AI, unlivable wages, and a split workforce

70

Awesome, not awesome.
#Awesome
“DeepMind specializes in “deep learning,” a type of artificial intelligence that is rapidly changing drug discovery science. A growing number of companies are applying similar methods to other parts of the long, enormously complex process that produces new medicines. These A.I. techniques can speed up many aspects of drug discovery and, in some cases, perform tasks typically handled by scientists.” — Cade Metz, Technology Correspondent Learn More from The New York Times >

#Not Awesome
“By merely relying on historical data and current definitions of fairness, we will lock in the accumulated unfairnesses of the past, and our algorithms and the products they support will always trail the norms, reflecting past norms rather than future ideals and slowing social progress rather than supporting it.” — Joi Ito, Director of the MIT Media Lab. Learn More from WIRED >

What we’re reading.

1/ Certain “categories of labor” may not be able to earn livable wages because of task automation facilitated by machine learning. Learn More from The New York Times >

2/ Many people believe that financial firms will start to act more like tech firms over time, but if companies like Google and Facebook have the data and technological prowess to make investment decisions, maybe they’ll start looking more like financial firms. Learn More from Logic >

3/ Thanks to machine learning, your camera will begin to understand the pictures you take, help you to discover more images like them, and remember the ones you forget. Learn More from Benedict Evans >

4/ Major news organizations are experimenting with using machine generated text throughout their journalistic process — from writing articles themselves to transcribing interviews to personalizing newsletters. Learn More from The New York Times >

5/ When it comes to all the video footage captured by self-driving cars, autonomous vehicle companies say they take privacy ‘very seriously,’ but privacy experts worry that they aren’t taking bystanders’ (people outside of the car) rights seriously. Learn More from Axios >

6/ A San Francisco lawmaker wants to ban local government agencies from using facial recognition software — but won’t ask private companies to do the same. Learn More from The Atlantic >

7/ Inertia within the Defense Department may keep the US military from making truly breakthrough leaps in AI technology. Learn More from Axios >

Links from the community.

“Why Captchas have gotten so difficult” submitted by Cecelia Shao (@ceceliashao). Learn More from The Verge >

“Can we build AI without losing control over it? | Sam Harris” submitted by Will Jessop (@willjessop). Learn More from YouTube >

“Machine Learning for Everyone” submitted by Avi Eisenberger (@aeisenberger). Learn More from vas3k Blog >

“Our Extended Minds” by KS Abhinav (@ksabhinav38). Learn More from Noteworthy >

“Stock Market Prediction by Recurrent Neural Network on LSTM Model” by Aniruddha Choudhury (@aniruddha.choudhury94). Learn More from Noteworthy >

“Swift Text-To-Speech tool AVSpeechSynthesizer” by Myrick Chow (@myrickchow32). Learn More from Noteworthy >

“Quantifying Accuracy and SoftMax Prediction Confidence For Making Safe and Reliable Deep Neural Network Based AI System” by AiOTA Labs (@aiotalabs). Learn More from Noteworthy >

“AI Technology & Effect.ai | Easy” by Melicio Sergio de Bel (@meliciosergiobel). Learn More from Noteworthy >

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