinidad and Tobago and the High Seas expedition.For the first time, a Trinidad and Tobago-led team will spend a month exploring the nation's deep waters aboard Schmidt Ocean Institute's research vessel Falkor Too searching for species never before seen by science.In this conversation, we discuss:• Why 70% of Trinidad and Tobago is deep ocean• Why less than 0.001% has ever been explored• The strange creatures living kilometres beneath us• New medicines hidden in the deep sea• Swimming with sperm whales• Taking Will Smith to the bottom of the ocean• Why the deep ocean may be Earth's final frontierThis is one of the most fascinating conversations we've ever had.Join the WhatsApp Channel to follow the expedition: https://whatsapp.com/channel/0029Vb7WmCK4o7qPcYbUuY0O

Send us Fan MailFor decades, fusion researchers were chasing a single goal: ignition. But what if ignition isn't the finish line? What if it's only the starting gun? Today we're joined by Dr. Will Regan, Founder and Chief Scientist of Pacific Fusion, a company betting that the future of fusion won't be won by building bigger machines - but by solving one of engineering's toughest manufacturing challenges.Dr. Will Regan, Ph.D. is the Founder and Chief Scientist of Pacific Fusion ( https://www.pacificfusion.com/ ), where he is helping lead one of the most ambitious efforts in advanced inertial fusion energy: developing a practical, scalable pathway to commercial fusion power using pulsed power systems and high-yield target physics.Before founding Pacific Fusion, Will served as a Project Lead and Rapid Evaluator at X, the moonshot factory, where he helped originate and develop breakthrough concepts in agricultural and systems innovation, including early foundational work behind the Mineral initiative focused on sustainable food production at planetary scale.Earlier in his career, he was an ARPA-E Fellow at Advanced Research Projects Agency–Energy, where he worked at the intersection of high-risk, high-reward energy technologies and national-scale innovation programs—an experience that helped shape his approach to large-scale energy system design and technology acceleration.Will earned his PhD in Physics from University of California, Berkeley, following his undergraduate degree in Physics from Cornell University. Across academia, government, and moonshot industry environments, he has consistently focused on translating frontier physics into real-world systems—now centered on one of the most consequential engineering challenges of the century: fusion energy.At Pacific Fusion, he is working to turn high-gain fusion concepts into repeatable, engineered systems that could fundamentally reshape global energy infrastructure.#FusionEnergy #NuclearFusion #PacificFusion #WillRegan #CleanEnergy #FutureOfEnergy #FusionPower #InertialConfinementFusion #ICF #MagLIF #SandiaLabs #LawrenceLivermore #NIF #AdvancedEnergy #EnergyInnovation #Physics #Engineering #ClimateTech #MoonshotFactory #ARPAE #SciencePodcast #TechnologyPodcast #ProgressPotentialPossibilities #STEM #DeepTechSupport the show

It's official, El Nino is here and it's likely to pack a punch. Two weeks ago we heard Earth Sciences New Zealand was on the precipice of declaring the conditions had been met, but today it made it official. Earth Sciences' Chief Scientist of Weather and Seasonal Forecasting, Chris Brandolino spoke to John Campbell.

Send us Fan MailWhat if a device small enough to fit in your pocket could detect a potentially fatal heart rhythm before symptoms even appear? My guest today helped make that possible. Dr. David Albert, MD - Founder of AliveCor, pioneered smartphone ECG technology and transformed how millions of people monitor their hearts.Dr. David Albert, MD is a physician, inventor, and serial entrepreneur whose career has helped define the modern era of digital cardiology and personal health technology. A graduate of Harvard College and Duke University School of Medicine, Dr. Albert has spent more than four decades translating breakthrough ideas into life-saving products. He founded Corazonix in the 1980s, Data Critical in the 1990s - which later became part of GE Healthcare - and ultimately served as Chief Scientist and Chief Clinical Scientist for GE Cardiology before returning to entrepreneurship.In 2010, Dr. Albert founded AliveCor ( https://alivecor.com/ ), pioneering the smartphone-based ECG and helping launch one of the earliest and most clinically validated digital health platforms. AliveCor's KardiaMobile technology transformed cardiac monitoring by placing medical-grade electrocardiograms in the hands of millions of consumers and patients worldwide. Beyond AliveCor, Dr. Albert founded InnovAlarm and Lifetone Technology, developing innovations that improve home safety and emergency alerting. An inventor with more than 80 U.S. patents and over 100 scientific publications, Dr. Albert has devoted his career to democratizing healthcare technology and empowering people to better understand - and protect - their own health.#DavidAlbert #AliveCor #KardiaMobile #DigitalHealth #Cardiology #HeartHealth #ECG #AtrialFibrillation #WearableTech #HealthcareInnovation #MedTech #RemoteMonitoring #AIinHealthcare #PrecisionMedicine #PreventiveMedicine #Entrepreneurship #HealthcareTechnology #GEHealthcare #MedicalDevices #FutureOfMedicine #DigitalBiomarkers #HealthTech #Inventor #StartupFounder #Cardiologist #PatientEmpowerment #HeartDisease #MedicalInnovation #ProgressPotentialPossibilitiesSupport the show

Save Ocean Sensors: The Fight Over the Ocean Observatories Initiative A $386 million network of nearly 900 ocean sensors stretching across five sites from Oregon to Alaska to Greenland was being quietly dismantled, with no warning to Congress and no explanation to the public. In this episode, we trace the fight to save the Ocean Observatories Initiative: what this network actually does, why it matters far more than most people realize, and how a Democrat from Oregon and a Republican from Alaska teamed up to pass a bill stopping it in the span of a day and a half. We talk with Craig McLean, who spent more than 40 years at NOAA, including two stints as NOAA's Chief Scientist, about what's really at stake when ocean monitoring infrastructure disappears, and why this fight is part of a much bigger pattern. We also revisit a warning from a past guest, marine ecologist Dr. Andrew Thaler, that's looking less hypothetical by the day, and lay out exactly what you can do to help make sure this win sticks. In This Episode What the Ocean Observatories Initiative actually measures, and why it matters for your weather forecast, your insurance rates, and what farmers plant each season The Coastal Endurance Array off Oregon and Washington, and why pulling it out right as an El Nino forms is especially bad timing, per OSU oceanographer Jack Barth How Senators Jeff Merkley (D-OR) and Lisa Murkowski (R-AK) found out sensors were already being removed, and how they got the Saving the OOI Act passed unanimously in under two days Craig McLean's read on why this kept happening, from the role of the Office of Management and Budget to the broader pattern of cuts to federal science A look back at a warning from marine ecologist Dr. Andrew Thaler about Project 2025's plans for NOAA, and how that warning is playing out in real time What happens next: the NSF's review process, and how you can track it and find out where your own senators stand Featured Voices Craig McLean spent over 40 years at NOAA, where he founded the Ocean Exploration Program and served as NOAA's Chief Scientist, including being reinstated after being removed from the role for defending scientific integrity. He is now a senior fellow at the Ocean Foundation. Senator Jeff Merkley (D-OR) and Senator Lisa Murkowski (R-AK) led the bipartisan push for the Saving the OOI Act, featured here via Senate floor remarks. Dr. Andrew Thaler, marine ecologist and conservation technologist, returns in spirit from a past episode where he broke down Project 2025's plans to dismantle NOAA. Jack Barth, oceanography professor at Oregon State University, provided background on the Coastal Endurance Array via KATU News. The Bill The Saving the OOI Act passed the Senate unanimously, pausing NSF's decommissioning of the Ocean Observatories Initiative pending a full review with stakeholder input. It was led by Senators Merkley and Murkowski and cosponsored by Senators Dan Sullivan (R-AK), Maria Cantwell (D-WA), Ron Wyden (D-OR), Jack Reed (D-RI), Tammy Baldwin (D-WI), Sheldon Whitehouse (D-RI), Patty Murray (D-WA), Elizabeth Warren (D-MA), Edward J. Markey (D-MA), and Chris Van Hollen (D-MD). Resources & Links Saving the OOI Act, full text and cosponsor list: Congress.gov NSF Ocean Observatories Initiative updates: oceanobservatories.org Senator Merkley's press release on the Saving the OOI Act: merkley.senate.gov "Scientists warn Oregon could lose critical ocean data under federal cuts," KATU News Take Action If your senator is one of the twelve who championed or cosponsored the Saving the OOI Act, send them a thank you. If they didn't, let them know this issue matters to you. Watch for the NSF's Dear Colleague Letter and expert panel process, your chance to weigh in on what happens to OOI long-term.

The Founding of OpenAI. Guest Author: Keach Hagey. In this opening segment, Keach Hagey discusses the January 2016 founding of OpenAI as a nonprofit research lab. Key figures included co-founder Greg Brockman and chief scientist Ilya Sutskever, a renowned researcher whose recruitment from Google signaled the lab's potential. Backed by a billion-dollar commitment from Elon Musk, Peter Thiel, and Jessica Livingston, the project was designed as a safe, non-commercial counterweight to Google's DeepMind. Operating initially out of Brockman's apartment, the team aimed to achieve Artificial General Intelligence (AGI) for the benefit of humanity. The technical foundation relied heavily on GPUs—hardware originally designed for video games—which proved essential for training the deep learning neural networks necessary for their research. This era was characterized by an ambitious, "pirate" spirit funded through YC Research to explore radical ideas outside the profit motive. 1JANUARY 1931

What happens when cutting-edge biotechnology meets artificial intelligence, and who's responsible for governing it? Kate Sixt, Executive Director of the Office of the Chief Scientist at the USDA and former Principal Director for Biotechnology at the Department of Defense, breaks down why biological data is uniquely personal, why governance frameworks need to balance promotion with protection, and how diverse perspectives are essential to navigating biotech's biggest risks and opportunities. Listen now to this exciting episode interviewing one of the world's leading experts in biotechnology governance!

Dean Radin is a researcher and author specializing in the scientific study of consciousness, psychic phenomena, and the intersection of mind and matter. He joined Rep. Crenshaw to talk about the history of the U.S. government's parapsychology programs, the evidence behind remote viewing and psychokinetic experiments, consciousness, and the nature of reality. A fascinating conversation that explores the lines of science fiction and science fact.   Dean Radin is the Chief Scientist at the Institute of Noetic Sciences. He has worked at Bell Labs, Princeton University, and Stanford Research Institute, where he contributed to research on remote viewing and other anomalous human abilities. He is the author of "The Conscious Universe", "Entangled Minds", and "Real Magic". Find him on X at @DeanRadin.

This lecture was recorded by Robin May on the 20th of May 2026 at Barnard's Inn Hall, LondonProfessor of Infectious Disease at the University of Birmingham, and (interim) Chief Scientist at the UK Health Security Agency, Robin May was appointed Gresham Professor of Physic in May 2022. Between July 2020 and September 2025 he served as Chief Scientific Adviser at the Food Standards Agency (FSA).Professor May's early training was in Plant Sciences at the University of Oxford, followed by a PhD on mammalian cell biology at University College London and the University of Birmingham. After postdoctoral research on gene silencing at the Hubrecht Laboratory, The Netherlands, he returned to the UK in 2005 to establish a research program on human infectious diseases. He was Director of the Institute of Microbiology and Infection at the University of Birmingham from 2017-2020. The transcript and downloadable versions of the lecture are available from the Gresham College website: https://www.gresham.ac.uk/watch-now/peoples-emotionsGresham College has offered free public lectures for over 400 years, thanks to the generosity of our supporters. There are currently over 2,500 lectures free to access. We believe that everyone should have the opportunity to learn from some of the greatest minds. To support Gresham's mission, please consider making a donation: https://gresham.ac.uk/support/Website:  https://gresham.ac.ukTwitter:  https://twitter.com/greshamcollegeFacebook: https://facebook.com/greshamcollegeInstagram: https://instagram.com/greshamcollegeSupport the show

Send us Fan MailDr. Jaime Lien is the Chief Scientist and a co-founder of Archetype AI, where she is building a foundation model that turns complex sensor data into meaning people can actually act on. A signal processing scientist by training, she earned her PhD working on RF imaging from satellites and later helped develop the first radar embedded in consumer smartphones at Google, before leaving with four colleagues to start Archetype AI. Recorded live from the floor of HumanX 2026, this lightning round explores how AI can read the rich, non-human signals all around us and translate them for the people who rely on them.Jaime and host Dan Turchin dig into how machines distill signal from noise, why a reasoning and communication model matters more than a straight-to-action one, and why keeping a human in the loop is a requirement, not a feature, when the stakes are physical.What You'll LearnHow AI separates meaningful signal from noise across the sensors already around usWhy Archetype builds a reasoning and communication model, not a straight-to-action oneWhy keeping a human in the loop is treated as a requirement, not an optional featureHow interpretability and explainability have to be designed in from the start, not added laterWhy discovering something new about the physical world matters more than automation alone

