Linear Digressions

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Optimization Problems Aug 22, 2016

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If modeling is about predicting the unknown, optimization tries to answer the question of what to do, what decision to make, to get the best results out of a given situation. Sometimes that's straightforward, but sometimes... not so much. What makes an optimization problem easy or hard, and what are some of the methods for finding optimal solutions to problems? Glad you asked! May we recommend our latest podcast episode to you?

Multi-level modeling for understanding DEADLY RADIOACTIVE GAS Aug 15, 2016

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Ok, this episode is only sort of about DEADLY RADIOACTIVE GAS. It's mostly about multilevel modeling, which is a way of building models with data that has distinct, related subgroups within it. What are multilevel models used for? Elections (we can't get enough of 'em these days), understanding the effect that a good teacher can have on their students, and DEADLY RADIOACTIVE GAS. Relevant links: http://www.stat.columbia.edu/~gelman/research/published/multi2.pdf

How Polls Got Brexit "Wrong" Aug 08, 2016

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Continuing the discussion of how polls do (and sometimes don't) tell us what to expect in upcoming elections--let's take a concrete example from the recent past, shall we? The Brexit referendum was, by and large, expected to shake out for "remain", but when the votes were counted, "leave" came out ahead. Everyone was shocked (SHOCKED!) but maybe the polls weren't as wrong as the pundits like to claim. Relevant links: http://www.slate.com/articles/news_and_politics/moneybox/2016/07/why_political_betting_markets_are_failing.html http://andrewgelman.com/2016/06/24/brexit-polling-what-went-wrong/

Election Forecasting Aug 01, 2016

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Not sure if you heard, but there's an election going on right now. Polls, surveys, and projections about, as far as the eye can see. How to make sense of it all? How are the projections made? Which are some good ones to follow? We'll be your trusty guides through a crash course in election forecasting. Relevant links: http://www.wired.com/2016/06/civis-election-polling-clinton-sanders-trump/ http://election.princeton.edu/ http://projects.fivethirtyeight.com/2016-election-forecast/ http://www.nytimes.com/interactive/2016/upshot/presidential-polls-forecast.html?rref=collection%2Fsectioncollection%2Fupshot&action=click&contentCollection=upshot&region=rank&module=package&version=highlights&contentPlacement=5&pgtype=sectionfront

Machine Learning for Genomics Jul 25, 2016

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Genomics data is some of the biggest #bigdata, and doing machine learning on it is unlocking new ways of thinking about evolution, genomic diseases like cancer, and what really makes each of us different for everyone else. This episode touches on some of the things that make machine learning on genomics data so challenging, and the algorithms designed to do it anyway.

Climate Modeling Jul 18, 2016

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Hot enough for you? Climate models suggest that it's only going to get warmer in the coming years. This episode unpacks those models, so you understand how they work. A lot of the episodes we do are about fun studies we hear about, like "if you're interested, this is kinda cool"--this episode is much more important than that. Understanding these models, and taking action on them where appropriate, will have huge implications in the years to come. Relevant links: https://climatesight.org/

Reinforcement Learning Gone Wrong Jul 11, 2016

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Last week’s episode on artificial intelligence gets a huge payoff this week—we’ll explore a wonderful couple of papers about all the ways that artificial intelligence can go wrong. Malevolent actors? You bet. Collateral damage? Of course. Reward hacking? Naturally! It’s fun to think about, and the discussion starting now will have reverberations for decades to come. https://www.technologyreview.com/s/601519/how-to-create-a-malevolent-artificial-intelligence/ http://arxiv.org/abs/1605.02817 https://arxiv.org/abs/1606.06565

Reinforcement Learning for Artificial Intelligence Jul 03, 2016

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There’s a ton of excitement about reinforcement learning, a form of semi-supervised machine learning that underpins a lot of today’s cutting-edge artificial intelligence algorithms. Here’s a crash course in the algorithmic machinery behind AlphaGo, and self-driving cars, and major logistical optimization projects—and the robots that, tomorrow, will clean our houses and (hopefully) not take over the world…

Differential Privacy: how to study people without being weird and gross Jun 27, 2016

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Apple wants to study iPhone users' activities and use it to improve performance. Google collects data on what people are doing online to try to improve their Chrome browser. Do you like the idea of this data being collected? Maybe not, if it's being collected on you--but you probably also realize that there is some benefit to be had from the improved iPhones and web browsers. Differential privacy is a set of policies that walks the line between individual privacy and better data, including even some old-school tricks that scientists use to get people to answer embarrassing questions honestly. Relevant links: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42852.pdf

How the sausage gets made Jun 20, 2016

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Something a little different in this episode--we'll be talking about the technical plumbing that gets our podcast from our brains to your ears. As it turns out, it's a multi-step bucket brigade process of RSS feeds, links to downloads, and lots of hand-waving when it comes to trying to figure out how many of you (listeners) are out there.

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