Linear Digressions

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 it's also an artifact of strange incentive structures in academic machine learning, and the fact that sometimes machine learning is just really hard. At the same time, a high q...

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 wearers run faster. Causal claims like this one are really tough to verify, because even if the data suggests that people wearing the shoe are faster that might be because of correlation, not causa...

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 assigned to the treatment arm actually gets the treatment, everyone in the control arm doesn't, and that the two groups get their treatment instantaneously. None of these things...

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

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 science and machine learning and stuff, and b) are usually overwhelmed by how much content i...

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 information to and from Mars.  Mars is very far away, and expensive to get to, and the bandwidth for passing messages...

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 look very different.  It's easy to think that having a good set of summary statistics (like mean, varian...

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.

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 GRPD in the world of data science.

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 strong following among software engineers but, in our anecdotal experience, data scientists are less likely to know how to use this powerful tool. Never fear: in this episode we'll talk through s...

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 succeeding. That was the motivation for writing a whitepaper on the analytics maturity of an organization, and that's what we're talking about today. In particular, we break it down into fiv...

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 across several contributors, originally to understand how impactful different actors are in building coalitions (hence the game theory background) but now t...

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 is. This is motivating a lot of work into feature important and model interpretability tools, and one of the most exciting new ones is based on Shapley Values fro...

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 ago, the algorithms were probably already implemented in popular open-source libraries like scikit-learn, but you still might have spent a lot of time trying different...

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 of tunable parameters. This week we'll pretend to be electrical engineers and talk about how modern machine learning is enabled by hardware.

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 your mind stretched.

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 to do image classification on far fewer training cases than convolutional nets, and they're posting results that are competitive with much...

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 good at image tasks.

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 Control by monitoring searches and looking for spikes in searches for flu symptoms, doctors appointments, and other related terms. It's a cool idea, but after a few years turned into a...

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 on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head...

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.

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 data and code is a little too open. In this episode, we'll talk about two interesting recent examples: a de-identified medical dataset in Australia that was re-identified so spe...

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 again this week with further developments, as the author of the original blog post pointed us toward more developments. All in all, there's a lot more clari...

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 a timely counterpoint, read this article ), but we seem to be moving toward a new normal where data science conclusions are expected to be shown, not j...

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 production ML system. Relevant links: https://research.google.com/pubs/pub45742.html