Send us Fan MailIn this special compilation episode of AI and the Future of Work, we are bringing you four conversations recorded live on the show floor at HumanX 2026. This is the second and final compilation of our three-part HumanX Live series.Our first compilation explored how AI amplifies the human potential it can never replace. This one turns to the technical side, and to a question that gets harder the more these systems touch our lives: how do you build AI you can actually trust not to fail when it matters most? As technology reaches further into the moments that count (the systems that monitor our health, drive our cars, and work alongside us on the job), these four builders share how they design for accountability, safety, and harmony between people and machines.What You'll LearnWhy reading code is no longer enough, and how observing real outcomes (not system metrics alone) is the only way to know whether AI is actually serving the people who depend on itWhat "AI values" are, and why companies will soon need shared norms for how people disclose, review, and engage with work produced by agentic systemsHow robots earn trust on a job site by measuring their own uncertainty, asking questions, and communicating their intentions before they actWhy the most valuable place for automation is the dirty, dull, and dangerous work humans were never meant to do, and what that means for keeping people safeWhy physical AI should be built first as a reasoning and communication model that can explain its thinking to people, rather than one that jumps straight to actionHow "harness engineering" moves teams beyond prompt and context engineering, and why orchestrating several frontier models together can outperform any single oneFeatured GuestsChristine Yen, CEO and Co-Founder of Honeycomb. Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/19362645 Dr. Ali Agha, CEO and Co-Founder of FieldAI. Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/19362711 Dr. Jaime Lien, Co-Founder & Chief Scientist of Archetype AI. Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/19363341 XD Huang, Chief Technology Officer of Zoom. Listen to the full conversation here: https://www.buzzsprout.com/520474/episodes/19363454 Inspired by something you heard in this episode? Share your favorite insight about the future of work and tag us on social:LinkedIn: https://www.linkedin.com/showcase/ai-and-the-future-of-work Instagram: https://www.instagram.com/aifutureofwork/ And don't forget to subscribe to AI and the Future of Work for more conversations with the leaders shaping what comes next.Explore the Full HumanX 2026 SeriesThis episode is part of a special three-part series recorded live at HumanX 2026:Episode 1: Special conversation with Stefan Weitz, CEO of HumanX: https://www.buzzsprout.com/520474/episodes/19210355Episode 2: Compilation featuring the CEOs of Scribe, Operative Games, and Dataiku, and the Chief Business Officer of Zensai: https://www.buzzsprout.com/520474/episodes/19257142LIVE EVENT: See how leading enterprises are using agentic AI to give employees back 4–6 productive hours every week. Join PeopleReign CEO Dan Turchin for a live demo on June 25, 2026.Register here: https://go.peoplereign.io/live-demo-how-agentic-ai-is-being-used-by-global-enterprises

A hundred million degrees Celsius. That's the brutal reality of commercial fusion. This week, we're sitting down with industry leaders to discuss the front lines of an energy revolution, from surviving extreme thermal stress to building brand-new supply chains.We Meet:  Joe Paluska is the CMO of Commonwealth Fusion SystemsAnnie Kritcher is the Co-Founder and Chief Scientist at InertiaAlice Brooks is a Board Member & Partner at Khosla VenturesCary Forest is the Co-Founder & Chief Scientific Officer at Realta FusionCredits:This episode of SHIFT was produced by Jennifer Strong with help from Emma Cillekens. It was mixed by Garret Lang, with original music from him and Jacob Gorski. Art by Meg Marco.

Dean Radin, PhD, is Chief Scientist at the Institute of Noetic Sciences (IONS), Associate Distinguished Professor at the California Institute of Integral Studies, and co-founder and chairman of the neuroengineering company Cognigenics. His latest book is “The Science of Magic: How the Mind Weaves the Fabric of Reality.”www.youtube.com/@InstituteofNoeticScienceswww.penguinrandomhouse.com/books/750262/the-science-of-magic-by-dean-radin-phd/www.deanradin.com Perplexity: Download the app or ask Perplexity anything at https://pplx.ai/rogan. Switch today at https://www.Visible.com for just 25/mo. Or Save $10 on your first month of Visible+ Pro with code ROGAN.  This video is sponsored by BetterHelp. Visit https://BetterHelp.com/JRE Learn more about your ad choices. Visit podcastchoices.com/adchoices

How do we inspire the next generation of aerospace innovators and connect them with the global space industry? Today we speak with Lisa Vitaris, Strategic Advisor for the American Institute of Aeronautics and Astronautics (AIAA) in the Indo-Pacific, and Dr. Paddy Neumann, Chief Scientist at Neumann Space, to find out! We dive into how students can showcase their research on a global stage, why the regional space economy is booming, and everything you need to know about the upcoming 2026 AIAA Region VII Student Conference in Adelaide. About Lisa VitarisLisa is a prominent leader in the aerospace sector, dedicated to elevating and growing the space industry across the Indo-Pacific region. As the Founder and CEO of Indo-Pacific Space, she provides high-level advisory and business consulting services to expand the region's footprint. Lisa brings a wealth of governance and major project expertise to the table, currently serving as a Board Director for Engineers Australia—the peak professional body representing over 130,000 members globally. Additionally, she acts as a strategic advisor for the AIAA and is leading the United States' bid to host the prestigious International Astronautical Congress (IAC) in Houston for 2029.https://www.linkedin.com/in/lisavitaris/ About Dr. Paddy NeumannPaddy is a pioneer in aerospace propulsion technology and a passionate advocate for student development. He is the Chief Scientist and co-founder of Neumann Space, an Australian company developing cutting-edge electric propulsion systems for spacecraft. Deeply involved in the aerospace community, Paddy also serves as the Chair of the Adelaide Section Council for the AIAA, working to bridge the gap between academic research and commercial space operations.https://www.linkedin.com/in/paddy-neumann-b7b6aa40/ About the American Institute of Aeronautics and Astronautics (AIAA)AIAA is the world’s largest aerospace technical society, dedicated to igniting the imagination, advancing engineering excellence, and fostering a global community of aerospace professionals and students.https://aiaa.org/https://aiaa.org/get-involved/ Spotlight: 2026 Region VII Student ConferenceThere are massive opportunities waiting for students at the 2026 AIAA Region VII Student Conference. Hosted by the Adelaide University Student Branch, this hybrid event welcomes high schoolers (grades 9–12), undergraduate, and graduate students of all majors from around the world (excluding Canada and Mexico, which participate in North American regions). When30 November – 1 December 2026 WhereAdelaide University, Adelaide, Australia & Online (Hybrid format) WhatA technical paper and oral presentation competition where students can pitch their work, win cash prizes, receive expert industry feedback, and network with leading aerospace professionals. Abstract Deadline: 31 July 2026 Registration Opens: August 2026 Submit Your Abstract & RegisterVisit the official AIAA events page to get involved and review eligibility guidelines.https://aiaa.org/events/2026-region-vii-student-conference/See omnystudio.com/listener for privacy information.

Jure Leskovec, Professor of Computer Science at Stanford University and Chief Scientist at Kumo.ai, speaks with host Sriram Panyam about relational and graph language models and their transformative impact on enterprise decision-making and predictive modeling. Jure begins by establishing the critical importance of predictive modeling across industries - from fraud detection in financial institutions to customer churn prediction, lifetime value estimation, product recommendations, and healthcare risk assessment. He notes that while AI has made remarkable advances in natural language understanding and computer vision, predictive modeling over enterprise operational data stored in relational databases has been largely left behind, still relying on 30-year-old machine learning approaches that are expensive, time-consuming, and require manual feature engineering. His proposed solution to the fundamental problem with current approaches is relational deep learning and relational transformers. The discussion explores how this approach differs from traditional graph neural networks (GNNs), which Jure pioneered and deployed successfully at Pinterest. Jure concludes with practical guidance for software engineers and data scientists interested in exploring this technology.

Christof Koch is a neuroscientist at the Allen Institute for Brain Science and the Chief Scientist of the Tiny Blue Dot Foundation. As a professor for 25 years at Cal Tech, he pioneered research on the neural basis of consciousness, and he has advanced one of the leading theories of consciousness called Integrated Information Theory. Among his many publications, Christof's most recent book is entitled Then I Am Myself the World: What Consciousness Is and How to Expand It. In our mini-series on the nature of intelligence, we have talked with Simon Conway Morris about the evolution of intelligence, with Susan Schneider about the rise of artificial intelligence, and with Laszlo Barabasi about the structure of collective intelligence. Check our Templeton Ideas podcast feed to listen to these episodes. Follow us on social media: Twitter, Facebook, Instagram, LinkedIn, and YouTube.

Colonel Eileen Collins was the first woman to pilot and command a Space Shuttle, and the person NASA trusted to lead the program back into space after the loss of Columbia. But her story is about so much more than the milestones. In this episode, Sarah Al-Ahmed sits down with Eileen Collins to discuss “Spacewoman,” a new documentary written and directed by Hannah Berryman, based on Collins' book “Through the Glass Ceiling to the Stars: The Story of the First American Woman to Command a Space Mission.” They talk about what drove her to keep pushing forward, the personal cost of pursuing an extraordinary career, and what it means to break barriers, not just for yourself, but for everyone who comes after you. Then, Bruce Betts, our Chief Scientist, joins us for What's Up to explore what distinguished pilots and commanders from mission specialists in the space shuttle era, and why that distinction was so critical to Eileen's path to the commander's seat. Discover more at: https://www.planetary.org/planetary-radio/2026-spacewomanSee omnystudio.com/listener for privacy information.

Dr. Alison Heather, Chief Scientist at Insitugen Limited shares with us the rapid advancements in anti-doping technology, the rise of online testosterone replacement therapies and the importance of understanding inadvertent contamination versus intentional doping. We discuss the necessity for fair processes, the role of sensitive testing methods and the need for athlete education to prevent false positives and unfair sanctions. Key Topics: The emergence of online testosterone replacement therapy services. Risks associated with unregulated use of testosterone and potential for misuse in sports The importance of comprehensive blood and metabolic testing before initiating hormone therapy How anti-doping testing has evolved since 2004 with increased sensitivity and the role of AI The significance of non-targeted testing in detecting unnoticed performance-enhancing drug use Challenges related to inadvertent contamination, such as semen or supplement trace amounts The importance of fair investigation procedures and the concept of adverse analytical findings The impact of media sensationalism on athletes who are not guilty The necessity for athletes and the public to understand the complexities of doping tests and contamination How technology, biology and AI are advancing doping detection capabilities The role of education and support for athletes facing potential sanctions Timestamps: 00:00 - Introduction: rise of online testosterone therapy and concerns 02:00 - Risks of unregulated hormone use  04:00 - Importance of blood work and medical consultation before hormone therapy 06:00 - Evolution of anti-doping testing methods since 2004 08:00 - Role of AI in non-targeted doping detection 10:00 - Contamination scenarios and their implications in doping cases 12:00 - Investigative processes and fairness in doping sanctions 14:00 - False positives due to contamination and trace amounts 16:00 - The case of Imogen Simmonds and the importance of fair procedures 18:00 - Threshold levels and resource allocation in doping testing 20:00 - Future directions: AI, fingerprinting, and supplement testing 22:00 - The importance of transparency and athlete support during investigations 24:00 - The cultural shift needed around doping allegations and fair play 26:00 - The impact of media and public perception on innocent athletes 28:00 - The significance of education and preventative measures in sports LINKS: Dr Alison Heather - Insitugen Limited at https://insitugen.com/

"What are the stories that people can participate in and see themselves in? I think that's one of the keys that we can unlock when it comes to the whole climate narrative is telling stories that build the scaffolding blocks to a larger narrative that people want to be a part of, and we need to make it feel inevitable….The way that justice prevails, whether it's environmental justice or any kind of justice, I think it's that the leaders make it feel inevitable. And that's the climate movement's job. And I think we have every opportunity and ability to do that." Melissa Jun Rowley on Electric Ladies Podcast The movements have been struggling to connect with people to communicate the vital messages about protecting the planet and its inhabitants.  How can the climate and justice movements engage people again? Listen to Melissa Jun Rowleg, author of "Beyond the Mic Drop: How Stories Can Shift Culture, Power & Policy" and communications expert and journalist in this fascinating conversation with Electric Ladies Podcast host Joan Michelson.   You'll hear about: ●        How stories and narratives work to engage people of all stripes. ●        How emotion is key to connecting with people, helping them see themselves in the story ●        Tips on how to develop and tell stories and tie them into a narrative campaign to drive a positive message for the planet and its inhabitants ●        Plus, career advice, such as:   "You really need to start looking at yourself differently. You're looking at yourself as one thing, but your skills, your assets, your talents, your passion can be so many things to so many other people…Try to look outside yourself a bit.…It's very hard to see ourselves clearly, and I don't know if we ever really do…So, if you're able to, just talk to other people and say…'What do you see in my skillset and in my energy and in what I've accomplished so far and what I could do that maybe I'm not looking at?' Because it is so hard to see ourselves…(and) it's important to celebrate our wins." Melissa Jun Rowley on Electric Ladies Podcast   Subscribe to our newsletter to receive our podcasts, blog, events and special coaching offers. Read Joan's Forbes articles here.   You'll also like: ·       People Leveraging Carbon Markets to Save Their Land - with Stacey Solie, Documentary Producer of "From the Ground Up" - telling stories to show the power of carbon markets ·       How To Talk 'Climate' To Keep People Safe -  with Allison Agsten, USC Center for Climate Journalism & Communications ·       How to Talk About Climate in a Polarized Culture - with Katharine Hayhoe, Ph.D., Climate Scientist, Professor at Texas Tech University and Chief Scientist at The Nature Conservancy ·       Seek First to Understand - with Jennifer Hough, Advisor, TEDx Speaker, Author ·       How Do We Talk About Climate? - with Jill Tidman, Executive Director of The Redford Center, nonprofit producing environmental documentaries and media Subscribe to our newsletter to receive our podcasts, blog, events and special coaching offers. Thanks for subscribing on Apple Podcasts or iHeartRadio and leaving us a review! Follow us on Twitter @joanmichelson

Keach Hagey recounts the January 2016 founding of OpenAI in San Francisco, initially established as a modest nonprofit research lab in Greg Brockman's apartment. Co-founded by Sam Altman, Brockman, and chief scientist Ilya Sutskever, the organization aimed to develop artificial general intelligence (AGI) safely outside of profit motives. Major initial backers included Elon Musk and Peter Thiel, who sought to create a counterweight to Google's DeepMind. The discussion explains how neural networks utilize Nvidia's GPUs—originally designed for video games—to mimic human thought, forming the technical foundation for the current AI race. (1/4)MARCH 1959

