Networking Optimizations for Multi-Node Deep ...

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, H ...  Show more

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 r ...  Show more

The world is hitting a data tipping point where vast connectivity requires massive network capacity. In this episode of Smart Talks, Malcolm Gladwell talks to David Shacochis, Vice President for Enterprise Technology and Field CTO at Lumen, and Howard Boville, Head of IBM Cloud P ...  Show more

<span style= "font-weight: 400;">Stephanie Wong</span> <span style= "font-weight: 400;">and</span> <span style= "font-weight: 400;">Carter Morgan</span> <span style= "font-weight: 4 ...

Today’s episode is a conversation about four big trends in the tech world. Any one of these trends would be notable on its own, but we cover all four in this hallway-style chat, as a16z General Partner Chris Dixon talks with Sep Kamvar (professor of Media Arts and Sciences at MIT ...  Show more

Autonomous vehicles are one of the most complex AI challenges of our time. The networks running in the car must act as an intricate symphony, requiring intensive training, testing and validation on massive amounts of data to operate safely in the real world. Clément Farabet is th ...  Show more