最高の Sam Charrington ポッドキャスト (2024)

1
An Agentic Mixture of Experts for DevOps with Sunil Mallya - #708 1:15:09

6d ago1:15:09

1:15:09

Today we're joined by Sunil Mallya, CTO and co-founder of Flip AI. We discuss Flip’s incident debugging system for DevOps, which was built using a custom mixture of experts (MoE) large language model (LLM) trained on a novel "CoMELT" observability dataset which combines traditional MELT data—metrics, events, logs, and traces—with code to efficientl…

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Building AI Voice Agents with Scott Stephenson - #707 1:01:44

13d ago1:01:44

1:01:44

Today, we're joined by Scott Stephenson, co-founder and CEO of Deepgram to discuss voice AI agents. We explore the importance of perception, understanding, and interaction and how these key components work together in building intelligent AI voice agents. We discuss the role of multimodal LLMs as well as speech-to-text and text-to-speech models in …

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Is Artificial Superintelligence Imminent? with Tim Rocktäschel - #706 55:52

20d ago55:52

55:52

Today, we're joined by Tim Rocktäschel, senior staff research scientist at Google DeepMind, professor of Artificial Intelligence at University College London, and author of the recently published popular science book, “Artificial Intelligence: 10 Things You Should Know.” We dig into the attainability of artificial superintelligence and the path to …

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ML Models for Safety-Critical Systems with Lucas García - #705 1:16:06

27d ago1:16:06

1:16:06

Today, we're joined by Lucas García, principal product manager for deep learning at MathWorks to discuss incorporating ML models into safety-critical systems. We begin by exploring the critical role of verification and validation (V&V) in these applications. We review the popular V-model for engineering critical systems and then dig into the “W” ad…

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AI Agents: Substance or Snake Oil with Arvind Narayanan - #704 54:22

1M ago54:22

54:22

Today, we're joined by Arvind Narayanan, professor of Computer Science at Princeton University to discuss his recent works, AI Agents That Matter and AI Snake Oil. In “AI Agents That Matter”, we explore the range of agentic behaviors, the challenges in benchmarking agents, and the ‘capability and reliability gap’, which creates risks when deploying…

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AI Agents for Data Analysis with Shreya Shankar - #703 48:24

1M ago48:24

48:24

Today, we're joined by Shreya Shankar, a PhD student at UC Berkeley to discuss DocETL, a declarative system for building and optimizing LLM-powered data processing pipelines for large-scale and complex document analysis tasks. We explore how DocETL's optimizer architecture works, the intricacies of building agentic systems for data processing, the …

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Stealing Part of a Production Language Model with Nicholas Carlini - #702 1:03:30

2M ago1:03:30

1:03:30

Today, we're joined by Nicholas Carlini, research scientist at Google DeepMind to discuss adversarial machine learning and model security, focusing on his 2024 ICML best paper winner, “Stealing part of a production language model.” We dig into this work, which demonstrated the ability to successfully steal the last layer of production language mode…

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Supercharging Developer Productivity with ChatGPT and Claude with Simon Willison - #701 1:14:15

2M ago1:14:15

1:14:15

Today, we're joined by Simon Willison, independent researcher and creator of Datasette to discuss the many ways software developers and engineers can take advantage of large language models (LLMs) to boost their productivity. We dig into Simon’s own workflows and how he uses popular models like ChatGPT and Anthropic’s Claude to write and test hundr…

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Automated Design of Agentic Systems with Shengran Hu - #700 59:30

2M ago59:30

59:30

Today, we're joined by Shengran Hu, a PhD student at the University of British Columbia, to discuss Automated Design of Agentic Systems (ADAS), an approach focused on automatically creating agentic system designs. We explore the spectrum of agentic behaviors, the motivation for learning all aspects of agentic system design, the key components of th…

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The EU AI Act and Mitigating Bias in Automated Decisioning with Peter van der Putten - #699 45:34