Strategies for implementing governance guardrails for agentic and shadow AIPrioritising risk reduction through design-led controls that balance innovation, governance, and cost efficiencyMitigating cascading risk across increasingly complex ecosystems of vendors, contractors and platformsThom Langford, Host, teissTalkhttps://www.linkedin.com/in/thomlangford/Cameron Brown, Head of Cyber Threat and Risk Analytics, Ariel Rehttps://www.linkedin.com/in/analyticalcyber/Benoit Heynderickx, Principal Research Analyst, Information Security Forum (ISF)https://www.linkedin.com/in/benoithey/Yaroslav Rosomakho, Chief Scientist, Zscalerhttps://www.linkedin.com/in/yaroslavrosomakho/

Summary In this episode, Andy welcomes back Marcus Buckingham, bestselling author and researcher, to discuss his new book, Design Love In: How to Unleash the Most Powerful Force in Business. For 25 years, Marcus studied the most productive teams, loyal customers, and effective leaders in the world, and the word that kept appearing in his data was one he kept changing: love. Andy and Marcus explore what love actually means in a business context, including how leaders are really experience makers whether they know it or not. You will hear the remarkable story of Josh D'Amaro, the CEO of Disney, and what his leadership reveals about designing love into a team's daily experience. Marcus unpacks the five feelings that lead people to say they love working for a leader, starting with something counterintuitive: control. The conversation also covers tough love, AI's limits as an experience maker, and how these principles can transform how we lead our families too. If you're looking for a fresh, evidence-based look at what drives sustained high performance, this episode is for you! Sound Bites "I kept hearing that word (love) and shame on me, but I did keep changing it because I felt like it was a careless exaggeration of the word like or something." "Don't keep changing the word (love). The word's the word. The question really should be why and how do we replicate it?" "You're paid to change behavior. That's all you're paid to do. You're not paid to run a project. You're paid to change behavior as a leader." "When you send an email, it's not an email. It's an experience for the person on the other end. When you call that team meeting, it's not a team meeting. It's an experience." "You join a company and then you quit your boss." "Undesigned experiences lead to unpredictable outcomes." "It's cowardly, not loving. It's cowardly to leave them in that job." "I am for you. I am for you. That doesn't always mean that I am going to tell you what you wanna hear. It means I want you to flourish." "Loving's an ingredient, right? Loving isn't, 'Be nicer.' Loving's like, 'What are you trying to do for me?'" "The beginning of love is rules. The beginning of love is clarity." Chapters 00:00 Introduction 01:48 Start of Interview 01:57 Why Marcus Spent Decades Avoiding the Word "Love" 05:47 Misconceptions About Love in Business 11:29 Inside the "Josh Effect" 18:02 What Great Leaders Don't Do 22:13 Local Leadership and Variation in Team Experience 27:54 When Senior Leaders Couldn't Say the Word 31:04 Applying the "Is This Loving or Unloving?" Lens 37:43 Tough Love and Difficult Performance Conversations 46:20 Practical Takeaways: The Five Feelings of Love 50:25 AI and the Role of Love in Leadership 56:34 Designing Love Into Parenting and Family 1:01:26 End of Interview 1:01:57 Andy Comments After the Interview 1:05:03 Outtakes Learn More You can learn more about Marcus and his work at BuckinghamInstitute.com. For more learning on this topic, check out: Episode 252, which is our earlier interview with Marcus Buckingham. That book still impacts how Andy leads years after having Marcus on the first time. Episode 332 with Kevin Eikenberry and Wayne Turmel. A discussion about keeping your teams engaged and connected, even if they're not co-located. Episode 324 with Jim Harter. Jim is the Chief Scientist at Gallup and they have an insightful discussion about building resilient and thriving teams. Chat with PMeLa You can chat directly with PMeLa—the podcast's AI persona—to get episode recommendations and answers to your project management and leadership questions. Visit PeopleAndProjectsPodcast.com/PMeLa to chat with her. Pass the PMP Exam If you or someone you know is thinking about getting PMP certified, we've put together a helpful guide called The 5 Best Resources to Help You Pass the PMP Exam on Your First Try. We've helped thousands of people earn their certification, and we'd love to help you too. It's totally free, and it's a great way to get a head start. Just go to 5BestResources.PeopleAndProjectsPodcast.com to grab your copy. I'd love to help you get your PMP this year! Join Us for LEAD52 I know you want to be a more confident leader–that's why you listen to this podcast. LEAD52 is a global community of people like you who are committed to transforming their ability to lead and deliver. It's 52 weeks of leadership learning, delivered right to your inbox, taking less than 5 minutes a week. And it's all for free. Learn more and sign up at GetLEAD52.com. Thanks! Thank you for joining me for this episode of The People and Projects Podcast! Talent Triangle: Power Skills Topics: Leadership, Love in Business, Team Culture, Employee Engagement, Customer Experience, Project Management, AI, Artificial Intelligence, Parenting, Organizational Culture, Experience Design The following music was used for this episode: Music: Summer Morning Full Version by MusicLFiles License (CC BY 4.0): https://filmmusic.io/standard-license Music: Tuesday by Sascha Ende License (CC BY 4.0): https://filmmusic.io/standard-license

Testing new technology is one thing. Making it work in real conditions and earning trust is another. USDA's new National Proving Grounds Network is designed to close that gap, testing emerging agriculture technologies on working farms and ranches so they can move from idea to adoption. Here to explain how that model is taking shape is Under Secretary for Research, Education and Economics and Chief Scientist at the U.S. Department of Agriculture, Scott Hutchins.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This episode of the Gresham College Podcast features an interview with Robin May, hosted by Jeoffrey Sarpong. Professor Robin May is a Professor of Infectious Disease at the University of Birmingham, and (interim) Chief Scientist at the UK Health Security Agency.We cover what's actually happening in your brain when you lose someone, why grief is hardwired from childhood, whether animals grieve, and what ancient burial sites tell us about human emotion 78,000 years ago. Then we shift to love — the physical symptoms of infatuation, why your amygdala shuts down around a new partner, why the honeymoon phase lasts 12–18 months, and why heartbreak can literally feel like withdrawal.Plus: audience questions on anxiety and love, chatbot grief, abusive relationships, and whether oxytocin is really a "love drug."Watch Robin's Gresham College lectures here: https://youtu.be/5Yrf8IBn9gkhttps://youtu.be/5uQWglAwlpsGresham College has offered free public lectures for over 400 years, thanks to the generosity of our supporters. There are currently over 2,500 lectures free to access. We believe that everyone should have the opportunity to learn from some of the greatest minds. To support Gresham's mission, please consider making a donation: https://gresham.ac.uk/support/Website:  https://gresham.ac.ukTwitter:  https://twitter.com/greshamcollegeFacebook: https://facebook.com/greshamcollegeInstagram: https://instagram.com/greshamcollegeSupport the show

This lecture was recorded by Robin May on the 22nd of April 2026 at Barnard's Inn Hall, LondonProfessor of Infectious Disease at the University of Birmingham, and (interim) Chief Scientist at the UK Health Security Agency, Robin May was appointed Gresham Professor of Physic in May 2022. Between July 2020 and September 2025 he served as Chief Scientific Adviser at the Food Standards Agency (FSA).Professor May's early training was in Plant Sciences at the University of Oxford, followed by a PhD on mammalian cell biology at University College London and the University of Birmingham. After postdoctoral research on gene silencing at the Hubrecht Laboratory, The Netherlands, he returned to the UK in 2005 to establish a research program on human infectious diseases. He was Director of the Institute of Microbiology and Infection at the University of Birmingham from 2017-2020. The transcript and downloadable versions of the lecture are available from the Gresham College website: https://www.gresham.ac.uk/watch-now/music-mindGresham College has offered free public lectures for over 400 years, thanks to the generosity of our supporters. There are currently over 2,500 lectures free to access. We believe that everyone should have the opportunity to learn from some of the greatest minds. To support Gresham's mission, please consider making a donation: https://gresham.ac.uk/support/Website:  https://gresham.ac.ukTwitter:  https://twitter.com/greshamcollegeFacebook: https://facebook.com/greshamcollegeInstagram: https://instagram.com/greshamcollegeSupport the show

In this episode of The Work We Do, we sit down with Charles Spillane, Chief Scientist at FAO.  Charlie traces how his upbringing on a farm in Ireland and an early interest in science fiction shaped his techno-optimistic worldview and his belief in the power of science and innovation to improve society.  He discusses why scientific advances often fail to reach smallholder farmers, what it takes to close the gap between knowledge and adoption, and how financial, institutional, and systemic barriers can be addressed.  Charlie explores the growing role of data in agriculture, including questions of ownership and governance, and the limitations of current research funding models. And he shares vision for a more effective, future-ready agrifood science system.  00:00 Inequality and progress  01:00 From farm to FAO  08:28 Innovation in context  11:11 The technology gap  12:24 Data and control  16:00 The digital divide  21:10 Climate trade-offs  27:07 Science funding  32:46 Why FAO 

"If you don't tell your stories, then no one will ever know, …Basically I just said, I think we should tell the positive stories and we should do it through the people…Let's actually just go and meet the people at all levels from the people that are involved in the projects and organizing them. Also the people that are participating in them and benefiting from them and find out what they say. How does this change your life? Is this actually happening? Did trees actually get planted? Did this well actually get plugged?." Stacey Solie on Electric Ladies Podcast   Who are the regular people on the ground saving their land and helping address climate crisis through carbon markets? Today we're going to hear from one of the producers of a new documentary about them, and be inspired to maybe think a little differently.   Listen to Stacey Solie, coproducer of "From The Ground Up: Voices From The Carbon Markets" and founder and CEO of Strategic Story Craft, in this fascinating conversation with Electric Ladies Podcast host Joan Michelson.   You'll hear about: ●        How they found these remarkably normal people doing extraordinarily simple things and benefiting from the carbon markets to save their land. ●        How these carbon market deals work as creative business models ●        How to leverage creativity to reach more people about the climate crisis. ●        Plus, career advice, such as:   "One thing that I've done is just given myself permission to explore… sometimes saying yes to something for your community that I guess I just learned so much and I met so many people and I got exposed to really amazing artists in ways that are still playing out today. So…being open to exploring… There's different ways to do things. There's a lot of different kinds of people that are trying to make a difference, and they're all intersecting in really creative ways. And I think maybe we can take that model for solutions more broadly. Let's just get creative and work together and try to solve these problems." Stacey Solie on Electric Ladies Podcast   Read Joan's Forbes articles here.   You'll also like: ·       How To Talk 'Climate' To Keep People Safe -  with Allison Agsten, USC Center for Climate Journalism & Communications ·       How to Talk About Climate in a Polarized Culture - with Katharine Hayhoe, Ph.D., Climate Scientist, Professor at Texas Tech University and Chief Scientist at The Nature Conservancy ·       Seek First to Understand - with Jennifer Hough, Advisor, TEDx Speaker, Author ·       How Do We Talk About Climate? - with Jill Tidman, Executive Director of The Redford Center, nonprofit producing environmental documentaries and media ·       What's a Tech Humanist? - with Kate O'Neill, Speaker, Tech Humanist, Author ·       The Politics of Climate & Energy – with Congresswoman Chrissy Houlahan, Co-Chair, Bipartisan Climate Solutions Caucus ·       How Climate Modelling Affects Everything – Maria Caffrey, Ph.D., Principal Scientist, UK's National Physical Laboratory Subscribe to our newsletter to receive our podcasts, blog, events and special coaching offers. Thanks for subscribing on Apple Podcasts or iHeartRadio and leaving us a review! Follow us on Twitter @joanmichelson

Drifting back in time... these are the Nite Drift Archives Originally aired: October 19th, 2020 *** Produced & Hosted by Jim Perry Cohosted by Tim Rothschild | ⁠⁠The Third Thing Network⁠⁠ Edit & Original Music by Jon McEdward Featuring: Dean Radin, PhD | ⁠deanradin.com⁠ Dean Radin, PhD, is Chief Scientist at the Institute of Noetic Sciences and Distinguished Professor at the California Institute of Integral Studies. He earned a BS in electrical engineering (magna cum laude, with honors in physics), then an MS in electrical engineering and a PhD in psychology from the University of Illinois, Urbana-Champaign. Before joining the IONS research staff in 2001, Radin was at AT&T Bell Labs, Princeton University, University of Edinburgh, and SRI International. He has given over 500 talks and interviews worldwide, and he is author or coauthor of hundreds of scientific and popular articles, four dozen book chapters, two technical books, and four popular books translated into 15 foreign languages: The Conscious Universe (1997), Entangled Minds (2006), Supernormal (2013), and Real Magic (2018). At the Institute of Noetic Sciences (IONS), we are inspired by the power of science to explain phenomena not previously understood, harnessing the best of the rational mind to make advances that further our knowledge and enhance our human experience. The mission of the Institute of Noetic Sciences is to reveal the interconnected nature of reality through scientific exploration and personal discovery. https://noetic.org/ Share your experience with Euphomet ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Euphomet Contact Form⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Signal Hotline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or send your own recording to jim@euphomet.com Support Euphomet Join ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Society of The Strange⁠⁠⁠⁠⁠⁠⁠⁠⁠ for Ad-free Episodes of Euphomet Subscribe on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠iTunes⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@euphomet⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and #euphomet Transmission received at jim@euphomet.com Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Niall speaks with Dr. Christof Koch, Chief Scientist of the MindScope Program at the Allen Institute for Brain Science, former Professor at Caltech, and author of “Then I Am Myself the World”. Dr. Koch is a leading researcher in the science of consciousness and a key proponent of Integrated Information Theory. In this conversation, they explore: — Why consciousness may be fundamental, while physical matter exists only in relation to other things — How an experience on a beach in Brazil changed his understanding of reality — The discovery of “covert consciousness” in patients thought to be in vegetative states — How the perturbational complexity index (PCI) shows a clear boundary between conscious and unconscious states, and why this matters — How Integrated Information Theory approaches the question of free will You can learn more about Dr. Koch's work at https://christofkoch.com. --- Dr. Christof Koch is a Meritorious Investigator at the Allen Institute. Christof received his baccalaureate from the Lycée Descartes in Rabat, Morocco, his B.S. and M.S. in physics from the University of Tübingen in Germany and his Ph.D. from the Max-Planck Institute for biological Cybernetics in 1982. Subsequently, he spent four years as a postdoctoral fellow in the Artificial Intelligence Laboratory and the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology. From 1987 until 2013, Koch was a professor at the California Institute of Technology (Caltech) in Pasadena, from his initial appointment as Assistant Professor, Division of Biology and Division of Engineering and Applied Sciences in 1986, to his final position as Lois and Victor Troendle Professor of Cognitive & Behavioral Biology. See here for Christof's academic pedigree and his students. Christof joined the Allen Institute for Brain Science as Chief Scientific Officer in 2011 and became President in 2015. Christof writings and interests integrate theoretical, computational and experimental neuroscience with philosophy and contemporary trends, in particular artificial intelligence. His latest book, Then I Am Myself the World: What Consciousness Is and How to Expand It, publish in May 2024. His previous book, Consciousness: Confessions of a Romantic Reductionist, blends science and memoir to explore topics in discovering the roots of consciousness. Stemming in part from a long-standing collaboration with the late Nobel Laureate Francis Crick, Christof authored the book The Quest for Consciousness: A Neurobiological Approach. Koch also authored the technical books Biophysics of Computation: Information Processing in Single Neurons and Methods in Neuronal Modeling: From Ions to Networks, and served as editor for several books on neural modeling and information processing. --- Interview Links: — Dr. Koch's website: https://christofkoch.com — Dr. Koch's book: https://amzn.to/4mIKG9W

