The TWIML AI Podcast with Sam Charrington

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. By sharing and amplifying the voices of a broad and diverse spectrum of machine learning and AI researchers, practitioners, and innovators, we hope to help make ML and AI more accessible and enhance the lives of our audience and their communities.

Through the podcast, we bring 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.

The TWIML AI Podcast is hosted by Sam Charrington, a sought-after industry analyst, speaker, commentator, and thought leader. Sam’s research is focused on the business and consumer application of machine learning and AI, bringing AI-powered products to market, and AI-enabled and -enabling technology platforms

Vibe Coding's Uncanny Valley [Alexandre Pesant] - 752

Вычисления потоков данных для вывода ИИ [Kunle Okotun] - 751

Повторение и внимание для долгосрочных трансформеров [Джейкоб Бакман] - 750

The Decentralized Future of Private AI [Illia Polosukhin] - 749

Inside Nano Banana 🍌 and the Future of Vision-Language Models [Oliver Wang] - 748

Is It Time to Rethink LLM Pre-Training? [Aditi Raghunathan] - 747

Building an Immune System for AI Generated Software [Animesh Koratana] - 746

Autoformalization and Verifiable Superintelligence [Christian Szegedy] - 745

Multimodal AI Models on Apple Silicon with MLX [Prince Canuma] - 744

Genie 3: A New Frontier for World Models [Jack Parker-Holder and Shlomi Fruchter] - 743

Closing the Loop Between AI Training and Inference [Lin Qiao] - 742

Context Engineering for Productive AI Agents [Filip Kozera] - 741

Infrastructure Scaling and Compound AI Systems [Jared Quincy Davis] - 740

Building Voice AI Agents That Don’t Suck [Kwindla Kramer] - 739

Distilling Transformers and Diffusion Models for Robust Edge Use Cases [Fatih Porikli] - 738

What are the four requirements of agent-to-agent collaboration? #aiagents #agntcy

Is collaboration the right path over competition among protocols like A2A, MCP, and ACP? #aiagents

Building the Internet of Agents [Vijoy Pandey] - 737

LLMs for Equities Feature Forecasting at Two Sigma [Ben Wellington] - 736

Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision [Jason Corso] - 735

Grokking, Generalization Collapse, and Dynamics of Training Deep Neural Nets [Charles Martin] - 734

Google I/O 2025 Special Edition - 733

RAG Risks: Why Retrieval-Augmented LLMs Are Not Safer [Sebastian Gehrmann] - 732

From Prompts to Policies: How RL Builds Better AI Agents [Mahesh Sathiamoorthy] - 731

How OpenAI Builds AI Agents That Think and Act [Josh Tobin] - 730

CTIBench: How Good Are LLMs at Detecting Cyber Threats? [Nidhi Rastogi] - 729

Generative Benchmarking: Measuring AI Models Beyond Accuracy [Kelly Hong] - 728

Inside the “Neurons” of LLMs: Circuit Tracing Their Hidden Biology [Emmanuel Ameisen] - 727

Self-Reflecting LLMs: Reinforcement Learning That Boosts Reasoning [Maohao Shen] - 726

Inside Waymo’s New Foundation Model Powering Self-Driving Cars [Drago Anguelov] - 725