Best AI papers explained

Best AI papers explained

Enoch H. Kang

4.0from 22 ratings

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34Podcast AuthorityScore out of 100 · How it is scored
834
Episodes
370
Transcribed
22
Ratings
Daily
Cadence
2025
First episode

About Best AI papers explained

Cut through the noise. We curate and break down the most important AI papers so you don’t have to.

Publisher
Enoch H. Kang
Category
technology
Language
en
Explicit
No
First episode
13 Mar 2025
Latest episode
28 Oct 2026
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Latest episodes

370 of 834 episodes have a transcript.

  1. 28 Oct 2026TRANSCRIPT

    High-Value Training Data from Noisy, Diverse Sources

  2. 27 Oct 2026TRANSCRIPT

    Predictable Performance in Model Routing and Cascades

  3. 26 Oct 2026TRANSCRIPT

    Asymmetric On-Policy Distillation Learns In-Context Exploration

Inside a recent episode

High-Value Training Data from Noisy, Diverse Sources

Published 28 Oct 2026 · Transcript excerpt

[…] I know it does. Okay, think of a normal gradient as the AI learning the rules of chess. It moves a piece, sees what happens, and updates its internal parameters to get better at the game. Makes sense. A metagradient goes one level deeper. It is the AI learning which chess books are actually helping it win. Oh, wow. Right. It looks at the final checkmate, traces its steps backward, and asks, did reading page 42 of that specific book actually help me win this specific game? That is incredible. Mathematically, it calculates how a tiny change in the weight of one single training example ripples through the entire training process, all the way to affect the final score on the gold standard test. […]

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Apple Podcasts (US)
4.0 / 5
4 ratings
Spotify
3.9 / 5
18 ratings

Podcast Authority Score: 34 / 100

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