Pope Leo's repeated calls for peace has put the focus on the Catholic Church's Just War Theory- something which went on to form the basis of international law. But is that Theory withering today - both from the religious and political lexicons? Audrey speaks to Professor Tobias Winwright - considered the world's leading authority on Just War - he's currently in Maynooth university, and theologian Dr Elaine Storkey.It's described as the world's biggest humanitarian disaster - 14 million people forced from their homes, and yet the situation in Sudan barely makes the news. As the current conflict enters its 4th year Audrey talks to Birke Herzbruch from Trócaire, who has recently returned from Sudan.Professor Katharine HeyHoe is the Chief Scientist at Nature Conservancy. She will be in Belfast soon to give the annual McCosh Lecture at Queens University. Ahead of the visit she spoke to Audrey about Faith, climate change and why small actions matter.The new Michael Jackson movie has been panned by critics with accusations that it whitewashes the singers past and makes no mention of the child molestation charges he faced. He's not the first artist to be accused of heinous crimes but how are we supposed to view their work- whether it's music, poetry, books, art or films? Audrey speaks to Dr Leon Litvack, from the School of Arts, English and Languages at Queen's University and by BBC Music Presenter Steven Rainey.

"Try as best as you can not to be fear-driven. I think we are so driven by fear that we're never going to be enough, that we aren't going to contribute enough….(Y)ou actually are enough just as you are, right? Take this day, do what you can. Impact the people around you.…Become partners in your career with unlikely people, people who don't think like you, people who aren't doing the same career as you. You'll get a lot more joy out of, I think, your career because of the cross-pollination." Dr. Katherine Gergen-Barnett on Electric Ladies Podcast This Earth Day, we want to share inspiring career advice for women in mid-career who want to make a difference, which I ask every guest for on Electric Ladies Podcast. No matter what's going on in the economy, you have agency. You can control what you think about, focus on and your emotions. Listen to these amazing women from five different industries who were interviewed on Electric Ladies Podcast recently. Let them inspire you and tell us what resonates with you. Post it to us @joanmichelson on social media. You'll hear from: ·       Katharine Hayhoe, Ph.D., Chief Scientist at The Nature Conservancy, professor at Texas Tech University and one of the world's foremost climate scientists. ·       Dominique Browning, Founder/CEO of Mom's Clean Air Force, on how to pressure elected officials on climate and clean energy issues. ·       Maria Korsnick, CEO of the Nuclear Energy Institute, on the new nuclear energy industry and innovations bringing clean, reliable power to many ·       Maura Hodge, head of U.S. Sustainability Practice at global consulting firm KPMG, on why companies believe sustainability creates long-term value. ·       Dr. Katherine Gergen-Barnett and Dr. Anna Goldman, of Boston Medical Center, on how healthcare systems can protect the planet while providing top medical care to people. Subscribe to our newsletter - and Join the waitlist for our new Membership Group here. Read Joan's Forbes articles here. Elevate your career with expert coaching and ESG advisory with Electric Ladies Podcast. Unlock new opportunities, gain confidence, and achieve your career goals with the right guidance. Subscribe to our newsletter to receive our podcasts, articles, events and career advice – and special coaching offers.. Thanks for subscribing on Apple Podcasts or iHeartRadio and leaving us a review! Don't forget to follow us on our socials Twitter: @joanmichelson LinkedIn: Electric Ladies Podcast with Joan Michelson Twitter: @joanmichelson Facebook: Electric Ladies Podcast

Stronger, sharper years are possible when you target the source of aging.In this episode, Dr. Stephen Petteruti sits down with Chris Burris, Chief Scientist of MyVitalC, to focus on Carbon 60 (C60) and why it's gaining attention in longevity science. This Nobel Prize–recognized molecule acts as a potent antioxidant at the mitochondrial level, helping manage the oxidative stress that drives aging and many chronic conditions. Chris outlines how C60 is produced and why its unique structure allows it to neutralize large numbers of free radicals. The discussion ties this to real outcomes while stressing quality control, proper sourcing, and avoiding hype in the supplement space.  Dr. Stephen adds in the clinical context that aging accelerates when oxidative stress and inflammation outpace the body's defenses. Interventions like C60 may support resilience, but fundamentals remain non-negotiable.If longevity and performance matter, spend a few minutes with this episode. Tune in now: The Anti-Aging Breakthrough: Can This Molecule Help You Live Longer? (C60 Explained).Enjoy the podcast? Subscribe and leave a 5-star review on your favorite platforms.Chris Burres is the Chief Scientist at MyVitalC, known for his work on the groundbreaking molecule ESS60. His research is tied to one of the most significant longevity discoveries in history, demonstrating a remarkable 90% lifespan extension in Wistar rats. As the host of the Live Beyond the Norms podcast and Longevity Summit, Chris shares science-backed insights to help people live longer, healthier lives. He is also the author of Live Longer and Better, where he breaks down practical strategies for optimizing health and extending lifespan. Driven by a mission to make longevity science accessible, Chris continues to educate and inspire individuals to take control of their health and aging.LI: https://www.linkedin.com/in/chrisburres/IG: https://www.instagram.com/chrisburres/  MyVitalCWebsite: https://www.myvitalc.com/ IG: https://www.instagram.com/myvitalc/ LI: https://www.linkedin.com/company/myvitalc/X: https://x.com/myvitalc YT: https://www.youtube.com/myvitalc Pinterest: https://www.youtube.com/myvitalc TikTok: https://www.tiktok.com/@myvitalc  Dr. Stephen Petteruti is a board-certified physician specializing in longevity-focused, integrative medicine. He works with men navigating prostate cancer, testosterone and hormone health, aging, and performance using proactive, evidence-informed strategies grounded in real clinical practice. His approach prioritizes preserving function, strength, and quality of life while helping patients make clear, informed decisions beyond reactive, fear-driven care.Learn more: https://www.drstephenpetteruti.com/ Learn more: https://www.intellectualmedicine.com/ Connect with Dr. Petteruti on:⁠Instagram: ⁠https://www.instagram.com/dr.stephenpetteruti/⁠ Facebook: ⁠https://www.facebook.com/dr.stephenpetteruti⁠ Subscribe to Intellectual Medicine on:Apple Podcast: https://tinyurl.com/DrPetterutiApplePodcast Spotify: https://tinyurl.com/DrPetterutiSpotifyPodcast Disclaimer:The content presented in this video reflects the opinions and clinical experience of Dr. Stephen Petteruti and is intended for informational and educational purposes only. It is not medical advice and should not be used as a substitute for professional diagnosis, treatment, or guidance from your personal healthcare provider. Always consult your physician or qualified healthcare professional before making any changes to your health regimen or treatment plan.Produced by https://www.BroadcastYourAuthority.com

The U.S. Space Force revealed what it thinks could be the threats, where those threats are located, and what it will need by 2040. The revelations will not only serve the service branch, but should guide C-suites, entrepreneurs, investors, and technologists for years to come. Laura Winter speaks with Joel Mozer, the first Chief Scientist of the U.S. Space Force; and Peter Garretson, Senior Fellow in Defense Studies at the American Foreign Policy Council and author of three books on space power, the latest of which is "Space Shock: 18 Threats That Will Define Space Power."

In this episode of ACM ByteCast, Rashmi Mohan hosts 2024 ACM/AAAI Allen Newell Award recipient Peter Stone, Professor at the University of Texas at Austin and Chief Scientist at Sony AI. He received the award for significant contributions to the theory and practice of AI, especially in reinforcement learning (RL), multiagent systems, transfer learning, and intelligent robotics. As a leading figure in AI research, Stone has fundamentally advanced how autonomous agents learn, plan, and collaborate. His groundbreaking work on RL algorithms has enabled robots to acquire skills through experience. He is an ACM, AAAI, AAAS, and IEEE Fellow, an Alfred P. Sloan Research Fellow, and a Fulbright Scholar. At UT Austin, he is the founder and director of the Learning Agents Research Group (LARG) within the Artificial Intelligence Laboratory, as well as Founding Director of Texas Robotics. In the past, he also worked at AT&T Labs - Research and co-founded Cogitai, Inc. (acquired by Sony). Peter explores the intersection of professional research and personal passion, detailing how his lifelong love for soccer fueled his involvement in RoboCup, where he aims to develop humanoid robots capable of competing at a World Cup level by 2050. The conversation highlights his leadership as the Chief Scientist of Sony AI, focusing on landmark projects like GT Sophy, an AI that mastered the complexities of Gran Turismo, and the development of FHIBE, an ethically sourced dataset designed to mitigate bias in machine learning. Throughout the interview, Stone emphasizes the importance of ad hoc teamwork—the ability of autonomous agents to collaborate on the fly with unfamiliar partners. He also shares his passion for undergraduate research and advocacy for AI education at all levels.

Hablamos con Jaime Blasco (@jaimeblascob) de campañas de Corea del Norte contra el resto del mundo. Jaime es el CEO y cofundador de Nudge Security, con más de 15 años de experiencia en ciberseguridad y uno de los referentes mundiales en Threat Intelligence. Anteriormente fue Chief Scientist en AlienVault y lideró Alien Labs en AT&T Cybersecurity, además de ser cofundador de Open Threat Exchange, una de las mayores comunidades de inteligencia de amenazas del mundo. Con él analizamos cómo Corea del Norte ha evolucionado desde ataques técnicos tradicionales hacia modelos mucho más sofisticados basados en infiltración laboral, ingeniería social y ataques a la cadena de suministro, por qué este enfoque está funcionando tan bien y cómo está cambiando por completo la superficie de ataque de las empresas, terminando con su visión sobre hacia dónde van estas amenazas y qué pueden hacer las organizaciones para prepararse ante un escenario donde el atacante ya no está fuera, sino dentro. ⭐️ SPONSORS ⭐️ ️‍♂️ Flare Flare es una plataforma de inteligencia de amenazas y monitoreo de la Dark Web que te ayuda a estar un paso por delante de los ciber-delincuentes. Puedes solicitar una prueba gratuita como oyente de Tierra de Hackers aquí:  https://try.flare.io/martin-vigo/ REDES SOCIALES - Twitter: https://twitter.com/tierradehackers - Instagram: https://instagram.com/tierradehackers - TikTok: https://tiktok.com/@tierradehackers - LinkedIn: https://linkedin.com/company/tierradehackers - Facebook: https://facebook.com/tierradehackers Únete al canal oficial de Discord para conectar con la comunidad de Tierra de Hackers: https://tierradehackers.com/discord Apóyanos en Patreon y obtén beneficios exclusivos y merchandising: https://patreon.com/tierradehackers Notas, links y referencias del episodio: https://www.tierradehackers.com/episodio-143

On America at Night with McGraw Milhaven, Craig Sumner, retired NASA aerospace engineer and former Artemis II propulsion manager, joined the program to discuss the long road that led to NASA's Artemis II mission. Sumner explained the history of the Artemis program, the technological progress behind NASA's return to human deep-space missions, and how decades of engineering and testing are paving the way for astronauts to once again travel around the Moon. Next, Nikki Gerber, co-organizer for Ohio Residents for Responsible Development, discussed growing opposition to the construction of large data centers in parts of Ohio. Gerber explained concerns from residents about energy consumption, environmental impact, and the strain that large-scale tech infrastructure could place on local communities. Later, Dr. Bruce Betts, Chief Scientist and LightSail Program Manager for The Planetary Society, joined the show to talk about the Artemis II splashdown and what it means for the future of space exploration. Betts discussed how Artemis missions are setting the stage for Artemis III and eventually human missions to Mars, and why the success of these missions is critical for the future of human spaceflight. Finally, Theo Lewis Clark, the show's Hollywood Executive for a Day, returned for the weekly movie trivia segment, challenging McGraw and listeners with film questions and pop culture trivia. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Matt Watson sits down with Mohan Reddy, serial entrepreneur and Chief Scientist at Cornerstone AI Labs, to explore how AI is fundamentally reshaping the way we think about work, skills, and human potential. Mohan shares the origin story of Skyhive—a workforce intelligence platform built to reskill and upskill people at a global scale—and how its acquisition by Cornerstone brought that mission to a larger stage.The conversation digs into why AI doesn't eliminate skills but transforms them, the distinction between tasks that can be automated versus those that require human judgment, and why "vibe coding" is both a breakthrough and a danger. Mohan also makes the case for reverse engineering as the most critical skill in an AI-driven world, and why sandbox environments will be essential for building trust in AI-assisted workflows.Whether you're a founder, engineer, or business leader trying to navigate the AI transition, this episode offers a grounded, optimistic perspective from someone who has spent decades at the intersection of human potential and machine intelligence.If you enjoyed today's episode, subscribe to the Starter Hustle podcast and leave us a review!⏱️ Episode Breakdown00:30 The Journey of Mohan Reddy and Skyhive03:34 Transition to Cornerstone AI Labs06:22 AI's Impact on Skills and Workforce09:33 The Evolution of Software Engineering12:29 The Future of Coding and AI Collaboration15:33 Upskilling in the Age of AI18:30 Curiosity and Learning in Tech21:34 Final Thoughts and AdviceLinks & ResourcesConnect with Mohan Reddy on LinkedInWhat Smart CTOs Are Doing Differently With Offshore Teams in 2025Subscribe to the Global Talent SprintFull Scale – Build your dev team quickly and affordablyIf you're trying to get your team out of the basement and into real product ownership, this episode is your playbook. Stop being a ticket factory. Build teams that think, create, and lead.Follow the show, rate it, and send this to someone who's still trying to do “real Scrum.” They need it more than you do.

