What's *really* so hard about feature engineering?

What's *really* so hard about feature engineering?

Feature engineering is ubiquitous but gets surprisingly difficult surprisingly fast. What could be so complicated about just keeping track of what data you have, and how you made it? A lot, as it turns out—most data science platforms at this point include explicit features (in the product sense, not the data sense) just for keeping track of and sharing features (in the data sense, not the product sense). Just like a good library needs a catalogue, a city needs a map, and a home chef needs a cookbook to stay organized, modern data scientists need feature libraries, data dictionaries, and a general discipline around generating and caring for their datasets.

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(320)

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