Linear Digressions19 Jun 2017

Anscombe's Quartet

Anscombe's Quartet is a set of four datasets that have the same mean, variance and correlation but look very different. It's easy to think that having a good set of summary statistics (like mean, variance and correlation) can tell you everything important about a dataset, or at least enough to know if two datasets are extremely similar or extremely different, but Anscombe's Quartet will always be standing behind you, laughing at how silly that idea is. Anscombe's Quartet was devised in 1973 as an example of how summary statistics can be misleading, but today we can even do one better: the Datasaurus Dozen is a set of twelve datasets, all extremely visually distinct, that have the same summary stats as a source dataset that, there's no other way to put this, looks like a dinosaur. It's an example of how datasets can be generated to look like almost anything while still preserving arbitrary summary statistics. In other words, Anscombe's Quartets can be generated at-will and we all should be reminded to visualize our data (not just compute summary statistics) if we want to claim to really understand it.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(331)

KV Cache Fundamentals

The unsung hero of every LLM conversation you've ever had? A clever optimization called the KV cache. Self-attention is computationally brutal — every token attending to every other token means costs ...

2 Nov 24min

AI Inference 101

Everyone talks about training the model — the data, the compute, the alignment magic. But what happens after the weights are frozen and millions of users start hammering it with prompts? That's infere...

26 Okt 27min

Back Next Week

Back Next Week by Katie Malone

19 Okt 18s

Back in Two Weeks

Back in Two Weeks by Katie Malone

12 Okt 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 Okt 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 Sep 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 Sep 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 Sep 32min