Brought to you by the folks at Weights & Biases, Gradient Dissent is a weekly machine learning podcast that takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at Facebook, Google, Lyft, OpenAI, Salesforce, iRobot, Stanford and more.
Listeners of Gradient Dissent - A Machine Learning Podcast by W&B that love the show mention: machine learning, real world, looking forward, guests, great.
The Gradient Dissent - A Machine Learning Podcast by W&B is an incredible podcast that delves deep into the world of machine learning and its applications in various industries. The presenters of this podcast excel in asking insightful questions that prompt thought-provoking discussions. What sets this podcast apart is its accessibility- even those with limited knowledge in AI can easily follow along and gain a deeper understanding of the subject matter. The focus on ML product and systems integration, as well as the exploration of challenges faced in production, provides valuable insights into real-world application.
One of the best aspects of The Gradient Dissent is its emphasis on how machine learning is contributing to commercial industries and guiding product development. This gives listeners a unique perspective on how AI is revolutionizing different sectors and making a significant impact on businesses. Additionally, the hosts do an exceptional job in highlighting how challenges in production are tackled, giving listeners a realistic view of what it takes to implement machine learning systems successfully.
Another standout feature of this podcast is the thoughtful interviews with interesting machine learning practitioners. These interviews provide valuable insights into real-world experiences, allowing listeners to learn from industry experts who have hands-on experience with ML projects. It's refreshing to hear casual and authentic conversations that give a genuine glimpse into the world of machine learning.
As for drawbacks, it would be great to have more technical discussions for those with a deeper understanding of AI concepts. While the accessibility aspect is commendable, more advanced topics could enhance the listening experience for experienced individuals in the field.
In conclusion, The Gradient Dissent - A Machine Learning Podcast by W&B is a must-listen for anyone interested in machine learning and its applications. With insightful questions, accessible discussions, and an emphasis on real-world implementation, this podcast offers valuable insights into how AI is shaping various industries. Whether you're new to AI or an experienced practitioner, there's something for everyone in this podcast.
In this episode of Gradient Dissent, Lukas Biewald sits down with Thomas Dohmke, CEO of GitHub, to talk about the future of software engineering in the age of AI. They discuss how GitHub Copilot was built, why agents are reshaping developer workflows, and what it takes to make tools that are not only powerful but also fun.Thomas shares his experience leading GitHub through its $7.5B acquisition by Microsoft, the unexpected ways it accelerated innovation, and why developer happiness is crucial to productivity. They explore what still makes human engineers irreplaceable and how the next generation of developers might grow up coding alongside AI.Follow Thomas Dohmke: https://www.linkedin.com/in/ashtom/Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb
In this episode of Gradient Dissent, Lukas Biewald talks with Martin Shkreli — the infamous "pharma bro" turned founder — about his path from hedge fund manager and pharma CEO to convicted felon and now software entrepreneur. Shkreli shares his side of the drug pricing controversy, reflects on his prison experience, and explains how he rebuilt his life and business after being "canceled."They dive deep into AI and drug discovery, where Shkreli delivers a strong critique of mainstream approaches. He also talks about his latest venture in finance software, building Godel Terminal “a Vim for traders", and why he thinks the AI hype cycle is just beginning. It's a wide-ranging and candid conversation with one of the most controversial figures in tech and biotech.Follow Martin Shkreli on TwitterGodel Terminal: https://godelterminal.com/Follow Weights & Biases on Twitterhttps://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, host Lukas Biewald talks with Sualeh Asif, the CPO and co-founder of Cursor, one of the fastest-growing and most loved AI-powered coding platforms. Sualeh shares the story behind Cursor's creation, the technical and design decisions that set it apart, and how AI models are changing the way we build software. They dive deep into infrastructure challenges, the importance of speed and user experience, and how emerging trends in agents and reasoning models are reshaping the developer workflow.Sualeh also discusses scaling AI inference to support hundreds of millions of requests per day, building trust through product quality, and his vision for how programming will evolve in the next few years.⏳Timestamps:00:00 How Cursor got started and why it took off04:50 Switching from Vim to VS Code and the rise of CoPilot08:10 Why Cursor won among competitors: product philosophy and execution10:30 How user data and feedback loops drive Cursor's improvements12:20 Iterating on AI agents: what made Cursor hold back and wait13:30 Competitive coding background: advantage or challenge?16:30 Making coding fun again: latency, flow, and model choices19:10 Building Cursor's infrastructure: from GPUs to indexing billions of files26:00 How Cursor prioritizes compute allocation for indexing30:00 Running massive ML infrastructure: surprises and scaling lessons34:50 Why Cursor chose DeepSeek models early36:00 Where AI agents are heading next40:07 Debugging and evaluating complex AI agents42:00 How coding workflows will change over the next 2–3 years46:20 Dream future projects: AI for reading codebases and papers
In this episode of Gradient Dissent, host Lukas Biewald talks with Christopher Ahlberg, CEO of Recorded Future, a pioneering cybersecurity company leveraging AI to provide intelligence insights. Christopher shares his fascinating journey from founding data visualization startup Spotfire to building Recorded Future into an industry leader, eventually leading to its acquisition by Mastercard.They dive into gripping stories of cyber espionage, including how Recorded Future intercepted a hacker selling access to the U.S. Electoral Assistance Commission. Christopher also explains why the criminal underworld has shifted to platforms like Telegram, how AI is transforming both cyber threats and defenses, and the real-world implications of becoming an "undesirable enemy" of the Russian state.This episode offers unique insights into cybersecurity, AI-driven intelligence, entrepreneurship lessons from a two-time founder, and what happens when geopolitical tensions intersect with cutting-edge technology. A must-listen for anyone interested in cybersecurity, artificial intelligence, or the complex dynamics shaping global security.
