This machine learning algorithm beat the human champions at Jeopardy. What is... Watson?
Bayesian Psychics
Come get a little "out there" with us this week, as we use a meta-study of extrasensory perception (or ESP, often used in the same sentence as "psychics") to chat about Bayesian vs. frequentist statistics.
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Troll Detection
Ever found yourself wasting time reading online comments from trolls? Of course you have; we've all been there (it's 4 AM but I can't turn off the computer and go to sleep--someone on the internet is WRONG!). Now there's a way to use machine learning to automatically detect trolls, and minimize the impact when they try to derail online conversations.
Yiddish Translation
Imagine a language that is mostly spoken rather than written, contains many words in other languages, and has relatively little written overlap with English. Now imagine writing a machine-learning-based translation system that can convert that language to English. That's the problem that confronted researchers when they set out to automatically translate between Yiddish and English; the tricks they used help us understand a lot about machine translation.
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Modeling Particles in Atomic Bombs
In a fun historical journey, Katie and Ben explore the history of the Manhattan Project, discuss the difficulties in modeling particle movement in atomic bombs with only punch-card computers and ingenuity, and eventually come to present-day uses of the Metropolis-Hastings algorithm... mentioning Solitaire along the way.
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Random Number Generation
Let's talk about randomness! Although randomness is pervasive throughout the natural world, it's surprisingly difficult to generate random numbers. And even if your numbers look random (but actually aren't), it can have interesting consequences on the security of systems, and the accuracy of models and research.
In this episode, Katie and Ben talk about randomness, its place in machine learning and computation in general, along with some random digressions of their own.
Links: Mersenne Twister
Electoral Insights Part 2
Following up on our last episode about how experiments can be performed in political science, now we explore a high-profile case of an experiment gone wrong.
An extremely high-profile paper that was published in 2014, about how talking to people can convince them to change their minds on topics like abortion and gay marriage, has been exposed as the likely product of a fraudulently produced dataset. We’ll talk about a cool data science tool called the Kolmogorov-Smirnov test, which a pair of graduate students used to reverse-engineer the likely way that the fraudulent data was generated.
But a bigger question still remains—what does this whole episode tell us about fraud and oversight in science?
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Electoral Insights Part 1
The first of our two-parter discussing the recent electoral data fraud case. The results of the study in question were covered widely, including by This American Life (who later had to issue a retraction).
Data science for election research involves studying voters, who are people, and people are tricky to study—every one of them is different, and the same treatment can have different effects on different voters. But with randomized controlled trials, small variations from person to person can even out when you look at a larger group. With the advent of randomized experiments in elections a few decades ago, a whole new door was opened for studying the most effective ways to campaign.
Link: The Victory Lab
Falsifying Data
In the first of a few episodes on fraud in election research, we’ll take a look at a case study from a previous Presidential election, where polling results were faked.
What are some telltale signs that data fraud might be present in a dataset? We’ll explore that in this episode.
Reporter Bot
There’s a big difference between a table of numbers or statistics, and the underlying story that a human might tell about how those numbers were generated.
Think about a baseball game—the game stats and a newspaper story are describing the same thing, but one is a good input for a machine learning algorithm and the other is a good story to read over your morning coffee. Data science and machine learning are starting to bridge this gap, taking the raw data on things like baseball games, financial scenarios, etc. and automatically writing human-readable stories that are increasingly indistinguishable from what a human would write.
In this episode, we’ll talk about some examples of auto-generated content—you’ll be amazed at how sophisticated some of these reporter-bots can be. By the way, this summary was written by a human. (Or was it?)
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Careers in Data Science
Let’s talk money. As a “hot” career right now, data science can pay pretty well. But for an individual person matched with a specific job or industry, how much should someone expect to make?
Since Katie was on the job market lately, this was something she’s been researching, and it turns out that data science itself (in particular linear regressions) has some answers.
In this episode, we go through a survey of hundreds of data scientists, who report on their job duties, industry, skills, education, location, etc. along with their salaries, and then talk about how this data was fed into a linear regression so that you (yes, you!) can use the patterns in the data to know what kind of salary any particular kind of data scientist might expect.
That's "Dr. Katie" To You
Katie successfully defended her thesis! We celebrate her return, and talk a bit about what getting a PhD in Physics is like.
