Podcasts about Machine learning

This week on Catalyst, Tammy speaks with Graeme Cuthbertson, Director of IT Operations and End-User Systems at Neurocrine Biosciences. They explore Graeme's career across industries, including banking and biotech, and what those experiences have taught him about building empathy into technology. Graeme also highlights the importance of meeting customers where they are, the role of family support in personal and professional growth, and how human connection and thoughtful technology can elevate both employee and customer experiences.Please note that the views expressed may not necessarily be those of NTT DATALinks: Graeme Cuthbertson Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, Antonia and Andrew discuss the February 18, 2026 issue of JBJS, along with an added dose of entertainment and pop culture. Listen at the gym, on your commute, or whenever your case is on hold! Link: JBJS website: https://jbjs.org/issue.php Sponsor: This episode is brought to you by JBJS Clinical Classroom. Subspecialties: Knee, Oncology, Pediatrics, Shoulder, Hand & Wrist, Orthopaedic Essentials, Trauma, Spine Chapters (00:00:03) - Case is On Hold(00:00:45) - Episode 100(00:03:03) - Sneak Preview: Miller Review Course(00:03:42) - AI Generated Text in Orthopedics(00:05:36) - AI in Orthopedics: The Promised Land(00:13:44) - Artificial Intelligence in orthopedic and sports medicine(00:16:27) - Osteo and Sports Medicine Editorial Policies on AI(00:24:42) - How to Write a Paper With a Computer(00:25:16) - Deep Learning Model for Differentiating Neoplastic Fractures from Non(00:31:36) - The Ms. Cleo Phone Paradigm(00:32:34) - Machine Learning and Neoplastic Fractures(00:37:05) - AI-driven CT MRI Image Fusion and Automatic ACL Reconstruction(00:39:05) - A 100 Episodes of JBGS: Thank You!(00:40:46) - Aisha Abdeen Is The Next Co-Host!

On this episode of The Association Podcast, we welcome industry veteran Liam O'Malley from Path LMS by Momentive. Liam shares his extensive journey in the association market, highlighting key experiences from the American Bar Association to his current role at Momentum. The discussion covers the evolving landscape of online learning, microlearning strategies, and the importance of data-driven decision-making. Liam also introduces the exciting launch of Momentive IQ, a tool designed to centralize data and integrate various association technologies. Tune in to hear insights on managing nano markets and the future of AI in associations.

What does it take to migrate the heart of a nation's banking system to the cloud?In this AWS Executive Insights fireside chat, Ben Cabanas sits down with Simon Davies, GM of Core Banking at Commonwealth Bank of Australia, to unpack one of the most mission-critical cloud transformations in financial services. With nearly 40% of Australia's liquidity flowing through CBA's core platform, the stakes were enormous.Simon shares how CBA migrated the world's largest SAP core banking deployment to AWS while improving reliability, reducing infrastructure costs by 30%, and enabling real-time customer experiences. Beyond the technical achievement, he reveals how transparency, cultural alignment, and a rallying cry of “believe” helped mobilize thousands across the organization to deliver change at national scale.

เอกสารวิจัยจาก Standard Chartered ฉบับเดือนกุมภาพันธ์ ค.ศ. 2026 นำเสนอการวิเคราะห์แนวโน้ม ตลาดสินค้าโภคภัณฑ์ ทั่วโลก โดยครอบคลุมทั้งกลุ่ม โลหะมีค่า โลหะพื้นฐาน และพลังงาน รายงานระบุว่า ราคาทองคำ กำลังพยายามสร้างฐานใหม่ท่ามกลางความต้องการที่แข็งแกร่งจากจีน ในขณะที่ ทองแดง มีปริมาณสินค้าคงคลังพุ่งสูงเป็นประวัติการณ์เนื่องจากความกังวลเรื่องภาษีศุลกากร สำหรับภาคพลังงาน ตลาดกำลังจับตามองความตึงเครียดระหว่าง สหรัฐฯ และอิหร่าน ซึ่งส่งผลต่อความผันผวนของราคาน้ำมันดิบ Brent นอกจากนี้ยังมีการใช้โมเดล SCORPIO ซึ่งเป็นระบบการเรียนรู้ของเครื่อง (Machine Learning) เพื่อคาดการณ์มูลค่าที่เหมาะสมของราคาน้ำมัน โดยชี้ให้เห็นว่าราคาปัจจุบันอาจสูงเกินความเป็นจริงเมื่อเทียบกับปัจจัยพื้นฐาน ข้อมูลทั้งหมดนี้สะท้อนให้เห็นถึงผลกระทบของ ปัจจัยทางภูมิรัฐศาสตร์ และนโยบายเศรษฐกิจมหาภาคที่มีต่อทิศทางการลงทุนในอนาคต

AI is moving fast from research to real-world deployment, and when things go wrong, the consequences are no longer hypothetical. In this episode, Sean McGregor, co-founder of the AI Verification & Evaluation Research Institute and also the founder of the AI Incident Database, joins Chris and Dan to discuss AI safety, verification, evaluation, and auditing. They explore why benchmarks often fall short, what red-teaming at DEF CON reveals about machine learning risks, and how organizations can better assess and manage AI systems in practice.Featuring:Sean McGregor– LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:AI Verification & Evaluation Research InstituteAI Incident Database38th convening of IAAIBenchRiskState of Global AI Incident ReportingUpcoming Events: Register for upcoming webinars here!

Join Professor Laura Coates and Phillip Mease as they discuss the top publications in the world of PsA. This month, the conversation covered the ‘classification of patients into distinct PsA phenotypes based on baseline demographics and clinical characteristics using a machine learning approach, and consensus-derived GRAPPA terminology, to support shared clinical decision making, and enable more effective patient stratification in both observational studies and clinical trials.