3M ago45:34

45:34

Today, we're joined by Peter van der Putten, director of the AI Lab at Pega and assistant professor of AI at Leiden University. We discuss the newly adopted European AI Act and the challenges of applying academic fairness metrics in real-world AI applications. We dig into the key ethical principles behind the Act, its broad definition of AI, and ho…

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The Building Blocks of Agentic Systems with Harrison Chase - #698 59:17

3M ago59:17

59:17

Today, we're joined by Harrison Chase, co-founder and CEO of LangChain to discuss LLM frameworks, agentic systems, RAG, evaluation, and more. We dig into the elements of a modern LLM framework, including the most productive developer experiences and appropriate levels of abstraction. We dive into agents and agentic systems as well, covering the “sp…

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Simplifying On-Device AI for Developers with Siddhika Nevrekar - #697 46:37

3M ago46:37

46:37

Today, we're joined by Siddhika Nevrekar, AI Hub head at Qualcomm Technologies, to discuss on-device AI and how to make it easier for developers to take advantage of device capabilities. We unpack the motivations for AI engineers to move model inference from the cloud to local devices, and explore the challenges associated with on-device AI. We dig…

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Genie: Generative Interactive Environments with Ashley Edwards - #696 46:51

3M ago46:51

46:51

Today, we're joined by Ashley Edwards, a member of technical staff at Runway, to discuss Genie: Generative Interactive Environments, a system for creating ‘playable’ video environments for training deep reinforcement learning (RL) agents at scale in a completely unsupervised manner. We explore the motivations behind Genie, the challenges of data ac…

1
Bridging the Sim2real Gap in Robotics with Marius Memmel - #695 57:21

3M ago57:21

57:21

Today, we're joined by Marius Memmel, a PhD student at the University of Washington, to discuss his research on sim-to-real transfer approaches for developing autonomous robotic agents in unstructured environments. Our conversation focuses on his recent ASID and URDFormer papers. We explore the complexities presented by real-world settings like a c…

1
Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - #694 1:20:05

4M ago1:20:05

1:20:05

Today, we're joined by Hamel Husain, founder of Parlance Labs, to discuss the ins and outs of building real-world products using large language models (LLMs). We kick things off discussing novel applications of LLMs and how to think about modern AI user experiences. We then dig into the key challenge faced by LLM developers—how to iterate from a sn…

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Mamba, Mamba-2 and Post-Transformer Architectures for Generative AI with Albert Gu - #693 57:54

4M ago57:54

57:54

Today, we're joined by Albert Gu, assistant professor at Carnegie Mellon University, to discuss his research on post-transformer architectures for multi-modal foundation models, with a focus on state-space models in general and Albert’s recent Mamba and Mamba-2 papers in particular. We dig into the efficiency of the attention mechanism and its limi…

1
Decoding Animal Behavior to Train Robots with EgoPet with Amir Bar - #692 43:16

4M ago43:16

43:16

Today, we're joined by Amir Bar, a PhD candidate at Tel Aviv University and UC Berkeley to discuss his research on visual-based learning, including his recent paper, “EgoPet: Egomotion and Interaction Data from an Animal’s Perspective.” Amir shares his research projects focused on self-supervised object detection and analogy reasoning for general c…

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How Microsoft Scales Testing and Safety for Generative AI with Sarah Bird - #691 57:12

4M ago57:12

57:12

Today, we're joined by Sarah Bird, chief product officer of responsible AI at Microsoft. We discuss the testing and evaluation techniques Microsoft applies to ensure safe deployment and use of generative AI, large language models, and image generation. In our conversation, we explore the unique risks and challenges presented by generative AI, the b…

1
Long Context Language Models and their Biological Applications with Eric Nguyen - #690 45:41

5M ago45:41

45:41

Today, we're joined by Eric Nguyen, PhD student at Stanford University. In our conversation, we explore his research on long context foundation models and their application to biology particularly Hyena, and its evolution into Hyena DNA and Evo models. We discuss Hyena, a convolutional-based language model developed to tackle the challenges posed b…

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Accelerating Sustainability with AI with Andres Ravinet - #689 47:46

