Awesome, not awesome.

#Awesome

“An autonomous vehicle stopped at a red light might suddenly roar into an intersection to avoid being struck by a human-driven car approaching too quickly from behind. It might even be able to stop the cross traffic and flip the lights to red as it enters the intersection… smart swarms acting in concert to achieve the global goal of preserving life.” — Matt Conway, frog Seattle. Learn More on frog >

#Not Awesome

“We now have a methodology to automate people in [white collar jobs]. What this means is that if I have a company, I may not fire people — companies tend to try to minimize firings. But I may dramatically slow the rate at which I hire new people and instead invest in automation.” — Jack Clark, OpenAI.Learn More on The Initialized Capital Blog >

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

What we’re reading.

1/ In a future where humans are more dependent on machines, the ability to learn, unlearn, and relearn skills will be critical to kicking ass at work. Learn More on Slack’s Blog >

2/ Uber bought a small, two-year old AI startup to help transform itself from a ride-hailing company into one that dominates a world of self-driving cars and trucks. Learn More on Wired >

3/ Apple breaks from it’s typical tight-lipped way of doing business to open up about the problems it’s using AI to solve. Learn More on Quartz >

4/ If Tech companies aren’t realistic with consumers about what AI can currently achieve, we’re going to shoot ourselves in the feet. Learn More on Wired >

5/ A famous Japanese film director humiliates a guy in front of a room of peers for using AI to model a zombie’s movement in a video game. Learn More on YouTube >

6/ OpenAI launches a software platform to train an AI’s intelligence on video games and apps, so that it can eventually use a computer like a human. Learn More on OpenAI Blog >

7/ A Cornell professor who fears people abusing machine learning built a guide on how to use it to create social good. Learn more of The Harvard Business Review >

In real life.

Unfortunately, machine learning algorithms, like humans, can be discriminatory. A few months ago, an article in the New York Times explored the implications for society if machine learning technology is only created by affluent men. That’s why my heart hurt on Friday when I saw an AI researcher tweet about how few women were at a major AI conference:

I wanted to take this opportunity to give credit to a few of the many awesome humans (who happen to be women) doing incredible work in AI/ML: Kate Crawford, Fei-Fei Lee, and Shivon Zilis. Feel free to follow them on Twitter, and send more awesome humans my way so they get a shoutout in future posts/emails! ✌️💪🤖❤️

Your turn.

Speaking of the New York Times, its Opinion Pages section is home to a spirited debate on human job security in the not-too-distant future.

Do you think human ambition will be enough to fend off total automation? Check out the discussion and make sure your voice is heard. 📢🙊🤖

Machine Learning (ML) and Artificial Intelligence (AI) are having a huge impact on our lives. We help people make sense of how AI & ML will impact their lives by curating and creating AI content that is accessible and just plain interesting.