Linear Digressions

Technology Podcasts

Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago. 896520

Episodes

GPU Architecture for LLM Inference: Memory, Bandwidth and Hardware Explained

KV Cache Fundamentals

AI Inference 101

Back Next Week

Back in Two Weeks

Mechanistic Interpretability: How Researchers Try To See Inside Models

LLM As A Judge

The Impact of AI on Podcasting (Harvard Data Science Review Cross-Post)

Better Know A Benchmark: ExploitGym

Constitutional AI

A Data-Driven Reality Check on AI in Business (Interview with Tom Davenport, Babson College)

Understanding AI Text Watermarking

Better Know a Benchmark: Humanity's Last Exam

A Scientific Deep Dive into Overconfident LLMs: Interview with Kaitlyn Zhou (Cornell)

Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

Distillation, or, How to Steal a Model

Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

Still summer break: back next week

Summer break: back soon

Interviewing the Linear Digressions Agents (The Agents Season, Episode 11)