~ cueing up the show ~
~ pulling every episode ~
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) — Every episode — OmList
om
list
Swipe
Tournament
Lists
Friends
All
Movies
TV
Books
Games
Music
Podcasts
People
▾
Sign in
Every episode
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
30 episodes
Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773
Ep 773 · Aug 12, 2026 · 57 min
Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution image generated locally, and today’s models still struggle in surprising ways. In this episode, Fatih Porikli, Vice President of Technology at Qualcomm, joins me to discuss what remains unsolved in image generation and several approaches his team presented at CVPR to address those challenges. We ex…
Why Models Are AI’s Next Training Dataset with Damian Borth - #772
Ep 772 · Jul 27, 2026 · 47 min
For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, researchers are looking for new ways to keep foundation models improving. In this episode, Damian Borth, professor of AI and machine learning at the University of St. Gallen, argues we’ve been overlooking an important source of knowledge: the models we’ve already trained. His group’s work on weight s…
How AI Learns to Smell with Alex Wiltschko - #771
Ep 771 · Jul 8, 2026 · 60 min
In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the science behind smell, from the hundreds of olfactory receptors in the human nose to the challenge of mapping the relationship between molecular structure and odor, ensuring safety regulations are met, and building foundation models for smell. Alex explains how graph neural networks and advanced embeddin…
Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770
Ep 770 · Jun 16, 2026 · 56 min
In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We explore why the enterprise playbook of static guardrails plus human approval starts to break down in the agent era. Agents are useful because they can plan, call tools, update systems, write code, send messages, and operate across workflows at machine speed, but those same capabilities make them dif…
Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769
Ep 769 · Jun 9, 2026 · 52 min
As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries of documents, why bother with retrieval at all? In this episode, Alex Bowcut, Head of Engineering at Sphere, explains why the answer depends on the application. Sphere uses AI to automate global tax compliance—an environment where getting the answer right isn’t enough. Every conclusion must be back…
Relational Foundation Models for Enterprise Data with Jure Leskovec - #768
Ep 768 · May 21, 2026 · 1 hr 6 min
In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational deep…
How to Find the Agent Failures Your Evals Miss with Scott Clark - #767
Ep 767 · May 7, 2026 · 53 min
In 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…
How to Engineer AI Inference Systems with Philip Kiely - #766
Ep 766 · Apr 30, 2026 · 55 min
In this episode, Philip Kiely, head of AI education at Baseten, joins us to unpack the fast-evolving discipline of inference engineering. We explore why inference has become the stickiest and most critical workload in AI, how it blends GPU programming, applied research, and large-scale distributed systems, and where the line sits between inference and model serving. Philip shares how research-to-production can move in hours, not months, and why understanding “the knobs” of inference—batching, qu…
How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765
Ep 765 · Apr 16, 2026 · 54 min
In this episode, Rashmi Shetty, senior director of enterprise generative AI platform at Capital One, joins us to explore how the company is designing, deploying, and scaling multi-agent systems in a highly regulated environment. Rashmi walks us through Chat Concierge, a multi-agent chat experience for auto dealerships that handles intent disambiguation, tool invocation, and human handoffs to deliver safer, more personalized customer journeys. We discuss Capital One’s platform-centric approach to…
The Race to Production-Grade Diffusion LLMs with Stefano Ermon - #764
Ep 764 · Mar 26, 2026 · 1 hr 3 min
Today, 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…
Agent Swarms and Knowledge Graphs for Autonomous Software Development with Siddhant Pardeshi - #763
Ep 763 · Mar 10, 2026 · 1 hr 16 min
In this episode, Sid Pardeshi, co-founder and CTO of Blitzy, joins us to discuss building autonomous development systems able to deliver production-ready software at enterprise scale. Sid contrasts AI-assisted coding with end-to-end autonomy, arguing that “code is a commodity” and acceptance is the real metric—security, standards, tests, and maintainability included. We explore Blitzy’s hybrid graph-plus-vector approach, which grounds agents and combines semantic signals with keyword search to n…
AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with Sebastian Raschka - #762
Ep 762 · Feb 26, 2026 · 1 hr 19 min
In 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…
The Evolution of Reasoning in Small Language Models with Yejin Choi - #761
Ep 761 · Jan 29, 2026 · 1 hr 6 min
Today, we're joined by Yejin Choi, professor and senior fellow at Stanford University in the Computer Science Department and the Institute for Human-Centered AI (HAI). In this conversation, we explore Yejin’s recent work on making small language models reason more effectively. We discuss how high-quality, diverse data plays a central role in closing the intelligence gap between small and large models, and how combining synthetic data generation, imitation learning, and reinforcement learning can…
Intelligent Robots in 2026: Are We There Yet? with Nikita Rudin - #760
Ep 760 · Jan 8, 2026 · 1 hr 7 min
Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion Robotics to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore…
Rethinking Pre-Training for Agentic AI with Aakanksha Chowdhery - #759
Ep 759 · Dec 17, 2025 · 53 min
Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step…
Why Vision Language Models Ignore What They See with Munawar Hayat - #758
Ep 758 · Dec 9, 2025 · 58 min
In this episode, we’re joined by Munawar Hayat, researcher at Qualcomm AI Research, to discuss a series of papers presented at NeurIPS 2025 focusing on multimodal and generative AI. We dive into the persistent challenge of object hallucination in Vision-Language Models (VLMs), why models often discard visual information in favor of pre-trained language priors, and how his team used attention-guided alignment to enforce better visual grounding. We also explore a novel approach to generalized cont…
Scaling Agentic Inference Across Heterogeneous Compute with Zain Asgar - #757
Ep 757 · Dec 2, 2025 · 49 min
In 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…
Proactive Agents for the Web with Devi Parikh - #756
Ep 756 · Nov 19, 2025 · 56 min
Today, we're joined by Devi Parikh, co-founder and co-CEO of Yutori, to discuss browser use models and a future where we interact with the web through proactive, autonomous agents. We explore the technical challenges of creating reliable web agents, the advantages of visually-grounded models that operate on screenshots rather than the browser’s more brittle document object model, or DOM, and why this counterintuitive choice has proven far more robust and generalizable for handling complex web in…
AI Orchestration for Smart Cities and the Enterprise with Robin Braun and Luke Norris - #755
Ep 755 · Nov 12, 2025 · 55 min
Today, we're joined by Robin Braun, VP of AI business development for hybrid cloud at HPE, and Luke Norris, co-founder and CEO of Kamiwaza, to discuss how AI systems can be used to automate complex workflows and unlock value from legacy enterprise data. Robin and Luke detail high-impact use cases from HPE and Kamiwaza’s collaboration on an “Agentic Smart City” project for Vail, Colorado, including remediation and automation of website accessibility for 508 compliance, digitization and understand…
Building an AI Mathematician with Carina Hong - #754
Ep 754 · Nov 4, 2025 · 56 min
In this episode, Carina Hong, founder and CEO of Axiom, joins us to discuss her work building an "AI Mathematician." Carina explains why this is a pivotal moment for AI in mathematics, citing a convergence of three key areas: the advanced reasoning capabilities of modern LLMs, the rise of formal proof languages like Lean, and breakthroughs in code generation. We explore the core technical challenges, including the massive data gap between general-purpose code and formal math code, and the diffic…
High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753
Ep 753 · Oct 28, 2025 · 52 min
In this episode, Hung Bui, Technology Vice President at Qualcomm, joins us to explore the latest high-efficiency techniques for running generative AI, particularly diffusion models, on-device. We dive deep into the technical challenges of deploying these models, which are powerful but computationally expensive due to their iterative sampling process. Hung details his team's work on SwiftBrush and SwiftEdit, which enable high-quality text-to-image generation and editing in a single inference step…
Vibe Coding's Uncanny Valley with Alexandre Pesant - #752
Ep 752 · Oct 22, 2025 · 1 hr 13 min