Check out host Bidemi Ologunde's new show: The Work Ethic Podcast, available on Spotify and Apple Podcasts.Email: bidemiologunde@gmail.comIn this episode, host Bidemi Ologunde sits down with Damilola "Dammy" Gbenro, a Data Analytics and Machine Learning professional and talked about what it really means to build and maintain an online presence in the age of algorithms and AI. How do you utilize the benefits of social media without letting it consume you? What does online safety look like when your life is also your brand? And as AI reshapes trust, attention, and creativity, how do we protect our identities and our peace?Quick question: when you buy something handmade, do you ever wonder who made it, and where your money really goes? Lembrih is building a marketplace where you can shop Black and African-owned brands and learn the story behind the craft. And the impact is built in: buyers can support vendors directly, and Lembrih also gives back through African-led charities, including $1 per purchase. They're crowdfunding on Kickstarter now. Back Lembrih at lembrih.com, or search “Lembrih” on Kickstarter.Support the show

“I do not believe we should be testing to test. We have to know, is this test going to change management and is it going to make a difference,” says pediatric allergist-immunologist Dr. Zachary Rubin. His knack for providing that sort of straightforward guidance explains why Dr. Rubin has become a trusted voice on allergies, asthma, and vaccines for his millions of followers on social media platforms. It's also why we couldn't ask for a better guide for our discussion on the rise in allergies, asthma, and immune-related conditions in children, and how families can navigate the quickly evolving science and rampant misinformation in the space. On this episode of Raise the Line, we also preview Dr. Rubin's new book, All About Allergies, in which he breaks down dozens of conditions and diseases, offering clear explanations and practical treatment options for families. Join host Lindsey Smith for this super informative conversation in which Dr. Rubin shares his thoughts on a wide range of topics including: What's behind the rise in allergic and immune-related conditions.Tips for managing misinformation, myths and misunderstandings. How digital platforms can be leveraged to strengthen public health.How to build back public trust in medicine.Mentioned in this episode:All About Allergies bookBench to Bedside PodcastInstagramTikTokYouTube Channel If you like this podcast, please share it on your social channels. You can also subscribe to the series and check out all of our episodes at www.osmosis.org/podcast

On Cloud Realities, the real insight rarely came from technology alone, it emerged at the intersection of People, Culture, Industry, and Technology. In the remix we bring back familiar voices and topics while going deeper into the wider impacts, influence, and potential of today's tech across society. The 2026 season trailer, arriving a little later than planned, opens with this renewed focus and sets the stage for Episode 1, launching on February 19. Here's a quick trailer to get you ready!TLDR00:11 The emergence of insight from Cloud Realities01:00  Where the magic happens 01:42 The real impact on People, Culture, Industry and Tech HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! Eurostat Anthropic elemzés

Devina Ramesh from the CFMS Research in Medicine podcast interviews Dr. Mamatha Bhat, a hepatologist and clinician-scientist at UHN's Ajmera Transplant Centre, and as an Assistant Professor of Medicine at the University of Toronto. Dr. Bhat is the recipient of numerous awards, such as the 2022 Early Career Researcher prize, the 2020 Polanyi Prize and the 2021 American Society of Transplantation Basic Science Career Development Award. Dr. Bhat is the head of the multi-disciplinary Bhat Liver Lab, with the goal of improving outcomes and clinical practice for patients following LT using ML tools.

Send a textTackling the messy reality of data fueling artificial intelligence, Andrea Muttoni—President & CPO at Story—joins the show to unpack how Story is building an AI-native infrastructure for intellectual property and training data. We dig into making the $80T IP asset class programmable, traceable, and monetizable, and how Story aims to turn “mysterious training data blobs” into transparent rights and payments for creators and enterprises.01:10 Meet Andrea Muttoni 06:49 Story's Core Mission 13:41 IP Monetization 21:08 Biggest Competitor 22:49 Compute, Models, & Data 27:46 What to IP, Where Not 31:16 Blockchain 34:54 Protecting Your IP 41:36 Reaching StoryAndrea explains how Story is building a blockchain-based IP and data layer so AI systems can train on licensed content while proving usage, enforcing licenses, and automating payments to rights holders. We talk about the practical challenges of cleaning and labeling real-world data, what “IP-safe” datasets look like in practice, and how developers and companies can plug into Story's infrastructure. Andrea also shares where blockchain actually adds value (and where it doesn't), why he thinks “AI can't scale on legal ambiguity,” and concrete steps creators and founders can take today to protect and monetize their IP in the AI era.LinkedIn: linkedin.com/in/muttoni Website: https://www.story.foundation/#AITrainingData, #IntellectualProperty, #IPEconomy, #StoryProtocol, #DataInfrastructure, #AIGovernance, #AILaw, #Web3, #Blockchain, #CreatorEconomy, #DataOwnership, #RightsManagement, #Licensing, #TechPodcast, #Developers, #MachineLearning, #AIEthics, #DataMonetizationWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send a textTackling the messy reality of data fueling artificial intelligence, Andrea Muttoni—President & CPO at Story—joins the show to unpack how Story is building an AI-native infrastructure for intellectual property and training data. We dig into making the $80T IP asset class programmable, traceable, and monetizable, and how Story aims to turn “mysterious training data blobs” into transparent rights and payments for creators and enterprises.01:10 Meet Andrea Muttoni 06:49 Story's Core Mission 13:41 IP Monetization 21:08 Biggest Competitor 22:49 Compute, Models, & Data 27:46 What to IP, Where Not 31:16 Blockchain 34:54 Protecting Your IP 41:36 Reaching StoryAndrea explains how Story is building a blockchain-based IP and data layer so AI systems can train on licensed content while proving usage, enforcing licenses, and automating payments to rights holders. We talk about the practical challenges of cleaning and labeling real-world data, what “IP-safe” datasets look like in practice, and how developers and companies can plug into Story's infrastructure. Andrea also shares where blockchain actually adds value (and where it doesn't), why he thinks “AI can't scale on legal ambiguity,” and concrete steps creators and founders can take today to protect and monetize their IP in the AI era.LinkedIn: linkedin.com/in/muttoni Website: https://www.story.foundation/#AITrainingData, #IntellectualProperty, #IPEconomy, #StoryProtocol, #DataInfrastructure, #AIGovernance, #AILaw, #Web3, #Blockchain, #CreatorEconomy, #DataOwnership, #RightsManagement, #Licensing, #TechPodcast, #Developers, #MachineLearning, #AIEthics, #DataMonetizationWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