In this episode of Gradient Dissent, host Lukas Biewald speaks with Captain Jon Haase, United States Navy about real-world applications of AI and autonomy in defense. From underwater mine detection with autonomous vehicles to the ethics of lethal AI systems, this conversation dives into how the U.S. military is integrating AI into mission-critical operations — and why humans will always be at the center of warfighting.They explore the challenges of underwater autonomy, multi-agent collaboration, cybersecurity, and the growing role of large language models like Gemini and Claude in the defense space. Essential listening for anyone curious about military AI, defense tech, and the future of autonomous systems.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz
In this episode of Gradient Dissent, host Lukas Biewald sits down with João Moura, CEO & Founder of CrewAI, one of the leading platforms enabling AI agents for enterprise applications. Joe shares insights into how AI agents are being successfully deployed in over 40% of Fortune 500 companies, what tools these agents rely on, and how software companies are adapting to an agentic world.They also discuss:What defines a true AI agent versus simple automationHow AI agents are transforming business processes in industries like finance, insurance, and softwareThe evolving business models for APIs as AI agents become the dominant software usersWhat the next breakthroughs in agentic AI might look like in 2025 and beyondIf you're curious about the cutting edge of AI automation, enterprise AI adoption, and the real impact of multi-agent systems, this episode is packed with essential insights.
In this episode of Gradient Dissent, host Lukas Biewald sits down with Mike Knoop, Co-founder and CEO of Ndea, a cutting-edge AI research lab. Mike shares his journey from building Zapier into a major automation platform to diving into the frontiers of AI research. They discuss DeepSeek's R1, OpenAI's O-series models, and the ARC Prize, a competition aimed at advancing AI's reasoning capabilities. Mike explains how program synthesis and deep learning must merge to create true AGI, and why he believes AI reliability is the biggest hurdle for automation adoption.This conversation covers AGI timelines, research breakthroughs, and the future of intelligent systems, making it essential listening for AI enthusiasts, researchers, and entrepreneurs.Mentioned Show Notes:https://ndea.comhttps://arcprize.org/blog/r1-zero-r1-results-analysishttps://arcprize.org/blog/oai-o3-pub-breakthrough
In this episode of Gradient Dissent, host Lukas Biewald sits down with David Cahn, partner at Sequoia Capital, for a compelling discussion on the dynamic world of AI investments. They dive into recent developments, including DeepSeek and Stargate, exploring their implications for the AI industry. Drawing from his articles, "AI's $200 Billion Question" and "AI's $600 Billion Question," David unpacks the financial challenges and opportunities surrounding AI infrastructure spending and the staggering revenue required to sustain these investments. Together, they examine the competitive strategies of cloud providers, the transformative impact of AI on business models, and predictions for the next wave of AI-driven growth. This episode offers an in-depth look at the crossroads of AI innovation and financial strategy.Mentioned Articles:AI's $200B QuestionAI's $600B Question
In this episode of Gradient Dissent, Akshay Agrawal, Co-Founder of Marimo, joins host Lukas Biewald to discuss the future of collaborative AI development. They dive into how Marimo is enabling developers and researchers to collaborate seamlessly on AI projects, the challenges of scaling AI tools, and the importance of fostering open ecosystems for innovation. Akshay shares insights into building a platform that empowers teams to iterate faster and solve complex AI challenges together.Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, Joseph E. Gonzalez, EECS Professor at UC Berkeley and Co-Founder at RunLLM, joins host Lukas Biewald to explore innovative approaches to evaluating LLMs.They discuss the concept of vibes-based evaluation, which examines not just accuracy but also the style and tone of model responses, and how Chatbot Arena has become a community-driven benchmark for open-source and commercial LLMs. Joseph shares insights on democratizing model evaluation, refining AI-human interactions, and leveraging human preferences to improve model performance. This episode provides a deep dive into the evolving landscape of LLM evaluation and its impact on AI development.
In this episode of Gradient Dissent, Julian Green, Co-founder & CEO of Brightband, joins host Lukas Biewald to discuss how AI is transforming weather forecasting and climate solutions.They explore Brightband's innovative approach to using AI for extreme weather prediction, the shift from physics-based models to AI-driven forecasting, and the potential for democratizing weather data. Julian shares insights into building trust in AI for critical decisions, navigating the challenges of deep tech entrepreneurship, and the broader implications of AI in mitigating climate risks. This episode delves into the intersection of AI and Earth systems, highlighting its transformative impact on weather and climate decision-making.
