The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and
Filtered episodes(8)
- StandardSummaries onlyHow to Find the Agent Failures Your Evals Miss with Scott Clark
Published May 7, 2026
Scott ClarkIn this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Maslow’s hierarchy of observability: telemetry for logging, monitoring for known signals, and post-production or online analytics to surface unknown unknowns. We dig into examples of real-world failures Scott’s team has seen in production systems, such as “lazy” tool-use hallucinations that standard eva
- StandardSummaries onlyThe Race to Production-Grade Diffusion LLMs with Stefano Ermon
Published Mar 26, 2026
Stefano ErmonToday, we're joined by Stefano Ermon, associate professor at Stanford University and CEO of Inception Labs to discuss diffusion language models. We dig into how diffusion approaches—traditionally used for images—are being adapted for text and code generation, the technical challenges of applying continuous methods to discrete token spaces, and how diffusion models compare to traditional autoregressive LLMs. Stefano introduces Mercury 2, a commercial-scale diffusion LLM that can generate multiple
- StandardSummaries onlyAI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with Sebastian Raschka
Published Feb 26, 2026
Sebastian RaschkaIn this episode, Sebastian Raschka, independent LLM researcher and author, joins us to break down how the LLM landscape has changed over the past year and what is likely to matter most in 2026. We discuss the shift from raw model scaling to reasoning-focused post-training, inference-time techniques, and better tool integration. Sebastian explains why methods like self-consistency, self-refinement, and verifiable-reward reinforcement learning have become central to progress in domains like math a
- StandardSummaries onlyScaling Agentic Inference Across Heterogeneous Compute with Zain Asgar
Published Dec 2, 2025
Zain AsgarIn this episode, Zain Asgar, co-founder and CEO of Gimlet Labs, joins us to discuss the heterogeneous AI inference across diverse hardware. Zain argues that the current industry standard of running all AI workloads on high-end GPUs is unsustainable for agents, which consume significantly more tokens than traditional LLM applications. We explore Gimlet’s approach to heterogeneous inference, which involves disaggregating workloads across a mix of hardware—from H100s to older GPUs and CPUs—to optim
- StandardSummaries onlyIs It Time to Rethink LLM Pre-Training? with Aditi Raghunathan
Published Sep 16, 2025
Aditi RaghunathanToday, we're joined by Aditi Raghunathan, assistant professor at Carnegie Mellon University, to discuss the limitations of LLMs and how we can build more adaptable and creative models. We dig into her ICML 2025 Outstanding Paper Award winner, “Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction,” which examines why LLMs struggle with generating truly novel ideas. We dig into the "Roll the dice" approach, which encourages structured exploration by injec
- StandardSummaries onlyHow OpenAI Builds AI Agents That Think and Act with Josh Tobin
Published May 6, 2025
Josh TobinToday, we're joined by Josh Tobin, member of technical staff at OpenAI, to discuss the company’s approach to building AI agents. We cover OpenAI's three agentic offerings—Deep Research for comprehensive web research, Operator for website navigation, and Codex CLI for local code execution. We explore OpenAI’s shift from simple LLM workflows to reasoning models specifically trained for multi-step tasks through reinforcement learning, and how that enables agents to more easily recover from failures
- StandardSummaries onlyExploring the Biology of LLMs with Circuit Tracing with Emmanuel Ameisen
Published Apr 14, 2025
Emmanuel AmeisenIn this episode, Emmanuel Ameisen, a research engineer at Anthropic, returns to discuss two recent papers: "Circuit Tracing: Revealing Language Model Computational Graphs" and "On the Biology of a Large Language Model." Emmanuel explains how his team developed mechanistic interpretability methods to understand the internal workings of Claude by replacing dense neural network components with sparse, interpretable alternatives. The conversation explores several fascinating discoveries about large
- StandardSummaries onlyTeaching LLMs to Self-Reflect with Reinforcement Learning with Maohao Shen
Published Apr 8, 2025
Maohao ShenToday, we're joined by Maohao Shen, PhD student at MIT to discuss his paper, “Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search.” We dig into how Satori leverages reinforcement learning to improve language model reasoning—enabling model self-reflection, self-correction, and exploration of alternative solutions. We explore the Chain-of-Action-Thought (COAT) approach, which uses special tokens—continue, reflect, and explore—to guide the mo