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Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach...
Just like a memorable talk lives or dies by its opening and closing, LLMs have a surprisingly similar quirk: they pay close attention to wha...
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...
AI agents are having a moment — and unpacking them properly takes more than a single conversation. This episode kicks off a dedicated multi-...
What's actually happening when an LLM "thinks out loud"? Research on human decision-making suggests that much of the reasoning we believe dr...
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 fac...
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...
The paperclip maximizer — the classic AI doom scenario where a hyper-competent machine single-mindedly converts the universe into office sup...
Every AI builder knows the anxiety: you spend months engineering prompts, tuning pipelines, and chaining calls together — then a new model d...
From Atari to ChatGPT: How AI Learned to Follow Instructions by Katie Malone
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...
One of the things that LLMs can be really helpful with is brainstorming or generating new creative content. They are called Generative AI, a...
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 wo...
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 pr...
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...
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—...
The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions o...
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 s...
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 data f...
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open...
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 it's...
This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason Uni...
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 d...
A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.
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 a n...
This is a re-release of an episode that was originally released on February 26, 2017. When you're estimating something about some object tha...
The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in t...
What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually th...
You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectat...
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 migh...
The abundance of data in healthcare, and the value we could capture from structuring and analyzing that data, is a huge opportunity. It also...
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)...
Most data scientists bounce back and forth regularly between doing analysis in databases using SQL and building and deploying machine learni...
Many of us have the privilege of working from home right now, in an effort to keep ourselves and our family safe and slow the transmission o...
Covid-19 is turning the world upside down right now. One thing that’s extremely important to understand, in order to fight it as effectively...
This week’s episode is a re-release of a recent episode, which we don’t usually do but it seems important for understanding what we can all...
When you need to untangle cause and effect, but you can’t run an experiment, it’s time to get creative. This episode covers difference in di...
This is a re-release of an episode that originally ran on October 21, 2018. The Poisson distribution is a probability distribution function...
Recent research into neural networks reveals that sometimes, not all parts of the neural net are equally responsible for the performance of...
Data privacy is a huge issue right now, after years of consumers and users gaining awareness of just how much of their personal data is out...
Put yourself in the shoes of an executive at a big legacy company for a moment, operating in virtually any market vertical: you’re constantl...
As demand for data scientists grows, and it remains as relevant as ever that practicing data scientists have a solid methodological and tech...
Traditional A/B tests assume that whether or not one person got a treatment has no effect on the experiment outcome for another person. But...
Adversarial examples are really, really weird: pictures of penguins that get classified with high certainty by machine learning algorithms a...
Dimensionality reduction redux: this episode covers UMAP, an unsupervised algorithm designed to make high-dimensional data easier to visuali...
Picking a metric for a problem means defining how you’ll measure success in solving that problem. Which sounds important, because it is, but...
For something as multifaceted and ill-defined as data science, communication and sharing best practices across the field can be extremely va...
When data scientists run experiments, like A/B tests, it’s really easy to plan on a period of a few days to a few weeks for collecting data....
This episode features Prof. Andrew Lo, the author of a paper that we discussed recently on Linear Digressions, in which Prof. Lo uses data t...
One of the hottest areas in data science and machine learning right now is healthcare: the size of the healthcare industry, the amount of da...
Facial recognition being used in everyday life seemed far-off not too long ago. Increasingly, it’s being used and advanced widely and with i...