Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...
Effective Machine Learning Needs Leadership — Not AI Hype
 Originally published in BigThink, Feb 12, 2024.  Excerpted from The...

Machine Learning

How to Make Artificial Intelligence Less Biased

 Originally published in The Wall Street Journal, Nov 3, 2020. AI systems can unfairly penalize certain segments of the population—especially women and minorities. Researchers and tech companies are figuring out how to address that. As artificial intelligence spreads into more areas of public and private life, one thing has become abundantly clear: It can be

Walmart is Giving Up On Shelf-scanning Robots in Favor of Humans

 Originally published in The Verge, Nov 3, 2020. The retail giant has ended a contract with Bossa Nova Robotics. Retail robots that can scan shelves and update inventory have been one of the most visible faces of...

Machine Learning: Business Leaders Must Take an Enlightening Look Under Its Hood (New Training Program)

  In this article, I identify unmet learner needs that are addressed by my business-oriented machine learning course series, Machine Learning for Everyone. Machine learning runs the world. It drives millions of business-critical decisions more effectively, guided...

Train Your Team to Avoid This ML Management Pitfall and Unite the Business and Tech Sides

  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...

AI Recognizes COVID-19 in the Sound of a Cough

 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...

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...

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...

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