Ai & ml

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

Um Detector 2: The Dynamic Time Warp

In this session of the Super Data Science Podcast, I chat with machine learning expert Hadelin de Ponteves about his work at Google, Canal+ and what career implications may come from the rapid growth of the Data Science field.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/2

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 002 : Machine Learning, Recommender Systems and The Future of Data with Hadelin de Ponteves

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.”

Relevant links:
https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45a6cea2b9c101761ea1b51c961628093ec1d5da.pdf

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

Picking a metric for a problem means defining how you’ll measure success in solving that problem. Which sounds important, because it is, but oftentimes new data scientists only get experience with a few kinds of metrics when they’re learning and those metrics have real shortcomings when you think about what they tell you, or don’t, about how well you’re really solving the underlying problem. This episode takes a step back and says, what are some metrics that are popular with data scientists, why are they popular, and what are their shortcomings when it comes to the real world? There’s been a lot of great thinking and writing recently on this topic, and we cover a lot of that discussion along with some perspective of our own.

Relevant links:
https://www.fast.ai/2019/09/24/metrics/
https://arxiv.org/abs/1909.12475
https://medium.com/shoprunner/evaluating-classification-models-1-ff0730801f17
https://hbr.org/2019/09/dont-let-metrics-undermine-your-business

Data scientists: beware of simple metrics

Dimensionality reduction redux: this episode covers UMAP, an unsupervised algorithm designed to make high-dimensional data easier to visualize, cluster, etc. It’s similar to t-SNE but has some advantages. This episode gives a quick recap of t-SNE, especially the connection it shares with information theory, then gets into how UMAP is different (many say better).

Between the time we recorded and released this episode, an interesting argument made the rounds on the internet that UMAP’s advantages largely stem from good initialization, not from advantages inherent in the algorithm. We don’t cover that argument here obviously, because it wasn’t out there when we were recording, but you can find a link to the paper below.

Relevant links:
https://pair-code.github.io/understanding-umap/
https://www.biorxiv.org/content/10.1101/2019.12.19.877522v1

Unsupervised Dimensionality Reduction: UMAP vs t-SNE

As demand for data scientists grows, and it remains as relevant as ever that practicing data scientists have a solid methodological and technical foundation for their work, higher education institutions are coming to terms with what’s required to educate the next cohorts of data scientists. The heterogeneity and speed of the field makes it challenging for even the most talented and dedicated educators to know what a data science education “should” look like.

This doesn’t faze Xiao-Li Meng, Professor of Statistics at Harvard University and founding Editor-in-Chief of the Harvard Data Science Review. He’s our interview guest in this episode, talking about the pedagogically distinct classes of data science and how he thinks about designing curricula for making anyone more data literate. From new initiatives in data science to dealing with data science FOMO, this wide-ranging conversation with a leading scholar gives us a lot to think about.

Relevant links: 
https://hdsr.mitpress.mit.edu/

Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

Put yourself in the shoes of an executive at a big legacy company for a moment, operating in virtually any market vertical: you’re constantly hearing that data science is revolutionizing the world and the firms that survive and thrive in the coming years are those that execute on a data strategy. What does this mean for your company? How can you best guide your established firm through a successful transition to becoming data-driven? How do you balance the momentum your firm has right now, and the need to support all your current products, customers and operations, against a new and relatively unknown future?

If you’re working as a data scientist at a mature and well-established company, these are the worries on the mind of your boss’s boss’s boss. The worries on your mind may be similar: you’re trying to understand where your work fits into the bigger picture, you need to break down silos, you’re often running into cultural headwinds created by colleagues who don’t understand or trust your work. Congratulations, you’re in the midst of a classic set of challenges encountered by innovation initiatives everywhere. Harvard Business School professor Clayton Christensen wrote a classic business book (The Innovator’s Dilemma) explaining the paradox of trying to innovate in established companies, and why the structure and incentives of those companies almost guarantee an uphill climb to innovate. This week’s episode breaks down the innovator’s dilemma argument, and what it means for data scientists working in mature companies trying to become more data-centric.