The second Wednesday of every month, Spaced Out Radio welcomes its resident scientist, Dr. Bob McGwier—better known to listeners as “Science Bob”—to explore the scientific side of the supernatural and paranormal. Each episode dives into high strangeness through a grounded, analytical lens, often featuring a guest with a background in science or research. From UFOs and UAPs to unexplained phenomena, Science Bob brings clarity to the unknown, using cutting-edge approaches including artificial intelligence and advanced computer technology to detect anomalies in the skies across the United States.Dr. McGwier holds a BSEE in Electrical Engineering and a BS in Applied Mathematics from Auburn University, along with a PhD in Applied Mathematics from Brown University. His distinguished career includes work at Sandia National Laboratories beginning in 1977, faculty roles at Auburn University, and over two decades with the Institute for Defense Analyses' Center for Communications Research. Most recently, he served as Professor and Chief Scientist at Virginia Tech's Ted and Karyn Hume Center for National Security and Technology, where he helped lead advancements at the intersection of science, security, and innovation.Spaced Out Radio is your nightly source for alternative information, starting at 9pm Pacific, 12am Eastern.  We broadcast LIVE every night. #UFO #UAP #AlienDisclosure #UFOSightings #UFOCoverUp #Aliens #SpacedOutRadio #Paranormal #UFOCommunity #disclosure -------------------------------------------------------You can now join the Space Traveler's Club;Join us at  https://www.patreon.com/sor_space_travelers_club  --------------------------------------------------------Grab Our Latest Spaced Out Radio Gear At:http://spacedoutradio.com/shop  It's a great way to support our show!--------------------------------------------------------OUR LINKS:TWITTER: https://www.twitter.com/spacedoutradio   FACEBOOK:https://www.facebook.com/spacedoutradioshow  SPACED OUT RADIO - INSTAGRAM:https://www.instagram.com/spacedoutradioshow  DAVE SCOTT - INSTAGRAM:https://www.instagram.com/davescottsor   TWITCH: https://www.twitch.com/spacedoutradioshow  WEBSITE: http://www.spacedoutradio.comGUEST IDEAS OR QUESTIONS FOR SOR?Contact Klaus at bookings@spacedoutradio.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/spaced-out-radio--1657874/support.

Jakub Pachocki, OpenAI's Chief Scientist, sits down with Jacob to cover the full arc of where AI research stands today and where it's headed. The conversation spans the explosive growth of coding agents and what it signals about near-term AI capability, the use of math and physics benchmarks as proxies for general intelligence, how reinforcement learning is being extended beyond easily-verified domains toward longer-horizon tasks, and what it means to run a research organization at the precise moment the models themselves are starting to accelerate the research. Jakub shares a candid take on the competitive landscape, why chain-of-thought monitoring is one of the most promising tools in the alignment toolkit, and — with unusual directness — why the concentration of power enabled by highly automated AI organizations is a societal problem that doesn't yet have an obvious solution.   (0:00) Intro (1:53) Research Intern Capability Timelines (4:59) Math Breakthroughs (7:59) RL Beyond Verifiable Tasks (12:32) RL vs In-Context (19:01) Allocating Compute Internally (28:18) AI for Science (31:40) Pattern Matching (33:23) Solving the Hardest Math Problems (37:40) Chain of Thought Monitoring (44:33) Generalization and Value Alignment in Models (47:57) Inside OpenAI (51:55) Quickfire   With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

This episode features Dr. Christopher Mushquash.  Dr. Mushquash is a Tier 1 Canada Research Chair in Indigenous Mental Health and Addiction, and Professor in the Department of Psychology at Lakehead University and the Division of Human Sciences at the Northern Ontario School of Medicine University. He is also Vice President Research at the Thunder Bay Regional Health Sciences Centre and Chief Scientist and Chief Operating Officer at the Thunder Bay Regional Health Research Institute. He is the Director of the Centre for Rural and Northern Health Research at Lakehead University. In addition to his academic appointments, Dr. Mushquash is a registered clinical psychologist providing assessment, intervention, and consultation services for First Nations children, adolescents, and adults at Dilico Anishinabek Family Care. In 2025, Dr. Mushquash was inducted as a Fellow in the Royal Society of Canada. Dr. Mushquash is Anishinawbe (Ojibway) and a member of Pawgwasheeng (Pays Plat First Nation). https://amshealthcare.ca/

"If there are people who are willing to talk to you about it, the first thing you got to do is listen….I grew up in a very rural area and it's very conservative also. And when I go home, I don't talk about climate change. I do talk about what's going on with hunting season. 'Oh, there's no deer this year. Why do you think that is? Tell me about the rain'….It is again about that pivoting and you have to understand what's important to the people you're talking to… I might say, 'I just upgraded my mom's generator. It's great because with all the outages we're having out here, now she can get electricity and I know your husband's been sick, that could be something that could be really beneficial for you.' I do not mention climate change…I am putting out ideas that are going to save somebody's life, I'm responding to the situation of the people that I'm speaking with." Allison Agsten on Electric Ladies Podcast How do we keep people safe and sound in the face of ferocious weather, wildfires and floods, especially when they wince at the words "climate change"? Make it real for their world. How? Listen to Allison Agsten, Director of the Center for Climate Journalism and Communications at the University of Southern California in this fascinating conversation with Electric Ladies Podcast host Joan Michelson. She also curates art at the intersection of art and climate at the Wrigley Marine Science Center.   You'll hear about: How to keep people safe with adaptation strategies without talking about climate change. How art and climate intersect and how it helps people stay safe. How to combat disinformation and misinformation…and so much more Plus, career advice, such as: "When I talk to women who are at some point in their careers and they're thinking of joining our sustainability communicator cohort, they always say to me, I just don't know if my skills are translatable. Yeah, they are. I can help you think about the language…It's scary because I've done it myself. I've made a number of career pivots, but to me, even scarier is not taking that chance and having the opportunity to feel really good about the work you do every day….I sleep well at night because I know that I used the skills that I have to do the thing that I think is most important." Allison Agsten on Electric Ladies Podcast Subscribe to our newsletter here -- and Read Joan's Forbes articles here. You'll also like: How to Talk About Climate in a Polarized Culture - with Katharine Hayhoe, Ph.D., Climate Scientist, Professor at Texas Tech University and Chief Scientist at The Nature Conservancy Seek First to Understand - with Jennifer Hough, Advisor, TEDx Speaker, Author How Do We Talk About Climate? - with Jill Tidman, Executive Director of The Redford Center, nonprofit producing environmental documentaries and media What's a Tech Humanist? - with Kate O'Neill, Speaker, Tech Humanist, Author The Politics of Climate & Energy – with Congresswoman Chrissy Houlahan, Co-Chair, Bipartisan Climate Solutions Caucus How Climate Modelling Affects Everything – Maria Caffrey, Ph.D., Principal Scientist, UK's National Physical Laboratory Subscribe to our newsletter to receive our podcasts, blog, events and special coaching offers. Thanks for subscribing on Apple Podcasts or iHeartRadio and leaving us a review! Follow us on Twitter @joanmichelson

