Linear Digressions

Latest Episodes:

Yiddish Translation Aug 03, 2015

Show notes

Imagine a language that is mostly spoken rather than written, contains many words in other languages, and has relatively little written overlap with English. Now imagine writing a machine-learning-based translation system that can convert that language to English. That's the problem that confronted researchers when they set out to automatically translate between Yiddish and English; the tricks they used help us understand a lot about machine translation.

Modeling Particles in Atomic Bombs Jul 06, 2015

Show notes

In a fun historical journey, Katie and Ben explore the history of the Manhattan Project, discuss the difficulties in modeling particle movement in atomic bombs with only punch-card computers and ingenuity, and eventually come to present-day uses of the Metropolis-Hastings algorithm... mentioning Solitaire along the way.

Random Number Generation Jun 19, 2015

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Let's talk about randomness! Although randomness is pervasive throughout the natural world, it's surprisingly difficult to generate random numbers. And even if your numbers look random (but actually aren't), it can have interesting consequences on the security of systems, and the accuracy of models and research. In this episode, Katie and Ben talk about randomness, its place in machine learning and computation in general, along with some random digressions of their own.

Electoral Insights (Part 2) Jun 09, 2015

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Following up on our last episode about how experiments can be performed in political science, now we explore a high-profile case of an experiment gone wrong. An extremely high-profile paper that was published in 2014, about how talking to people can convince them to change their minds on topics like abortion and gay marriage, has been exposed as the likely product of a fraudulently produced dataset. We’ll talk about a cool data science tool called the Kolmogorov-Smirnov test, which a pair of graduate students used to reverse-engineer the likely way that the fraudulent data was generated. But a bigger question still remains—what does this whole episode tell us about fraud and oversight in science?

Electoral Insights (Part 1) Jun 05, 2015

Show notes

The first of our two-parter discussing the recent electoral data fraud case. The results of the study in question were covered widely, including by This American Life (who later had to issue a retraction). Data science for election research involves studying voters, who are people, and people are tricky to study—every one of them is different, and the same treatment can have different effects on different voters. But with randomized controlled trials, small variations from person to person can even out when you look at a larger group. With the advent of randomized experiments in elections a few decades ago, a whole new door was opened for studying the most effective ways to campaign.

Falsifying Data Jun 01, 2015

Show notes

In the first of a few episodes on fraud in election research, we’ll take a look at a case study from a previous Presidential election, where polling results were faked. What are some telltale signs that data fraud might be present in a dataset? We’ll explore that in this episode.

Reporter Bot May 20, 2015

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There’s a big difference between a table of numbers or statistics, and the underlying story that a human might tell about how those numbers were generated. Think about a baseball game—the game stats and a newspaper story are describing the same thing, but one is a good input for a machine learning algorithm and the other is a good story to read over your morning coffee. Data science and machine learning are starting to bridge this gap, taking the raw data on things like baseball games, financial scenarios, etc. and automatically writing human-readable stories that are increasingly indistinguishable from what a human would write. In this episode, we’ll talk about some examples of auto-generated content—you’ll be amazed at how sophisticated some of these reporter-bots can be. By the way, this summary was written by a human. (Or was it?)

Careers in Data Science May 16, 2015

Show notes

Let’s talk money. As a “hot” career right now, data science can pay pretty well. But for an individual person matched with a specific job or industry, how much should someone expect to make? Since Katie was on the job market lately, this was something she’s been researching, and it turns out that data science itself (in particular linear regressions) has some answers. In this episode, we go through a survey of hundreds of data scientists, who report on their job duties, industry, skills, education, location, etc. along with their salaries, and then talk about how this data was fed into a linear regression so that you (yes, you!) can use the patterns in the data to know what kind of salary any particular kind of data scientist might expect.

That's "Dr Katie" to You May 14, 2015

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Katie successfully defended her thesis! We celebrate her return, and talk a bit about what getting a PhD in Physics is like.

Neural Nets (Part 2) May 11, 2015

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In the last episode, we zipped through neural nets and got a quick idea of how they work and why they can be so powerful. Here’s the real payoff of that work: In this episode, we’ll talk about a brand-new pair of results, one from Stanford and one from Google, that use neural nets to perform automated picture captioning. One neural net does the object and relationship recognition of the image, a second neural net handles the natural language processing required to express that in an English sentence, and when you put them together you get an automated captioning tool. Two heads are better than one indeed...

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