Appalling behavior at a machine learning conference

December 18, 2017 Sam DeBrule

Awesome, not awesome.

“Scientists using advanced “neural network” machine-learning software to sift through thousands of weak, previously unstudied signals from NASA’s planet-hunting Kepler spacecraft have identified two new worlds orbiting distant suns…”What’s different about this discovery is that we used machine learning to help identify planets that were missed by previous searches of the Kepler data,” said Christopher Shallue, a senior software engineer at Google AI in Mountain View, Calif.” The key contribution of machine learning here is it was able to search a much larger number of signals than humans would have been able to do in a reasonable amount of time.”— William Harwood, News Space Analyst Learn More on CBS News >

#Not Awesome
“There’s a video of Gal Gadot having sex with her stepbrother on the internet. But it’s not really Gadot’s body, and it’s barely her own face. It’s an approximation, face-swapped to look like she’s performing in an existing incest-themed porn video. The video was created with a machine learning algorithm, using easily accessible materials and open-source code that anyone with a working knowledge of deep learning algorithms could put together.” — Samantha Cole, Editor Learn More on Motherboard > (NSFW)

What we’re reading.

1/ At NIPS, the largest conference for machine learning, a prominent researcher is alleged to have made appalling jokes about sexual assault. Learn More on Medium >

2/ If you feel like Netflix “just gets you,” you’re on to something. They use machine learning to personalize the title artwork they show you — whether it’s highlighting “an actor you recognize…or a dramatic scene that conveys the essence of a movie or TV show” — so you’re more likely to start watching. Learn More on The Netflix Tech Blog >

3/ If you want to understand what machine learning is, how it works, its applications, and what the future holds, this presentation by a senior creative engineer at Google is essential reading. Learn More on Google Slides >

4/ Researchers and tech companies use machine learning to understand the language people use on social media and their interaction with devices to identify suicidal thoughts in a way that no survey can. Learn More on Scientific American >

5/ Parents, worried that their young children will eventually work for robots or not at all because of robots, are coming up with strategies to automation-proof their kids. Learn More on The New York Times >

6/ Once forced to travel around the globe to find interesting startups, some venture capitalists now let machine learning algorithms do the heavy lifting in filtering out companies before making an investment decision. Learn More on Financial Times >

7/ Despite major advances in machine learning, employment in the US remains high and productivity low. Some will point to AI being overhyped, but it’s starting to look like we’re experiencing a “brief lull before an explosion of new technology” that shakes our world. Learn More on Tim Harford >

What we’re building.

(gif via: twitter/@nasa)

From enabling the discovery of new solar systems to the personalization of movie and show artwork, machine learning is the foundation for products that will reshape our world.

Like @Journal on Facebook to see awesome real-life applications of machine learning, and to understand the people and ideas that build the products that we love.

Links from the community.

“Artificial Intelligence is killing the uncanny valley and out grasp on reality” submitted by Avi Eisenberger (@aeisenberger). Learn More on WIRED >

“The Great AI Paradox” submitted by Samiur Rahman. Learn More on The Harvard Crimson >

“Machine Learning and AI trends for 2018: What to Expect?” submitted by Carl DeBrule (@carldebrule). Learn More on Hackernoon >

“AI in storytelling: Machines as co-creators” submitted by Wes Chow. Learn More on McKinsey & Company >

“Jason’s Machine Learning 101” submitted by Dakota McKenzie (@dakotajmckenzie). Learn More on Google >

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Appalling behavior at a machine learning conference was originally published in Machine Learnings on Medium, where people are continuing the conversation by highlighting and responding to this story.

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