Podcast profile
By Sam Charrington
Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live.
Latest episode
1 hr 5 min
Topics
Model development, learning methods, prediction systems, and applied machine learning.
Systems that generate text, images, audio, video, software, or other new content.
Large pretrained models, model capabilities, scaling, evaluation, and model ecosystems.
Formats
Detailed technical, architectural, or research-oriented examination.
Host-led conversation where a guest supplies most of the subject matter.
Audience level
Assumes basic technology literacy and familiarity with common products, roles, and terminology.
Stay connected
From the feed
Episodes shown — 10 of 25
Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion—and adding vision and visual presence only raises the stakes.
As reasoning models consume more tokens and AI systems become more expensive to run, understanding what those tokens actually buy is becoming increasingly important.
In this episode, Justin Johnson, co-founder of World Labs, joins us to discuss world models and the emerging field of spatial AI.
The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else?
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.
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, 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.
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.
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, 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.