Thinking of data science initiatives as innovation initiatives

Data privacy is a huge issue right now, after years of consumers and users gaining awareness of just how much of their personal data is out there and how companies are using it. Policies like GDPR are imposing more stringent rules on who can use what data for what purposes, with an end goal of giving consumers more control and privacy around their data. This episode digs into this topic, but not from a security or legal perspective—this week, we talk about some of the interesting technical challenges introduced by a simple idea: a company should remove a user’s data from their database when that user asks to be removed. We talk about two topics, namely using Bloom filters to efficiently find records in a database (and what Bloom filters are, for that matter) and types of machine learning algorithms that can un-learn their training data when it contains records that need to be deleted.

Interesting technical issues prompted by GDPR and data privacy concerns

Recent research into neural networks reveals that sometimes, not all parts of the neural net are equally responsible  for the performance of the network overall. Instead, it seems like (in some neural nets, at least) there are smaller subnetworks present where most of the predictive power resides. The fascinating thing is that, for some of these subnetworks (so-called “winning lottery tickets”), it’s not the training process that makes them good at their classification or regression tasks: they just happened to be initialized in a way that was very effective. This changes the way we think about what training might be doing, in a pretty fundamental way. Sometimes, instead of crafting a good fit from wholecloth, training might be finding the parts of the network that always had predictive power to begin with, and isolating and strengthening them. This research is pretty recent, having only come to prominence in the last year, but nonetheless challenges our notions about what it means to train a machine learning model.

The Lottery Ticket Hypothesis

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

Covid-19 is turning the world upside down right now. One thing that’s extremely important to understand, in order to fight it as effectively as possible, is how the virus spreads and especially how much of the spread of the disease comes from carriers who are experiencing no or mild symptoms but are contagious anyway. This episode digs into the epidemiological model that was published in Science this week—this model finds that the data suggests that the majority of carriers of the coronavirus, 80-90%, do not have a detected disease. This has big implications for the importance of social distancing of a way to get the pandemic under control and explains why a more comprehensive testing program is critical for the United States. 

Also, in lighter news, Katie (a native of Dayton, Ohio) lays a data-driven claim for just declaring the University of Dayton flyers to be the 2020 NCAA College Basketball champions.

Relevant links:
https://science.sciencemag.org/content/early/2020/03/13/science.abb3221

Understanding Covid-19 transmission: what the data suggests about how the disease spreads

Many of us have the privilege of working from home right now, in an effort to keep ourselves and our family safe and slow the transmission of covid-19. But working from home is an adjustment for many of us, and can hold some challenges compared to coming in to the office every day. This episode explores this a little bit, informally, as we compare our new work-from-home setups and reflect on what’s working well and what we’re finding challenging.

The work-from-home episode

AI is evolving incredibly quickly, and thinking now about where it might go next (and how we as a species and a society should be prepared) is critical. Professor Stuart Russell, an AI expert at UC Berkeley, has a formulation for modifications to AI that we should study and try implementing now to keep it much safer in the long run. Prof. Russell’s new book, “Human Compatible: Artificial Intelligence and the Problem of Control” gives an accessible but deeply thoughtful exploration of why he thinks runaway AI is something we need to be considering seriously now, and what changes in formulation might be a solution. This episodes features Prof. Russell as a special guest, exploring the topics in his book and giving more perspective on the long-term possible futures of AI: both good and bad.

Relevant links:
https://www.penguinrandomhouse.com/books/566677/human-compatible-by-stuart-russell/

Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell

The abundance of data in healthcare, and the value we could capture from structuring and analyzing that data, is a huge opportunity. It also presents huge challenges. One of the biggest challenges is how, exactly, to do that structuring and analysis—data scientists working with this data have hundreds or thousands of small, and sometimes large, decisions to make in their day-to-day analysis work. What data should they include in their studies? What method should they use to analyze it? What hyperparameter settings should they explore, and how should they pick a value for their hyperparameters? The thing that’s really difficult here is that, depending on which path they choose among many reasonable options, a data scientist can get really different answers to the underlying question, which makes you wonder how to conclude anything with certainty at all.

The paper for this week’s episode performs a systematic study of many, many different permutations of the questions above on a set of benchmark datasets where the “right” answers are known. Which strategies are most likely to yield the “right” answers? That’s the whole topic of discussion.

Relevant links:
https://hdsr.mitpress.mit.edu/pub/fxz7kr65

Keeping ourselves honest when we work with observational healthcare data

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.

Stein's Paradox

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

An interstitial word from Ben

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

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.”

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.

Ai & ml

Claus Birkebæk Kristensen