A Data Scientist's View of the War Against Cancer

In this episode, we're taking many episodes' worth of insights and unpacking an extremely complex and important question--in what ways are we winning the fight against cancer, where might that fight go in the coming decade, and how do we know when we're making progress? No matter how tricky you might think this problem is to solve, the fact is, once you get in there trying to solve it, it's even trickier than you thought.

Experiments and Causality

"People with a family history of heart disease are more likely to eat healthy foods, and have a high incidence of heart attacks." Did the healthy food cause the heart attacks? Probably not. But establishing causal links is extremely tricky, and extremely important to get right if you're trying to help students, test new medicines, or just optimize a website. In this episode, we'll unpack randomized experiments, like AB tests, and maybe you'll be smarter as a result. Will you be smarter BECAUSE of this episode? Well, tough to say for sure...

Relevant links:

Backpropagation

The reason that neural nets are taking over the world right now is because they can be efficiently trained with the backpropagation algorithm. In short, backprop allows you to adjust the weights of the neural net based on how good of a job the neural net is doing at classifying training examples, thereby getting better and better at making predictions. In this episode: we talk backpropagation, and how it makes it possible to train the neural nets we know and love.

 

Text Analysis on the State of the Union

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:

Paradigms in Artificial Intelligence

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

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.

Gravitational Waves

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

The Turing Test

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/

Item Response Theory: How Smart ARE You?

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

Great Social Networks in History

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

Going Once, Going Twice: Auctions Part 1

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