Mistral has been on an absolute tear - with frequent successful model launches it is easy to forget that they raised the largest European AI round in history last year. We were long overdue for a Mistral episode, and we were very fortunate to work with Sophia and Howard to catch up with Pavan (Voxtral lead) and Guillaume (Chief Scientist, Co-founder) on the occasion of this week's Voxtral TTS launch:Mistral can't directly say it, but the benchmarks do imply, that this is basically an open-weights ElevenLabs-level TTS model (Technically, it is a 4B Ministral based multilingual low-latency TTS open weights model that has a 68.4% win rate vs ElevenLabs Flash v2.5). The contributions are not just in the open weights but also in open research: We also spend a decent amount of the pod talking about their architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens (typically only applied in the Image Generation space, as seen in the Flow Matching NeurIPS workshop from the principal authors that we reference in the pod).You can catch up on the paper here and the full episode is live on youtube!Timestamps00:00 Welcome and Guests00:22 Announcing Voxtral TTS01:41 Architecture and Codec02:53 Understanding vs Generation05:39 Flow Matching for Audio07:27 Real Time Voice Agents13:40 Efficiency and Model Strategy14:53 Voice Agents Vision17:56 Enterprise Deployment and Privacy23:39 Fine Tuning and Personalization25:22 Enterprise Voice Personalization26:09 Long-Form Speech Models26:58 Real-Time Encoder Advances27:45 Scaling Context for TTS28:53 What Makes Small Models30:37 Merging Modalities Tradeoffs33:05 Open Source Mission35:51 Lean and Formal Proofs38:40 Reasoning Transfer and Agents40:25 Next Frontiers in Training42:20 Hiring and AI for Science44:19 Forward Deployed Engineering46:22 Customer Feedback Loop48:29 Wrap Up and ThanksTranscriptswyx: Okay, welcome to Latent Space. We're here in the studio with our gues co-host Vibh u. Welcome. Thanks. Excited for this one as well as Guillaume and Pavan from Mistral. Welcome. Excited to be here.Guillaume: Thank you.swyx: Pavan, you are leading audio research at Mistral and Guillaume, you're Chief Scientist,Announcing Voxtral TTSswyxHost(00:05) Okay. (00:05) Welcome to Lean Space. (00:06) We're here in the studio with trustee co-hosts, Vibhu. (00:09) Welcome.VibhuHost(00:11) Very excited for this one.swyxHost(00:12) As well as Guillaume and Pavan from Mistral. (00:15) Welcome. (00:16) Excited to be here. (00:17) Thank you for having us.(00:18) Pavan, you are leading audio research at Mistral and Guillaume, you're a chief scientist. (00:23) What are we announcing today where we're coordinating this release with you guys?GuillaumeGuest(00:26) Yeah, so we are releasing Voxtral TTS. So it's our first audio model that generates speech. It's not our first audio model. We had a couple of releases before.(00:35) We had one in the summer that was Voxtral, our first audio model, but it was like a transcription model, ASR. Like a few months later, we released some update on top of this, supporting more languages. Also a lot of table stack features for our customers, context biasing, precision, timestamping and transcription. We also have some real-time model that can transcribe not just at the end of the level.(00:56) You don't need to fill your entire audio file, but that can also come in real-time. And here, this is a natural extension in the audio, so basically speech generation. So yeah, so we support nine languages, and this is a pretty small model, 3D model, so very fast, and also state of the art. Performed at the same level as the base model, but it's much more efficient in terms of cost, and also much, in terms of cost, it's also much cheaper, only a fraction of the cost of our competitors.(01:22) And we are also releasing the work that this model is running.swyx What's the decision factor?Guillaume It's a good question.swyxThere will be more. Yeah, Pavan, any sort of research notes to add on?Architecture and CodecPavan: But it's a novel architecture that we develop inhouse.We traded on several internal architectures and ended up with a auto aggressive flow matching architecture. And also have a new in-house neural audio codec. Which, converts this audio into all point by herds latent [00:02:00] tokens, semantic and acoustic tokens. And yeah, that's that's their new part about this model and we're pretty excited that it's, it came out with such good quality and Jim was mentioning. Yeah, it's a three B model. It's based off of the TAL model that we actually released just a few months back and insert trunk and mainly meant for like the TTS stuff, but they need text capabilities are also there. Yeah.swyx: So there's a lot to cover.I always I love any, anything to do with novel encodings and all those things because I think that's obviously I creates a lot of efficiency, but also maybe bugs that sometimes happen. You were previously a Gemini and you worked on post training for language models, and maybe a lot of people will have less experience with audio models just in general compared to pure language.What did you find that you have to revisit from scratch as you joined this trial and started doing this? At leastUnderstanding vs GenerationPavan: when it comes to, for, I think the, there are two buckets, I guess the audio understanding and audio [00:03:00] generation. The audio understanding, like the walkthrough models that Kim was mentioning that we released earlier.The walkthrough chat that we released I think July last year, and the follow up transcription only, models family that we released in January, that would be one bucket, and the generation is another bucket. I think. You can also treat them as a unified set of models, but currently the approaches are a little different between these two.To your question on how audio is fed to the model? In the understanding model, it's very similar to actually Pixar models that we also released,swyx: yes.Pavan: That'sswyx: amazing.Pavan: It was pretty, I, that was the first project I worked on after joined Misra. It was pretty, pretty nice. And Wtu was very similar in spirit.I guess So we feed audio through an audio encoder similar to images through a vision encoder, and it produces continuous embeddings and which are fed as tokens to the main transformer decoded transformer model. Yeah. On the model output is just text. So on the output side, there is nothing that needs to be done in these kinds of mode.I [00:04:00] guess the interesting part of what the generation stuff is, the output now has to produce audio and. The approach that we have is this neural audio codec, which converts audio into these latent tokens. There is a lot of existing attrition and a lot of models which are based off of this kind of approach.And we took a slightly. A different, design decisions around this. But at the end of the day, the neural audio product converts audio into a 12.5 herdz set of latents. And each latent is, has a semantic token and a set of acoustic tokens. And the idea is that you take these discrete tokens and then feed it on the input side.There's several ways to use this at each frame, but we just sum the embedding. So it's like having key different vocabularies. Combine all of them because they all correspond to one audio frame on the input side. The output side is the interesting part on the output side, the, it's not the, I don't know if it's the most popular, but one.Popular technique is to have a depth transformer [00:05:00] because you have K tokens at each time step, like with a text, you just have one token at each time step. So you just do predict the token from the vocabulary with, yeah, with just, you get probabilityswyx: This's a very straightforward text. VeryPavan: straightforward.swyx: Yeah.Pavan: But if you have K tokens, then the name thing would be to predict all of them in paddle. That doesn't work. At least that doesn't work that well because audio has more entropy. And the, one of the techniques people use is this depth transformer where you you almost have a small transformer, or it can be L-S-T-M-R in as well, but people use transformers and you predict the K tokens in auto aggressive fashion in that.So you have two auto reive things going on.Flow Matching for AudioPavan: So the thing we did differently is in, instead of having this auto aggressive K step prediction, we have a flow matching model. Instead of modeling this as a discrete token set we trained the codec to be both discrete and continuous to have this flexibility.So we did try the discrete stuff too, and which it works well, but the continuous stuff works just better. So yeah, we took this flow matching, so the, it's a flow [00:06:00] matching head, which takes the latent from the main transformer and like kind in fusion, it's denoising, but in this flow matching itself, velocity estimate.So you go from this noise t all the way to there. Audio latent, which corresponds to the 80 millisecond audio and then, which is sent through the work order to get back the 80 millisecond audio frame.swyx: Yeah. Is this the first application of flow matching in audio? Because usually I come across this in the image.Pavan: Yeah. Actually, in some sense there are models flow matching models in audio, but I think this specific combination I could be wrong. There could be somewhat. No. I haven't seen. I haven't seen much work in this, so I think it's novel and a lot of it's just a way bigger community, so they, I think they pioneer a lot of these diffusion flow matching work, and it's interesting to adopt some of the ideas there into audio and,swyx: yeah.Pavan: Yeah, I'm, personally that's the think part which is trying out about. One of more meta point is unlike text, even in vision, I think this is true, but in [00:07:00] audio step literature that there is no.Winner model, yet there is no, okay, this is the way you do things. It's it's still by, I think people are still iterating and figuring out like what's the best overall recipe. I guess the idea. Pretty sure there are models which are also completely end-to-end, like NATO audio. NATO audio, but it's still not come to a convergence point where this, the right way to think that.That also makes. A space pretty exciting to explore.Real Time Voice AgentsVibhu: What are some of the ways to look at it?Vibhu: There are ways where you can do diffusion for audio generation, but if you want like real time generation, that's a big thing with the approach I'm assuming that you took. Yeah. And also like how do you go about evaluating different axes of what you care about, yeah,Pavan: good point. I think we so you can do just flow matching diffusion for the whole audio. We didn't even go down that path because one of the main applications is voice agents and we want real time streaming, and that's the use case. That's not the only use case, but that's one of the primary use cases we want to get to.So we [00:08:00] picked the auto aggressive approach for that. And within the auto aggressive space, again, you can do chunk by chunk or you can do so we picked the. I think at least personally prefer the operations, which are the simplest, and so we try to see, can we just add audio as just another head to our regular transformer decode model because that kind of makes it easier for eventual end-to-end modeling of audio text native modeling.Yeah. And it works pretty well. So I guess we went with that and we tried a little bit, but the flow matching head itself, like we had a discreet. Diffusion kind of approach, which also works well, but the flow matching work better.swyx: I was just curious about how you also think about this overall direction of research.Do you basically, when you work with the audio team, do you set some high level parameters and then let them explore whatever, or how does it work between you guys?Guillaume: No I think the way it works is that we are the, we are prioritizing together, I think, what are the most important features because there are many things we can do [00:09:00] in audio.Yeah, I think we try to. These are like how we should do things, for instance. Ultimately what we want to do is to build this through duplex model, but we are not going to start this start there directly, I think is. Some of the project people are doing, butswyx: just to confirm, full effects means it can speak while I'm speaking or,Guillaume: yeah.Okay. Audio. Yeah. Yeah. So intimately we're going to get there, but for us it was, we decided to take it like a step by step. So we start with whatever is the most important. I think support customers, which is the transcription is the most popular use case. Then the speech generation, Soviet time, just a bit before that.And then actually to be like more, but try combining everything all together. But but yeah, we thought it was also important to like separate things and optimize each capability one by one before weswyx: measure of that together. And the super omni model. ButGuillaume: very interesting because as Par said, it's when you work on some other domains of this airline and everything, there are many areas where I think it's not as interesting.For instance. Many places, it's essentially just around data or like creating new environments on a lot of kind [00:10:00] of easy things. But things were, I think the research is maybe not as interesting. Were in audio. There are so many ways to actually build this model. So many ways to go around it. That's the sense I think is really interesting.And what we also tried for speed generation is that we tried multiple approaches. What was interesting that even though they were extremely different, they under the big know the particles but the for matching turned out to be quite more natural. So we are happy with this.swyx: Is there intuition why it maybe like flow matching is just models speech better in some natural fundamental, latent dimension?Pavan: No, I think the main thing is e even at a particular time step, there is a distribution of things.swyx: Yes.Pavan: To be predicted like the way you inflate. So you already know the word that you're speaking and Yeah. The intake space, let's say the word maps register a single token for simplicity.In most cases it does. So there is not a lot of so you just pick the word, but with within audio, even the same word could, even with your own voice, could be inflicted in so many different ways. And I think [00:11:00] any approach which like models this distribution and. And flow matching is one, one of the take.It's not the only one at all, but it's a one which works pretty reasonably well. I think that's better. So you have to pick across several different, the intuition I have is it's, there are some, several different clusters each corresponding to some specific way you would inflict, pronounce that thing.And you can't predict the mean of it because that corresponds to some blurred out speech or something like that. But you have to pick one. And then like sharpswyx: conditional inference.Pavan: Yeah, exactly.swyx: Is that all covered under disfluencies, which is I think the normal term of art. Pauses intonations. By the way, I have to thank Sophia for setting all this up, including like some of these really good notes becausePavan: Yeah.swyx: I'm less familiar with the audios for me.Pavan: No. I think dis dismisses are definitely one such Eno defenses is more likeswyx: which is arms are.Pavan: Yeah, arms. And also repeat like you like,swyx: yeah.Pavan: You do this full of words, your thinking, so you repeat the word.swyx: Okay. Whereas intonation is like a diff, it's up up [00:12:00] speak and all this.Okay.Pavan: Yeah. So I think there is a lot of like entropy. And modeling it as a distribution. And a, any technique which helps with it and the depth transformer is a conditional way of modeling this. And Transformers actually really good at it, even though that's a mini transformers. So I think that worked pretty well too for us too.It's just that the main concentration is when you have a depth transformer. If you have K tokens, you need to do K auto steps, right? Even though it's a small thing, it's K steps, which is very vacant, say heavy, but flow matching. We were able to cut it down significantly. So we are able to do the inference in quad steps or 16 steps and it works pretty well.And there are more normal techniques to bring it down even further to like, in extreme case, one step like we're not doing it yet, but it at least the framework, LEDs itself to more efficient and Yes.swyx: And the image guys have done.Pavan: Yeah.swyx: Incredible work guys. Yeah.Pavan: It now you just. Send a prompt and you get an image.swyx: Yeah. Surprisingly not enough. I think image model labs use those techniques in production. I think it's, I feel like it's a lot of research demos, but [00:13:00] nothing I can use on my phone today.Guillaume: The thing, there's a thing that would be interesting here is that since, indeed I've been so much sure that has been done in the vision community compared to radio dys, stomach, I think there are so many long infra Yeah.And there are so many things we can do to actually improve this further. So it's our first version, but we have so many ways to exist, much better and much more efficient, cost efficient, soswyx: yeah.Guillaume: So really it's not a new field at all, of course, but there are still so many things that can be done.Perfect. It'sswyx: nice. I should also mention for those who are newer to flow matching, I think the creator, this guy's name is Alex, he's done I think in Europe's maybe two Europes as ago. There was, there's a very good workshop. There's one hour on like this matching is I would recommend people look that up.That's the other thing, right?Efficiency and Model Strategyswyx: The efficiency wise, like I, I imagine like the reason is open weights the reason you pick 3.6 B backbone it you are 3.4 B you are, try to fit to some kinda hardware constraints. You kinda fits some kinda basic constraints. What are they?Guillaume: Not necessarily, I think something we care about in our model that they're efficient.So we have a [00:14:00] lot of separate model, for instance. So we have this that is very small, very efficient. We also have a small OCR model that is available. Good, highly efficient as well. And I think on a project maybe there, I think companies are going to take is to have a coverage general model that will do a bit of everything.But that is also going to be expensive. On here. What want say is if you care about this specific use case, if you can actually use this model, it just does that. It's extremely good at it. Survey, very efficient. That's why we can actually add. We do, but also OCR that are like really good at that.And that would be much more cost effective factors and the general model that will contain a lot of capabilities you don't really need. So yeah. So we're doing like general model, but also like more customized model. This,Open Weights and BenchmarksVibhu: how does it compare to other TTS models? It's, we are going follow open wave.We're just dropping it. I think it's pretty good.Pavan: Yeah, I think it's pretty good. Like it, it's definitely one of the best. For sure. It's probably I would say it's the best open source model, butVibhu: decipher themselves.swyx: Yeah.Voice Agents VisionVibhu: Why now? How does it fit into broader ral vision? How do you see voice agents?How do you see voice? I think every year I've heard, okay, you're a [00:15:00] voice. You're a voice. There's a lot of architectural stuff. There's a lot of end time that see it, your solving, but where do you see voice setting?Guillaume: We had so many customers asking for voice. That's also why we wanted to build it.What's interesting in this domain is that. In a sense, if you take something simple like transcription it doesn't seem like something that should be very hard to do for a model. It's essentially, it's pattern recognition. It's classification on this. Models are very good at classifying, right?Or nonetheless, when you talk to them it's not there yet, right? It's not, you don't talk to them the same way you talk to a person. On something, maybe people don't realize it. It's in English it's still much better than in any user language, even compared to French instance. If you talk to this million in French, when you see people talking to this they'll talk very