Today, we're joined by Alexandre Pesant, AI lead at Lovable, who joins us to discuss the evolution and practice of vibe coding. Alex shares his take on how AI is enabling a shift in software development from typing characters to expressing intent, creating a new layer of abstraction similar to how high-level code compiles to machine code. We explore the current capabilities and limitations of coding agents, the importance of context engineering, and the practices that separate successful vibe co…
Dataflow Computing for AI Inference with Kunle Olukotun - #751
Ep 751 · Oct 14, 2025 · 58 min
In this episode, we're joined by Kunle Olukotun, professor of electrical engineering and computer science at Stanford University and co-founder and chief technologist at Sambanova Systems, to discuss reconfigurable dataflow architectures for AI inference. Kunle explains the core idea of building computers that are dynamically configured to match the dataflow graph of an AI model, moving beyond the traditional instruction-fetch paradigm of CPUs and GPUs. We explore how this architecture is well-s…
Recurrence and Attention for Long-Context Transformers with Jacob Buckman - #750
Ep 750 · Oct 7, 2025 · 57 min
Today, we're joined by Jacob Buckman, co-founder and CEO of Manifest AI to discuss achieving long context in transformers. We discuss the bottlenecks of scaling context length and recent techniques to overcome them, including windowed attention, grouped query attention, and latent space attention. We explore the idea of weight-state balance and the weight-state FLOP ratio as a way of reasoning about the optimality of compute architectures, and we dig into the Power Retention architecture, which…
The Decentralized Future of Private AI with Illia Polosukhin - #749
Ep 749 · Sep 30, 2025 · 1 hr 5 min
In this episode, Illia Polosukhin, a co-author of the seminal "Attention Is All You Need" paper and co-founder of Near AI, joins us to discuss his vision for building private, decentralized, and user-owned AI. Illia shares his unique journey from developing the Transformer architecture at Google to building the NEAR Protocol blockchain to solve global payment challenges, and now applying those decentralized principles back to AI. We explore how Near AI is creating a decentralized cloud that leve…
Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748
Ep 748 · Sep 23, 2025 · 1 hr 4 min
Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capabilities of this newly released frontier vision-language model, beginning with the broader shift from specialized image generators to general-purpose multimodal agents that can use both visual and textual data for a variety of tasks. Oliver explains how Nano Banana can generate and iteratively edit i…
Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747
Ep 747 · Sep 16, 2025 · 58 min
Today, 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…
Building an Immune System for AI Generated Software with Animesh Koratana - #746
Ep 746 · Sep 9, 2025 · 1 hr 5 min
Today, we're joined by Animesh Koratana, founder and CEO of PlayerZero to discuss his team’s approach to making agentic and AI-assisted coding tools production-ready at scale. Animesh explains how rapid advances in AI-assisted coding have created an “asymmetry” where the speed of code output outpaces the maturity of processes for maintenance and support. We explore PlayerZero’s debugging and code verification platform, which uses code simulations to build a "memory bank" of past bugs and leverag…
Autoformalization and Verifiable Superintelligence with Christian Szegedy - #745
Ep 745 · Sep 2, 2025 · 1 hr 12 min
In this episode, Christian Szegedy, Chief Scientist at Morph Labs, joins us to discuss how the application of formal mathematics and reasoning enables the creation of more robust and safer AI systems. A pioneer behind concepts like the Inception architecture and adversarial examples, Christian now focuses on autoformalization—the AI-driven process of translating mathematical concepts from their human-readable form into rigorously formal, machine-verifiable logic. We explore the critical distinct…
Multimodal AI Models on Apple Silicon with MLX with Prince Canuma - #744
Ep 744 · Aug 26, 2025 · 1 hr 10 min
Today, we're joined by Prince Canuma, an ML engineer and open-source developer focused on optimizing AI inference on Apple Silicon devices. Prince shares his journey to becoming one of the most prolific contributors to Apple’s MLX ecosystem, having published over 1,000 models and libraries that make open, multimodal AI accessible and performant on Apple devices. We explore his workflow for adapting new models in MLX, the trade-offs between the GPU and Neural Engine, and how optimization methods…
← Back to the show
