Machine Learning Times
Machine Learning Times
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Switchback Tests and Randomized Experimentation Under Network Effects at DoorDash

 Originally published in DoorDash Engineering Feb 13, 2018. To A/B or not to A/B, that is the question Overview On the Dispatch team at DoorDash, we use simulation, empirical observation, and experimentation to make progress towards our goals; however, given the systemic nature of many of our products, simple A/B tests are often ineffective due

What Twitter Learned From The Recsys 2020 Challenge

 Originally published in Towards Data Science on Oct 26, 2020. This year, Twitter sponsored the RecSys 2020 Challenge, providing a large dataset of user engagements. In this post, we describe the challenge and the insights we had...

Split-Second ‘Phantom’ Images Can Fool Tesla’s Autopilot

 Originally posted to Wired.com, Oct 11, 2020. Researchers found they could stop a Tesla by flashing a few frames of a stop sign for less than half a second on an internet-connected billboard. Safety concerns over automated...

Ethical Machine Learning as a Wicked Problem

 In the 1950 and 1960s, social and behavioral sciences were at the cutting edge of innovation. Scientific techniques and quantitative analyses were being applied to some of the most pressing social problems. The thinking was “If NASA...

Artificial Intelligence Model Detects Asymptomatic Covid-19 Infections Through Cellphone-Recorded Coughs

 Originally published in MIT News, Oct 29, 2020. Results might provide a convenient screening tool for people who may not suspect they are infected. Asymptomatic people who are infected with Covid-19 exhibit, by definition, no discernible physical...

Six Ways Machine Learning Threatens Social Justice

 Originally published in Big Think When you harness the power and potential of machine learning, there are also some drastic downsides that you’ve got to manage. Deploying machine learning, you face the risk that it be discriminatory,...

AI Can Help Patients—but Only If Doctors Understand It

 Originally published in Wired.com, October 2, 2020. Algorithms can help diagnose a growing range of health problems, but humans need to be trained to listen. Nurse Dina Sarro didn’t know much about artificial intelligence when Duke University...

The Computational Limits of Deep Learning Are Closer Than You Think

 Originally posted to DiscoverMagazine, July 24, 2020. Deep learning eats so much power that even small advances will be unfeasible give the massive environmental damage they will wreak, say computer scientists. Deep in the bowels of the...

Traffic Prediction With Advanced Graph Neural Networks

  By partnering with Google, DeepMind is able to bring the benefits of AI to billions of people all over the world.  From reuniting a speech-impaired user with his original voice, to helping users discover personalised apps,...

Multi-Armed Bandits and the Stitch Fix Experimentation Platform

 Multi-armed bandits have become a popular alternative to traditional A/B testing for online experimentation at Stitch Fix. We’ve recently decided to extend our experimentation platform to include multi-armed bandits as a first-class feature. This post gives an...

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