Awesome, not awesome.
#Awesome
"In search of a solution to this problem [of the variable nature of wind as a renewable energy source], last year DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms—part of Google’s global fleet of renewable energy projects—collectively generate as much electricity as is needed by a medium-sized city... Although we continue to refine our algorithm, our use of machine learning across our wind farms has produced positive results. To date, machine learning has boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to the grid." - Carl Elkin, Sims Witherspoon and Will Fadrhonc of DeepMind and Google Learn More from DeepMind >
#Not Awesome
"Researchers at the Georgia Institute of Technology found that state-of-the-art object recognition systems are less accurate at detecting pedestrians with darker skin tones...The researchers tested eight image-recognition systems (each trained on a standard data set) against a large pool of pedestrian images. They divided the pedestrians into two groups for lighter and darker skin tones according to the Fitzpatrick skin type scale, a way of classifying human skin color...The detection accuracy of the systems was found to be lower by an average of five percentage points for the group with darker skin. This held true even when controlling for time of day and obstructed view.” - Karen Hao, Reporter. Learn More from MIT Technology Review >
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