Linear Digressions

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AlphaStar Mar 11, 2019

Show notes

It’s time for our latest installation in the series on artificial intelligence agents beating humans at games that we thought were safe from the robots. In this case, the game is StarCraft, and the AI agent is AlphaStar, from the same team that built the Go-playing AlphaGo AI last year. StarCraft presents some interesting challenges though: the gameplay is continuous, there are many different kinds of actions a player must take, and of course there’s the usual complexities of playing strategy games and contending with human opponents. AlphaStar overcame all of these challenges, and more, to notch another win for the computers.

Are machine learning engineers the new data scientists? Mar 04, 2019

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For many data scientists, maintaining models and workflows in production is both a huge part of their job and not something they necessarily trained for if their background is more in statistics or machine learning methodology. Productionizing and maintaining data science code has more in common with software engineering than traditional science, and to reflect that, there’s a new-ish role, and corresponding job title, that you should know about. It’s called machine learning engineer, and it’s what a lot of data scientists are becoming. Relevant links: https://medium.com/@tomaszdudek/but-what-is-this-machine-learning-engineer-actually-doing-18464d5c699 https://www.forbes.com/sites/forbestechcouncil/2019/02/04/why-there-will-be-no-data-science-job-titles-by-2029/#64e3906c3a8f

Interview with Alex Radovic, particle physicist turned machine learning researcher Feb 25, 2019

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You’d be hard-pressed to find a field with bigger, richer, and more scientifically valuable data than particle physics. Years before “data scientist” was even a term, particle physicists were inventing technologies like the world wide web and cloud computing grids to help them distribute and analyze the datasets required to make particle physics discoveries. Somewhat counterintuitively, though, deep learning has only really debuted in particle physics in the last few years, although it’s making up for lost time with many exciting new advances. This episode of Linear Digressions is a little different from most, as we’ll be interviewing a guest, one of my (Katie’s) friends from particle physics, Alex Radovic. Alex and his colleagues have been at the forefront of machine learning in physics over the last few years, and his perspective on the strengths and shortcomings of those two fields together is a fascinating one.

K Nearest Neighbors Feb 17, 2019

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K Nearest Neighbors is an algorithm with secrets. On one hand, the algorithm itself is as straightforward as possible: find the labeled points nearest the point that you need to predict, and make a prediction that’s the average of their answers. On the other hand, what does “nearest” mean when you’re dealing with complex data? How do you decide whether a man and a woman of the same age are “nearer” to each other than two women several years apart? What if you convert all your monetary columns from dollars to cents, your distances from miles to nanometers, your weights from pounds to kilograms? Can your definition of “nearest” hold up under these types of transformations? We’re discussing all this, and more, in this week’s episode.

Not every deep learning paper is great. Is that a problem? Feb 11, 2019

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Deep learning is a field that’s growing quickly. That’s good! There are lots of new deep learning papers put out every day. That’s good too… right? What if not every paper out there is particularly good? What even makes a paper good in the first place? It’s an interesting thing to think about, and debate, since there’s no clean-cut answer and there are worthwhile arguments both ways. Wherever you find yourself coming down in the debate, though, you’ll appreciate the good papers that much more. Relevant links: https://blog.piekniewski.info/2018/07/14/autopsy-dl-paper/ https://www.reddit.com/r/MachineLearning/comments/90n40l/dautopsy_of_a_deep_learning_paper_quite_brutal/ https://www.reddit.com/r/MachineLearning/comments/agiatj/d_google_ai_refuses_to_share_dataset_fields_for_a/

The Assumptions of Ordinary Least Squares Feb 03, 2019

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Ordinary least squares (OLS) is often used synonymously with linear regression. If you’re a data scientist, machine learner, or statistician, you bump into it daily. If you haven’t had the opportunity to build up your understanding from the foundations, though, listen up: there are a number of assumptions underlying OLS that you should know and love. They’re interesting, force you to think about data and statistics, and help you know when you’re out of “good” OLS territory and into places where you could run into trouble.

Quantile Regression Jan 28, 2019

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Linear regression is a great tool if you want to make predictions about the mean value that an outcome will have given certain values for the inputs. But what if you want to predict the median? Or the 10th percentile? Or the 90th percentile. You need quantile regression, which is similar to ordinary least squares regression in some ways but with some really interesting twists that make it unique. This week, we’ll go over the concept of quantile regression, and also a bit about how it works and when you might use it. Relevant links: https://www.aeaweb.org/articles?id=10.1257/jep.15.4.143 https://eng.uber.com/analyzing-experiment-outcomes/

Heterogeneous Treatment Effects Jan 20, 2019

Show notes

When data scientists use a linear regression to look for causal relationships between a treatment and an outcome, what they’re usually finding is the so-called average treatment effect. In other words, on average, here’s what the treatment does in terms of making a certain outcome more or less likely to happen. But there’s more to life than averages: sometimes the relationship works one way in some cases, and another way in other cases, such that the average isn’t giving you the whole story. In that case, you want to start thinking about heterogeneous treatment effects, and this is the podcast episode for you. Relevant links: https://eng.uber.com/analyzing-experiment-outcomes/ https://multithreaded.stitchfix.com/blog/2018/11/08/bandits/ https://www.locallyoptimistic.com/post/against-ab-tests/

Pre-training language models for natural language processing problems Jan 14, 2019

Show notes

When you build a model for natural language processing (NLP), such as a recurrent neural network, it helps a ton if you’re not starting from zero. In other words, if you can draw upon other datasets for building your understanding of word meanings, and then use your training dataset just for subject-specific refinements, you’ll get farther than just using your training dataset for everything. This idea of starting with some pre-trained resources has an analogue in computer vision, where initializations from ImageNet used for the first few layers of a CNN have become the new standard. There’s a similar progression under way in NLP, where simple(r) embeddings like word2vec are giving way to more advanced pre-processing methods that aim to capture more sophisticated understanding of word meanings, contexts, language structure, and more. Relevant links: https://thegradient.pub/nlp-imagenet/

Facial Recognition, Society, and the Law Jan 07, 2019

Show notes

Facial recognition being used in everyday life seemed far-off not too long ago. Increasingly, it’s being used and advanced widely and with increasing speed, which means that our technical capabilities are starting to outpace (if they haven’t already) our consensus as a society about what is acceptable in facial recognition and what isn’t. The threats to privacy, fairness, and freedom are real, and Microsoft has become one of the first large companies using this technology to speak out in specific support of its regulation through legislation. Their arguments are interesting, provocative, and even if you don’t agree with every point they make or harbor some skepticism, there’s a lot to think about in what they’re saying. https://blogs.microsoft.com/on-the-issues/2018/12/06/facial-recognition-its-time-for-action/

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