Data Science Roundup #86: AI in Medicine, Opportunity Sizing @ Etsy & Deep Learning Advice.
Unambiguously positive results in medicine and education. A great talk from Etsy, practical advice for deep learning projects, and more. Solid week. Enjoy đ
- Tristan
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Two Posts You Can't Miss
Applying Artificial Intelligence in Medicine: Our Early Results
Two quotes:
One year ago, we teamed up with UCSF Cardiology to start the mRhythm study, which 6,158 Cardiogram users enrolled in. Cardiogram trained a deep neural network on the Apple Watchâs heart rate readings and was able to obtain an AUC of 0.97, enabling us to detect atrial fibrillation with 98.04% sensitivity and 90.2% specificity.
The most promising finding of our study is that consumer-grade wearables can be used to detect disease.
Hardware and algorithm improvements in wearables will be tightly coupled, and it only takes a couple of consumer-grade successes to get the flywheel started.
Duolingo: How We Learn How You Learn
This is a fascinating use case. Duolingo took a piece of 100-year-old research on educational theory called the âforgetting curveâ and turned it into an algorithm that now sits at the heart of their product. The intuition is simple: student retention occurs best when practice is spaced out over time. Not shocking to anyone who has ever crammed for a test.
What I find so interesting about this piece is that the solution wasnât particularly hard. That is not at all to say that it wasnât challenging, but it didnât take a millennium of GPU time or fundamental breakthroughs in methods. It was a bunch of smart people thinking hard about how to apply existing techniques to a very specific, high-value domain. This will be increasingly common as data science moves further into the deployment phase.
This Week's Top Posts
Etsy: An Intro to Opportunity Sizing
The author walks through Etsyâs process for opportunity sizing. If youâve never spent much time thinking about opportunity sizing, this is a must-read. Itâs quite likely that many of the projects you engage in just arenât that valuable.
datadriven.club ⢠Share
Questions & Intuition for Tackling Deep Learning Problems
In this article, Iâd like to recount five key lessons that Iâve learned after one too many walks down dead alleyways.
Very, very good. If youâre thinking about tackling a problem using deep learning, read this first.
This microsite is intended to help newcomers (both non-technical and technical) begin exploring whatâs possible with AI. Weâve met with hundreds of Fortune 500 / Global 2000 companies, startups, and government policy makers asking: âHow do I get started with artificial intelligence?â and âWhat can I do with AI in my own product or company?â This site is designed as a resource for anyone asking those questions, complete with examples and sample code to help you get started.
Great resource, maybe for you, definitely for people you work with.
The Cost of Doing Data Science on Laptops
This article makes a great point: itâs just not a great idea to train a model on your laptop if youâre doing anything even moderately sophisticated. Using the cloud is just too easy and too cheap today.
While this is obviously content marketing for Domino (theyâre selling a cloud-based data science product), that doesnât make it any less true. Sure, use DominoâŚor just spend a couple of hours getting off the ground with AWS.
blog.dominodatalab.com ⢠Share
The Behance Artistic Media Dataset
This is a gold mine. Amazing images, strong classifier, really straightforward to access. Iâm not sure whether this is more useful for making memes or as training data, but this data set is one-of-a-kind.
bam-dataset.org ⢠Share
A Deep Reinforced Model for Abstractive Summarization
Abstractive summarizationâlast covered in #49â remains only partially solved. Existing models donât perform well enough to eliminate humans in critical use cases. MetaMind just released research that improves upon existing modelsâ performance, getting us one step closer.
If youâre new to abstractive summarization, this post is a great intro.
metamind.io ⢠Share
From Physics to Finance: My First Year in Industry
The author has two masterâs, a doctorate, and a post-doc fellowship under his belt, but after making the switch he has this to say:
According to Vitae, only 18% of the young researchers who made this transition would go back to academia. I am confident I am one of the 82%.
Good read if youâre considering your options.
Data Viz of the week
"What parties support what policies?" Perfect answer.
Thanks to our sponsors!
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End note:
Iâm attending CogX London 2017, a conference which will explore the impact of AI across industry. I have a 50% discount off the Early Bird tickets for Data Science Roundup readers. Just visit and use the code 877f!5ht0wn50.
Let me know if you plan on being there!
The internet's most useful data science articles. Curated with â¤ď¸ by Tristan Handy.
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