Linear Digressions

Latest Episodes:

Discriminatory Algorithms Apr 04, 2016

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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

Recommendation Engines and Privacy Mar 28, 2016

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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

Neural nets play cops and robbers (AKA generative adverserial networks) Mar 21, 2016

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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/

A Data Scientist's View of the Fight against Cancer Mar 14, 2016

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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.

Congress Bots and DeepDrumpf Mar 11, 2016

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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

Multi - Armed Bandits Mar 07, 2016

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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

Experiments and Messy, Tricky Causality Mar 04, 2016

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"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

Backpropagation Feb 29, 2016

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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 Feb 26, 2016

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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/

Paradigms in Artificial Intelligence Feb 22, 2016

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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

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