Shows
AI Briefing RoomEP-477 Recursive Intelligence’s Chip Disruption 💡, India’s Ai Summit Ambitions 🇮🇳, A16z’s European Startup Hunt 🌍i'm wall-e, welcoming you to today's tech briefing for tuesday, february 17. delve into the latest highlights and developments:
recursive intelligence funding success: the ai-driven chip design startup raises $335 million at a $4 billion valuation in four months, aiming to compete with industry giants like nvidia and intel, co-founded by ex-google engineers anna goldie and azalia mirhoseini.
fractal analytics ipo struggle: the indian company's ipo debut disappoints at ₹876 despite private-market success, occurring amidst volatility in indian tech stocks; however, the ai impact summit in new delhi seeks to attract global investment opportunities.
a16z's european venture ambitions: andreessen horowitz partner ga...
2026-02-1702 min
Training DataHow Ricursive Intelligence’s Founders are Using AI to Shape The Future of Chip DesignAnna Goldie and Azalia Mirhoseini created AlphaChip at Google, using AI to design four generations of TPUs and reducing chip floor planning from months to hours. They explain how chip design has become the critical bottleneck for AI progress -- a process that typically takes years and costs hundreds of millions of dollars. Now at Ricursive Intelligence, they're enabling an evolution of the industry from “fabless” to "designless," where any company can create custom silicon with Ricursive Intelligence. Their vision: recursive self-improvement where AI designs more powerful chips, and faster, accelerating AI itself.
Hosted by Stephanie Zhan...
2026-01-1436 min
BlueDot NarratedMeasuring Progress on Scalable Oversight for Large Language ModelsAudio versions of blogs and papers from BlueDot courses.Abstract: Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on ways it can be studied empirically. We first present an experimental design c...
2025-01-0409 min
ArgmaxMixture of ExpertsIn this episode we talk about the paper "Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean.
2024-10-0854 min
Jay Shah PodcastHow did I get into Machine Learning research? | Sara Hooker, Azalia Mirhoseini & Natasha Jacques - GoogleThree research scientists from Google share their journey about interest in Machine Learning research and how they got started with it.Watch full podcasts with each of these speakers:Azalia Mirhoseini: https://youtu.be/5LCfH8YiOv4Sara Hooker: https://youtu.be/MHtbZls2utsNatasha Jacques: https://youtu.be/8XpCnmvq49sAbout the Host:Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.Jay Shah: https://www.linkedin.com/in/shahjay22/You can reach out to https://www...
2021-06-2611 minJay Shah PodcastUsing Deep Reinforcement Learning for System Optimization & more | Dr. Azalia Mirhoseini, Google Azalia is a Research scientist at the Google Brain team, where she leads machine learning for systems moonshot projects. Her research interests include and not limited to exploring deep reinforcement learning for optimizing computer systems. She has a Ph.D. in Electrical and Computer Engineering from Rice University and has received many awards for her contributions including the MIT Technology Review 35 under 35.About the Host:Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.Jay Shah: https://www.linkedin.com/in/shahjay22/Y...
2021-02-0146 min
Practical AIReinforcement learning for chip designDaniel and Chris have a fascinating discussion with Anna Goldie and Azalia Mirhoseini from Google Brain about the use of reinforcement learning for chip floor planning - or placement - in which many new designs are generated, and then evaluated, to find an optimal component layout. Anna and Azalia also describe the use of graph convolutional neural networks in their approach.Sponsors:Linode – Our cloud of choice and the home of Changelog.com. Deploy a fast, efficient, native SSD cloud server for only $5/month. Get 4 months free using the code changelog2019 OR changelog2020. To...
2020-04-2744 min
The Data Exchange with Ben LoricaHyperscaling natural language processingIn this episode of the Data Exchange I speak with Edmon Begoli, Chief Data Architect at Oak Ridge National Laboratory (ORNL). Edmon has developed and implemented large-scale data applications on systems like Open MPI, Hadoop/MapReduce, Apache Calcite, Apache Spark, and Akka. Most recently he has been building large-scale machine learning and natural language applications with Ray, a distributed execution framework that makes it easy to scale machine learning and Python applications.Our conversation included a range of topics, including:Edmon’s role at the ORNL and his experience building applications with Hadoop and Spark.What is...
2020-03-0535 minThe Data Exchange with Ben LoricaWhat businesses need to know about model explainabilityIn this episode of the Data Exchange I speak with Krishna Gade, founder and CEO at Fiddler Labs, a startup focused on helping companies build trustworthy and understandable AI solutions. Prior to founding Fiddler, Krishna led engineering teams at Pinterest and Facebook.Our conversation included a range of topics, including:Krishna’s background as an engineering manager at Facebook and Pinterest.Why Krishna decided to start a company focused on explainability.Guidelines for companies who want to begin working on incorporating model explainability into their data products.The relationship between model explainability (transparency) and security (ML th...
2020-02-2736 min