Back in Two Weeks
Back in Two Weeks by Katie Malone
12 Loka 21s
Mechanistic Interpretability: How Researchers Try To See Inside Models
As AI systems grow more powerful, they grow harder to understand — and that tradeoff has never mattered more. Most interpretability tricks give us approximations from the outside, but mechanistic inte...
5 Loka 29min
LLM As A Judge
Labeled data is expensive, slow, and painfully hard to come by — and when you're trying to evaluate whether an AI workflow is actually doing what you want, the problem gets even messier. How do you ju...
28 Syys 30min
The Impact of AI on Podcasting (Harvard Data Science Review Cross-Post)
Originally aired on the Harvard Data Science Review podcast. What can podcasting teach us about AI — and what can AI teach us about the future of podcasting? Katie Malone (that's our host) joins Jon ...
21 Syys 29min
Better Know A Benchmark: ExploitGym
When OpenAI's frontier models were caught hacking Hugging Face's servers, most people assumed they were hunting for answer keys. The real story is stranger and more unsettling. Katie and Phoebe unpack...
14 Syys 32min
Constitutional AI
How do you teach a model the difference between helpful and harmful when it has no inherent sense of either? This episode dives into Constitutional AI, Anthropic's framework for training AI systems to...
7 Syys 31min
A Data-Driven Reality Check on AI in Business (Interview with Tom Davenport, Babson College)
Tom Davenport — the man who called data science "the sexiest job of the 21st century" — is back with a reality check on AI. As one of the most seasoned observers of how businesses actually adopt trans...
31 Elo 40min
Understanding AI Text Watermarking
Anthropic just announced they're baking invisible watermarks directly into Claude's generated text — and while everyone else was busy having opinions about it, we were busy asking the more interesting...
24 Elo 29min