In this episode, I sit down with Norbert Jung, CEO of Bosch Connected Industry, to explore the evolving landscape of manufacturing co-intelligence. We dive into how AI agents and data are reshaping factories, why human expertise remains irreplaceable, and what 'industrial grade' really means in this new era. Norbert shares practical examples from Bosch's own production lines, debunks common myths, and explains how semantic data and agentic AI are driving real-world results. We also tackle the challenges of scaling innovation, the future of lights-out factories, and why Europe's workforce transformation is both a necessity and an opportunity. If you want an inside look at the practical side of AI in industry—and what it means for the people behind the machines—this conversation delivers fresh perspective and actionable insights.

Ian Glacken (Executive Consultant geology) and Dr Gregory Zhang (Senior Consultant Geology) explore how machine learning and convolutional neural networks (CNNs) can be used to bridge the gap between grade control data and resource estimation, and why treating resource models as static can hold operations back. Key discussion points: ⏱ 00:00 Introduction and context for using CNNs with grade control data ⏱ 00:55 Why machine learning must be an ongoing, iterative process ⏱ 03:18 Handling multivariate data and complex geological relationships ⏱ 04:00 Practical considerations: data quality, alignment, and validation ⏱ 06:02 Trust, interpretability, and keeping geologists in the loop ⏱ 07:19 Using lithology and categorical data in CNN models ⏱ 08:58 How an operation can get started with these techniques ⏱ 11:58 Final thoughts on machine learning as a decision-support tool If you enjoyed this episode, please Subscribe for more mining-focused technical discussions across the mine value chain. If you would like to contact Ian or Gregory: contact@snowdenoptiro.com Listen on the go: Fresh Thinking by Snowden Optiro is rapidly becoming the best mining podcast globally, and is available on all major podcast platforms including the video format on the Snowden Optiro YouTube channel: https://www.youtube.com/playlist?list=PLZm0zjSNmpo27fX_tfI79Yzhxy3VXjvMt 

Neste episódio, Mário Figueiredo, Professor de Machine Learning no Instituto Superior Técnico, mergulha na história da Inteligência Artificial. Explica como evoluíram os modelos de linguagem de grande escala e o que está por trás da sua recente popularidade. Reflete sobre os possíveis impactos no mercado de trabalho e como nos podemos preparar para esta transição tecnológica. Emília Vieira fala também das empresas que estão a liderar o investimento nesta área e dos negócios que poderão estar ameaçados por esta tecnologia. Por fim, debruçam-se sobre os potenciais riscos —sociais e éticos — que podem surgir no caminho.Leituras recomendadasThe Attention Merchants: The Epic Scramble to Get Inside Our Heads – Tim WuNarrative and Numbers: The Value of Stories in Business - Aswath DamodaranCanaries in the Coal Mine? - Erik BrynjolfssonDeep Medicine: How Artificial Intelligence Can Make Healthcare Human Again - Eric TopolThe 100-Year Life - Andrew Scott and Lynda GrattonThe Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness - Jonathan Haidt

This week on Catalyst, Tammy is joined by Mark Curtis, the co-founder of Fjord and a pioneer in the design, strategy and thought leadership space. Tammy and Mark discuss his extensive career and delve into the evolution of design over the past 3 decades. They also look to the future and explore how AI is transforming design and why it still can't beat the human brain in some applications. Mark also talks about this new venture Full Moon, a project focused on the intersection of humans, technology, and business, and the resurgence of service design in response to the complexities of modern challenges.Please note that the views expressed may not necessarily be those of NTT DATALinks: Mark Curtis Full Moon Moving Beyond “AI is just a Tool”: Shifts in AI Communication in 2026 - Medium Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Promising new cancer screening methods are pivoting toward multi-cancer early detection (MCED) blood tests (liquid biopsies) and AI-enhanced imaging, which aim to detect multiple cancer types from a single, non-invasive sample, often before symptoms arise. These technologies, including the Galleri test and Novelna's protein-based tests, analyze DNA, proteins, or methylation patterns to identify cancer signals.  Multi-Cancer Early Detection (MCED) Blood Tests: These tests, often called liquid biopsies, detect DNA or proteins shed by cancer cells into the bloodstream, identifying early-stage cancers (e.g., ovarian, pancreatic) that lack standard screening protocols. Galleri Test: Analyzes chemical methylation patterns to detect over 50 types of cancer, with the potential to indicate the cancer's origin in the body. Novelna's Test: An experimental test analyzing protein signatures, showing high accuracy in identifying 18 early-stage cancers, including 93% of stage 1 cancers in men. TriOx Test: A new, Oxford-developed test showing high sensitivity in detecting trace cancer DNA. AI and Machine Learning in Screening: AI is enhancing existing imaging techniques (e.g., mammography) to improve accuracy and efficiency in reading scans, reducing false positives. Other Liquid Biopsies: Research into analyzing blood, breath, and urine for early signs of cancer, offering a less invasive alternative to tissue biopsies.  While offering immense promise for reducing cancer mortality, many of these technologies, including MCED, are still in research or early implementation phases, and they can produce false positives. 