5M ago47:46

47:46

Today, we're joined by Andres Ravinet, sustainability global black belt at Microsoft, to discuss the role of AI in sustainability. We explore real-world use cases where AI-driven solutions are leveraged to help tackle environmental and societal challenges, from early warning systems for extreme weather events to reducing food waste along the supply…

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Gen AI at the Edge: Qualcomm AI Research at CVPR 2024 with Fatih Porikli - #688 1:10:41

5M ago1:10:41

1:10:41

Today we’re joined by Fatih Porikli, senior director of technology at Qualcomm AI Research. In our conversation, we covered several of the Qualcomm team’s 16 accepted main track and workshop papers at this year’s CVPR conference. The papers span a variety of generative AI and traditional computer vision topics, with an emphasis on increased trainin…

1
Energy Star Ratings for AI Models with Sasha Luccioni - #687 48:26

5M ago48:26

48:26

Today, we're joined by Sasha Luccioni, AI and Climate lead at Hugging Face, to discuss the environmental impact of AI models. We dig into her recent research into the relative energy consumption of general purpose pre-trained models vs. task-specific, non-generative models for common AI tasks. We discuss the implications of the significant differen…

1
Language Understanding and LLMs with Christopher Manning - #686 56:10

6M ago56:10

56:10

Today, we're joined by Christopher Manning, the Thomas M. Siebel professor in Machine Learning at Stanford University and a recent recipient of the 2024 IEEE John von Neumann medal. In our conversation with Chris, we discuss his contributions to foundational research areas in NLP, including word embeddings and attention. We explore his perspectives…

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Chronos: Learning the Language of Time Series with Abdul Fatir Ansari - #685 43:05

6M ago43:05

43:05

Today we're joined by Abdul Fatir Ansari, a machine learning scientist at AWS AI Labs in Berlin, to discuss his paper, "Chronos: Learning the Language of Time Series." Fatir explains the challenges of leveraging pre-trained language models for time series forecasting. We explore the advantages of Chronos over statistical models, as well as its prom…

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Powering AI with the World's Largest Computer Chip with Joel Hestness - #684 55:06

6M ago55:06

55:06

Today we're joined by Joel Hestness, principal research scientist and lead of the core machine learning team at Cerebras. We discuss Cerebras’ custom silicon for machine learning, Wafer Scale Engine 3, and how the latest version of the company’s single-chip platform for ML has evolved to support large language models. Joel shares how WSE3 differs f…

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AI for Power & Energy with Laurent Boinot - #683 49:41

6M ago49:41

49:41

Today we're joined by Laurent Boinot, power and utilities lead for the Americas at Microsoft, to discuss the intersection of AI and energy infrastructure. We discuss the many challenges faced by current power systems in North America and the role AI is beginning to play in driving efficiencies in areas like demand forecasting and grid optimization.…

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Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682 42:09

7M ago42:09

42:09

Today we're joined by Azarakhsh (Aza) Jalalvand, a research scholar at Princeton University, to discuss his work using deep reinforcement learning to control plasma instabilities in nuclear fusion reactors. Aza explains his team developed a model to detect and avoid a fatal plasma instability called ‘tearing mode’. Aza walks us through the process …

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GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681 47:08

7M ago47:08

47:08

Today we're joined by Kirk Marple, CEO and founder of Graphlit, to explore the emerging paradigm of "GraphRAG," or Graph Retrieval Augmented Generation. In our conversation, Kirk digs into the GraphRAG architecture and how Graphlit uses it to offer a multi-stage workflow for ingesting, processing, retrieving, and generating content using LLMs (like…

1
Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680 46:24

7M ago46:24

46:24

Today we're joined by Alex Havrilla, a PhD student at Georgia Tech, to discuss "Teaching Large Language Models to Reason with Reinforcement Learning." Alex discusses the role of creativity and exploration in problem solving and explores the opportunities presented by applying reinforcement learning algorithms to the challenge of improving reasoning…

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Localizing and Editing Knowledge in LLMs with Peter Hase - #679 49:46

7M ago49:46

49:46

Today we're joined by Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab. We discuss "scalable oversight", and the importance of developing a deeper understanding of how large neural networks make decisions. We learn how matrices are probed by interpretability researchers, and explore the two schools of thought regardi…

