The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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 wha...

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. Galle...

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 sa...

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, up...

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 us...