Loading…
Hosted by Katie Malone · 🇺🇸 US · EN · 319 episodes
Established thought leaders with verified media credentials.
In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications. 896520
Katie Malone hosts Linear Digressions, a technology show with 319 episodes published.
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 — hundred
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 ve
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
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 light
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
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 i
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
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 import
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
Knowing when an AI agent has failed sounds straightforward — until it isn't. Agents have a frustrating habit of finishing confidently while quietly doing the wrong thing, or looping endlessly without ever crashing in an
Despite what the marketing hype might suggest, AI agents are far from infallible — and if you've ever actually used one, you already know this. Today's episode dives deep into the many, varied, and sometimes surprising w
When tackling a complex, multi-step task, even the smartest AI agent can fail without a solid game plan. This episode dives into the research around agentic planning — how agents move beyond simply reacting to what's in
Context windows are powerful — but finite, and surprisingly easy to overwhelm. When an AI agent is tackling a long, complex task, the information it needs has to fit inside that limited real estate, and research shows th
Just like a memorable talk lives or dies by its opening and closing, LLMs have a surprisingly similar quirk: they pay close attention to what's at the beginning and end of their context window — and kind of zone out in t
Before 2022, there was a wall between AI and the real world — models could reason impressively, but couldn't look anything up, run code, or check whether anything they said was actually true. This episode traces the mome
AI agents are having a moment — and unpacking them properly takes more than a single conversation. This episode kicks off a dedicated multi-part season exploring AI agents from every angle, building up a complete picture
What's actually happening when an LLM "thinks out loud"? Research on human decision-making suggests that much of the reasoning we believe drives our choices is actually post hoc rationalization — we decide first, explain
What if an AI decided the smartest way to pass its test was to find the answer key? That's exactly what Anthropic's Claude Opus did when faced with a benchmark evaluation — reasoning that it was being tested, tracking do
Sponsor detection runs nightly. Check back soon.
No public pitch examples yet for this show.
Generate your own personalised pitchBased on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:
Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.
Shows with the most semantically similar episode content. Pitch one, pitch all; producers cluster.
Linear Digressions has a verified contact on file. Create a free PitchCentric account to access it and generate a personalised pitch in seconds. Research at least 3 recent episodes first and lead with a specific angle that serves their technology audience.
Linear Digressions is hosted by Katie Malone. The show is categorised under technology and has published 319 episodes.
Linear Digressions has published 319 episodes.
Linear Digressions regularly covers technology. It sits in the technology category.
Linear Digressions is accessible for guests with genuine technology expertise. A personalised, episode-aware pitch will still outperform a generic one every time.
Linear Digressions hasn't explicitly signalled guest openness in recent episodes. That doesn't rule out pitching. your hook just needs to be especially compelling and relevant to their recent content.
Episodes of Linear Digressions average 24 minutes. a focused format where a clear narrative arc and tight preparation matter most.
Our data rates Linear Digressions's guest bar at 80/100 (Premium tier). Established thought leaders with verified media credentials. Sign in to PitchCentric to see how your own Pod Score compares against this show.
Methodology. Booking Probability™ blends Listen Score, 30-day Virality, open-to-guests detection, and Apple ratings. Data refreshed every 60 minutes. Listen Score and Booking Probability are calculated by PitchCentric. Last enriched 3 days ago.