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Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678 48:27

7M ago48:27

48:27

Today we're joined by Jonas Geiping, a research group leader at the ELLIS Institute, to explore his paper: "Coercing LLMs to Do and Reveal (Almost) Anything". Jonas explains how neural networks can be exploited, highlighting the risk of deploying LLM agents that interact with the real world. We discuss the role of open models in enabling security r…

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V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677 47:47

8M ago47:47

47:47

Today we’re joined by Mido Assran, a research scientist at Meta’s Fundamental AI Research (FAIR). In this conversation, we discuss V-JEPA, a new model being billed as “the next step in Yann LeCun's vision” for true artificial reasoning. V-JEPA, the video version of Meta’s Joint Embedding Predictive Architecture, aims to bridge the gap between human…

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Video as a Universal Interface for AI Reasoning with Sherry Yang - #676 49:34

8M ago49:34

49:34

Today we’re joined by Sherry Yang, senior research scientist at Google DeepMind and a PhD student at UC Berkeley. In this interview, we discuss her new paper, "Video as the New Language for Real-World Decision Making,” which explores how generative video models can play a role similar to language models as a way to solve tasks in the real world. Sh…

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Assessing the Risks of Open AI Models with Sayash Kapoor - #675 40:26

8M ago40:26

40:26

Today we’re joined by Sayash Kapoor, a Ph.D. student in the Department of Computer Science at Princeton University. Sayash walks us through his paper: "On the Societal Impact of Open Foundation Models.” We dig into the controversy around AI safety, the risks and benefits of releasing open model weights, and how we can establish common ground for as…

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OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674 32:12

8M ago32:12

32:12

Today we’re joined by Akshita Bhagia, a senior research engineer at the Allen Institute for AI. Akshita joins us to discuss OLMo, a new open source language model with 7 billion and 1 billion variants, but with a key difference compared to similar models offered by Meta, Mistral, and others. Namely, the fact that AI2 has also published the dataset …

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Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673 25:03

9M ago25:03

25:03

Today we’re joined by Ben Prystawski, a PhD student in the Department of Psychology at Stanford University working at the intersection of cognitive science and machine learning. Our conversation centers on Ben’s recent paper, “Why think step by step? Reasoning emerges from the locality of experience,” which he recently presented at NeurIPS 2023. In…

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Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - #672 45:38

9M ago45:38

45:38

Today we're joined by Armineh Nourbakhsh of JP Morgan AI Research to discuss the development and capabilities of DocLLM, a layout-aware large language model for multimodal document understanding. Armineh provides a historical overview of the challenges of document AI and an introduction to the DocLLM model. Armineh explains how this model, distinct…

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Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671 1:05:40

9M ago1:05:40

1:05:40

Today we’re joined by Sanmi Koyejo, assistant professor at Stanford University, to continue our NeurIPS 2024 series. In our conversation, Sanmi discusses his two recent award-winning papers. First, we dive into his paper, “Are Emergent Abilities of Large Language Models a Mirage?”. We discuss the different ways LLMs are evaluated and the excitement…

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AI Trends 2024: Reinforcement Learning in the Age of LLMs with Kamyar Azizzadenesheli - #670 1:10:25

9M ago1:10:25

1:10:25

Today we’re joined by Kamyar Azizzadenesheli, a staff researcher at Nvidia, to continue our AI Trends 2024 series. In our conversation, Kamyar updates us on the latest developments in reinforcement learning (RL), and how the RL community is taking advantage of the abstract reasoning abilities of large language models (LLMs). Kamyar shares his insig…

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Building and Deploying Real-World RAG Applications with Ram Sriharsha - #669 35:29

10M ago35:29

35:29

Today we’re joined by Ram Sriharsha, VP of engineering at Pinecone. In our conversation, we dive into the topic of vector databases and retrieval augmented generation (RAG). We explore the trade-offs between relying solely on LLMs for retrieval tasks versus combining retrieval in vector databases and LLMs, the advantages and complexities of RAG wit…

