Linear Digressions
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Linear Digressions
Demystifying AI for the intelligently curious
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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...
An interstitial word from Ben
A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.
Convolutional Neural Networks
This is a re-release of an episode that originally aired on April 1, 2018
If you've done image recognition or computer vision tasks with...
Stein's Paradox
This is a re-release of an episode that was originally released on February 26, 2017.
When you're estimating something about some object...
Protecting Individual-Level Census Data with Differential Privacy
The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset...
Causal Trees
What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually these two do...
The Grammar Of Graphics
You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectations about...
Gaussian Processes
It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most...
Keeping ourselves honest when we work with observational healthcare data
The abundance of data in healthcare, and the value we could capture from structuring and analyzing that data, is a huge opportunity. It also presents...
Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell
AI is evolving incredibly quickly, and thinking now about where it might go next (and how we as a species and a society should be prepared) is critica...
Putting machine learning into a database
Most data scientists bounce back and forth regularly between doing analysis in databases using SQL and building and deploying machine learning pipelin...