Linear Digressions

By Ben Jaffe and Katie Malone

Technology

What's Linear Digressions about?

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 even a few short years ago.

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LAST EPISODE

A Technical Deep Dive on St...

08.12.2019

This is a re-release of an episode that first ran on April 9, 2017. In our follow-up episode to last week's introduction to the first self-driving car, we will be doing a technical deep dive this week and talking about ...

Linear Digressions episodes:

08.13.2018 Re - Release: How To Lose At Kaggle We've got a classic for you this week as we take a week off for the dog days of summer. See you again next week! Competing in a machine learning competition on Kaggle is a kind of rite of passage ...
08.06.2018 Troubling Trends In Machine Learning Scholarship There's a lot of great machine learning papers coming out every day--and, if we're being honest, some papers that are not as great as we'd wish. In some ways this is symptomatic of a field that's growing really quickly, but ...
07.29.2018 Can Fancy Running Shoes Cause You To Run Faster? The stars aligned for me (Katie) this past weekend: I raced my first half-marathon in a long time and got to read a great article from the NY Times about a new running shoe that Nike claims can make its ...
07.22.2018 Compliance Bias When you're using an AB test to understand the effect of a treatment, there are a lot of assumptions about how the treatment (and control, for that matter) get applied. For example, it's easy to think that everyone who was ...
07.15.2018 AI Winter Artificial Intelligence has been widely lauded as a solution to almost any problem. But as we justapose the hype in the field against the real-world benefits we see, it raises the question: Are we coming up on an AI winter
07.08.2018 Rerelease: How to Find New Things to Learn We like learning on vacation. And we're on vacation, so we thought we'd re-air this episode about how to learn. Original Episode: https://lineardigressions.com/episodes/2017/5/14/how-to-find-new-things-to-learn Original Summary: If you're anything like us, you a) always are curious to learn more about data ...
07.02.2018 Rerelease: Space Codes We're on vacation on Mars, so we won't be communicating with you all directly this week. Though, if we wanted to, we could probably use this episode to help get started. Original Episode: http://lineardigressions.com/episodes/2017/3/19/space-codes Original Summary: It's hard to get ...
06.25.2018 Rerelease: Anscombe's Quartet We're on vacation, so we hope you enjoy this episode while we each sip cocktails on the beach. Original Episode: http://lineardigressions.com/episodes/2017/6/18/anscombes-quartet Original Summary: Anscombe's Quartet is a set of four datasets that have the same mean, variance and correlation but ...
06.18.2018 Rerelease: Hurricanes Produced Now that hurricane season is upon us again (and we are on vacation), we thought a look back on our hurricane forecasting episode was prudent. Stay safe out there.
06.11.2018 GDPR By now, you have probably heard of GDPR, the EU's new data privacy law. It's the reason you've been getting so many emails about everyone's updated privacy policy. In this episode, we talk about some of the potential ramifications of ...
06.03.2018 Git for Data Scientists If you're a data scientist, chances are good that you've heard of git, which is a system for version controlling code. Chances are also good that you're not quite as up on git as you want to be--git has a ...
05.20.2018 Analytics Maturity Data science and analytics are hot topics in business these days, but for a lot of folks looking to bring data into their organization, it can be hard to know where to start and what it looks like when they're ...
05.13.2018 SHAP: Shapley Values in Machine Learning Shapley values in machine learning are an interesting and useful enough innovation that we figured hey, why not do a two-parter? Our last episode focused on explaining what Shapley values are: they define a way of assigning credit for outcomes ...
05.07.2018 Game Theory for Model Interpretability: Shapley Values As machine learning models get into the hands of more and more users, there's an increasing expectation that black box isn't good enough: users want to understand why the model made a given prediction, not just what the prediction itself ...
04.30.2018 AutoML If you were a machine learning researcher or data scientist ten years ago, you might have spent a lot of time implementing individual algorithms like decision trees and neural networks by hand. If you were doing that work five years ...
04.23.2018 CPUs, GPUs, TPUs: Hardware for Deep Learning A huge part of the ascent of deep learning in the last few years is related to advances in computer hardware that makes it possible to do the computational heavy lifting required to build models with thousands or even millions ...
04.16.2018 A Technical Introduction to Capsule Networks Last episode we talked conceptually about capsule networks, the latest and greatest computer vision innovation to come out of Geoff Hinton's lab. This week we're getting a little more into the technical details, for those of you ready to have ...
04.09.2018 A Conceptual Introduction to Capsule Networks Convolutional nets are great for image classification... if this were 2016. But it's 2018 and Canada's greatest neural networker Geoff Hinton has some new ideas, namely capsule networks. Capsule nets are a completely new type of neural net architecture designed ...
04.02.2018 Convolutional Neural Nets If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so ...
03.26.2018 Google Flu Trends It's been a nasty flu season this year. So we were remembering a story from a few years back (but not covered yet on this podcast) about when Google tried to predict flu outbreaks faster than the Centers for Disease ...
03.19.2018 How to pick projects for a professional data science team This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working ...
03.12.2018 Autoencoders Autoencoders are neural nets that are optimized for creating outputs that... look like the inputs to the network. Turns out this is a not-too-shabby way to do unsupervised machine learning with neural nets.
03.05.2018 When Private Data Isn't Private Anymore After all the back-patting around making data science datasets and code more openly available, we figured it was time to also dump a bucket of cold water on everyone's heads and talk about the things that can go wrong when ...
02.26.2018 What makes a machine learning algorithm "superhuman"? A few weeks ago, we podcasted about a neural network that was being touted as "better than doctors" in diagnosing pneumonia from chest x-rays, and how the underlying dataset used to train the algorithm raised some serious questions. We're back ...