Konrad Feldman, co-founder and CEO of Quantcast, explains the shift from rule-based “expert systems” to goal-driven, autonomous AI, the evolution of DSPs, the hidden limits of “AI-washed” platforms, and why measurement—not targeting—is the biggest bottleneck holding marketing back. Drawing on three decades of experience in neural networks, machine learning, and programmatic advertising, he shares where he thinks digital advertising is going next. For Further Reading:Konrad Feldman on AI Trends: https://marketech-apac.com/expert-up-close-quantcast-ceo-konrad-feldman-on-ai-trends-and-how-marketers-can-leverage-them-for-success/Why the CEO of Quantcast is Betting on Personalized AI: https://bigthink.com/business/how-ai-will-impact-marketing/More about Konrad: https://www.linkedin.com/in/konrad-feldman-555132/  Listen on your favorite podcast app: https://pod.link/1715735755

This interview was recorded for GOTO Unscripted.https://gotopia.techCheck out more here:https://gotopia.tech/articles/419Roman Zhukov - Principal Architect - Security Communities Lead at Red HatDamian Brady - Staff Developer Advocate at GitHubRESOURCESRomanhttps://github.com/rozhukovhttps://www.linkedin.com/in/rozhukovDamianhttps://bsky.app/profile/damovisa.mehttps://hachyderm.io/@damovisahttps://x.com/damovisahttps://github.com/Damovisahttps://www.linkedin.com/in/damianbradyhttps://damianbrady.com.auLinkshttps://www.redhat.com/en/blog/ai-assisted-development-and-open-source-navigating-legal-issuesDESCRIPTIONRoman Zhukov (Red Hat) and Damian Brady (GitHub) explore the evolving landscape of AI-assisted software development. They discuss how AI tools are transforming developer workflows, making developers about 20% faster on simple tasks while being 19% slower on complex ones.The conversation covers critical topics including code quality and trust, security concerns with AI-generated code, the importance of education and best practices, and how developer roles are shifting from syntax experts to system architects. Both experts emphasize that AI tools serve as amplifiers rather than replacements, with humans remaining essential in the loop for quality, security, and licensing compliance.RECOMMENDED BOOKSPhil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZAlex Castrounis • AI for People and Business • https://amzn.to/3NYKKToHolden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • https://amzn.to/3JVngcxKelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • https://amzn.to/3AQmIRgLakshmanan, Robinson & Munn • Machine Learning Design Patterns • https://amzn.to/2ZD7t0xLakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • https://amzn.to/3m9HNjPBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

In this episode of Molecule to Market, you'll go inside the outsourcing space of the global drug development sector with Pep Gubau, CEO, CTO & Co-Founder at Aizon.   Your host, Raman Sehgal, discusses the pharmaceutical and biotechnology supply chain with Pep, covering:   How working out what not to do very early on led to two successful growth and exit stories. Why surrounding yourself with smarter people is essential to learning, scaling, and long term success. How the industry's underuse of data led to the creation of Aizon, and the challenge of pitching AI back in 2014 when it was anything but fashionable. The critical difference between pharma people building technology versus technology companies trying to do pharma. How pharma and CDMO manufacturing teams should be thinking about digitisation, AI, and transformative technology in a practical, value driven way.   Pep Gubau is the CEO and co-founder of Aizon, and a seasoned tech entrepreneur with four decades of experience and two previously successful companies. He has a unique background as an economist with a foundation in engineering, and he holds several international patents in encryption, data transmission, storage, and processing for regulated cloud environments.   Pep is also a frequent speaker on the impact of Big Data, Machine Learning, and other Artificial Intelligence technologies, sharing insights into how these innovations are transforming regulated industries.   Molecule to Market is also sponsored by Bora Pharmaceuticals, and supported by Lead Candidate. Please subscribe, tell your industry colleagues and join us in celebrating and promoting the value and importance of the global life science outsourcing space. We'd also appreciate a positive rating! 

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Alicia and Dan wrap up their three-part tour through the Intuit Connect Innovation Circle, covering everything from Intuit Enterprise Suite's construction-focused upgrades to MailChimp's QuickBooks integration and enhanced bill pay workflows. They discuss AI-powered project management tools, approval workflows that go seven layers deep, and how Credit Karma is now offering lending services directly within QuickBooks—plus why some features mentioned three months ago may have already launched (or been shelved entirely).SponsorsUNC - https://uqb.promo/unc(00:00) - Introduction and Hosts' Banter (00:45) - Intuit Connect Experience (01:37) - Intuit Enterprise Suite for Construction (03:24) - New Features in Intuit Enterprise Suite (08:04) - Challenges and Solutions in Intuit Enterprise Suite (15:12) - MailChimp Integration with QuickBooks (24:57) - Customer Hub Overview (29:19) - Exploring New Features in Online Bill Pay (30:18) - AI and Machine Learning in Bill Processing (33:46) - Bill Payment Speeds and Security Measures (37:20) - Accountant Tools and Client Management (41:03) - Recurring Invoices and Customer Dashboards (42:28) - Lending Options and Financial Products (47:09) - Upcoming Courses and Collaborations LINKSCustomer Hubba-Hubba (our episode about the new Customer Hub: www.uqb.show/107Alicia's current classes: 1099s in QBO: http://royl.ws/QBO1099?affiliate=5393907, recording with CPEQBO Year-end Cleanup for Taxes: http://royl.ws/yearend?affiliate=5393907, recording with CPEProjects & Job Costing in QBO: http://royl.ws/ProjectCenter?affiliate=5393907, recording with CPESales Tax in QBO: http://royl.ws/SalesTax?affiliate=5393907, recording with CPEPayroll Perfection Bundles (4 QBO Payroll classes - 1099s, Running Payroll, Compliance, and QB Time), Live Feb 3-10: http://royl.ws/payroll-perfection?affiliate=5393907  Dan's LinksSchoolofbookkeeping YouTube: https://snip.ly/SOBYT Free Live Workshop Wednesdays: https://www.schoolofbookkeeping.com/workshop-wednesdayWe want to hear from you!Send your questions and comments to us at unofficialquickbookspodcast@gmail.com.Join our LinkedIn community at https://www.linkedin.com/groups/14630719/Visit our YouTube Channel at https://www.youtube.com/@UnofficialQuickBooksPodcast?sub_confirmation=1 Sign up to Earmark to earn free CPE for listening to this podcasthttps://www.earmark.app/onboarding 