In this episode of Gradient Dissent, Jonathan Siddharth, CEO & Co-Founder of Turing, joins host Lukas Biewald to discuss the path to AGI.They explore how Turing built a "developer cloud" of 3.7 million engineers to power AGI training, providing high-quality code and reasoning data to leading AI labs. Jonathan shares insights on Turing's journey, from building coding datasets to solving enterprise AI challenges and enabling human-in-the-loop solutions. This episode offers a unique perspective on the intersection of human intelligence and AGI, with an eye on the expansion of new domains beyond coding.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz
In this episode of Gradient Dissent, Guillermo Rauch, CEO & Founder of Vercel, joins host Lukas Biewald for a wide ranging discussion on how AI is changing web development and front end engineering. They discuss how Vercel's v0 expert AI agent is generating code and UI based on simple ChatGPT-like prompts, the importance of releasing daily for AI applications, and the changing landscape of frontier model performance between open and closed models.Listen on Apple Podcasts: http://wandb.me/apple-podcastsListen on Spotify: http://wandb.me/spotify Subscribe to Weights & Biases: https://bit.ly/45BCkYzGet our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleYouTube: http://wandb.me/youtubeConnect with Guillermo Rauch:https://www.linkedin.com/in/rauchg/ https://x.com/rauchgFollow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, Snowflake CEO Sridhar Ramaswamy joins host Lukas Biewald to explore how AI is transforming enterprise data strategies.They discuss Sridhar's journey from Google to Snowflake, diving into the evolving role of foundation models, Snowflake's AI strategy, and the challenges of scaling AI in business. Sridhar also shares his thoughts on leadership, rapid iteration, and creating meaningful AI solutions for enterprise clients. Tune in to discover how Snowflake is driving innovation in the AI and data space.Connect with Sridhar Ramaswamy:https://www.linkedin.com/in/sridhar-ramaswamy/ Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, Erik Bernhardsson, CEO & Founder of Modal Labs, joins host Lukas Biewald to discuss the future of machine learning infrastructure. They explore how Modal is enhancing the developer experience, handling large-scale GPU workloads, and simplifying cloud execution for data teams. If you're into AI, data pipelines, or building robust ML systems, this episode is packed with valuable insights!
In this episode of Gradient Dissent, Howie Lou, CEO of Airtable, joins host Lukas Biewald to dive into Airtable's transformation from a no-code app builder to a platform capable of supporting complex AI-driven workflows. They discuss the strategic decisions that propelled Airtable's growth, the challenges of scaling AI in enterprise settings, and the future of AI in business operations. Discover how Airtable is reshaping digital transformation and why flexibility and innovation are key in today's tech landscape. Tune in now to learn about the evolving role of AI in business and product development.
In this episode of Gradient Dissent, Andrew Feldman, CEO of Cerebras Systems, joins host Lukas Biewald to discuss the latest advancements in AI inference technology. They explore Cerebras Systems' groundbreaking new AI inference product, examining how their wafer-scale chips are setting new benchmarks in speed, accuracy, and cost efficiency. Andrew shares insights on the architectural innovations that make this possible and discusses the broader implications for AI workloads in production. This episode provides a comprehensive look at the cutting-edge of AI hardware and its impact on the future of machine learning.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz
In this episode of Gradient Dissent, Kanjun Qiu, CEO and Co-founder of Imbue, joins host Lukas Biewald to discuss how AI agents are transforming code generation and software development. Discover the potential impact and challenges of creating autonomous AI systems that can write and verify code and and learn about the practical research involved.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYzConnect with Kanjun Qiu: https://www.linkedin.com/in/kanjun/ https://x.com/kanjunGeneral Intelligent Podcast: https://imbue.com/podcast/Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, Stephen Balaban, CEO of Lambda Labs, joins host Lukas Biewald to discuss the journey of scaling Lambda Labs to an impressive $400M in revenue. They explore the pivotal moments that shaped the company, the future of GPU technology, and the impact of AI data centers on the energy grid. Discover the challenges and triumphs of running a successful hardware and cloud business in the AI industry. Tune in now to explore the evolving landscape of AI hardware and cloud services.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYzConnect with Stephen Balaban:https://www.linkedin.com/in/sbalaban/ https://x.com/stephenbalaban Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb
In this episode of Gradient Dissent, Jake Heller, Head of Product, CoCounsel, joins host Lukas Biewald to discuss how AI is innovating legal practices and reshaping educational approaches for aspiring lawyers. From automating document review to enhancing legal research capabilities, explore the potential impact and challenges AI presents in the legal field. Whether you're a legal professional, a student, or simply curious about the future of law and technology, this conversation provides valuable insights and perspectives. Tune in now to explore the evolving landscape of AI in legal education.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz
In this episode of Gradient Dissent, Denis Yarats, CTO of Perplexity, joins host Lukas Biewald to discuss the innovative use of AI in creating high-quality, fast search engine answers.Discover how Perplexity combines advancements in search engines and LLMs to deliver precise answers. Yarats shares insights on the technical challenges, the importance of speed, and the future of AI in search.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz
In this episode of Gradient Dissent, Sergiy Nesterenko, CEO of Quilter, joins host Lukas Biewald to discuss the groundbreaking use of reinforcement learning in PCB design. Learn how Quilter automates the complex, manual process of creating PCBs, making it faster and more efficient. Nesterenko shares insights on the challenges and successes of integrating AI with real-world applications and discusses the future of electronic design.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYzConnect with Sergiy Nesterenko:https://www.linkedin.com/in/sergiynesterenko/ Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, Varun Mohan, Co-Founder & CEO of Codeium, joins host Lukas Biewald to discuss the transformative power of AI in coding. They explore how Codeium evolved from GPU virtualization to a widely used AI coding tool and tackled the technical challenges and future prospects of AI-assisted software development. Varun shares insights on overcoming performance and latency issues and how AI can significantly enhance engineering velocity. This episode offers an in-depth look at the intersection of AI and coding, highlighting both technological advancements and the potential for more efficient development processes.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYzConnect with Varun Mohan:https://www.linkedin.com/in/varunkmohan/ https://x.com/_mohansolo Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, Together AI co-founder and Stanford Associate Professor Percy Liang joins host, Lukas Biewald, to discuss advancements in AI benchmarking and the pivotal role that open-source plays in AI development.He shares his development of HELM—a robust framework for evaluating language models. The discussion highlights how this framework improves transparency and effectiveness in AI benchmarks. Additionally, Percy shares insights on the pivotal role of open-source models in democratizing AI development and addresses the challenges of English language bias in global AI applications. This episode offers in-depth insights into how benchmarks are shaping the future of AI, highlighting both technological advancements and the push for more equitable and inclusive technologies.✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYzConnect with Percy Liang:https://www.linkedin.com/in/percy-liang-717b8a/ https://twitter.com/percyliang Anticipatory Music Composer:https://stanford.io/3y5VycN Blog Post:https://crfm.stanford.edu/2024/02/18/helm-instruct.htmlFollow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3
In this episode of Gradient Dissent, Isomorphic Labs Chief AI Officer Max Jaderberg, and Chief Technology Officer Sergei Yakneen join our host Lukas Biewald to discuss the advancements in biotech and drug discovery being unlocked with machine learning.With backgrounds in advanced AI research at DeepMind, Max and Sergei offer their unique insights into the challenges and successes of applying AI in a complex field like biotechnology. They share their journey at Isomorphic Labs, a company dedicated to revolutionizing drug discovery with AI. In this episode, they discuss the transformative impact of deep learning on the drug development process and Isomorphic Labs' strategy to innovate from molecular design to clinical trials.You'll come away with valuable insights into the challenges of applying AI in biotech, the role of AI in streamlining the drug discovery pipeline, and peer into the future of AI-driven solutions in healthcare.Connect with Sergei Yakneen & Max Jaderberg:https://www.linkedin.com/in/maxjaderberg/ https://www.linkedin.com/in/yakneensergei/ https://twitter.com/SergeiIakhnin https://twitter.com/maxjaderberg Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb
In the newest episode of Gradient Dissent, we explore the intersecting worlds of AI and Healthcare with John Halamka, President of the Mayo Clinic Platform.Journey with us down John Halamka's remarkable path from his early tech startup days to leading innovations as the President of the Mayo Clinic Platform, one of the world's most esteemed healthcare institutions. This deep dive into AI's role in modern medicine covers the technology's evolution, its potential to redefine patient care, and the visionary work of Mayo Clinic Platform in harnessing AI responsibly.Explore the misconceptions surrounding AI in healthcare and discover the ethical and regulatory frameworks guiding its application. Glimpse into the future with Halamka's visionary perspective on AI's potential to democratize and revolutionize healthcare across the globe. Join us for an enlightening discussion on the challenges, triumphs, and the horizon of AI in healthcare through the lens of John Halamka's pioneering experiences.
In the newest episode of Gradient Dissent, Chelsea Finn, Assistant Professor at Stanford's Computer Science Department, discusses the forefront of robotics and machine learning.Discover her groundbreaking work, where two-armed robots learn to cook shrimp (messes included!), and discuss how robotic learning could transform student feedback in education.We'll dive into the challenges of developing humanoid and quadruped robots, explore the limitations of simulated environments and discuss why real-world experience is key for adaptable machines. Plus, Chelsea will offer a glimpse into the future of household robotics and why it may be a few years before a robot is making your bed.Whether you're an AI enthusiast, a robotics professional, or simply curious about the potential and future of the technology, this episode offers unique insights into the evolving world of robotics and where it's headed next.*Subscribe to Weights & Biases* → https://bit.ly/45BCkYzTimestamps:0:00- Introduction13:00 - Reinforcement Learning in Robotics15:00 - Using Simulation vs. Real Data in Robotics17:00 - The Complexity of Grasping and Manipulation Tasks20:00 - Future of Household Robotics23:00 - Humanoids and Quadrupeds in Robotics25:00 - Public Perception and Design of Robots27:00 - Performance of Robot Dogs29:00 - Chelsea's Work on Student Feedback31:00 - Training the Auto-Grading System33:00 - Potential Expansion to Other Classes and Projects35:00 - Impact of AI Coding Tools on Education37:00 - Chelsea's Exciting Research in Robotics39:00 - Cooking Shrimp with a Two-Armed Robot41:00 - Evaluating Robotic Cooking Experiments43:00 - Vision Systems in Robotics50:00 - Conclusion