02.19.2018 Open Data and Open Science One interesting trend we've noted recently is the proliferation of papers, articles and blog posts about data science that don't just tell the result--they include data and code that allow anyone to repeat the analysis. It's far from universal (for ...
02.12.2018 Defining the quality of a machine learning production system Building a machine learning system and maintaining it in production are two very different things. Some folks over at Google wrote a paper that shares their thoughts around all the items you might want to test or check for your ...
02.04.2018 Auto-generating websites with deep learning We've already talked about neural nets in some detail (links below), and in particular we've been blown away by the way that image recognition from convolutional neural nets can be fed into recurrent neural nets that generate descriptions and captions ...
01.29.2018 The Case for Learned Index Structures, Part 2: Hash Maps and Bloom Filters Last week we started the story of how you could use a machine learning model in place of a data structure, and this week we wrap up with an exploration of Bloom Filters and Hash Maps. Just like last week, ...
01.22.2018 The Case for Learned Index Structures, Part 1: B-Trees Jeff Dean and his collaborators at Google are turning the machine learning world upside down (again) with a recent paper about how machine learning models can be used as surprisingly effective substitutes for classic data structures. In this first part ...
01.15.2018 Challenges with Using Machine Learning to Classify Chest X-Rays Another installment in our "machine learning might not be a silver bullet for solving medical problems" series. This week, we have a high-profile blog post that has been making the rounds for the last few weeks, in which a neural ...
01.08.2018 The Fourier Transform The Fourier transform is one of the handiest tools in signal processing for dealing with periodic time series data. Using a Fourier transform, you can break apart a complex periodic function into a bunch of sine and cosine waves, and ...
01.02.2018 Statistics of Beer What better way to kick off a new year than with an episode on the statistics of brewing beer?
12.24.2017 Re - Release: Random Kanye We have a throwback episode for you today as we take the week off to enjoy the holidays. This week: what happens when you have a markov chain that generates mashup Kanye West lyrics with Bible verses? Exactly what you ...
12.18.2017 Debiasing Word Embeddings When we covered the Word2Vec algorithm for embedding words, we mentioned parenthetically that the word embeddings it produces can sometimes be a little bit less than ideal--in particular, gender bias from our society can creep into the embeddings and give ...
12.11.2017 The Kernel Trick and Support Vector Machines Picking up after last week's episode about maximal margin classifiers, this week we'll go into the kernel trick and how that (combined with maximal margin algorithms) gives us the much-vaunted support vector machine.
12.04.2017 Maximal Margin Classifiers Maximal margin classifiers are a way of thinking about supervised learning entirely in terms of the decision boundary between two classes, and defining that boundary in a way that maximizes the distance from any given point to the boundary. It's ...
11.27.2017 Re - Release: The Cocktail Party Problem Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!
11.20.2017 Clustering with DBSCAN DBSCAN is a density-based clustering algorithm for doing unsupervised learning. It's pretty nifty: with just two parameters, you can specify "dense" regions in your data, and grow those regions out organically to find clusters. In particular, it can fit irregularly-shaped ...
11.13.2017 The Kaggle Survey on Data Science Want to know what's going on in data science these days? There's no better way than to analyze a survey with over 16,000 responses that recently released by Kaggle. Kaggle asked practicing and aspiring data scientists about themselves, their tools, ...
11.06.2017 Machine Learning: The High Interest Credit Card of Technical Debt This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows. If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that ...
10.30.2017 Improving Upon a First-Draft Data Science Analysis There are a lot of good resources out there for getting started with data science and machine learning, where you can walk through starting with a dataset and ending up with a model and set of predictions. Think something like ...
10.23.2017 Survey Raking It's quite common for survey respondents not to be representative of the larger population from which they are drawn. But if you're a researcher, you need to study the larger population using data from your survey respondents, so what should ...
10.16.2017 Happy Hacktoberfest It's the middle of October, so you've already made two pull requests to open source repos, right? If you have no idea what we're talking about, spend the next 20 minutes or so with us talking about the importance of ...
10.09.2017 Re - Release: Kalman Runners In honor of the Chicago marathon this weekend (and due in large part to Katie recovering from running in it...) we have a re-release of an episode about Kalman filters, which is part algorithm part elaborate metaphor for figuring out, ...
10.02.2017 Neural Net Dropout Neural networks are complex models with many parameters and can be prone to overfitting. There's a surprisingly simple way to guard against this: randomly destroy connections between hidden units, also known as dropout. It seems counterintuitive that undermining the structural ...
09.25.2017 Disciplined Data Science As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level. Most of our episodes are about the cool work being done by other ...
09.18.2017 Hurricane Forecasting It's been a busy hurricane season in the Southeastern United States, with millions of people making life-or-death decisions based on the forecasts around where the hurricanes will hit and with what intensity. In this episode we'll deconstruct those models, talking ...
09.11.2017 Finding Spy Planes with Machine Learning There are law enforcement surveillance aircraft circling over the United States every day, and in this episode, we'll talk about how some folks at BuzzFeed used public data and machine learning to find them. The fun thing here, in our ...
09.04.2017 Data Provenance Software engineers are familiar with the idea of versioning code, so you can go back later and revive a past state of the system. For data scientists who might want to reconstruct past models, though, it's not just about keeping ...
08.28.2017 Adversarial Examples Even as we rely more and more on machine learning algorithms to help with everyday decision-making, we're learning more and more about how they're frighteningly easy to fool sometimes. Today we have a roundup of a few successful efforts to ...

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