Linear Digressions

Katie Malone

Demystifying AI for the intelligently curious

  1. -18 ч

    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 this — making lighter, faster, more focused models for specific tasks, and the more contentious use case of effectively copying a rival's flagship model by hammering its API with questions (with a callback to the old Bing/Google search controversy). They also get into why it's so hard to prove distillation happened, why some models occasionally introduce themselves as "Claude," and a surprisingly old idea: a 2015 paper by Geoffrey Hinton, Jeff Dean, and Oriol Vinyals on distilling knowledge using the full probability distribution over a model's outputs — not just its single most likely answer — and what that "soft label" approach captures about how a model relates concepts to each other.

  2. 20 июл.

    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 failure modes in how we interact with AI, what it really means to become a more fluent user, and why these language-wielding systems are genuinely alien in ways we're only beginning to reckon with. His perspective sits at a rare intersection of linguistics, cognition, and machine learning — and it shows.

  3. 13 июл.

    Still summer break: back next week

    Still summer break: back next week by Katie Malone

  4. 6 июл.

    Summer break: back soon

    Summer break: back soon by Katie Malone

  5. 28 июн.

    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 lens on the podcast itself, putting the AI agents built throughout the season to work on real production tasks. It's a fitting, self-referential close to a season spent dissecting how agents actually function — and a honest look at what they can (and can't) take off your plate.

  6. 22 июн.

    Agent Economics (The Agents Season, Episode 10)

    What if building more highways made your commute *slower*? That's the paradox at the heart of AI agent economics: even as per-token inference costs have plummeted dramatically over the past two years, total LLM spending keeps climbing. Drawing on a surprising lesson from Robert Moses's mid-century New York infrastructure projects, this episode unpacks why cheaper compute doesn't necessarily mean cheaper AI — and what's really driving the economics of running agents at scale.

  7. 15 июн.

    Agent Trust, Oversight and Control (The Agents Season, Episode 9)

    Capabilities get all the attention when it comes to AI agents — but what happens when a highly capable agent makes a bad decision in the real world? Trust, oversight, and control are the unglamorous but critically important flip side of the agentic AI story. This episode digs into the security concerns that emerge when you combine powerful models with real-world tool access, and why judgment (or the lack of it) might matter just as much as raw capability. --- Website: https://lineardigressions.com Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323 Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT Substack: https://substack.com/@lineardigressions

  8. 8 июн.

    Many Agents, Many Problems (The Agents Season, Episode 8)

    Whether you work best solo or thrive in a team, you know collaboration is complicated — and it turns out AI agents face the same tensions. This episode dives into multi-agent systems, exploring how networks of AI agents can overcome the individual limitations of a single model, and what the research says about when collaboration actually helps versus when it just adds noise. Think scaling laws, but for teamwork. --- Website: https://lineardigressions.com Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323 Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT Substack: https://substack.com/@lineardigressions

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Demystifying AI for the intelligently curious

Информация

  • Автор
    Katie Malone
  • Годы выхода
    2014 - 2026
  • Выпуски
    317
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © All rights reserved
  • Сайт подкаста
    Linear Digressions

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