In the latest episode of Gradient Dissent, Richard Socher, CEO of You.com, shares his insights on the power of AI in search. The episode focuses on how advanced language models like GPT-4 are transforming search engines and changing the way we interact with digital platforms. The discussion covers the practical applications and challenges of integrating AI into search functionality, as well as the ethical considerations and future implications of AI in our digital lives. Join us for an enlightening conversation on how AI and you.com are reshaping how we access and interact with information online.*Subscribe to Weights & Biases* → https://bit.ly/45BCkYzTimestamps: 00:00 - Introduction to Gradient Dissent Podcast 00:48 - Richard Socher's Journey: From Linguistic Computer Science to AI 06:42 - The Genesis and Evolution of MetaMind 13:30 - Exploring You.com's Approach to Enhanced Search 18:15 - Demonstrating You.com's AI in Mortgage Calculations 24:10 - The Power of AI in Search: A Deep Dive with You.com 30:25 - Security Measures in Running AI-Generated Code 35:50 - Building a Robust and Secure AI Tech Stack 42:33 - The Role of AI in Automating and Transforming Digital Work 48:50 - Discussing Ethical Considerations and the Societal Impact of AI 55:15 - Envisioning the Future of AI in Daily Life and Work 01:02:00 - Reflecting on the Evolution of AI and Its Future Prospects 01:05:00 - Closing Remarks and Podcast Wrap-Up
Explore the Future of Investment & Impact in AI with Host Lukas Biewald and Guests Elad Gill and Sarah Guo of the No Priors podcast.Sarah is the founder of Conviction VC, an AI-centric $100 million venture fund. Elad, a seasoned entrepreneur and startup investor, boasts an impressive portfolio in over 40 companies, each valued at $1 billion or more, and wrote the influential "High Growth Handbook."Join us for a deep dive into the nuanced world of AI, where we'll explore its broader industry impact, focusing on how startups can seamlessly blend product-centric approaches with a balance of innovation and practical development.*Subscribe to Weights & Biases* → https://bit.ly/45BCkYzTimestamps:0:00 - Introduction 5:15 - Exploring Fine-Tuning vs RAG in AI10:30 - Evaluating AI Research for Investment15:45 - Impact of AI Models on Product Development20:00 - AI's Role in Evolving Job Markets25:15 - The Balance Between AI Research and Product Development30:00 - Code Generation Technologies in Software Engineering35:00 - AI's Broader Industry Implications40:00 - Importance of Product-Driven Approaches in AI Startups45:00 - AI in Various Sectors: Beyond Software Engineering50:00 - Open Source vs Proprietary AI Models55:00 - AI's Impact on Traditional Roles and Industries1:00:00 - Closing Thoughts Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Follow Weights & Biases:YouTube: http://wandb.me/youtubeTwitter: https://twitter.com/weights_biases LinkedIn: https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3#OCR #DeepLearning #AI #Modeling #ML
In the latest episode of Gradient Dissent, we explore the innovative features and impact of LlamaIndex in AI data management with Jerry Liu, CEO of LlamaIndex. Jerry shares insights on how LlamaIndex integrates diverse data formats with advanced AI technologies, addressing challenges in data retrieval, analysis, and conversational memory. We also delve into the future of AI-driven systems and LlamaIndex's role in this rapidly evolving field. This episode is a must-watch for anyone interested in AI, data science, and the future of technology.Timestamps:0:00 - Introduction 4:46 - Differentiating LlamaIndex in the AI framework ecosystem.9:00 - Discussing data analysis, search, and retrieval applications.14:17 - Exploring Retrieval Augmented Generation (RAG) and vector databases.19:33 - Implementing and optimizing One Bot in Discord.24:19 - Developing and evaluating datasets for AI systems.28:00 - Community contributions and the growth of LlamaIndex.34:34 - Discussing embedding models and the use of vector databases.39:33 - Addressing AI model hallucinations and fine-tuning.44:51 - Text extraction applications and agent-based systems in AI.49:25 - Community contributions to LlamaIndex and managing refactors.52:00 - Interactions with big tech's corpus and AI context length.54:59 - Final thoughts on underrated aspects of ML and challenges in AI.Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Connect with Jerry:https://twitter.com/jerryjliu0https://www.linkedin.com/in/jerry-liu-64390071/Follow Weights & Biases:YouTube: http://wandb.me/youtubeTwitter: https://twitter.com/weights_biases LinkedIn: https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3#OCR #DeepLearning #AI #Modeling #ML
In the latest episode of Gradient Dissent, we hear from Joseph Spisak, Product Director, Generative AI @Meta, to explore the boundless impacts of AI and its expansive role in reshaping various sectors. We delve into the intricacies of models like GPT and Llama2, their influence on user experiences, and AI's groundbreaking contributions to fields like biology, material science, and green hydrogen production through the Open Catalyst Project. The episode also examines AI's practical business applications, from document summarization to intelligent note-taking, addressing the ethical complexities of AI deployment. We wrap up with a discussion on the significance of open-source AI development, community collaboration, and AI democratization. Tune in for valuable insights into the expansive world of AI, relevant to developers, business leaders, and tech enthusiasts.We discuss:0:00 Intro0:32 Joe is Back at Meta3:28 What Does Meta Get Out Of Putting Out LLMs?8:24 Measuring The Quality Of LLMs10:55 How Do You Pick The Sizes Of Models16:45 Advice On Choosing Which Model To Start With24:57 The Secret Sauce In The Training26:17 What Is Being Worked On Now33:00 The Safety Mechanisms In Llama 237:00 The Datasets Llama 2 Is Trained On38:00 On Multilingual Capabilities & Tone43:30 On The Biggest Applications Of Llama 247:25 On Why The Best Teams Are Built By Users54:01 The Culture Differences Of Meta vs Open Source57:39 The AI Learning Alliance1:01:34 Where To Learn About Machine Learning1:05:10 Why AI For Science Is Under-rated1:11:36 What Are The Biggest Issues With Real-World ApplicationsThanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
