Alex Tamkin on Self-Supervised Learning and Large Language Models
Alex Tamkin on Self-Supervised Learning and Large Language Models
An interview Stanford PhD candidate Alex Tamkin, whose research focuses on understanding, building, and controlling pretrained models, especially in domain-general or multimodal settings
Appears in this episode
In episode 15 of The Gradient Podcast, we talk to Stanford PhD Candidate Alex Tamkin
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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
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"