Author Archive
It’s the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections. But while there are so many how-to training programs for hands-on techies, there are practically none that also serve business
Originally published in IEEE Spectrum, Nov 4, 2020. Based on a cellphone-recorded cough, machine learning models accurately detect coronavirus even in people with no symptoms. Again and again, experts have pleaded that we need more and faster...
By: James Vincent, Senior Reporter,
The Verge
Originally published in The Verge, Nov 20, 2020. AI researchers sometimes refer to machine learning technology as “brittle.” By this, they mean that artificial intelligence lacks a human’s understanding of real world complexities, with the end result...
David Kastelman, Data Scientist & Raghav Ramesh,
Machine Learning Engineer
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...
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...
By: Andy Greenberg,
WIRED
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...
By: Sherril Hayes, Executive Director, Analytics and Data Science Institute and Professor of Conflict Management, Analytics & Data Science Institute, College of Computing and Software Engineering,
Kennesaw State University
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...
Jennifer Chu, MIT News Office
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...
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,...
By: Sam Koslowsky, Senior Analytic Consultant,
Harte Hanks
Models predicting the potential spread of the COVID-19 pandemic have become a fixture of American life. Many of these models use typical demographic data, coupled with underlying medical conditions, infection rates, etc. Indeed, the spread of this...
