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 >
What we’re reading.
1/ The biggest (easiest) thing individuals can do to prevent society from racing towards a dystopian AI future is to think twice before handing their most precious data over to a Tech product. Learn More from MIT Technology Review >
2/ A leading expert in artificial intelligence believes that machine learning techniques are not on the trajectory to displace jobs that require compassion and or creativity. Learn More from Andreessen Horowitz >
3/ AI researchers are studying the brains of young children to in hopes of finding new ways to make machines smarter. Learn More from Vox >
4/ The US’ largest nonprofit dedicated to advocating for vaccinations had to stop posting their educational videos on YouTube — because each time they did, YouTube’s recommendation algorithm would suggest anti-vaccination conspiracy theories alongside them. Learn More from NBC News >
5/ For “AI-first” startups to grow, they should act like a normal startup — and not get too wrapped up in doing deeply technical research. Learn More from Matt Turck’s Blog >
6/ Agree with their positions or not, it’s great to see government leaders drafting policies in order to help people whose jobs are displaced by AI technologies. Learn More from Brookings >
7/ To preserve “our constitutional and legal freedoms,” we need to think trough the ehtical complexities that AI will introduce into our society. Learn More from Princeton University >
What we’re building.
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Links from the community.
“Machine Learning For Stronger Military Vehicles” submitted by Avi Eisenberger (@aeisenberger). Learn More from Texas A&M Today >
“Seven Myths in Machine Learning Research” submitted by Samiur Rahman (@samiur1204). Learn More from Oscar Chang’s blog >
“A Beginner’s Guide to Machine Learning” by Shaurya Bhandari (@shauryabhandari). Learn More from Noteworthy >
“Cancer is an intelligent killing machine — let’s defeat it with Artificial Intelligence” by Suleiman Khan (@suleiman.khan). Learn More from Noteworthy >
“The Butter Chicken Theory of Machine Learning” by Mohit Bindra (@bindra.mohit88). Learn More from Noteworthy >
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