The AI-related stories landing in your Facebook and Twitter feeds are mostly about doomsday Artificial General Intelligence scenarios. While interesting, they distract from the more pressing issue at hand.

AI-driven automation will create new jobs and help people to be more productive, but the painful truth is that AI-driven automation of both specific work activities and entire jobs will be incredibly disruptive to hundreds of millions of people around the globe. The McKinsey Global institute and The Obama Whitehouse, respectively, believe that “60% of all occupations have at least 30% of activities that are technically automate-able,” and “47% of U.S. jobs are at risk of being replaced by AI technologies” over the next 10–20 years.

When a company stands to gain enormous financial benefits from automating a certain work task, and said task is automate-able with current artificial intelligence techniques, you can expect for it to be automated quickly. If a job consists mostly doing that single task, you can expect people to be replaced, rather than complemented, by AI software. While automation is incredibly painful for us who lose jobs, it can simultaneously improve the quality of life, and in some cases save, the lives of hundreds of millions of people.

Learn more about the activities, jobs, and industries that experts predict to experience the impacts of AI-driven automation in the next 5 years in “WTF is Artificial Intelligence?”

“What else are you working on besides Machine Learnings?” Usejournal.com

Awesome, not awesome.

#Awesome

“Using cameras with facial recognition software and other biometric indicators, automakers are looking to personalize the driving experience with cars that stare back at you, quietly adjusting seats and driving modes…Engineers can then dynamically adjust the so-called human machine interface, putting critical information, say, about a stalled car up ahead, or the fact that you are about to exceed the speed limit, directly in a driver’s line of sight on the dashboard or in a display on the windshield.” — John Quain, Reporter. Learn More on The New York Times >

#Not Awesome

“There’s some algorithm somewhere that predicted, hey, for this user right now who is experimental subject 79B3 in experiment 231, we think we can see an improvement in his behavior if you give it to him in this burst instead of that burst. You’re part of a controlled set of experiments that are happening in real time across you and millions of other people…You’re guinea pigs. You are guinea pigs in the box pushing the button and sometimes getting the likes. And they’re doing this to keep you in there. The longer we look at our screens, the more data companies collect about us, and the more ads we see.” — Ramsay Brown, Founder. Learn More on 60 Minutes >

Join 13,000+ readers and subscribe to the 🤖Machine Learnings🤖 newsletter to feel prepared for the future.

What we’re reading.

1/ In a future where more jobs and work activities are automated by artificial intelligence, the people who master “soft” skills will be the most successful:

2/ Canada pledges $93 million to support AI research, reboot its Tech industry, and convince top talent to leave Google, Facebook, and Apple:

3/ Automakers, not drivers or car owners, will have to cover the costs of auto insurance when driverless cars dominate the road:

4/ Machine learning algorithms will soon be better than doctors at spotting strokes before they ravage a brain, but doctors fear these algorithms may ravage the medical field too:

5/ No matter how powerful an AI algorithm is, the user experience will ultimately determine how valuable it is for a business:

6/ A recent report on the progress of self-driving car technology shows Ford and General Motors leading the pack while Uber and Honda are being left in the dust:

7/ Scientists think brain scans and machine learning will one day make it possible for doctors to treat patients with “precision psychiatry” and reduce the feeling of demoralization cause by failed treatments:

Links from the community.

“The Boundaries of Artificial Emotional Intelligence” submitted by Avi Eisenberger Eisenberger (@aeisenberger). Learn More on How We Get to Next >

“Artificial Intelligence, Deep Learning, and Neural Networks Explained” by Alex Castrounis (@innoarchitech). Learn More on InnoArchiTech >

“Understanding the Limits of Deep Learning” by Mariya Yao (@thinkmariya). Learn more on TOPBOTS >

Artificial Intelligence & Machine Learning will radically change the way we work and live. Machine Learnings covers the most remarkable news in AI, so you’ll feel prepared for the future.