Neural Nets Part 2
In the last episode, we zipped through neural nets and got a quick idea of how they work and why they can be so powerful. Here’s the real payoff of that work:
In this episode, we’ll talk about a brand-new pair of results, one from Stanford and one from Google, that use neural nets to perform automated picture captioning. One neural net does the object and relationship recognition of the image, a second neural net handles the natural language processing required to express that in an English sentence, and when you put them together you get an automated captioning tool. Two heads are better than one indeed...
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Neural Nets Part 1
There is no known learning algorithm that is more flexible and powerful than the human brain. That's quite inspirational, if you think about it--to level up machine learning, maybe we should be going back to biology and letting millions of year of evolution guide the structure of our algorithms.
This is the idea behind neural nets, which mock up the structure of the brain and are some of the most studied and powerful algorithms out there. In this episode, we’ll lay out the building blocks of the neural net (called neurons, naturally) and the networks that are built out of them.
We’ll also explore the results that neural nets get when they're used to do object recognition in photographs.
Inferring Authorship Part 2
Now that we’re up to speed on the classic author ID problem (who wrote the unsigned Federalist Papers?), we move onto a couple more contemporary examples.
First, J.K. Rowling was famously outed using computational linguistics (and Twitter) when she wrote a book under the pseudonym Robert Galbraith.
Second, we’ll talk about a mystery that still endures--who is Satoshi Nakamoto? Satoshi is the mysterious person (or people) behind an extremely lucrative cryptocurrency (aka internet money) called Bitcoin; no one knows who he, she or they are, but we have plenty of writing samples in the form of whitepapers and Bitcoin forum posts. We’ll discuss some attempts to link Satoshi Nakamoto with a cryptocurrency expert and computer scientist named Nick Szabo; the links are tantalizing, but not a smoking gun. “Who is Satoshi” remains an example of attempted author identification where the threads are tangled, the conclusions inconclusive and the stakes high.
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Juola: Rowling and "Galbraith": an authorial analysis
LikeInAMirror Blog: Occam's Razor: who is most likely to be Satoshi Nakamoto?
Inferring Authorship Part 1
This episode is inspired by one of our projects for Intro to Machine Learning: given a writing sample, can you use machine learning to identify who wrote it? Turns out that the answer is yes, a person’s writing style is as distinctive as their vocal inflection or their gait when they walk.
By tracing the vocabulary used in a given piece, and comparing the word choices to the word choices in writing samples where we know the author, it can be surprisingly clear who is the more likely author of a given piece of text.
We’ll use a seminal paper from the 1960’s as our example here, where the Naive Bayes algorithm was used to determine whether Alexander Hamilton or James Madison was the more likely author of a number of anonymous Federalist Papers.
Link: Mosteller and Wallace, Inference in an Authorship Problem
Statistical Mistakes and the Challenger Disaster
After the Challenger exploded in 1986, killing all 7 astronauts aboard, an investigation into the cause was immediately launched.
In the cold temperatures the night before the launch, the o-rings that seal off the fuel tanks from the rocket boosters became inflexible, so they did not seal properly, which led to the fuel tank explosion. NASA knew that there could be o-ring problems, but performed the analysis of their data incorrectly and ended up massively underestimating the risk associated with the cold temperatures.
In this episode, we'll unpack the mistakes they made. We'll talk about how they excluded data points that they thought were irrelevant but which actually were critical to recognizing a fatal pattern.
Genetics and Um Detection (HMMs Part 2)
In part two of our series on Hidden Markov Models (HMMs), we talk to Katie and special guest Francesco about more useful and novel applications of HMMs. We revisit Katie's "Um Detector," and hear about how HMMs are used in genetics research.
Introducing Hidden Markov Models (HMMs Part 1)
Wikipedia says, "A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states." What does that even mean?
In part one of a special two-parter on HMMs, Katie, Ben, and special guest Francesco explain the basics of HMMs, and some simple applications of them in the real world. This episode sets the stage for part two, where we explore the use of HMMs in Modern Genetics, and possibly Katie's "Um Detector."
Monte Carlo for Physicists
This is another physics-centered podcast, about an ML-backed particle identification tool that we use to figure out what kind of particle caused a particular blob in the detector. But in this case, as in many cases, it looks hard at the outset to use ML because we don't have labeled training data. Monte Carlo to the rescue!
Monte Carlo (MC) is fake data that we generate for ourselves, usually following certain sets of rules (often a Markov chain; in physics we generate MC according to the laws of physics as we understand them) and since you generated the event, you "know" what the correct label is.
Of course, it's a lot of work to validate your MC, but the payoff is that then you can use Machine Learning where you never could before.