slow.They'll articulate as much as they can. So it's not natural, right? We're not yet to this. And I think, yeah, maybe the next generation will not know this, but yeah, I think people that. But our edge will actually always keep this bias speaking very slowly when they talk to this model. Even if maybe, probably in a couple of years, maybe next year it'll not be necessary anymore.But yeah. But what's interesting is to see that yeah, even for like languages [00:16:00] like yeah, French and Spanish Germans that are not no, no resource on religion. You have a lot of audios there on still it's not as good. And I think a consequence. Because then for this, I suppose just is not as much energy, as much effort that has been put done in some other mod that for some vision or like coding.But but yeah, there's still a lot of progress to be done. I think it's just a question of doing the work and it's clear path I think to get there.Pavan: It's a little fascinating because I worked on Google Assistant I think while back at this point, but it's, I think it's, it like when you take a step back, it's fascinating.It's not that long ago. It was like four years ago or five years ago, and it's now it's completely audio in, audio out and the function calling and the whole thing happens completely end to end. And in a very natural,swyx: yeah,Pavan: natural way and still ways to go. Kim was telling, even despite all the previous, it's not like you're speaking to a person.When you talk to any of these agents, bots, or voice mode kind of situation, it's still like a gap. I think that's the great part and I feel like with even the existing [00:17:00] stack, we should be able to get to this very natural speech conversational abilities soon enough I guess.And we'll also hope. I get thatGuillaume: on this kind of the next step, right? Because when you talk to these agents, like usually people are just writing to them and sometimes they'll this very clear, for instance, you are, you want to write code, but you are, you have a very clear idea of how you want the model to implement what you in mind.But so here you are able to spend a lot of time writing. So it's not really efficient on audio is really like a natural interface that is just not there yet, but I think it's just gonna be the place.Vibhu: How's it like building, serving, inferencing, like we see a lot about, it's very easy to take LMS off the shelf, serve them.Fine tuning, deploying. I know you guys have a whole you have Ford, you have a whole stack of customizing, deploying. Is there a lag in getting that. Like distribution channel. Are you helping? There is. So like prompting, lms, you can have them be concise, verbose, all that.They're built on LM backbones, these models. How do you see all that?Enterprise Deployment and PrivacyGuillaume: Yeah, I think this is a lot of what we're doing with our own customers. Very [00:18:00] often they come to us, so it's for different reasons. I think one reason is sometimes they have this lot of privacy concerns.They have this data that it's very sensitive. They don't want data to leave. The companies, they wanted to stay. Inside the company. So we have them deploy model in-house. So either on a, either on premise or on private cloud. So they're not worried that it's given to a third party on the there some leakage.Sometimes they have this kind of many companies have this different, sensitivity of data they have like sometimes channel chat can send it to the cloud has to stay there. So then it creates some kind of heterogeneous workflows where it's annoying. You cannot send some data to the cloud.This one you can, so here, when we actually deploy the model for them, they don't have this consideration. They are like not worried that, this is going to leak. Everything is much easier. So we help them basically do this on the, so it's one of the very proposition. But but the other is very often, when customers use this off the shelf close model, but very sad is that they are not leveraging, these data that have been collecting for four years or something for decades.So much data. Sometimes it's trillions of tokens of [00:19:00] data in a very specific domain. Their domain, which is data that you'll not find in the public, on the public internet. So data on which, like close model, we actually not have access to one, which that's going to be really good. So if they're using like closed source models are basically not benefiting from all these insights.All these data they have collected three years, they can always give it into the context that in France, but is never as good as if you actually train the modern analysis. So yes, that's basically what we help them to do. We actually provide them some purchase, basically what we announced at GTC this week.So we provide them with this, it's basically like a platform with a lot of tools to actually help them process data. Trained on that. Yeah, it's actually the same thing that we're using in the science team. So it's actually very better tested infrastructure, like a lot of efficient training cut base.For a quality pre-training like a fine tuning, even doing S-F-T-I-L. So we help them do this using the same tools as what our science team is building is using. So since it's tools that we've been using for two years now, it's really better tested. It's really sophisticated.So it's the same thing. We are giving to them, giving the company the same thing [00:20:00] that what are same still using internally actually build their own ai and it makes a really big difference. I think sometimes customers. And many in general don't realize how much better the model becomes when you fine tune it on your own data.And you can have a, your model is here. You start from there. You have a cross source model, which is sort here, but if you actually fine tune it can actually really go much further than this. And then you have a very big advantage. The model is trained on your entire company knowledge, so it knows everything.You don't have to feed like 10 K tokens of contact at every query. So it's it's much easier. It's a bit, I think using a closed source model is really sad because it basically puts. You are not leveraging all this data and you are going to be using the same model as all your old competitors when you're actually using, everything you have been collected for years, which is really valuable.So yeah. So we help basically customers do this. We have a lot of solution I mean deployed for engineers that go in the company that basically look at the problem customers are facing to look at what they're struggling to do what we should do to solve it. So we help them solve them together.So it's I think our approach is a bit different, but here. [00:21:00] Some of their companies and competitors, it's, we don't just release an endpoint on sale, do some stuff on top of that, or we don't just give a checkpoint. We really look very closely with customers. We look at the issues they have, we had them solve them.We really make some tailored solution for the client are facing. Some example are also going to be, sometime we have some customers. They really wanted to have a really good model, really performance on some, like Asian languages on the, if you take some of the shelf models, they can speak it, they can write in this language, but it's not amazing.This language would be like maybe zero 1% of the mixture. So it has been included during training, but very little. So what we did here is upgrade. We trained a new model for them, but so this language was 50% of the mix, so it's much, much stronger. It knows of the dialects, it knows the, so it's yeah.So it's some example of things we can do and it's really arbitrary, custom. I think you had some of their customers, for instance, they wanted some. They wanted some 3D model that can do audio with a very good function cable. So something you wanted to put in the car in particular, they wanted this to be offline because in a car you don't necessarily have access to internet.So [00:22:00] yeah. So here we can actually build the solutions. There is no like model out of the box on this. In the internet you have this very, you have this very general model generalist, like he's strong model. But for things like this, they always want at specific solutions and on some other reasons.Sometimes they come to us is because, like they, they experiment with some closed source model. They get some prototype. They're happy with what they build. They, it works well. They're happy with the performance, and then they want to go to production and then they analyze. But it's extremely expensive.You cannot push this. It's so then they come back to us on this. They can help us build the same thing as this, but using something much cheaper on here. And here we can sometime be something 10 x cheaper by just functioning a model and it'll be better OnPrem on their old server and also much cheaper as well.So yeah,swyx: that's the drop pitch right there. Take all themoney.Vibhu: And outside of that you do, we do put open wave models so people can do this themselves. I feel like not enough people go outta their way.swyx: They're not going to, they're gonna ask them to do it as the expert. IGuillaume: think initially we didn't know, [00:23:00] we wanted completely short at the beginning of the company because, I think our study was not exactly the same as what it is today, but what we underestimated initially is the complexity of deploying this model and connecting them to everything to be sure it has access to the company knowledge on the, and it was, yeah, on, we were seeing customers struggling with this, but it was even, that was three years ago and no, things are much more complicated because now you don't just have, text on SFT on a simple instruction following.You have reasoning like your agents, you have like tools. You have a multimodal audio, so it's much more complicated than before. And even back then it was hard for customers. So they really need, have some support and this is why actually providing like always some four D position as well. The processFine Tuning and Personalizationswyx: I'm curious is there also voice fine tuning that people do?Pavan: So in this forge we also have a say unified framework. And the hope is like the er speech to text that we released earlier this year. And even the ER chart that we released last year. And I think a big people, I think there's a big, rich ecosystem [00:24:00] of people fine tuning whisper, and people want the same thing with w so it's much stronger than Whisper.And yeah, the the platform offers that kind of fine tuning yeah, which could be any kind of fine tuning. Like for instance, even sometimes people want to support new languages to this, which are tail languages, which we hope to cover. Certain natively, but if there is a language where you data and you want to frank you, I think this is a good use case.Or the other use cases, you, it's the same language, like even English but it's in a very domain specific way.swyx: Yeah. Terminology, jargon, medical stuff.Pavan: Exactly. And also there's specific acoustic conditions like there's a lot of noise or the, and. The model will do decently in most conditions, but you can always make it better.And that those are some of the use cases where you can improve it e even further. And that's one good use case for this and for text to speech. We're just releasing it so we'll have support for that soon too. I think it's similar use case.Voice Personalization Pavan: It's little different the kind of things that you want to extend a [00:25:00] text to speech model to, which could be like voice personalization, voice adaptation for enterprises.Many enterprises need very specific kind of tone, very specific kind of like personality for this kind of voice. And all of those are like good use cases for fine tuning.swyx: This one I was gonna ask you, we never talked about cloning voice clothing here. How important is it, right?Like I can clone a famous person's voice. Okay. ButPavan: the main use case would be like for enterprise personalization, like enterprises need like a lot of customization. You don't want the same. Voice for all the enterprises. Each enterprise want a customized, specialized something which is representative both their brand and also their, I guess safety considerations and the use case I think the kind of thing that you would deploy as a empathetic assistant in the context of a healthcare domain would be very different from the kind of thing that would be in a customer support bot and would be different from like more conversational aspects.I think those are the. [00:26:00] Customizations you would expect from enterprise. And that's the main use case, at least from our side.Vibhu: My, my basic example is you don't want to call to customer services and have the same exact voice. It's just, it's gonna be weird.Long-Form Speech ModelsLong-Form Speech ModelsVibhu: But also on the technical side of this, so there's like a few things in TRO that I thought were pretty interesting.He's a big fan of this paper. Oh, he said very good paper. He said this is the best SR paper he's ever read. Yeah. I've hyped up this voice paper enough. We covered it. Somewhere, but a big thing. So Whisper is known for 32nd generation a 32nd processing. You extended this to 40 minutes. There was a lot of good detail in the paper about how this was done.Even little niches of how the padding is. So it's very much needed. You need to have that padding in there, the synthetic data generation around this. I'm wondering if you can share the same about the new speech to text, right? Text to speech. So how do you. How do you generate long form, coherent?How do you generate, how do you do that? And then any gems? Is there gonna be a paper?Pavan: Yeah. Yeah. They would be a technical report. Okay. Yeah. I think I could have a lot of details.Real-Time Encoder AdvancesPavan: But me I think the [00:27:00] summary of it, actually, some of the considerations in this paper were, because we started with the wipa encoder as the starting point, and now we have in-house encoders, like the bigger time model, for instance, which we released in January.Also release a technical report for that real time model as well, which is this dual stream architecture. It's an interesting architecture. You should check it out. And there we have a causal encoder and I don't think there's any strong, multilingual causal encoder out in the community. So we thought it's a good contribution.So that's one nice encoder there. Other people want to adapt. That's a good end code. And we train it from scratch. I think her. Post stack is now mature enough that we are able to train super strong ENC codes. And some of these considerations, like spatting and stuff, is a function of the Whisper ENC code.And now that we train encoders, inhouse the design concentrations are different.Scaling Context for TTSPavan: And for the question on text to speech, I think that's also leans onto the original auto aggressive decoder backbone. I think, it says very, almost identical considerations. I think the long context in it's not even long con, [00:28:00] so the model processes audio at 12.5 herds, so one second maps to like 12.5 tokens.So I think one minute is like 7.8 tokens. You can get like up to 10 minutes in eight K context window and get half an hour and 30 K context window. So that's and 30 2K context is something that's we are very comfortable training on. We can extend it even much longer. 1 48 K. Okay. You can naturally see how it can extend to even our long generations.Yeah. We need the. Like data recipe and the whole algorithm to work coherently enough through such long context. But the techniques are some way very similar to the text, long context modeling. And the key differences, it's just doing flow matching order regressively instead of a text open prediction.swyx: Okay. I think that was most, most of the sort of voice questions that we had. ButWhat Makes a Model SmallVibhu: I have a big question on Mr. Al, Mr. Small. So what is small? How do we define [00:29:00] small? What is this? What is this? I remember the days of Misal seven B on my laptop. The snuff fitting on my laptop. I could run it on the big laptop, butGuillaume: it's just additional.Question of terminology, like here what we did, baseball is north active parameters, but it's true. Really not give it another name, but yeah, we could have called it medium, but only, I,I suppose it's a model that we released mixture of experts. It's a model that combines different model before which we were doing the same, is that we had one model, general model for Israel. Doing instruction following, were like a separate model that was Devrel trial. So qu coding specify specific to code with another model for Reason Maal.So this were separate artifacts built by different team at trial on what we're doing is basically merging all of this. It was, you had pixel trial was the first vision model. We was like a separate model on the way we do things internally is that we have one team focus on one capability, build one model.On the means mature, mature enough, we decide to merge this into the [00:30:00] matrix. But here it was the first time we basically match all of this into one. But there are some other things we did at first time to merge time, for instance, like more capabilities or function coding I think would be, are, it's going to be much, much better in this trial, small platform.But but yeah, so it's our latest model on the working is,Vibhu: and yeah, key things is it's very sparse. Six, be active pretty efficient to serve. 