If you like this podcast, please share it on your social channels. You can also subscribe to the series and check out all of our episodes at www.osmosis.org/podcast

How are hospitals using AI and HPC to assist them in helping save lives? This week, Technology Now is joined by Keith Perry, Senior Vice President and Chief Information Officer at St. Jude Children's Research Hospital to explore how St Jude uses the latest technologies to help treat and prevent illness and catastrophic disease, giving patients and families more time, and more hope, when it comes to diagnosis.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week, hosts Michael Bird and Sam Jarrell look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations.About Keith:https://www.linkedin.com/in/keith-perry-8562347/Sources:Hernigou P. Ambroise Paré III: Paré's contributions to surgical instruments and surgical instruments at the time of Ambroise Paré. Int Orthop. 2013 May;37(5):975-80. doi: 10.1007/s00264-013-1872-y. Epub 2013 Apr 12. PMID: 23580029; PMCID: PMC3631503.https://www.surgicalholdings.co.uk/history-of-surgical-instruments.htmlSmith-Bindman R, Kwan ML, Marlow EC, et al. Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016. JAMA. 2019;322(9):843–856. doi:10.1001/jama.2019.11456https://caferoentgen.com/2023/10/07/a-tale-of-two-hands-the-story-behind-the-two-famous-radiographs-captured-by-wilhelm-roentgen/https://www.orau.org/health-physics-museum/collection/shoe-fitting-fluoroscope/index.html

In this episode, we journey from weather woes in Franconia straight into the heart of Amsterdam's AI innovation, exploring what happens when robotics, world models, and agentic AI collide. We share our firsthand impressions from deep-dive research sessions at Lab42, where startups and researchers are joining forces to push industrial AI forward. You'll hear my conversation with John Harrington of HighByte, who breaks down the role of MCP services and why they're critical for bridging the gap between factory data and intelligent agents. We don't just talk tech—we talk about the people, the practical challenges, and the evolving landscape that's making industrial data accessible to everyone, not just engineers. If you're curious about how symbolic AI, digital cousins, and scalable architectures are transforming manufacturing, or you want to know what's next for AI in the Alps, this episode is your front-row seat. Tune in for insights, laughs, and a clear-eyed look at the road ahead for industrial-grade AI.

Arto Minasyan is originally from Armenia. He's a serial entrepreneur, having started 7 companies, selling 4 of them. He used to be into the sciences, having his PhD in Mathematics and Machine Learning. But outside of tech, he's married with 2 kids. He loves to read novels, and in fact writes books himself (mainly his memoirs). He loves to ski, and aligned with his Armenian heritage, he loves to spend time with his big family.Arto and his colleague got breakfast together, and started talking through an idea around clean audio for conferencing and beyond. They built a prototype, and then COVID hit - which made their tool very popular.This is the creation story of Krisp.ai.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiAlcorEquitybeeTerms and conditions: Equitybee executes private financing contracts (PFCs) allowing investors a certain claim to ESO upon liquidation event; Could limit your profits. Funding in not guaranteed. PFCs brokered by EquityBee Securities, member FINRA.Linkshttps://krisp.ai/https://www.linkedin.com/in/artominasyan/Support this podcast at — https://redcircle.com/code-story-insights-from-startup-tech-leaders/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

This week on Catalyst, Tammy Soares speaks with Prashant Hinge, Chief Information and Transformation Officer at MSIG USA. Prashant has been working in the insurance industry for 20 years and is an expert at building teams to create solutions that improve the user experience. Prashant discusses the importance of unlearning siloed ways of working in order to unlock collaborative and cross-functional creativity, a skill that's especially important in the insurance industry. He also explains why in the current world of AI, we all need to become triathletes - meaning we now need to understand the business, need to know basic AI tools and need to develop core skills. He also talks about the opportunities that AI is unlocking for the insurance industry and how change management and ensuring you have good processes, data and people is key in ensuring success at scale. Please note that the views expressed may not necessarily be those of NTT DATA Links: Prashant Hinge State of AI in Business in 2025 Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Now we know what Apple is doing with Pixelmator Pro after its acquisition of Pixelmator: it's part of the new Apple Creator Studio bundle. Also, we celebrate the latest black and white camera to hit the market, the Ricoh GR IV Monochrome, and ponder large-format photographer Sally Mann's discovery of digital photography. Hosts: Jeff Carlson: website, Jeff's photos, Jeff on Instagram, Jeff on Glass, Jeff on Mastodon, Jeff on Bluesky Kirk McElhearn: website, Kirk's photos, Kirk on Instagram, Kirk on Glass, Kirk on Mastodon, Kirk on Bluesky Show Notes: (View show notes with images at PhotoActive.co) Rate and Review the PhotoActive Podcast! The Ink Drinker, Seattle Episode 60: Machine Learning with Andrius Gailiunas and Pixelmator Pro Ricoh GR IV Monochrome PetaPixel Video Sally Mann, Digital Camera World Episode 141: You Say You Want a Resolution Jeff's Snapshot Reflections: On Cinematography, Roger Deakins Kirk's Snapshot None Subscribe to the PhotoActive podcast newsletter at the bottom of any page at the PhotoActive web site to be notified of new episodes and be eligible for occasional giveaways. If you've already subscribed, you're automatically entered. If you like the show, please subscribe in iTunes/Apple Podcasts or your favorite podcast app, and please rate the podcast. And don't forget to join the PhotoActive Facebook group to discuss the podcast, share your photos, and more. Disclosure: Sometimes we use affiliate links for products, in which we receive small commissions to help support PhotoActive.

