Machine Learning & Data Science

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/

The Turing Test

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

Gravitational Waves

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.

Survival Analysis

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:
https://www.technologyreview.com/s/544606/can-this-man-make-aimore-human/
https://www.youtube.com/watch?v=B8J4uefCQMc
http://venturebeat.com/2013/11/29/sentient-code-an-inside-look-at-stephen-wolframs-utterly-new-insanely-ambitious-computational-paradigm/
http://www.slate.com/articles/technology/bitwise/2014/03/stephen_wolfram_s_new_programming_language_can_he_make_the_world_computable.html

Paradigms in Artificial Intelligence

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 link:
https://civisanalytics.com/blog/data-science/2016/01/15/data-science-on-state-of-the-union-addresses/

Text Analysis on the State Of The Union

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.

Backpropagation

"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 link:
http://tylervigen.com/spurious-correlations

Experiments and Messy, Tricky Causality

Multi-armed bandits: how to take your randomized experiment and make it harder better faster stronger.  Basically, a multi-armed bandit experiment allows you to optimize for both learning and making use of your knowledge at the same time.  It's what the pros (like Google Analytics) use, and it's got a great name, so... winner!

Relevant link: https://support.google.com/analytics/answer/2844870?hl=en

Multi - Armed Bandits

Hey, sick of the election yet?  Fear not, there are algorithms that can automagically generate political-ish speech so that we never need to be without an endless supply of Congressional speeches and Donald Trump twitticisms!  

Relevant links:
http://arxiv.org/pdf/1601.03313v2.pdf
http://qz.com/631497/mit-built-a-donald-trump-ai-twitter-bot-that-sounds-scarily-like-him/
https://twitter.com/deepdrumpf

Congress Bots and DeepDrumpf

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.

A Data Scientist's View of the Fight against Cancer

One neural net is creating counterfeit bills and passing them off to a second neural net, which is trying to distinguish the real money from the fakes.  Result: two neural nets that are better than either one would have been without the competition.

Relevant links:
http://arxiv.org/pdf/1406.2661v1.pdf
http://arxiv.org/pdf/1412.6572v3.pdf
http://soumith.ch/eyescream/

Neural nets play cops and robbers (AKA generative adverserial networks)

This episode started out as a discussion of recommendation engines, like Netflix uses to suggest movies.  There's still a lot of that in here.  But a related topic, which is both interesting and important, is how to keep data private in the era of large-scale recommendation engines--what mistakes have been made surrounding supposedly anonymized data, how data ends up de-anonymized, and why it matters for you.

Relevant links:
http://www.netflixprize.com/
http://bits.blogs.nytimes.com/2010/03/12/netflix-cancels-contest-plans-and-settles-suit/?_r=0
http://arxiv.org/PS_cache/cs/pdf/0610/0610105v2.pdf

Recommendation Engines and Privacy

Sometimes when we say an algorithm discriminates, we mean it can tell the difference between two types of items.  But in this episode, we'll talk about another, more troublesome side to discrimination: algorithms can be... racist?  Sexist?  Ageist?  Yes to all of the above.  It's an important thing to be aware of, especially when doing people-centered data science.  We'll discuss how and why this happens, and what solutions are out there (or not).

Relevant Links:
http://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html
http://techcrunch.com/2015/08/02/machine-learning-and-human-bias-an-uneasy-pair/
http://www.sciencefriday.com/segments/why-machines-discriminate-and-how-to-fix-them/
https://medium.com/@geomblog/when-an-algorithm-isn-t-2b9fe01b9bb5#.auxqi5srz




Discriminatory Algorithms

Simpson's paradox is the data science equivalent of looking through one eye and seeing a very clear trend, and then looking through the other eye and seeing the very clear opposite trend.  In one case, you see a trend one way in a group, but then breaking the group into subgroups gives the exact opposite trend.  Confused?  Scratching your head?  Welcome to the tricky world of ecological inference.

Relevant links:
https://gking.harvard.edu/files/gking/files/part1.pdf
http://blog.revolutionanalytics.com/2013/07/a-great-example-of-simpsons-paradox.html

Ecological Inference and Simpson's Paradox

We've got updates for you about topics from past shows!  First, the political science scandal of the year 2015 has a new chapter, we'll remind you about the original story and then dive into what has happened since.  Then, we've got an update on AlphaGo, and his/her/its much-anticipated match against the human champion of the game Go.

Relevant Links:
https://soundcloud.com/linear-digressions/electoral-insights-part-2
https://soundcloud.com/linear-digressions/go-1

http://www.sciencemag.org/news/2016/04/talking-people-about-gay-and-transgender-issues-can-change-their-prejudices
http://science.sciencemag.org/content/sci/352/6282/220.full.pdf

http://qz.com/639952/googles-ai-won-the-game-go-by-defying-millennia-of-basic-human-instinct/
http://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/
http://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go/

Updates! Political Science Fraud and AlphaGo

Machine learning algorithms can be black boxes--inputs go in, outputs come out, and what happens in the middle is anybody's guess.  But understanding how a model arrives at an answer is critical for interpreting the model, and for knowing if it's doing something reasonable (one could even say... trustworthy).  We'll talk about a new algorithm called LIME that seeks to make any model more understandable and interpretable.

Relevant Links:
http://arxiv.org/abs/1602.04938
https://github.com/marcotcr/lime/tree/master/lime

Model Interpretation (and Trust Issues)

Supervised machine learning assumes that the features and labels used for building a classifier are isolated from each other--basically, that you can't cheat by peeking.  Turns out this can be easier said than done.  In this episode, we'll talk about the many (and diverse!) cases where label information contaminates features, ruining data science competitions along the way.

Relevant links:
https://www.researchgate.net/profile/Claudia_Perlich/publication/221653692_Leakage_in_data_mining_Formulation_detection_and_avoidance/links/54418bb80cf2a6a049a5a0ca.pdf

Data Contamination

Data science and is often mentioned in the same breath as big data.  But how big is big data?  And who has the biggest big data?  CERN?  Youtube?  

... Something (or someone) else?

Relevant link: http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195



What's the biggest #bigdata?

It's storytime this week--the story, from beginning to end, of how Katie designed and built the main project for Udacity's Intro to Machine Learning class, when she was developing the course.  The project was to use email and financial data to hunt for signatures of fraud at Enron, one of the biggest cases of corporate fraud in history; that description makes the project sound pretty clean but getting the data into the right shape, and even doing some dataset merging (that hadn't ever been done before), made this project much more interesting to design than it might appear.  Here's the story of what a data analysis like this looks like...from the inside.

Inside a Data Analysis: Fraud Hunting at Enron

One tricky thing about working with time series data, like the audio data in our "um" detector (remember that?  because we barely do...), is that sometimes events look really similar but one is a little bit stretched and squeezed relative to the other.  Besides having an amazing name, the dynamic time warp is a handy algorithm for aligning two time series sequences that are close in shape, but don't quite line up out of the box.

Relevant link:
http://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf

Machine Learning & Data Science

Rivera Rodrigo