First up in this episode: a crash course in natural language processing, and important steps if you want to use machine learning techniques on text data. Then we'll take that NLP know-how and talk about a really cool analysis of State of the Union text, which analyzes the topics and word choices of every President from Washington to Obama.
Relevant links:
Artificial intelligence includes a number of different strategies for how to make machines more intelligent, and often more human-like, in their ability to learn and solve problems. An ambitious group of researchers is working right now to classify all the approaches to AI, perhaps as a first step toward unifying these approaches and move closer to strong AI. In this episode, we'll touch on some of the most provocative work in many different subfields of artificial intelligence, and their strengths and weaknesses.
Relevant links:
Survival analysis is all about studying how long until an event occurs--it's used in marketing to study how long a customer stays with a service, in epidemiology to estimate the duration of survival of a patient with some illness, and in social science to understand how the characteristics of a war inform how long the war goes on. This episode talks about the special challenges associated with survival analysis, and the tools that (data) scientists use to answer all kinds of duration-related questions.
All aboard the gravitational waves bandwagon--with the first direct observation of gravitational waves announced this week, Katie's dusting off her physics PhD for a very special gravity-related episode. Discussed in this episode: what are gravitational waves, how are they detected, and what does this announcement mean for future studies of the universe.
Relevant links:
http://www.nytimes.com/2016/02/12/science/ligo-gravitational-waves-black-holes-einstein.html
https://www.ligo.caltech.edu/news/ligo20160211
Let's imagine a future in which a truly intelligent computer program exists. How would it convince us (humanity) that it was intelligent? Alan Turing's answer to this question, proposed over 60 years ago, is that the program could convince a human conversational partner that it, the computer, was in fact a human. 60 years later, the Turing Test endures as a gold standard of artificial intelligence. It hasn't been beaten, either--yet.
Relevant links:
https://en.wikipedia.org/wiki/Turing_test
http://commonsensereasoning.org/winograd.html
http://consumerist.com/2015/09/29/its-not-just-you-robots-are-also-bad-at-assembling-ikea-furniture/
Psychometrics is all about measuring the psychological characteristics of people; for example, scholastic aptitude. How is this done? Tests, of course! But there's a chicken-and-egg problem here: you need to know both how hard a test is, and how smart the test-taker is, in order to get the results you want. How to solve this problem, one equation with two unknowns? Item response theory--the data science behind such tests and the GRE.
Relevant links:
https://en.wikipedia.org/wiki/Item_response_theory
As you may have heard, a computer beat a world-class human player in Go last week. As recently as a year ago the prediction was that it would take a decade to get to this point, yet here we are, in 2016. We'll talk about the history and strategy of game-playing computer programs, and what makes Google's AlphaGo so special.
Relevant link:
http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html
The Medici were one of the great ruling families of Europe during the Renaissance. How did they come to rule? Not power, or money, or armies, but through the strength of their social network. And speaking of great historical social networks, analysis of the network of letter-writing during the Enlightenment is helping humanities scholars track the dispersion of great ideas across the world during that time, from Voltaire to Benjamin Franklin and everyone in between.
Relevant links:
https://www2.bc.edu/~jonescq/mb851/Mar12/PadgettAnsell_AJS_1993.pdf
http://republicofletters.stanford.edu/index.html
A few small encores on auction theory, and then--how can you value a piece of information before you know what it is? Decision theory has some pointers. Some highly relevant information if you are trying to figure out how much to pay a spy.
Relevant links:
https://tuecontheoryofnetworks.wordpress.com/2013/02/25/the-origin-of-the-dutch-auction/
http://www.nowozin.net/sebastian/blog/the-fair-price-to-pay-a-spy-an-introduction-to-the-value-of-information.html
The Google ads auction is a special kind of auction, one you might not know as well as the famous English auction (which we talked about in the last episode). But if it's what Google uses to sell billions of dollars of ad space in real time, you know it must be pretty cool.
Relevant links:
https://en.wikipedia.org/wiki/English_auction
http://people.ischool.berkeley.edu/~hal/Papers/2006/position.pdf
http://www.benedelman.org/publications/gsp-060801.pdf
The Google AdWords algorithm is (famously) an auction system for allocating a massive amount of online ad space in real time--with that fascinating use case in mind, this episode is part one in a two-part series all about auctions. We dive into the theory of auctions, and what makes a "good" auction.
Relevant links:
https://en.wikipedia.org/wiki/English_auction
http://people.ischool.berkeley.edu/~hal/Papers/2006/position.pdf
http://www.benedelman.org/publications/gsp-060801.pdf
A data visualization extravaganza in this episode, as we discuss Chernoff faces (you: "faces? huh?" us: "oh just you wait") and the greatest data visualization of all time, or at least the Napoleonic era.
Relevant links:
http://lya.fciencias.unam.mx/rfuentes/faces-chernoff.pdf
https://en.wikipedia.org/wiki/Charles_Joseph_Minard
Ever tried to visualize a cluster of data points in 40 dimensions? Or even 4, for that matter? We prefer to stick to 2, or maybe 3 if we're feeling well-caffeinated. The t-SNE algorithm is one of the best tools on the market for doing dimensionality reduction when you have clustering in mind.
Relevant links:
https://www.youtube.com/watch?v=RJVL80Gg3lA
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/
Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!
Links: Deep learning machine solves the cocktail party problem
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.
Link: The beauty of capturing faces: rating the quality of digital portraits