Linear Digressions

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Space Codes! Mar 20, 2017

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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 with Earth is not huge. The messages you do pass have to traverse millions of miles, which provides ample opportunity for the message to get corrupted or scrambled. How, then, can you encode messages so that errors can be detected and corrected? How does the decoding process allow you to actually find and correct the errors? In this episode, we'll talk about three pieces of the process (Reed-Solomon codes, convolutional codes, and Viterbi decoding) that allow the scientists at NASA to talk to our rovers on Mars.

Finding (and Studying) Wikipedia Trolls Mar 13, 2017

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You may be shocked to hear this, but sometimes, people on the internet can be mean. For some of us this is just a minor annoyance, but if you're a maintainer or contributor of a large project like Wikipedia, abusive users can be a huge problem. Fighting the problem starts with understanding it, and understanding it starts with measuring it; the thing is, for a huge website like Wikipedia, there can be millions of edits and comments where abuse might happen, so measurement isn't a simple task. That's where machine learning comes in: by building an "abuse classifier," and pointing it at the Wikipedia edit corpus, researchers at Jigsaw and the Wikimedia foundation are for the first time able to estimate abuse rates and curate a dataset of abusive incidents. Then those researchers, and others, can use that dataset to study the pathologies and effects of Wikipedia trolls.

A Sprint Through What's New in Neural Networks Mar 06, 2017

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Advances in neural networks are moving fast enough that, even though it seems like we talk about them all the time around here, it also always seems like we're barely keeping up. So this week we have another installment in our "neural nets: they so smart!" series, talking about three topics. And all the topics this week were listener suggestions, too!

Stein's Paradox Feb 27, 2017

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When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you estimate it: use measurements of the individual, or get some extra information from the group? The James-Stein estimator tells you how to combine individual and group information make predictions that, taken over the whole group, are more accurate than if you treated each individual, well, individually.

Empirical Bayes Feb 20, 2017

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Say you're looking to use some Bayesian methods to estimate parameters of a system. You've got the normalization figured out, and the likelihood, but the prior... what should you use for a prior? Empirical Bayes has an elegant answer: look to your previous experience, and use past measurements as a starting point in your prior. Scratching your head about some of those terms, and why they matter? Lucky for you, you're standing in front of a podcast episode that unpacks all of this.

Endogenous Variables and Measuring Protest Effectiveness Feb 13, 2017

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Have you been out protesting lately, or watching the protests, and wondered how much effect they might have on lawmakers? It's a tricky question to answer, since usually we need randomly distributed treatments (e.g. big protests) to understand causality, but there's no reason to believe that big protests are actually randomly distributed. In other words, protest size is endogenous to legislative response, and understanding cause and effect is very challenging. So, what to do? Well, at least in the case of studying Tea Party protest effectiveness, researchers have used rainfall, of all things, to understand the impact of a big protest. In other words, rainfall is the instrumental variable in this analysis that cracks the scientific case open. What does rainfall have to do with protests? Do protests actually matter? What do we mean when we talk about endogenous and instrumental variables? We wouldn't be very good podcasters if we answered all those questions here--you gotta listen to this episode to find out.

Calibrated Models Feb 06, 2017

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Remember last week, when we were talking about how great the ROC curve is for evaluating models? How things change... This week, we're exploring calibrated risk models, because that's a kind of model that seems like it would benefit from some nice ROC analysis, but in fact the ROC AUC can steer you wrong there.

Rock the ROC Curve Jan 30, 2017

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This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

Ensemble Algorithms Jan 23, 2017

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If one machine learning model is good, are two models better? In a lot of cases, the answer is yes. If you build many ok models, and then bring them all together and use them in combination to make your final predictions, you've just created an ensemble model. It feels a little bit like cheating, like you just got something for nothing, but the results don't like: algorithms like Random Forests and Gradient Boosting Trees (two types of ensemble algorithms) are some of the strongest out-of-the-box algorithms for classic supervised classification problems. What makes a Random Forest random, and what does it mean to gradient boost a tree? Have a listen and find out.

How to evaluate a translation: BLEU scores Jan 16, 2017

Show notes

As anyone who's encountered a badly translated text could tell you, not all translations are created equal. Some translations are smooth, fluent and sound like a poet wrote them; some are jerky, non-grammatical and awkward. When a machine is doing the translating, it's awfully easy to end up with a robotic-sounding text; as the state of the art in machine translation improves, though, a natural question to ask is: according to what measure? How do we quantify a "good" translation? Enter the BLEU score, which is the standard metric for quantifying the quality of a machine translation. BLEU rewards translations that have large overlap with human translations of sentences, with some extra heuristics thrown in to guard against weird pathologies (like full sentences getting translated as one word, redundancies, and repetition). Nowadays, if there's a machine translation being evaluated or a new state-of-the-art system (like the Google neural machine translation we've discussed on this podcast before), chances are that there's a BLEU score going into that assessment.

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