1
Nightshade: Data Poisoning to Fight Generative AI with Ben Zhao - #668 39:45

10M ago39:45

39:45

Today we’re joined by Ben Zhao, a Neubauer professor of computer science at the University of Chicago. In our conversation, we explore his research at the intersection of security and generative AI. We focus on Ben’s recent Fawkes, Glaze, and Nightshade projects, which use “poisoning” approaches to provide users with security and protection against…

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Learning Transformer Programs with Dan Friedman - #667 38:48

10M ago38:48

38:48

Today, we continue our NeurIPS series with Dan Friedman, a PhD student in the Princeton NLP group. In our conversation, we explore his research on mechanistic interpretability for transformer models, specifically his paper, Learning Transformer Programs. The LTP paper proposes modifications to the transformer architecture which allow transformer mo…

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AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - #666 1:05:18

10M ago1:05:18

1:05:18

Today we continue our AI Trends 2024 series with a conversation with Thomas Dietterich, distinguished professor emeritus at Oregon State University. As you might expect, Large Language Models figured prominently in our conversation, and we covered a vast array of papers and use cases exploring current research into topics such as monolithic vs. mod…

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AI Trends 2024: Computer Vision with Naila Murray - #665 52:01

10M ago52:01

52:01

Today we kick off our AI Trends 2024 series with a conversation with Naila Murray, director of AI research at Meta. In our conversation with Naila, we dig into the latest trends and developments in the realm of computer vision. We explore advancements in the areas of controllable generation, visual programming, 3D Gaussian splatting, and multimodal…

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Are Vector DBs the Future Data Platform for AI? with Ed Anuff - #664 48:13

11M ago48:13

48:13

Today we’re joined by Ed Anuff, chief product officer at DataStax. In our conversation, we discuss Ed’s insights on RAG, vector databases, embedding models, and more. We dig into the underpinnings of modern vector databases (like HNSW and DiskANN) that allow them to efficiently handle massive and unstructured data sets, and discuss how they help us…

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Quantizing Transformers by Helping Attention Heads Do Nothing with Markus Nagel - #663 46:49

11M ago46:49

46:49

Today we’re joined by Markus Nagel, research scientist at Qualcomm AI Research, who helps us kick off our coverage of NeurIPS 2023. In our conversation with Markus, we cover his accepted papers at the conference, along with other work presented by Qualcomm AI Research scientists. Markus’ first paper, Quantizable Transformers: Removing Outliers by H…

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Responsible AI in the Generative Era with Michael Kearns - #662 36:04

11M ago36:04

36:04

Today we’re joined by Michael Kearns, professor in the Department of Computer and Information Science at the University of Pennsylvania and an Amazon scholar. In our conversation with Michael, we discuss the new challenges to responsible AI brought about by the generative AI era. We explore Michael’s learnings and insights from the intersection of …

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Edutainment for AI and AWS PartyRock with Mike Miller - #661 29:46

11M ago29:46

29:46

Today we’re joined by Mike Miller, director of product at AWS responsible for the company’s “edutainment” products. In our conversation with Mike, we explore AWS PartyRock, a no-code generative AI app builder that allows users to easily create fun and shareable AI applications by selecting a model, chaining prompts together, and linking different t…

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Data, Systems and ML for Visual Understanding with Cody Coleman - #660 38:27

11M ago38:27

38:27

Today we’re joined by Cody Coleman, co-founder and CEO of Coactive AI. In our conversation with Cody, we discuss how Coactive has leveraged modern data, systems, and machine learning techniques to deliver its multimodal asset platform and visual search tools. Cody shares his expertise in the area of data-centric AI, and we dig into techniques like …

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Patterns and Middleware for LLM Applications with Kyle Roche - #659 35:58

11M ago35:58

35:58

Today we’re joined by Kyle Roche, founder and CEO of Griptape to discuss patterns and middleware for LLM applications. We dive into the emerging patterns for developing LLM applications, such as off prompt data—which allows data retrieval without compromising the chain of thought within language models—and pipelines, which are sequential tasks that…

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