Machine learning and particle physics go together like peanut butter and jelly--but this is a relatively new development.  

For many decades, physicists looked through their fairly large datasets using the laws of physics to guide their exploration; that tradition continues today, but as ever-larger datasets get made, machine learning becomes a more tractable way to deal with the deluge.  

With this in mind, ATLAS (one of the major experiments at CERN, the European Center for Nuclear Research and home laboratory of the recently discovered Higgs boson) ran a machine learning contest over the summer, to see what advances could be found from opening up the dataset to non-physicists.  

The results were impressive--physicists are smart folks, but there’s clearly lots of advances yet to make as machine learning and physics learn from one another.  And who knows--maybe more Nobel prizes to win as well!

https://www.kaggle.com/c/higgs-boson

Hunting for the Higgs

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.

Inferring Authorship (Part 2)

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 used to do object recognition in photographs.

Neural Nets (Part 1)

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.

Careers in Data Science

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

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

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…

Reinforcement Learning for Artificial Intelligence

When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you estimate it: use measurements of the individual, or get some extra information from the group?  The James-Stein estimator tells you how to combine individual and group information make predictions that, taken over the whole group, are more accurate than if you treated each individual, well, individually.

Stein's Paradox

A few weeks ago, we podcasted about a neural network that was being touted as "better than doctors" in diagnosing pneumonia from chest x-rays, and how the underlying dataset used to train the algorithm raised some serious questions. We're back again this week with further developments, as the author of the original blog post pointed us toward more developments. All in all, there's a lot more clarity now around how the authors arrived at their original "better than doctors" claim, and a number of adjustments and improvements as the original result was de/re-constructed. 

Anyway, there are a few things that are cool about this. First, it's a worthwhile follow-up to a popular recent episode. Second, it goes *inside* an analysis to see what things like imbalanced classes, outliers, and (possible) signal leakage can do to real science. And last, it raises a really interesting question in an age when computers are often claimed to be better than humans: what do those claims really mean?

Relevant links:
https://lukeoakdenrayner.wordpress.com/2018/01/24/chexnet-an-in-depth-review/

What makes a machine learning algorithm "superhuman"?

This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on.

We have several goals in data science project selection at Civis Analytics (where I work), which can be summarized under "balance the best attributes of bottom-up and top-down decision-making." We achieve this balance, or at least get pretty close, using a process we've come to call the Idea Factory (after a great book about Bell Labs). This talk is about that process, how it works in the real world of a data science company and how we see it working in the data science programs of other companies.

Relevant links: https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63905

How to pick projects for a professional data science team

When you build a model for natural language processing (NLP), such as a recurrent neural network, it helps a ton if you’re not starting from zero. In other words, if you can draw upon other datasets for building your understanding of word meanings, and then use your training dataset just for subject-specific refinements, you’ll get farther than just using your training dataset for everything. This idea of starting with some pre-trained resources has an analogue in computer vision, where initializations from ImageNet used for the first few layers of a CNN have become the new standard. There’s a similar progression under way in NLP, where simple(r) embeddings like word2vec are giving way to more advanced pre-processing methods that aim to capture more sophisticated understanding of word meanings, contexts, language structure, and more.

Relevant links:
https://thegradient.pub/nlp-imagenet/

Pre-training language models for natural language processing problems

We often hear from folks wondering what advice we can give them as they search for their first job in data science. What does a hiring manager look for? Should someone focus on taking classes online, doing a bootcamp, reading books, something else? How can they stand out in a crowd? 

There’s no single answer, because so much depends on the person asking in the first place, but that doesn’t stop us from giving some perspective. So in this episode we’re sharing that advice out more widely, so hopefully more of you can benefit from it.

Advice to those trying to get a first job in data science

What do you get when you cross a support vector machine with matrix factorization?  You get a factorization machine, and a darn fine algorithm for recommendation engines.

Re - Release: Factorization Machines

The topic of bias in word embeddings gets yet another pass this week. It all started a few years ago, when an analogy task performed on Word2Vec embeddings showed some indications of gender bias around professions (as well as other forms of social bias getting reproduced in the algorithm’s embeddings). We covered the topic again a while later, covering methods for de-biasing embeddings to counteract this effect. And now we’re back, with a second pass on the original Word2Vec analogy task, but where the researchers deconstructed the “rules” of the analogies themselves and came to an interesting discovery: the bias seems to be, at least in part, an artifact of the analogy construction method. Intrigued? So were we…

Relevant link:
https://arxiv.org/abs/1905.09866

Revisiting Biased Word Embeddings

We talk often about which features in a dataset are most important, but recently a new paper has started making the rounds that turns the idea of importance on its head: Data Shapley is an algorithm for thinking about which examples in a dataset are most important. It makes a lot of intuitive sense: data that’s just repeating examples that you’ve already seen, or that’s noisy or an extreme outlier, might not be that valuable for using to train a machine learning model. But some data is very valuable, it’s disproportionately useful for the algorithm figuring out what the most important trends are, and Data Shapley is explicitly designed to help machine learning researchers spend their time understanding which data points are most valuable and why.

Relevant links:
http://proceedings.mlr.press/v97/ghorbani19c/ghorbani19c.pdf
https://blog.acolyer.org/2019/07/15/data-shapley/

Data Shapley

When data science is hard, sometimes it’s because the algorithms aren’t converging or the data is messy, and sometimes it’s because of organizational or business issues: the data scientists aren’t positioned correctly to bring value to their organization. Maybe they don’t know what problems to work on, or they build solutions to those problems but nobody uses what they build. A lot of this can be traced back to the way the team is organized, and (relatedly) how it interacts with the rest of the organization, which is what we tackle in this issue. There are lots of options about how to organize your data science team, each of which has strengths and weaknesses, and Pardis Noorzad wrote a great blog post recently that got us talking.