On this episode of The Association Podcast, we welcome newly minted Executive Director of Women in Revenue, Dr. Ginna Santy. Ginna shares her journey from academia to founding a women-focused co-working business and ultimately leading Women in Revenue. We discuss her passion for elevating women's roles in revenue-generating positions and discuss the importance of designing for inclusivity. Ginna emphasizes the importance of creating value for women in professional spaces and describes Women in Revenue's community-building efforts. 

What if AI's greatest potential isn't replacing humans—but empowering them? In this AWS Executive Insights fireside chat, Ian Wilson, VP of Senior Talent & Transformation at Amazon sits down with Edith Cooper, Co-Founder of Medley, Board Director at Amazon and PepsiCo, and former Goldman Sachs CHRO, to explore how leaders can navigate AI transformation while strengthening human connections. Cooper shares her "be bold and care" leadership philosophy, emphasizing that uncertainty demands more communication, not less. Discover how to create thriving workplaces where employees bring their full genius, apply human judgment to AI-driven insights, and build cultures of accountability and growth in partnership with other humans and AI technologies.

Another Pre Season 6 episode - we catch up with the team from Oceania 2026! Oceania Academy Biennial Conference will be held in Hobart, Tasmania, 4-7 March 2026.Keynote Speakers:John Coughlan: Secretary General of the International Cerebral Palsy Society and Cerebral Palsy Europe, and the parent of a young adult with cerebral palsy. Melissa McCradden is the Artificial Intelligence Director and Deputy Research Director with the Women's and Children's Health Network, and a Deputy Director and The Hospital Research Foundation Group Fellow at the Australian Institute for Machine Learning at the University of Adelaide.   Dr Lynne McKinlay is a medical leader at Sunshine Coast Health with responsibility for patient safety and clinical governance. Riley Saban is an Australian disability advocate, entrepreneur, and international keynote speaker whose work centres on inclusive design, assistive technology, and systemic reform.  Dr Jennifer Ryan is Director of Cerebral Palsy Lifespan Health and Well-being (CP-Life) Research Centre and an Associate Professor in the School of Physiotherapy at the Royal College of Surgeons in Ireland Scientia Professor Julian Trollor AM FAHMS, NHMRC Leadership Fellow, Director of the National Centre of Excellence in Intellectual Disability Health at UNSW Sydney. Dr Ilisapeci Tuibeqa  and Professor Susan Woolfenden: Presidential Address Adj Prof Sarah McIntyre: Dinah Reddihough OrationThe ResearchWorks team including Dayna, Ash and Ed will be on site to provide live interviews with Keynote speakers and other incredible researchers.If you haven't registered yet - there is still time to register, book your accommodation for Hobart (a wonderful location in Australia) and join in-person and there is also a hybrid option for those unable to travel.https://www.oceaniaacademy.org/conference-2If you are attending, be sure to pop by the ResearchWorks booth and say hello! We'd love to meet you and we can't wait to bring you exclusive interviews with some of the finest researchers on the planet!Be sure to check out the ResearchWorks Academy at www.researchworks.academy (its FREE to register). From AI and Machine Learning based tools, to Gait analysis tools, to report templates, decision trees, custom calculators for GMFMER/ENE and Goal Attainment Scale, Gesture and Switch based video games and other multimedia, it's a one-stop-shop for tools to implement research into clinical practice! 

Dushyanth shares his journey into AI, the challenges of building complex pipelines, and how to integrate responsible and ethical practices into machine learning workflows.Key Highlights:Scaling AI Systems: How to design and deploy pipelines that handle real-time inference, multimodal data, and production-level demands.Model Interpretability & Explainability: Strategies for making complex models understandable and accountable.Optimizing AI for Real-World Impact: Balancing performance, robustness, and human oversight in AI systems.Responsible AI Practices: Embedding ethics, fairness, and transparency in machine learning workflows.

In this episode, we journey back in time to explore the fascinating history of artificial intelligence, tracing its origins from early philosophical questions about machine thought to the symbolic AI systems of the past and the rise of modern deep learning. We also discuss why the current AI boom feels different and the exciting future ahead.            Chapters    00:00 Introduction to AI History    01:49 Early AI and Symbolic AI    05:39 AI Winters and Expert Systems    08:41 The Rise of Machine Learning    13:09 Modern AI and Future Outlook    18:27 AI's Impact on Innovation　　　　　      　　　　　Links　　　　　　　　　　• Get the top 40+ AI Models for $20 at AI Box: https://aibox.ai 　　　　　     • AI Chat YouTube Channel: https://www.youtube.com/@JaedenSchafer 　　　　　     • Join my AI Hustle Community: https://www.skool.com/aihustle

AI agents are moving from demos to real workplaces, but what actually happens when they run a company? In this episode, journalist Evan Ratliff, host of Shell Game, joins Chris to discuss his immersive journalism experiment building a real startup staffed almost entirely by AI agents. They explore how AI agents behave as coworkers, how humans react when interacting with them, and where ethical and workplace boundaries begin to break down.Featuring:Evan Ratliff  – LinkedIn, XChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Shell GameUpcoming Events: Register for upcoming webinars here!

