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Linear Digressions

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

Demystifying AI for the intelligently curious

PublishesWeeklyEpisodes319Founded12 years ago
Number of ListenersCategory
Technology

Listen to this Podcast

Artwork for Linear Digressions

Latest Episodes

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

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, exploring how the internal reasoning pr... more

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 model... more

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 r... more

Key Facts

Contact Information
Podcast Host
Number of Listeners
Find out how many people listen to this podcast per episode and each month.

Recent Guests

Chris Potts
Stanford Linguistics Professor
Stanford University
Episode: Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

Host

Katie
Host of Linear Digressions; co-anchored discussions that translate data science concepts into practical insights.

Reviews

4.8 out of 5 stars from 666 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
    4 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.

Listeners praise the approachable yet rigorous explanations and the dynamic host pairing.
Many reviews emphasize the consistency and breadth of topics, from foundational data science to cutting-edge AI methods.
Listeners appreciate beginner-friendly episodes that still offer technical depth and practical takeaways.
Audiences highlight the show's ability to teach complex AI topics through real-world examples and engaging conversations.

Chart Rankings

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

Apple Podcasts
#33
Chile/Technology
Apple Podcasts
#111
New Zealand/Technology
Apple Podcasts
#200
Mexico/Technology
Apple Podcasts
#217
Poland/Technology
Apple Podcasts
#218
Netherlands/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

Listeners, social reach, demographics and more for this podcast.

Listeners per Episode
Gender Skew
Location
Interests
Professions
Age Range
Household Income
Social Media Reach

Frequently Asked Questions About Linear Digressions

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

This show consistently explores practical AI, data science, and machine learning concepts through approachable conversations between two seasoned hosts. Across episodes, listeners encounter clear explanations of complex topics—from AI planning and multi-agent systems to retrieval-augmented generation and memory management—paired with real-world examples and thoughtful digressions. The tone stays accessible and engaging, often bridging theory with hands-on implications for professionals building or using AI-powered tools. A standout quality is the host dynamic: they balance rigorous, technical discussion with lively storytelling and approachable prompts that make cutting-edge topics digestible for a broad audience.

Listeners likely value pr... more

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How many episodes of Linear Digressions are there?

Linear Digressions launched 12 years ago and published 319 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. Chris Potts

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