In the premiere episode of Gradient Dissent Business, we're joined by Weights & Biases co-founder Chris Van Pelt for a deep dive into the world of large language models like GPT-3.5 and GPT-4. Chris bridges his expertise as both a tech founder and AI expert, offering key strategies for startups seeking to connect with early users, and for enterprises experimenting with AI. He highlights the melding of AI and traditional web development, sharing his insights on product evolution, leadership, and the power of customer conversations—even for the most introverted founders. He shares how personal development and authentic co-founder relationships enrich business dynamics. Join us for a compelling episode brimming with actionable advice for those looking to innovate with language models, all while managing the inherent complexities. Don't miss Chris Van Pelt's invaluable take on the future of AI in this thought-provoking installment of Gradient Dissent Business.We discuss:0:00 - Intro5:59 - Impactful relationships in Chris's life13:15 - Advice for finding co-founders16:25 - Chris's fascination with challenging problems22:30 - Tech stack for AI labs30:50 - Impactful capabilities of AI models36:24 - How this AI era is different47:36 - Advising large enterprises on language model integration51:18 - Using language models for business intelligence and automation52:13 - Closing thoughts and appreciationThanks for listening to the Gradient Dissent Business podcast, with hosts Lavanya Shukla and Caryn Marooney, brought to you by Weights & Biases. Be sure to click the subscribe button below, to keep your finger on the pulse of this fast-moving space and hear from other amazing guests#OCR #DeepLearning #AI #Modeling #ML
On this episode, we're joined by Brandon Duderstadt, Co-Founder and CEO of Nomic AI. Both of Nomic AI's products, Atlas and GPT4All, aim to improve the explainability and accessibility of AI.We discuss:- (0:55) What GPT4All is and its value proposition.- (6:56) The advantages of using smaller LLMs for specific tasks. - (9:42) Brandon's thoughts on the cost of training LLMs. - (10:50) Details about the current state of fine-tuning LLMs. - (12:20) What quantization is and what it does. - (21:16) What Atlas is and what it allows you to do.- (27:30) Training code models versus language models.- (32:19) Details around evaluating different models.- (38:34) The opportunity for smaller companies to build open-source models. - (42:00) Prompt chaining versus fine-tuning models.Resources mentioned:Brandon Duderstadt - https://www.linkedin.com/in/brandon-duderstadt-a3269112a/Nomic AI - https://www.linkedin.com/company/nomic-ai/Nomic AI Website - https://home.nomic.ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
On this episode, we're joined by Soumith Chintala, VP/Fellow of Meta and Co-Creator of PyTorch. Soumith and his colleagues' open-source framework impacted both the development process and the end-user experience of what would become PyTorch.We discuss:- The history of PyTorch's development and TensorFlow's impact on development decisions.- How a symbolic execution model affects the implementation speed of an ML compiler.- The strengths of different programming languages in various development stages.- The importance of customer engagement as a measure of success instead of hard metrics.- Why community-guided innovation offers an effective development roadmap.- How PyTorch's open-source nature cultivates an efficient development ecosystem.- The role of community building in consolidating assets for more creative innovation.- How to protect community values in an open-source development environment.- The value of an intrinsic organizational motivation structure.- The ongoing debate between open-source and closed-source products, especially as it relates to AI and machine learning.Resources:- Soumith Chintalahttps://www.linkedin.com/in/soumith/- Meta | LinkedInhttps://www.linkedin.com/company/meta/- Meta | Websitehttps://about.meta.com/- Pytorchhttps://pytorch.org/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
On this episode, we're joined by Andrew Feldman, Founder and CEO of Cerebras Systems. Andrew and the Cerebras team are responsible for building the largest-ever computer chip and the fastest AI-specific processor in the industry.We discuss:- The advantages of using large chips for AI work.- Cerebras Systems' process for building chips optimized for AI.- Why traditional GPUs aren't the optimal machines for AI work.- Why efficiently distributing computing resources is a significant challenge for AI work.- How much faster Cerebras Systems' machines are than other processors on the market.- Reasons why some ML-specific chip companies fail and what Cerebras does differently.- Unique challenges for chip makers and hardware companies.- Cooling and heat-transfer techniques for Cerebras machines.- How Cerebras approaches building chips that will fit the needs of customers for years to come.- Why the strategic vision for what data to collect for ML needs more discussion.Resources:Andrew Feldman - https://www.linkedin.com/in/andrewdfeldman/Cerebras Systems - https://www.linkedin.com/company/cerebras-systems/Cerebras Systems | Website - https://www.cerebras.net/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
On this episode, we're joined by Harrison Chase, Co-Founder and CEO of LangChain. Harrison and his team at LangChain are on a mission to make the process of creating applications powered by LLMs as easy as possible.We discuss:- What LangChain is and examples of how it works. - Why LangChain has gained so much attention. - When LangChain started and what sparked its growth. - Harrison's approach to community-building around LangChain. - Real-world use cases for LangChain.- What parts of LangChain Harrison is proud of and which parts can be improved.- Details around evaluating effectiveness in the ML space.- Harrison's opinion on fine-tuning LLMs.- The importance of detailed prompt engineering.- Predictions for the future of LLM providers.Resources:Harrison Chase - https://www.linkedin.com/in/harrison-chase-961287118/LangChain | LinkedIn - https://www.linkedin.com/company/langchain/LangChain | Website - https://docs.langchain.com/docs/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