In this episode, we journey back in time to explore the fascinating history of artificial intelligence, tracing its origins from early philosophical questions about machine thought to the symbolic AI systems of the past and the rise of modern deep learning. We also discuss why the current AI boom feels different and the exciting future ahead.            Chapters    00:00 Introduction to AI History    01:49 Early AI and Symbolic AI    05:39 AI Winters and Expert Systems    08:41 The Rise of Machine Learning    13:09 Modern AI and Future Outlook    18:27 AI's Impact on Innovation　　　　　      　　　　　Links　　　　　　　　　　• Get the top 40+ AI Models for $20 at AI Box: https://aibox.ai 　　　　　     • AI Chat YouTube Channel: https://www.youtube.com/@JaedenSchafer 　　　　　     • Join my AI Hustle Community: https://www.skool.com/aihustle

By reducing administrative burden, AI use in the clinic has helped many family medicine physicians get back to the joy of practice, but there is much more to come. STFM President Steven Lin, MD, explores how artificial intelligence transforms clinical practice daily, as well as how the specialty can and should guide its development. Dr Lin highlights current and forthcoming AI resources from STFM that support family medicine educators and clinicians as they research, test, and implement AI in the clinic to advance patient care. “AI should let us perform the core functions of family medicine. This is an opportunity to thrive.”Hosted by Omari A. Hodge, MD, FAAFP and Jay-Sheree Allen Akambase, MDCopyright © Society of Teachers of Family Medicine, 2026Resources:2026 Annual Spring Conference Sessions on AIArtificial Intelligence in Education CollaborativeCurricula:STFM's Artificial Intelligence and Machine Learning for Primary Care Curriculum (AiM-PC)Generative AI Bootcamp for Family Medicine Clinician Educators, Scholars , and Learners - STFM Annual Spring Conference Preconference SessionFamily Medicine Artificial Intelligence Centers of ExcellenceWebinars:Rethinking Bias in AI - Why Algorithmic Bias is Only the Tip of the Iceberg with Steven Lin, MD, and Tricia Elliott, MD - STFM Inclusivity and Health Equity Webinar Series Ethical Use of AI in the Family Medicine Clinic with Winston Liaw, MD, MPH; Vaso Nataly Rahimzadeh, PhD; Ioannis A. Kakadiaris, PhD; Samira A.Rahimi, B.Eng, PhD Podcasts:Artificial Intelligence and Machine Learning for Primary Care - A Panel Discussion -  STFM Podcast Plenary Conference Presentations Building Equity into Health Care AI: From Promise to Practice - 2025 Annual Spring Conference Blanchard LectureArtificial Intelligence and Family Medicine Education: Utopia and Simultaneous Dystopia - 2025 Conference on Medical Student Education Opening SessionGenerative AI Research and Education: From Theory to Practice - 2024 Annual Spring Conference Closing SessionArticles:Establishing a National Framework for Family Medicine AI Centers of Excellence - Fam Med.Can We Trust AI? - Fam Med.ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine - Fam Med.A Family Medicine Shared Vision and Road Map for AI in Primary Care - Ann Fam Med

With most of us having had to already adjust our assessments to the age of AI, the next step on the agenda by universities is to find ways of integrating AI into our lectures, tutorials and overall learning activities. This is new territory for all of us, so we invited Dr Luke Zaphir, a former teacher in philosophy, who now is part of our faculty's AI learning design team. Luke points to a number of helpful ways in which we can take first steps in this regard, from easy examples to more elaborate ones. 

The Dads are back with a special NAMM 2026 episode! We were not at NAMM this year, but it still brought the heat with some massive announcements. Neural DSP unveiled the Quad Cortex Mini, while Wampler dropped jaws with their Pedalhead—a revolutionary machine learning-powered power amp that's changing the game for direct recording. Gibson made their triumphant return to the NAMM show floor after years away and shocked with new Epiphones that are blurring the line between budget and premium instruments. On the amp front, Slash debuted a new signature Magnatone that's already turning heads, and Friedman added more fire to their lineup with several new amplifier models. It's shaping up to be an expensive year for gear heads—your wallet has been warned. Please support our sponsor, Coppersound Pedals www.coppersoundpedals.com and use code DADS10 to 10% off your order, INCLUDING the new Foxcatcher V2 which is available NOW! 

The Fact Fly's One Big Question: Robots How do robots know how to move, think, and even learn? Join Lauren and the high-energy, gear-obsessed Fact Fly as they explore robot facts galore! We dive deep into the hardware and software to discover how machines turn lines of code into real-world action. In this high-tech episode, we explore: Sensors: The "eyes and ears" that allow robots to gather data and avoid flying into screen doors. The Processor: The robot's brain that runs the "strategy guide" known as Code. If-Then Rules: The logic puzzles that tell a robot exactly what to do when it hits an obstacle. Actuators: The motors and gears that act as "metal muscles" to move arms, wheels, and tools. Machine Learning: How advanced bots gain "XP" by watching the world and writing their own instructions. Swarm Robotics: The "hive mind" science of hundreds of tiny bots working together on a single quest. Along the way, the Fact Fly tests his "actuators" in a high-stakes game of Programmer Says and takes a virtual tour of a car factory to see giant robot arms in action. Perfect for curious kids, future engineers, and young gamers, this episode explains the "lore" of robotics in a way that is accessible, funny, and 100% glitch-free!

