Alex Tamkin on Self-Supervised Learning and Large Language Models
The Gradient: Perspectives on AI · Andrey Kurenkov
Audio is streamed directly from the publisher (api.substack.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
In episode 15 of The Gradient Podcast, we talk to Stanford PhD Candidate Alex Tamkin
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSS
Alex Tamkin is a fourth-year PhD student in Computer Science at Stanford, advised by Noah Goodman and part of the Stanford NLP Group. His research focuses on understanding, building, and controlling pretrained models, especially in domain-general or multimodal settings.
We discuss:
* Viewmaker Networks: Learning Views for Unsupervised Representation Learning
* DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning
* On the Opportunities and Risks of Foundation Models
* Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models
* Mentoring, teaching and fostering a healthy and inclusive research culture
* Scientific communication and breaking down walls between fields
Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music"
Get full access to The Gradient at thegradientpub.substack.com/subscribe