On this episode, we're joined by Jean Marc Alkazzi, Applied AI at idealworks. Jean focuses his attention on applied AI, leveraging the use of autonomous mobile robots (AMRs) to improve efficiency within factories and more.We discuss:- Use cases for autonomous mobile robots (AMRs) and how to manage a fleet of them. - How AMRs interact with humans working in warehouses.- The challenges of building and deploying autonomous robots.- Computer vision vs. other types of localization technology for robots.- The purpose and types of simulation environments for robotic testing.- The importance of aligning a robotic fleet's workflow with concrete business objectives.- What the update process looks like for robots.- The importance of avoiding your own biases when developing and testing AMRs.- The challenges associated with troubleshooting ML systems.Resources: Jean Marc Alkazzi - https://www.linkedin.com/in/jeanmarcjeanazzi/idealworks |LinkedIn - https://www.linkedin.com/company/idealworks-gmbh/idealworks | Website - https://idealworks.com/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
On this episode, we're joined by Stella Biderman, Executive Director at EleutherAI and Lead Scientist - Mathematician at Booz Allen Hamilton.EleutherAI is a grassroots collective that enables open-source AI research and focuses on the development and interpretability of large language models (LLMs).We discuss:- How EleutherAI got its start and where it's headed.- The similarities and differences between various LLMs.- How to decide which model to use for your desired outcome.- The benefits and challenges of reinforcement learning from human feedback.- Details around pre-training and fine-tuning LLMs.- Which types of GPUs are best when training LLMs.- What separates EleutherAI from other companies training LLMs.- Details around mechanistic interpretability.- Why understanding what and how LLMs memorize is important.- The importance of giving researchers and the public access to LLMs.Stella Biderman - https://www.linkedin.com/in/stellabiderman/EleutherAI - https://www.linkedin.com/company/eleutherai/Resources:- https://www.eleuther.ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
On this episode, we're joined by Aidan Gomez, Co-Founder and CEO at Cohere. Cohere develops and releases a range of innovative AI-powered tools and solutions for a variety of NLP use cases.We discuss:- What “attention” means in the context of ML.- Aidan's role in the “Attention Is All You Need” paper.- What state-space models (SSMs) are, and how they could be an alternative to transformers. - What it means for an ML architecture to saturate compute.- Details around data constraints for when LLMs scale.- Challenges of measuring LLM performance.- How Cohere is positioned within the LLM development space.- Insights around scaling down an LLM into a more domain-specific one.- Concerns around synthetic content and AI changing public discourse.- The importance of raising money at healthy milestones for AI development.Aidan Gomez - https://www.linkedin.com/in/aidangomez/Cohere - https://www.linkedin.com/company/cohere-ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Resources:- https://cohere.ai/- “Attention Is All You Need”#OCR #DeepLearning #AI #Modeling #ML
Jonathan Frankle, Chief Scientist at MosaicML and Assistant Professor of Computer Science at Harvard University, joins us on this episode. With comprehensive infrastructure and software tools, MosaicML aims to help businesses train complex machine-learning models using their own proprietary data.We discuss:- Details of Jonathan's Ph.D. dissertation which explores his “Lottery Ticket Hypothesis.”- The role of neural network pruning and how it impacts the performance of ML models.- Why transformers will be the go-to way to train NLP models for the foreseeable future.- Why the process of speeding up neural net learning is both scientific and artisanal. - What MosiacML does, and how it approaches working with clients.- The challenges for developing AGI.- Details around ML training policy and ethics.- Why data brings the magic to customized ML models.- The many use cases for companies looking to build customized AI models.Jonathan Frankle - https://www.linkedin.com/in/jfrankle/Resources:- https://mosaicml.com/- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural NetworksThanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML
About This EpisodeShreya Shankar is a computer scientist, PhD student in databases at UC Berkeley, and co-author of "Operationalizing Machine Learning: An Interview Study", an ethnographic interview study with 18 machine learning engineers across a variety of industries on their experience deploying and maintaining ML pipelines in production.Shreya explains the high-level findings of "Operationalizing Machine Learning"; variables that indicate a successful deployment (velocity, validation, and versioning), common pain points, and a grouping of the MLOps tool stack into four layers. Shreya and Lukas also discuss examples of data challenges in production, Jupyter Notebooks, and reproducibility.Show notes (transcript and links): http://wandb.me/gd-shreya---
About this episodeIn this episode of Gradient Dissent, Lukas interviews Dave Rogenmoser (CEO & Co-Founder) and Saad Ansari (Director of AI) of Jasper AI, a generative AI company with a focus on text generation for content like blog posts, articles, and more. The company has seen impressive growth since it's launch at the start of 2021.Lukas talks with Dave and Saad about how Jasper AI was able to sell the capabilities of large language models as a product so successfully, and how they are able to continually improve their product and take advantage of steps forward in the AI industry at large.They also speak on how they keep their business ahead of the competition, where they put their focus on in terms of R&D, and how they are able to keep the insights they've learned over the years relevant at all times as their company grows in employee count and company value.Other topics include the potential use of generative AI in domains it hasn't necessarily seen yet, as well as the impact that community and user feedback plays on the constant tweaking and tuning processes that machine learning models go through.Connect with Dave & Saad:Find Dave on Twitter and LinkedIn.Find Saad on LinkedIn.---
