Linear Digressions

When you are running an AB test, one of the most important questions is how much data to collect. Collect too little, and you can end up drawing the wrong conclusion from your experiment. But in a world where experimenting is generally not free, and you want to move quickly once you know the answer, there is such a thing as collecting too much data. Statisticians have been solving this problem for decades, and their best practices are encompassed in the ideas of power, statistical significance, and especially how to generally think about hypothesis testing. This week, we’re going over these important concepts, so your next AB test is just as data-intensive as it needs to be.

OpenAI recently created a cutting-edge new natural language processing model, but unlike all their other projects so far, they have not released it to the public. Why? It seems to be a little too good. It can answer reading comprehension questions, summarize text, translate from one language to another, and generate realistic fake text. This last case, in particular, raised concerns inside OpenAI that the raw model could be dangerous if bad actors had access to it, so researchers will spend the next six months studying the model (and reading comments from you, if you have strong opinions here) to decide what to do next. Regardless of where this lands from a policy perspective, it’s an impressive model and the snippets of released auto-generated text are quite impressive. We’re covering the methodology, the results, and a bit of the policy implications in our episode this week.

Imagine you have two choices of how to build something: top-down and controlled, with a few people playing a master designer role, or bottom-up and free-for-all, with nobody playing an explicit architect role. Which one do you think would make the better product? “The Cathedral and the Bazaar” is an essay exploring this question for open source software, and making an argument for the bottom-up approach. It’s not entirely intuitive that projects like Linux or scikit-learn, with many contributors and an open-door policy for modifying the code, would be able to resist the chaos of many cooks in the kitchen. So what makes it work in some cases? And sometimes not work in others? That’s the topic of discussion this week. Relevant links: http://www.catb.org/~esr/writings/cathedral-bazaar/cathedral-bazaar/index.html

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.

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

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 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.

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/

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.

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/