Relevant links: https://medium.com/swlh/models-for-integrating-data-science-teams-within-organizations-7c5afa032ebd

Organizational Models for Data Scientists

Data science management isn’t easy, and many data scientists are finding themselves learning on the job how to manage data science teams as they get promoted into more formal leadership roles. O’Reilly recently release a report, written by yours truly (Katie) and another experienced data science manager, Michelangelo D’Agostino, where we lay out the most important tasks of a data science manager and some thoughts on how to unpack those tasks and approach them in a way that makes a new manager successful. This episode is an interview episode, the first of three, where we discuss some of the common paths to data science management and what distinguishes (and unifies) different types of data scientists and data science teams.

Relevant links:
https://oreilly-ds-report.s3.amazonaws.com/Care_and_Feeding_of_Data_Scientists.pdf

The Care and Feeding of Data Scientists: Becoming a Data Science Manager

When you want to understand if doing something causes something else to happen, like if a change to a website causes and dip or rise in downstream conversions, the gold standard analysis method is to use randomized controlled trials. Once you’ve properly randomized the treatment and effect, the analysis methods are well-understood and there are great tools in R and python (and other languages) to find the effects. However, when you’re operating at scale, the logistics of running all those tests, and reaching correct conclusions reliably, becomes the main challenge—making sure the right metrics are being computed, you know when to stop an experiment, you minimize the chances of finding spurious results, and many other issues that are simple to track for one or two experiments but become real challenges for dozens or hundreds of experiments. Nonetheless, the reality is that there might be dozens or hundreds of experiments worth running. So in this episode, we’ll work through some of the most important issues for running experiments at scale, with strong support from a series of great blog posts from Airbnb about how they solve this very issue.

For some blog post links relevant to this episode, visit lineardigressions.com

Varsity A/B Testing

This is a re-release of an episode that originally ran on October 21, 2018.

The Poisson distribution is a probability distribution function used to for events that happen in time or space. It’s super handy because it’s pretty simple to use and is applicable for tons of things—there are a lot of interesting processes that boil down to “events that happen in time or space.” This episode is a quick introduction to the distribution, and then a focus on two of our favorite everyday applications: using the Poisson distribution to identify supernovas and study army deaths from horse kicks.

Better know a distribution: the Poisson distribution

When you need to untangle cause and effect, but you can’t run an experiment, it’s time to get creative. This episode covers difference in differences and synthetic controls, two observational causal inference techniques that researchers have used to understand causality in complex real-world situations.

Causal inference when you can't experiment: difference-in-differences and synthetic controls

It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most appropriate—linear? quadratic? sinusoidal? some combination of these, and perhaps others? Gaussian processes introduce a nonparameteric option where you can fit over all the possible types of functions, using the data points in your datasets as constraints on the results that you get (the idea being that, no matter what the “true” underlying function is, it produced the data points you’re trying to fit). What this means is a very flexible, but depending on your parameters not-too-flexible, way to fit complex datasets.

The math underlying GPs gets complex, and the links below contain some excellent visualizations that help make the underlying concepts clearer. Check them out!

Relevant links:
http://katbailey.github.io/post/gaussian-processes-for-dummies/
https://thegradient.pub/gaussian-process-not-quite-for-dummies/
https://distill.pub/2019/visual-exploration-gaussian-processes/

Gaussian Processes

You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the information in the underlying data using just visual cues. It’s a bit abstract but very profound, and these principles underlie the ggplot2 package in R that makes famously beautiful plots with minimal code. This episode covers a paper by Hadley Wickham (author of ggplot2, among other R packages) that unpacks the layered approach to graphics taken in ggplot2, and makes clear the assumptions and structure of many familiar data visualizations.

The Grammar Of Graphics

This is a re-release of an episode that was originally released on February 26, 2017.

When you're estimating something about some object that's a member of a  larger group of similar objects (say, the batting average of a baseball  player, who belongs to a baseball team), how should you estimate it: use  measurements of the individual, or get some extra information from the  group?  The James-Stein estimator tells you how to combine individual  and group information make predictions that, taken over the whole group,  are more accurate than if you treated each individual, well,  individually.

This is a re-release of an episode that originally aired on April 1, 2018

If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

Convolutional Neural Networks

A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.

An interstitial word from Ben

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. 

We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fairness” of the predictions and what is causing its racially disparate output (to wit: race is explicitly not an input to the algorithm, and yet the algorithm gives outputs that correlate with race—what gives?) Unfortunately it’s not an open-and-shut case of a tuning parameter being off, or the wrong metric being used: instead the biases in the justice system itself are being captured in the algorithm outputs, in such a way that a self-fulfilling prophecy of harsher treatment for black defendants is all but guaranteed. Like many other things this week, this episode left us thinking about bigger, systemic issues, and why it’s proven so hard for years to fix what’s broken.

Racism, the criminal justice system, and data science

This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after our last episode (about racially complicated recidivism algorithms). Our conversation covers a wide range of topics—common misconceptions around race and crime statistics, how methodologically-driven criminology scholars think about building crime prediction models, and how to think about policy changes when we don’t have a complete understanding of cause and effect in criminology. For the many of us currently re-thinking race and criminal justice, but wanting to be data-driven about it, this conversation with Zach is a must-listen.

Criminology and Data Science

This is a re-release of an episode that first ran on January 29, 2017.

This  week: everybody's favorite WWII-era classifier metric!  But it's not  just for winning wars, it's a fantastic go-to metric for all your  classifier quality needs.

Rock the ROC Curve

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben

Linear Digressions

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