Sarah Catanzaro is a General Partner at Amplify Partners, and one of the leading investors in AI and ML. Her investments include RunwayML, OctoML, and Gantry.Sarah and Lukas discuss lessons learned from the "AI renaissance" of the mid 2010s and compare the general perception of ML back then to now. Sarah also provides insights from her perspective as an investor, from selling into tech-forward companies vs. traditional enterprises, to the current state of MLOps/developer tools, to large language models and hype bubbles.Show notes (transcript and links): http://wandb.me/gd-sarah-catanzaro---⏳ Timestamps: 0:00 Intro1:10 Lessons learned from previous AI hype cycles11:46 Maintaining technical knowledge as an investor19:05 Selling into tech-forward companies vs. traditional enterprises25:09 Building point solutions vs. end-to-end platforms36:27 LLMS, new tooling, and commoditization44:39 Failing fast and how startups can compete with large cloud vendors52:31 The gap between research and industry, and vice versa1:00:01 Advice for ML practitioners during hype bubbles1:03:17 Sarah's thoughts on Rust and bottlenecks in deployment1:11:23 The importance of aligning technology with people1:15:58 Outro---
Cristóbal Valenzuela is co-founder and CEO of Runway ML, a startup that's building the future of AI-powered content creation tools. Runway's research areas include diffusion systems for image generation.Cris gives a demo of Runway's video editing platform. Then, he shares how his interest in combining technology with creativity led to Runway, and where he thinks the world of computation and content might be headed to next. Cris and Lukas also discuss Runway's tech stack and research.Show notes (transcript and links): http://wandb.me/gd-cristobal-valenzuela---⏳ Timestamps: 0:00 Intro1:06 How Runway uses ML to improve video editing6:04 A demo of Runway's video editing capabilities13:36 How Cris entered the machine learning space18:55 Cris' thoughts on the future of ML for creative use cases28:46 Runway's tech stack32:38 Creativity, and keeping humans in the loop36:15 The potential of audio generation and new mental models40:01 Outro---
Jeremy Howard is a co-founder of fast.ai, the non-profit research group behind the popular massive open online course "Practical Deep Learning for Coders", and the open source deep learning library "fastai".Jeremy is also a co-founder of #Masks4All, a global volunteer organization founded in March 2020 that advocated for the public adoption of homemade face masks in order to help slow the spread of COVID-19. His Washington Post article "Simple DIY masks could help flatten the curve." went viral in late March/early April 2020, and is associated with the U.S CDC's change in guidance a few days later to recommend wearing masks in public.In this episode, Jeremy explains how diffusion works and how individuals with limited compute budgets can engage meaningfully with large, state-of-the-art models. Then, as our first-ever repeat guest on Gradient Dissent, Jeremy revisits a previous conversation with Lukas on Python vs. Julia for machine learning.Finally, Jeremy shares his perspective on the early days of COVID-19, and what his experience as one of the earliest and most high-profile advocates for widespread mask-wearing was like.Show notes (transcript and links): http://wandb.me/gd-jeremy-howard-2---⏳ Timestamps: 0:00 Intro1:06 Diffusion and generative models14:40 Engaging with large models meaningfully20:30 Jeremy's thoughts on Stable Diffusion and OpenAI26:38 Prompt engineering and large language models32:00 Revisiting Julia vs. Python40:22 Jeremy's science advocacy during early COVID days1:01:03 Researching how to improve children's education1:07:43 The importance of executive buy-in1:11:34 Outro1:12:02 Bonus: Weights & Biases---
Jerome Pesenti is the former VP of AI at Meta, a tech conglomerate that includes Facebook, WhatsApp, and Instagram, and one of the most exciting places where AI research is happening today.Jerome shares his thoughts on Transformers-based large language models, and why he's excited by the progress but skeptical of the term "AGI". Then, he discusses some of the practical applications of ML at Meta (recommender systems and moderation!) and dives into the story behind Meta's development of PyTorch. Jerome and Lukas also chat about Jerome's time at IBM Watson and in drug discovery.Show notes (transcript and links): http://wandb.me/gd-jerome-pesenti---⏳ Timestamps: 0:00 Intro0:28 Jerome's thought on large language models12:53 AI applications and challenges at Meta18:41 The story behind developing PyTorch26:40 Jerome's experience at IBM Watson28:53 Drug discovery, AI, and changing the game36:10 The potential of education and AI40:10 Meta and AR/VR interfaces43:43 Why NVIDIA is such a powerhouse47:08 Jerome's advice to people starting their careers48:50 Going back to coding, the challenges of scaling52:11 Outro---Connect with Jerome:
D. Sculley is CEO of Kaggle, the beloved and well-known data science and machine learning community.D. discusses his influential 2015 paper "Machine Learning: The High Interest Credit Card of Technical Debt" and what the current challenges of deploying models in the real world are now, in 2022. Then, D. and Lukas chat about why Kaggle is like a rain forest, and about Kaggle's historic, current, and potential future roles in the broader machine learning community.Show notes (transcript and links): http://wandb.me/gd-d-sculley---⏳ Timestamps: 0:00 Intro1:02 Machine learning and technical debt11:18 MLOps, increased stakes, and realistic expectations19:12 Evaluating models methodically25:32 Kaggle's role in the ML world33:34 Kaggle competitions, datasets, and notebooks38:49 Why Kaggle is like a rain forest44:25 Possible future directions for Kaggle46:50 Healthy competitions and self-growth48:44 Kaggle's relevance in a compute-heavy future53:49 AutoML vs. human judgment56:06 After a model goes into production1:00:00 Outro---Connect with D. and Kaggle:
Emad Mostaque is CEO and co-founder of Stability AI, a startup and network of decentralized developer communities building open AI tools. Stability AI is the company behind Stable Diffusion, the well-known, open source, text-to-image generation model.Emad shares the story and mission behind Stability AI (unlocking humanity's potential with open AI technology), and explains how Stability's role as a community catalyst and compute provider might evolve as the company grows. Then, Emad and Lukas discuss what the future might hold in store: big models vs "optimal" models, better datasets, and more decentralization.-
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