February 11, 2020 – Vildana Hajric – Bloomberg
“Fresh signs of robot supremacy are emerging on Wall Street in the form of machine stock analysts that make more profitable investment choices than humans.”
January 1, 2020 – Denise Grady – The New York Times
“Unlike humans, computers do not get tired, bored or distracted toward the end of a long day of reading mammograms…But in some instances, A.I. missed a cancer that all six radiologists found — and vice versa.”
December 11, 2019 – Victor Tangermann – Futurism
“MRI scans are able to spot any disruption in this network — but recognizing the patterns that could indicate a case of ADHD is much more difficult.”
August 5, 2019 – William D. Eggers, Amrita Datar, David Parent & Jenn Gustetic – Deloitte
“As humans and intelligent machines working together becomes the norm in the workplace, organizations have an opportunity to maximize the potential of both.”
July 21, 2018 – Alison Snyder – Axios
“As the workforce ages in some countries and middle-aged workers are in short supply, industrial robots are stepping in to perform their jobs.”
November 19, 2019 – Rick Wartzman – Fast Company
“Some 25 million workers in the U.S. stand to be touched the most by AI. That’s about 15% of the nation’s labor force.”
11/14/19 – Andrew Yang – The New York Times
“Automation doesn’t just affect millions of factory workers and truck drivers. Bookkeepers, journalists, retail and food service workers, office clerks, call center employees and even teachers also face the threat of being replaced by machines.”
10/31/19 – Wired – Sara Harrison
“In the study, researchers divided the listings into three groups based on the advertised pay, then examined how different tasks were being valued. What they found is that how we value tasks may be starting to change.”
October 24, 2019 – Danielle Kurtzleben – NPR
“The question of whether trade or automation affects employment more not only divides economists, but appears set to drive major questions in the Democratic primary.”
October 15, 2018 – Charlotte Jee – MIT Technology Review
“The firm’s deep-learning tool was able to correctly distinguish metastatic cancer 99% of the time, a greater accuracy rate than human pathologists.”