Labels and Where To Find Them
Linear Digressions4 Helmi 2015

Labels and Where To Find Them

Supervised classification is built on the backs of labeled datasets, but a good set of labels can be hard to find. Great data is everywhere, but the corresponding labels can sometimes be really tricky. Take a few examples we've already covered, like lie detection with an MRI machine (have to take pictures of someone's brain while they try to lie, not a trivial task) or automated image captioning (so many images! so many valid labels!) In this epsiode, we'll dig into this topic in depth, talking about some of the standard ways to get a labeled dataset if your project requires labels and you don't already have them. www.higgshunters.org

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Better Know a Benchmark: Humanity's Last Exam

Better Know a Benchmark: Humanity's Last Exam

Humanity's Last Exam was designed with a bold premise: questions that human experts can answer, but AI models can't. Originally dubbed "Humanity's Last Stand," this benchmark is a massive academic col...

17 Elo 23min

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

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

When a language model tells you it's absolutely certain, is it actually more likely to be right? Kaitlyn Zhou's research says: not necessarily — sometimes confident phrasing correlates with *worse* ac...

10 Elo 33min

Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

Reasoning models don't just answer your question — they *think out loud* first. In this episode we dig into the class of AI models that generate intermediate chains of thought before arriving at a fin...

3 Elo 25min

Distillation, or, How to Steal a Model

Distillation, or, How to Steal a Model

This week we’re covering model distillation: the technique of using a large "teacher" model's outputs to train a smaller, cheaper "student" model that mimics it. They cover the two big reasons labs do...

27 Heinä 23min

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

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

What happens when a Stanford linguistics professor turns his attention to AI chatbots — and the surprisingly invisible ways humans misunderstand them? Chris Potts joins the show to unpack the hidden f...

20 Heinä 41min

Still summer break: back next week

Still summer break: back next week

Still summer break: back next week by Katie Malone

13 Heinä 25s

Summer break: back soon

Summer break: back soon

Summer break: back soon by Katie Malone

6 Heinä 36s

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

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

After a five-year hiatus, the podcast that burned out partly over the tedium of writing episode descriptions is back — and using AI agents to handle exactly that task. The season-11 finale turns the l...

28 Kesä 37min