Linear Digressions

The town of [expletive deleted], England, is responsible for the clbuttic [expletive deleted] problem. This week on Linear Digressions: we try really hard not to swear too much. Related links: https://en.wikipedia.org/wiki/Scunthorpe_problem https://www.washingtonpost.com/news/worldviews/wp/2016/01/05/where-is-russia-actually-mordor-in-the-world-of-google-translate/

In order to do supervised learning, you need a labeled training dataset. Or do you...? Relevant links: http://www.cs.columbia.edu/~dplewis/candidacy/goldman00enhancing.pdf

Machine learning: it can be fooled, just like you or me. Here's one of our favorite examples, a study into hacking neural networks. Relevant links: http://arxiv.org/pdf/1412.1897v4.pdf

Zipf's law is related to the statistics of how word usage is distributed. As it turns out, this is also strikingly reminiscent of how income is distributed, and populations of cities, and bug reports in software, as well as tons of other phenomena that we all interact with every day. Relevant links: http://economix.blogs.nytimes.com/2010/04/20/a-tale-of-many-cities/ http://arxiv.org/pdf/cond-mat/0412004.pdf https://terrytao.wordpress.com/2009/07/03/benfords-law-zipfs-law-and-the-pareto-distribution/

We've gone indie! Which shouldn't change anything about the podcast that you know and love, but we're super excited to keep bringing you Linear Digressions as a fully independent podcast. Some links mentioned in the show: https://twitter.com/lindigressions https://twitter.com/benjaffe https://twitter.com/multiarmbandit https://soundcloud.com/linear-digressions http://lineardigressions.com/

It's Da Vinci meets Skynet: what makes a portrait beautiful, according to a machine learning algorithm. Snap a selfie and give us a listen.

Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!

Because there are more interesting problems than there are labeled datasets, semi-supervised learning provides a framework for getting feedback from the environment as a proxy for labels of what's "correct." Of all the machine learning methodologies, it might also be the closest to how humans usually learn--we go through the world, getting (noisy) feedback on the choices we make and learn from the outcomes of our actions.

Overfitting to your training data can be avoided by evaluating your machine learning algorithm on a holdout test dataset, but what about overfitting to the test data? Turns out it can be done, easily, and you have to be very careful to avoid it. But an algorithm from the field of privacy research shows promise for keeping your test data safe from accidental overfitting

How many data scientists are there, where do they live, where do they work, what kind of tools do they use, and how do they describe themselves? RJMetrics wanted to know the answers to these questions, so they decided to find out and share their analysis with the world. In this very special interview episode, we welcome Tristan Handy, VP of Marketing at RJMetrics, who will talk about "The State of Data Science Report."

There's a good chance that great data science is going on close to you, and that it's going toward making your city, state, country, and planet a better place. Not all the data science questions being tackled out there are about finding the sleekest new algorithm or billion-dollar company idea--there's a whole world of social data science that just wants to make the world a better place to live in.

The Kalman Filter is an algorithm for taking noisy measurements of dynamic systems and using them to get a better idea of the underlying dynamics than you could get from a simple extrapolation. If you've ever run a marathon, or been a nuclear missile, you probably know all about these challenges already. By the way, we neglected to mention in the episode: Katie's marathon time was 3:54:27!

When you sleep, the neural pathways in your brain take the "white noise" of your resting brain, mix in your experiences and imagination, and the result is dreams (that is a highly unscientific explanation, but you get the idea). What happens when neural nets are put through the same process? Train a neural net to recognize pictures, and then send through an image of white noise, and it will start to see some weird (but cool!) stuff.

Sometimes numbers are... weird. Benford's Law is a favorite example of this for us--it's a law that governs the distribution of the first digit in certain types of numbers. As it turns out, if you're looking up the length of a river, the population of a country, the price of a stock... not all first digits are created equal.

Not to oversell it, but the student's t-test has got to have the most interesting history of any statistical test. Which is saying a lot, right? Add some boozy statistical trivia to your arsenal in this epsiode.

Doing some science, and want to know if you might have found something? Or maybe you've just accomplished the scientific equivalent of going fishing and reeling in an old boot? Frequentist p-values can help you distinguish between "eh" and "oooh interesting". Also, there's a lot of physics in this episode, nerds.

This machine learning algorithm beat the human champions at Jeopardy. What is... Watson?

Come get a little "out there" with us this week, as we use a meta-study of extrasensory perception (or ESP, often used in the same sentence as "psychics") to chat about Bayesian vs. frequentist statistics.

Ever found yourself wasting time reading online comments from trolls? Of course you have; we've all been there (it's 4 AM but I can't turn off the computer and go to sleep--someone on the internet is WRONG!). Now there's a way to use machine learning to automatically detect trolls, and minimize the impact when they try to derail online conversations.

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.