From DSC:<\/span> How Amazon is tackling the A.I. talent crunch<\/strong> <\/a>— from fortune.com by Jonathan Vanian<\/p>\n Excerpt:<\/em><\/p>\n \u201cOne way Amazon has adapted to the tight labor market is to require potential new programming hires to take classes in machine learning, said Bratin Saha, a vice president and general manager of machine learning services at Amazon. The company’s executives believe they can teach these developers machine learning basics over a few weeks so that they can work on more cutting-edge projects after they’re hired.\u201d Machine Learning Can Predict Rapid Kidney Function Decline<\/strong><\/a> — from sicklecellanemianews.com by Steve Bryson PhD; with thanks to Sam DeBrule for this resource<\/p>\n Excerpt:<\/em><\/p>\n Machine learning tools can identify sickle cell disease (SCD) patients at high risk of progressive kidney disease as early as six months in advance, a study shows.\u00a0 The study, \u201cUsing machine learning to predict rapid decline of kidney function in sickle cell anemia,\u201d was published in the journal\u00a0eJHaem.<\/p>\n NYPD\u2019s Sprawling Facial Recognition System Now Has More Than 15,000 Cameras<\/strong><\/a> — from vice.com by Todd Feathers; with thanks to Sam DeBrule for this resource Excerpt:<\/em><\/p>\n The New York City Police Department has built a sprawling facial recognition network that may include more than 15,000 surveillance cameras in Manhattan, Brooklyn, and the Bronx, according to a massive\u00a0crowdsourced investigation\u00a0by Amnesty International.
\nAgain, as you can see from the items below…there are various plusses and minuses regarding the use of Artificial Intelligence (AI). <\/span><\/strong>Some of the items below are neither positive or negative, but I found them interesting nonetheless.<\/span><\/p>\n
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\n\u201cThese are not formal college courses, and Saha said the recruits aren\u2019t graded like they would be in school. Instead, the courses are intended to give new developers a foundation in machine learning and statistics so they can understand the theoretical underpinnings.\u201d<\/p>\n
\nThe massive camera network is concentrated in predominantly Black and brown neighborhoods, according to a new crowdsourced report.<\/em><\/p>\n
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\n\u201cThis sprawling network of cameras can be used by police for invasive facial recognition and risk turning New York into an Orwellian surveillance city,\u201d Matt Mahmoudi, an artificial intelligence and human rights researcher at Amnesty, wrote in the group\u2019s report. \u201cYou are never anonymous. Whether you\u2019re attending a protest, walking to a particular neighbourhood, or even just grocery shopping\u2014your face can be tracked by facial recognition technology using imagery from thousands of camera points across New York.\u201d<\/p>\n