Why Models Are AI’s Next Training Dataset with Damian Borth - #772
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, r...
27 Heinä 47min
How AI Learns to Smell with Alex Wiltschko - #771
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 sci...
8 Heinä 59min
Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769
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...
9 Kesä 51min
Relational Foundation Models for Enterprise Data with Jure Leskovec - #768
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 lea...
21 Touko 1h 6min
How to Find the Agent Failures Your Evals Miss with Scott Clark - #767
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 Masl...
7 Touko 53min
How to Engineer AI Inference Systems with Philip Kiely - #766
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 cri...
30 Huhti 54min
How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765
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 h...
16 Huhti 54min
