technology
Hosted by Katie Malone · technology · EN
In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications. 896520
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Booking probability
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Signup to Generate a PitchLinear Digressions is hosted by Katie Malone. The show has published 329 episodes to date.
Recent episodes include "AI Inference 101" (26 October 2026, 28 minutes), "Back Next Week" (19 October 2026, 1 minute), and "Back in Two Weeks" (12 October 2026, 1 minute).
Linear Digressions publishes roughly weekly, with episodes averaging 24 minutes across 329 episodes.
Booking probability for Linear Digressions is 37%, and the host is rated Selective on guests. Linear Digressions requires a Pod Score of 80 or higher. Linear Digressions's contact route on record: verified email.
The strongest fits for Linear Digressions are Scientific Research & Development and IT Services & Software Development. Linear Digressions has a technology audience.
Katie Malone hosts Linear Digressions, a technology podcast with 329 episodes published.
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Linear Digressions
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 inference, and it's a...
Linear Digressions
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 interpretability takes...
Linear Digressions
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 judge whether a...
Linear Digressions
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 Krohn of...
Linear Digressions
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 ExploitGym — the...
Linear Digressions
How do you teach a model the difference between helpful and harmful when it has no inherent sense of either? This episode dives into Constitutional AI, Anthropic's framework for training AI systems to be both useful and...
Linear Digressions
Tom Davenport — the man who called data science "the sexiest job of the 21st century" — is back with a reality check on AI. As one of the most seasoned observers of how businesses actually adopt transformative...
Linear Digressions
Anthropic just announced they're baking invisible watermarks directly into Claude's generated text — and while everyone else was busy having opinions about it, we were busy asking the more interesting question: how does...
Linear Digressions
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 collaboration —...
Linear Digressions
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* accuracy, echoing a...
Linear Digressions
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 final answer...
Linear Digressions
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...
Linear Digressions
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...
Linear Digressions
Still summer break: back next week by Katie Malone
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...
Linear Digressions
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...
Linear Digressions
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...
Recent guests on Linear Digressions. Study who booked and why before you pitch.
Charles E. and Susan T. Harris Professor at MIT Sloan School of Management, MIT Sloan School of Management
SuperDataScience
Harvard Data Science Review
UC Berkeley
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