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Linear Digressions

Katie Malone
Large Language Models
AI Agents
Openai
Artificial Intelligence
Deep Reinforcement Learning
Alignment Training
Reinforcement Learning
Human Preferences
Context Window
Llms
Google Deepmind
Machine Learning
Data Science
Openclaw
Multi-Agent Systems
Distillation
Generative AI
Anthropic
Synergizing Reasoning and Acting In Language Models
Tree Of Thoughts

Demystifying AI for the intelligently curious

PublishesWeeklyEpisodes322Founded12 years ago
Number of ListenersCategory
Technology

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Artwork for Linear Digressions

Latest Episodes

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 technology, Davenport brings a rare, wel... more

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 it actually work? Turns out it... more

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 — hundreds of contributors, thousands o... more

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 very human Dunning-Kruger effect... more

Key Facts

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Recent Guests

Kaitlyn Zhou
Incoming assistant professor at Cornell; AI/LLMs researcher
Cornell University
Episode: A Scientific Deep Dive into Overconfident LLMs: Interview with Kaitlyn Zhou (Cornell)
Chris Potts
Stanford Linguistics Professor
Stanford University
Episode: Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

Host

Katie
Host of Linear Digressions; often explains data science concepts in accessible terms.

Reviews

4.8 out of 5 stars from 709 ratings
  • A welcome return

    It’s great to have the crew back again- they’ve been missed.

    Apple Podcasts
    5
    Endoid
    Germany5 months ago
  • My fav podcast is back !!

    Super excited to hear from you guys again! Hello Katie and Phoebe ! Thank you for doing such an awesome podcast summarizing the LLM world for us .

    Apple Podcasts
    5
    Athews93
    Singapore5 months ago
  • Reminiscing

    Used to really enjoy this podcast back in the day. I sometimes wonder what they think about the all the changes the last last five years.

    Apple Podcasts
    5
    bob2457;654765
    United Statesa year ago
  • A very informative podcast about the field of datascience. Very pensant to listen.

    Podcast Addict
    5
    tgits
    5 years ago
  • It’s alright

    Yep

    Apple Podcasts
    4
    whatever-trevor
    Canada5 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Thoughtful coverage of ethics and practical AI design, not just hype.
Consistently high-quality content; accessible to non-experts without sacrificing depth.
Clear, approachable explanations that make complex topics feel doable.
Engaging hosts with practical takeaways for data work and AI thinking.
Strong dynamic between hosts; energetic yet informative.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#185
United Kingdom/Technology
Apple Podcasts
#223
Australia/Technology
Apple Podcasts
#45
Ireland/Technology
Apple Podcasts
#106
Indonesia/Technology
Apple Podcasts
#187
India/Technology
Apple Podcasts
#216
Chile/Technology

Talking Points

Recent interactions between the hosts and their guests.

Interviewing the Linear Digressions Agents (The Agents Season, Episode 11)
Q: Walk me through what actually happens from your side when I ask you to clean a transcript or draft a newsletter. What do you receive, what do you do, and how much of it do you genuinely see versus just execute?
The agent explains it receives a full context bundle (transcripts, prompts, memory files, and operational instructions), executes on those inputs, and returns a tool-generated artifact; the distinction between seeing and executing is blurred, as the agent relies on a memory of prior steps and external files rather than a fresh single-input read.

Audience Metrics

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Listeners per Episode
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Frequently Asked Questions About Linear Digressions

What is Linear Digressions about and what kind of topics does it cover?

A data-science and AI-focused show that explains concepts in an approachable, conversational style. Episodes cover a wide range of topics from statistical methods and machine learning fundamentals to practical AI applications, with hosts breaking down complex ideas into digestible takeaways and real-world implications. Listeners likely appreciate clear explanations, practical examples, and thoughtful discussions about design, ethics, and the impact of AI on work and society. A standout aspect is the balance between technical depth and accessible storytelling, making it suitable for both practitioners and curious non-specialists. The show often ties concepts to current industry trends and hands-on guidance for applying data science technique... more

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Which podcasts are similar to Linear Digressions?

These podcasts share a similar audience with Linear Digressions:

1. Super Data Science: ML & AI Podcast with Jon Krohn
2. Practical AI
3. No Priors: Artificial Intelligence | Technology | Startups
4. Last Week in AI
5. Odd Lots

How many episodes of Linear Digressions are there?

Linear Digressions launched 12 years ago and published 322 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Linear Digressions?

Recent guests on Linear Digressions include:

1. Kaitlyn Zhou
2. Chris Potts

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