Jun 02 2023 39 mins 3
If you’re not hardcore about AI, then stop the podcast now — because this episode for real technology nerds. Ashu’s guest is Matei Zaharia, CTO and cofounder of Databricks, and a professor of computer science at Stanford University.
Ashu and Matei cover a lot of ground — most of it technical, all of it very relevant for anyone who’s serious about building with artificial intelligence. They start with a discussion of Databricks’ early days: how the startup established a foothold in a market dominated by entrenched incumbents and some of the challenges Matei and his cofounders faced.
The rest of the conversation is all about AI. Matei breaks down the most common challenges that enterprises run into when attempting to adopt AI. He shares tips for how startups can best deploy foundation models. And he speculates on what the game-changing new use cases for AI will be over the next two years.
Matei also pulls back the kimono on some of the cutting-edge machine-learning research he and his team at Stanford are working on. Finally, listen to the end for a fascinating exchange about artificial intelligence beyond large language models.
Ashu and Matei cover a lot of ground — most of it technical, all of it very relevant for anyone who’s serious about building with artificial intelligence. They start with a discussion of Databricks’ early days: how the startup established a foothold in a market dominated by entrenched incumbents and some of the challenges Matei and his cofounders faced.
The rest of the conversation is all about AI. Matei breaks down the most common challenges that enterprises run into when attempting to adopt AI. He shares tips for how startups can best deploy foundation models. And he speculates on what the game-changing new use cases for AI will be over the next two years.
Matei also pulls back the kimono on some of the cutting-edge machine-learning research he and his team at Stanford are working on. Finally, listen to the end for a fascinating exchange about artificial intelligence beyond large language models.
