Linear Digressions
Detalls del Canal
Linear Digressions
Demystifying AI for the intelligently curious
Episodis Recents
323 episodis
Constitutional AI
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...
A Data-Driven Reality Check on AI in Business (Interview with Tom Davenport, Babson College)
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 seasone...
Understanding AI Text Watermarking
Anthropic just announced they're baking invisible watermarks directly into Claude's generated text — and while everyone else was busy having opinions...
Better Know a Benchmark: Humanity's Last Exam
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 Las...
A Scientific Deep Dive into Overconfident LLMs: Interview with Kaitlyn Zhou (Cornell)
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 — somet...
Reasoning Models: When LLMs Went Beyond Fancy Autocomplete
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 inter...
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...
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?...
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 e...
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 ha...
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? T...
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 episod...
How Do You Evaluate An AI Agent? (The Agents Season, Episode 7)
Knowing when an AI agent has failed sounds straightforward — until it isn't. Agents have a frustrating habit of finishing confidently while quietly do...
AI Agent Failure Modes (The Agents Season, Episode 6)
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'...
Agentic Planning (The Agents Season, Episode 5)
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 a...
Memory Management for AI Agents (The Agents Season, Episode 4)
Context windows are powerful — but finite, and surprisingly easy to overwhelm. When an AI agent is tackling a long, complex task, the information it n...
Lost in the Middle (The Agents Season, Episode 3)
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...
ReAct and Tool Usage (The Agents Season, Episode 2)
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 whet...
What's an AI Agent? And Why's That Hard to Define? (The Agents Season, Episode 1)
AI agents are having a moment — and unpacking them properly takes more than a single conversation. This episode kicks off a dedicated multi-part seaso...
Unfaithful Chain of Thought
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 c...
Benchmark Bank Heist
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...
Benchmarking AI Models
How do you know if a new AI model is actually better than the last one? It turns out answering that question is a lot messier than it sounds. This wee...
The Hot Mess of AI (Mis-)Alignment
The paperclip maximizer — the classic AI doom scenario where a hyper-competent machine single-mindedly converts the universe into office supplies — mi...
The Bitter Lesson
Every AI builder knows the anxiety: you spend months engineering prompts, tuning pipelines, and chaining calls together — then a new model drops and h...
From Atari to ChatGPT: How AI Learned to Follow Instructions
From Atari to ChatGPT: How AI Learned to Follow Instructions by Katie Malone
It's RAG time: Retrieval-Augmented Generation
Today we are going to talk about the feature with the worst acronym in generative AI: RAG, or Retrieval Augmented Generation. If you've ever used some...
Chasing Away Repetitive LLM Responses with Verbalized Sampling
One of the things that LLMs can be really helpful with is brainstorming or generating new creative content. They are called Generative AI, after all—n...
We're Back
It's been (*checks watch*) about five and a half years since we last talked. Fortunately nothing much has happened in the AI/data science world in tha...
A Key Concept in AI Alignment: Deep Reinforcement Learning from Human Preferences
Modern AI chatbots have a few different things that go into creating them. Today we're going to talk about a really important part of the process: the...
The Impact of Generative AI on Critical Thinking
I use LLMs a lot. I use them in my work, I use them in my personal life, and sometimes I use them to help me with stuff that I already know how to do....
So long, and thanks for all the fish
All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostl...
A Reality Check on AI-Driven Medical Assistants
The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the heal...
A Data Science Take on Open Policing Data
A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied wit...
Procella: YouTube's super-system for analytics data storage
This is a re-release of an episode that originally ran in October 2019.
If you’re trying to manage a project that serves up analytics dat...
The Data Science Open Source Ecosystem
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source pr...
Rock the ROC Curve
This is a re-release of an episode that first ran on January 29, 2017.
This week: everybody's favorite WWII-era classifier metric! But...
Criminology and Data Science
This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Z...
Racism, the criminal justice system, and data science
As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into on...