2 56 K context. Yeah,Merging Capabilities vs Specialistsswyx: I think what's interesting is just this general theory of developing individual capabilities in different teams and then merging them.Where is this going gonna end up?Vibhu: Like we've seen the five things put together in this. Yeah. What are the next five teams?swyx: I think actually OpenAI has gone away from the original four Oh. Vision of the Omni model. This was what they were selling. All modalities and all modalities out.But I feel like you might do it.Guillaume: I think there's some mod where it's not competitive use, for instance for audio. For audio here, if you want to do transcription, I think it makes no sense to use a model. If you just want to trans tech it, it'll be very inefficient. If you want to do audio, you probably just want to be the [00:31:00] one VR 3D model performance essentiallyswyx: the same.It's going to be incredibly cheaper. So here, that's why we wantGuillaume: to have a separate but just does this. Yeah, I think the question is just, yeah. If you are to, to your model. By speech and you asking like a very complex questions on how you do this on the, just to cascade things. Do you want to put a d in a model that has like a one key around it?It's like a, not a competitive discussion, I think unaware if you doing into the direction, but that's possible. Of course. But yeah. But I think for us, the next capabilities we want to try to integrate into these models when we are going to be yes, like marketing or no reasoning better, I think more capabilities that people don't talk too much about, but at high bottom, I think for our customers in our, on different industries, for instance, things are around like a legal computer.I design all these things that is this males out of the box are to put at that. Because people, if you don't prioritize this, there is not like too benchmark on that. Butswyx: this done how toGuillaume: make this good and this just start to do the work. Extracting some that processing it [00:32:00] expression. So yeah.But we are offering the imagine to this.swyx: I think for voice. Yeah. The key thing I think over maybe like the last year or so with VO and gr Imagine and all these things is joining voice with video, right? Which people don't understand spatial audio because like most TTS is just oh, I'm speaking to a microphone in perfect studio quality.But when you have video, like the voice moves around.Pavan: That's true. The constitution was a little different in the sense that there it's like a a standalone artifact where you get the whole thing and you consume it. But in a conversational setting, it's a, you need the extreme low latency.swyx: Yeah,Pavan: streaming would be one of the primary concentrations.swyx: You can build a giant company just doing that, right? So you don't need to do the voice, but I was just know on the theme of merging modalities, that is something I, I am like, wow. Like I didn't, everyone up till, let's say mid last year was just doing these like pipelines of okay, we'll stitch a TTS model with a voice thing and a lip sync [00:33:00] thing and what have you.Nope. Just giant model. Yeah.Open Source MissionVibhu: I have a two part question. So one is, it's still open. It seems like open source is still very core to what you guys do and I just have to plug your paper. Jan 2024. This is the one trial of experts like. Very fundamental research on how to do good.Moes paper comes out very good paper for anyone. That's just side tangent. No.swyx: This thing caused, we bring back, eight by 22 was like the nuclear bomb for open source. I think it takes Shouldn be more seven B more. Yeah. Yeah. But this is a bigger opposite than me.Yeah. Yeah I don't remember this. I remember, I don't think it was January, right? It was like new reps it was, it dropped during new reps and everyone in Europes was December of 25th, I think. Yeah. The model was did as well.Vibhu: It's just a little update probably.swyx: Yeah. No, but you have a point to make.Vibhu: No, you gotta check that. But then, I just want to hear more broadly on open source for you guys, and when you had asked earlier [00:34:00] about what's next, what are the other, side tapes working on you. You put out Lean straw. This,swyx: it's not necessarily surprise. I was like, I don't, this doesn't fit my mental model or Misra.Guillaume: Yeah. First for open source in general, I think it's really something which looks to the January of the company. I think we started it per once, is we so we have open sourcing with, since the beginning and even before this. So before this, so me and Tim were at Meta, we released LA and I think what was really nice.To see that before this, for most researchers like universities, it was impossible to work on elements. There was no alien outside. And if you look at many of the techniques that were developed after, for instance, was open source all this post-training approaches like even DPOD, like preference optimization, all of this were done by people that had access to this portal.And it'll have been impossible to do without this. So it's really making sense, move faster. So we really want to contribute to this ecosystem. I think like the deep and also like very lot of impact. All these papers that are I think in the open source community are really helping the science community as a whole to move faster.So [00:35:00] we want contribute to this ecosystem. That's why we're releasing very detailed technical reports. So ma trial and our first reason model, and ation, lot of results, things that work, things that did not work as well. Think helpful on the, yeah, so for the audio model also to share a lot of details, share of them for real time model.And the, yeah, so we really want to continue this, basically belong to this community of people who share science. I think we really don't want to be, leading in a world where the smartest model, the best models are only behind, close doors. Only accessible to a shoe companies that we, as a power to decide we can use them on it.I think it's a scary future. We don't want to live in, we really want this model to be accessible to anyone that want. Intelligence to be used unaccessible by anyone who can use it. So yeah, so that's why we are pushing this mission and source model. Yeah. So not, so yeah, no strategy. So it's open source, not the first model, so not the best on the Yeah.Lean and Formal ProofsGuillaume: LIN trial I think is also one step into this direction. So it's yeah, a bit different than what we are usually releasing. But we have a small team internally [00:36:00] working on them. Formal proofing, formal math. So I think a subject we care about in general and we were working on reasoning. I think we started too early before doing reasoning without LMD is very hard, especially when you work with formal systems because the amount of data you have is negligible.It's addressable community of people writing like formal proofs. But the reason why we like it is because I think there is if you look at what people are doing with reasoning, is there, the problems that you can use. Are usually going to be problems where you can verify the output. So for instance, all this ai ME problem where the solution is a number between 100, like a thousand.So you can verify, compare this with a reference or it's an expression. You can actually compare the output expression generic with the reference. But there are many, most of them have problem and most of the reason problem. There is no like way to easily verify the solution. If the question is show that F is continuous, cannot compare in the reference, right?If it's a probe that this is true or probes is properties, there is no way to. You cannot act, simply verify the correctness of your proof. So it's hard to apply the, there is no referable reward here. So [00:37:00] what you could provide is of course, like a judge and judge that will look at your proof. But it's very hard and it's very, you could do certain, some reward hacking happening there.So it's difficult. You could provide like a reference proof, but then there are also many ways to prove the same thing. So if the model says give negative reward because it's a different poop, maybe it was still digit proof, just different. So it's not going to work well. What's nice with lean and with formal probing is that you don't have to worry about this whatsoever.We just,swyx: they're all function is largely compiles in lean is functionally the same. Exactly.Guillaume: It's like a problem if it compiles it's correct. It's very easy. And you can apply this and then you can,swyx: it's just way too small. So no human will actually go and do it.Guillaume: Yeah, that's exactly.It's the only people can do it. It's like a very small committee of people doing a PhD on that. So it's super small. And it's sad because it's actually very useful on not just mat, but also in software verification. So for instance, software verification today. So tiny market. Very few industries work on this and we need that.It's usually going to be like companies like building airplanes, air robotics,swyx: likeGuillaume: things [00:38:00] where they absolutely want to be sure. Life depend on this, but it's very rare that people formally verify the correctness of their software. But I think one of the reasons for this is simply that it's just hard to do.swyx: Are you think of TLA plus? It's the language that some people do for software verification? No. That people use in a ference, but but yeah, it's the reason I think why people don't use it more and why this industry is not as big as could be is because it's very hard. But now with cutting edges that are there, it's going to be very different.Guillaume: We're going to see much more of this. So I think yes, industry there is going to be much larger in the future that we, these models. So yeah. Here also anticipating this a little bit, we wanted to work on that because it's proving like a math theory and like a, essentially the same tools.swyx: Yeah.Reasoning Transfer and Agentsswyx: One of my theories is that because the proofs takes so long, it's actually just a proxy for long horizon reasoning and coherence and planning. Maybe a lot of people will say okay, it's for people who like math. It's for being okay. It's like a niche math language. Who cares? But actually, and you use this as part of your data mixture for [00:39:00] post-training and reasoning, actually, it might spike everywhere else.Yeah. And I think that's un under explored or no one's like really put out a definitive paper on how this generalizes.Guillaume: Yeah, absolutely. AndPavan: I think evenGuillaume: that's what we're seeing already. For instance, you should do some reasoning on math as then the American should do reason even.Yeah. In the early stage. So we, the, there is some transfer, some sort of emergence that happens. And I think some, it's also interesting, it's not just I think the topic in general, but it's, there is a lot of connection with this on including agents because. Sometimes the model can see like a three that it has to prove it's very complex, but then it can take the initiative to say, I'm going to prove this three lr.I'm going to suggest three Rs, and I'm going to in parallel prove each R. So three of them in parallel with sub agents, but I'm also going to prove them in theory and the three tool so you can do this also. Pretty interesting. You can, even if you fail to put one of the LeMar, you can actually, maybe you succeed to put the normal lema too, so you get some possible reward here.So it's a bit less Spartan issue, just get to zero one for the entire thing. [00:40:00] So it's pretty interesting. I think we can actually,Vibhu: yeah, it's also an interesting case just for specialized models in general, right? Like the cost thing you show is pretty interesting yeah, similar score wise, you are, thirty, seventy, a hundred fifty, three hundred bucks.Smaller.swyx: I think cost is a bit unfair, right? ‘cause this one is at like inference cost. It's always there on top with their margins on top of it. But, we don't know anything else, so we gotta figure it out.Vibhu: Okay.Next Frontiers in TrainingVibhu: I did wanna actually push on that more. Not on cost, but you mentioned about, okay, it's a great way to have verifiable long context reasoning.What are other frontiers that, I'm sure you guys are working on internally, there's a lot of push of people pushing back on pre-training. Scaling, RL pushing, compute towards having more than half of your training budget. All on rl. Where are you guys seeing the frontier of research in that?Guillaume: You mean theVibhu: just in foundation model training in the next, one thing that you guys do actually is you do fundamental research from the ground up, right? So you probably have a really good look at where you can [00:41:00] forecast this out.Guillaume: Yeah. I think for us we're still working a lot on the pre-training side.I think we are very far from situational, the pre-training. I think ML four preprinting will be like big step compared to everything we have done before. So we are pretty excited about this. And I think on the other side, I think now we have more and more to think about this algorithm that will actually support this very long trajectories.I think when it was, for instance, GRPO for it doesn't really work this any bit of policy. Which was okay initially because you are solving math problem that can be solved in like a few thousand tokens. So the model can alize them pretty quickly. So when you do your update, the model is never too far off.It's never too far off. But now when you are moving towards this kind of problems where certain takes hours, like six hours to get a reward, then your model is co pick places. So you have bi new infrastructure that supports this, but also new A, so now everything we're doing internally, we're trying to. Build some infra that we actually anticipate is what we have in six months, one now, which is this extremely no scenarios on the, I think when we started Missal, part of me and [00:42:00] we wanted to, is very nice under element where people are there, they can do research, they like with a lot of resources.So it was nice. I think things changed a lot when I think when J Pity came out. I think after that I think was. This one is same again. But but yeah, but it was nice. And I think we also want to work part of this descrip beforeswyx: coming to the end.Hiring and Team Footprintswyx: We're just, obviously, I think you guys are doing incredible work.You've, they are a very impressive vision for open source and for voice. What are you hiring for? What's the what are you looking for that you are trying to join the company?Guillaume: Yeah, so we are hiring a lot of people in our sense team. We're hiring, in all our offices. So we have a, our H two is in France in Paris.We have a small team in London. We like a team in Pato as well. Co we open some offices in in SAU, in Poland. So one in Zurich. We also like some presence in New York as well on Sooner one in San Francisco. So we all bit either way also like hiring remotely. So we're going the team trying to hire like very strong people.I think we want to stay, so the team is not. Instead of fairly small team. [00:43:00] But I think we want to keep it that way. ‘Cause we we find it quite efficient. So like a small team they agile so yeah.swyx: Okay.AI for Science Partnershipsswyx: Let's focus on science and the forward deployed. We actually are strong believers in science.We started the our new science pod that focuses specifically on the air for science. What areas do you think are the most promis.Guillaume: What we're pretty excited about right now, and something we have already started doing or that we'd probably be able to share more about this in a couple of months, is that we are exploring AI for science.And there are a lot of areas where we think that you could get some extremely promising buzz. If you were to apply AI in these domains. There are a lot of long inputs. You just have to find these domains where actually AI has not been yet applied, and it's usually hard to do because the people working in those domains don't necessarily know the capability of these models.They don't know. How I would just have to pair them with Yeah, exactly. Your researcher slashing, which is actually hard to do. But this matching, we're doing it naturally with our customers. So we have some company we are very closely with. So for instance, ISM Andreesen are one of our partners, so we're doing some research with them on their other, like tons of extremely interesting problems.Columns in physics, in [00:44:00] science matter science that they're essentially the only ones to work on. ‘cause they're doing something No, no one else is doing on the, yeah. So there are many domains where AI can actually revolutionize things. Just you have to think about it on you familiar with what can do or to apply it.So yeah, it's something where more modeling with our partners, with our customers sort AI for s, but.swyx: Yeah. Okay.Forward Deployed Skillsswyx: And then for deployed what it makes a good four deployed engineer, what do they need? Where do people fail?Guillaume: I think it's usually you need people that are very familiar with the tech and not necessarily with a lot of research expertise, but that are actually pretty good at using this model that can actually like that know how to do functioning, that know how to like, start some error pipeline.And it's it's not easy. It's something that mucus. Majority of companies will not be able to do this on their own. So here I think we need people that are, that like to solve problems that are accept solving some complex, very concrete problem. It's applied science basically.And yeah, so I think it's not too different. I think from the case you need in research because it's essentially you are trying to find solutions to problems that in [00:45:00] customers have not yet. So sometimes it's easy. Sometimes you're here to do the work. You have to like create synthetic data.Find some edge case. So it can be, yeah. Depends on the problem. But but yeah, you have to, I think it also a bit of patience on the be creative. I think very similar skill is Asian,Pavan: the diversity of the work they do. It always surprises me. It's it's, it goes all the way from the kind of stuff they encounter in industries.It's just very interesting. I think.swyx: Any fun like success anecdotes.Guillaume: Yeah, it can be actually training this small model on edge that just we do one specific thing can be like training some very large model without some specific languages as well. Making models really good at some tube use, like for instance, computer ID design, these kind of things.Is that pairing with vision as well? Yeah,Pavan: and the fact detection for chips or like in, in factories identifying things like it, the. Diversity could be anything where you can deploy these foundation models. So yeah the work to make it work in that specific setting, basically whatever it takes to make it like add value in that, by the way, workflow.Vibhu: Yeah. [00:46:00] And it goes across the stack, right? Like even just pulling up the website like.swyx: It's so broad on compute. It is so broad.Vibhu: We didn't even touch on if you have a coding CLI tool. One thing you guys were actually like, I think the first tool was agents, ral agents. You had the agent builder, you can serve it via API and all that.And I'm guessing forward deploy people.Guillaume: Yeah.Vibhu: Help build that out and stuff.Customer Feedback LoopGuillaume: It is also why we are, so we're doing many things, but I think that's also part of the value proposition that sometime know customers. They're always very. Extremely careful about their data and they don't want to, they don't like, trusting so many partners, trusting one partner for code, giving the data to another third party for like audios and another one.So they don't like this here. What they really like with our approach that we can help them on anything so they don't have to send the data to so many clouds. So yeah,swyx: I think that there can be many orders of magnitude more. F Ds then research scientists and they don't need your full experience, but they're still super variable to customersGuillaume: in practice.These two teams [00:47:00] are still quite intertwine, very often. Yeah. So first of all, they're using the same tools, the same data pipeline and everything on the, it's it's very helpful for the science team to get the feedback and the solution team ‘cause they can. Look at these customers are trying to do this.This is not working. It can really be show in the next version. Yeah. But this is basically a real world eval. Yeah, it's real world eval and it's not something, for instance, if you're just working in the lab, it's just ships model. But you don't do this work of for customers. You have no idea for whether your model is good at this H case.For instance, you even in year found this, right? So yeah, there is a very gap, big gap between the public benchmarks that are very like academic. OnPavan: the rare cases are just very diverse and in the specific concept of a customer, you can fine tune and make it like first evaluate, create a solid eval, benchmark, and then measure in the context of their, the kind of audio.Like for instance, one use case is literally just, there's the word for kids and they have to just say it out. It's a very specific thing. You're just saying one word and then you have to you, you'll grade the kid whether they did it right or not. It's [00:48:00] like R for, but so there're very diverse use cases and the idea is that they, the.Applied scientist engineer will go and make it better. And then from the learnings we incorporate it into the base model itself. So it's it's just better out of the box.Vibhu: Yeah. It's a good full circle system. Like the foundation model evals are all just proxies of what you really, you're never gonna have one that says it, it doesn't make sense for there to be, a one word transcription like that.It's not something you wanna fit on. Perfect.Wrap Up and Thanksswyx: Everyone should go check out everything that Michelle has to offer and try the TTS model, which will link in the show notes. But thank you so much for coming tha thanks. Such a stretch. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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They informed and entertained together throughout the first 20 years of Planetary Radio. Listen in as the Society’s chief scientist and book club edition host Mat Kaplan share the mic once again for a delightful conversation about Dr. Betts’ two new space books for young people. “Are We Alone?” introduces the search for life across the Universe, while “The Size of Space” collects many of Bruce’s brilliant and hilarious ways to cut our Solar System down to human size. Discover more at: https://www.planetary.org/planetary-radio/book-club-bruce-bettsSee omnystudio.com/listener for privacy information.