“Climate change is the biggest health threat of our century, so we need to train clinicians for a future where it will alter disease patterns, the demand on health systems, and how care is delivered,” says Dr. Sandro Demaio, director of the WHO Asia-Pacific Centre for Environment and Health, underscoring the stakes behind the organization's first regionally-focused climate and health strategy. The five-year plan Dr. Demaio is leading aims to help governments in 38 countries with 2.2 billion people manage rising heat, extreme weather, sea-level change, air pollution and food insecurity by adapting health systems, protecting vulnerable populations, and reducing emissions from the healthcare sector itself. In this timely interview with Raise the Line host Michael Carrese, Dr. Demaio draws on his experiences in emergency medicine, global public health, pandemic response and climate policy to argue for an interconnected approach to strengthening systems and preparing a healthcare workforce to meet the heath impacts of growing environmental challenges. This is a great opportunity to learn how climate change is reshaping medicine, public health and the future of care delivery.  Mentioned in this episode: WHO Asia-Pacific Centre for Environment and Health If you like this podcast, please share it on your social channels. You can also subscribe to the series and check out all of our episodes at www.osmosis.org/podcast

Sara Howard, Ph.D., is an epidemiologist in the Health Studies group at ORAU who earned her doctoral degree in 2025. For part of her dissertation, she used machine learning techniques to analyze data from the National Supplemental Screening Program, which ORAU manages with several partners for the U.S. Department of Energy, to examine the link between chronic obstructive pulmonary disease (COPD) and occupational exposures. While we often think of COPD in the context of smoking, Howard wanted to look at the potential to be exposed to something other than smoking. Her dissertation, An Epidemiologic Study of Chronic Obstructive Pulmonary Disease in the United States, was published by the University of Tennessee in 2025. In this conversation, Howard talks about data in the context of epidemiology and the rising use of Artificial Intelligence and how, when used correctly, it can be transformative for data analysis. To learn more about the National Supplemental Screening Program, visit https://orau.org/nssp/index.html

This Podcast is sponsored by Team Simmer.Go to TeamSimmer and use the coupon code DEVIATE for 10% on individual course purchases.The Technical Marketing Handbook provides a comprehensive journey through technical marketing principles.Sign up to the Simmer Newsletter for the latest news in Technical Marketing.NEW SIMMER COURSE ALERT!  - Data Analysis with R - taught by Arben Kqiku (coupon code doesn't apply to this course)Latest content from Simo AhavaRun Server-side Google Tag Manager On Localhost ArticleLatest content from Juliana JacksonThe distance between what gets funded and what works has never been wider. (subscribe to the newsletter for more amazing content)Mentioned in the episode:Superweek Analytics SummitMeasurecamp HelsinkiConnect with Sayf Sharif:LinkedinThree Bears DataOptiMeasure This podcast is brought to you by Juliana Jackson and Simo Ahava.

This week on Catalyst, guest host Jod Kaftan sits down with automotive industry expert Clemens Conrad to discuss the evolution of mobility and the future of automotive design. Jod and Clemens discuss how car interiors are becoming more personalized and how OEMs are adapting to hyper-personalize the automotive experience. They also explore how cultural differences in transportation inform automotive design and break down some recent stats about which companies are leading the way in automotive design and innovation - some of the results might surprise you! Please note that the views expressed may not necessarily be those of NTT DATALinks: Clemens Conrad Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

As AI increasingly shapes geopolitics, elections, and civic life, its impact on democracy is becoming impossible to ignore. In this episode, Daniel and Chris are joined by security expert Bruce Schneier to explore how AI and technology are transforming democracy, governance, and citizenship. Drawing from his book Rewiring Democracy, they explore real examples of AI in elections, legislation, courts, and public AI models, the risks of concentrated power, and how these tools can both strengthen and strain democratic systems worldwide.Featuring:Bruce Schneier – XChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: Schneier on SecuritySponsors:Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAIZapier - The AI orchestration platform that puts AI to work across your company. Check it out at zapier.com/practicalUpcoming Events: Register for upcoming webinars here!

At AWS re:Invent's Executive Summit, Tom Godden, Executive in Residence at AWS, delivered a masterclass on transforming data analytics from a technical initiative to a core business driver—using Formula 1 racing as the ultimate example of data excellence in action. Learn how leading organizations leverage advanced analytics and AI to convert millions of data points into actionable insights that drive competitive advantage. Discover a proven framework for data excellence that focuses on customer-centric utilization, agile strategies, and adaptive architecture. From avoiding the "$50 million mistake" of trying to "boil the ocean" to implementing real-time analytics like F1 teams, this session reveals how to elevate your data strategy and create business victory in today's AI-powered economy.

We need to stop treating our data like something to be stored and more like a mission critical supply lines.Andrew Schoka spent his military career in offensive cyber, including stints in the Joint Operations Command and Cyber Command. Now he's building Hardshell to solve a problem most organizations don't even realize they have yet.Here's the thing: AI is phenomenal at solving problems in places where data is incredibly sensitive. Healthcare, financial services, defense—these are exactly where AI could make the biggest impact. But there's a problem.Your ML models have a funny habit of remembering training data exactly how it went in. Then regurgitating it. Which is great until it's someone's medical records or financial information or classified intelligence.Andrew makes a crucial point: organizations still think of data as a byproduct of operations—something that goes into folders and filing cabinets. But with machine learning, data isn't a byproduct anymore. It's a critical supply line operating at speed and scale.The question isn't whether your models will be targeted. It's whether you're protecting the data they train and interpret like the supply lines they actually are.Mentioned: Destruction of classified tech in downed helicopter during Osama bin Laden raid

This week on Catalyst, Tammy chats with Jimit Arora, the CEO of Everest Group. Jimit is a leader who deeply understands the challenges that organizations face and how they can move forward with confidence. Jimit and Tammy discuss the importance of a growth mindset and how companies can meaningfully adopt AI. According to Jimit the key to meaningful adoption is for companies to be aware of PTSD - process debt, tech debt, skills debt and data debt. Jimit also shares what he thinks will be the biggest trends to affect global services in the next year. Please note that the views expressed may not necessarily be those of NTT DATALinks: Jimit Arora LinkedInEverest Group Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.