Identifying ISIS operatives with machine learning

September 5, 2017 Sam DeBrule


Awesome, not awesome.

“Physicists at Stanford University have developed a new technique of using neural networks for analyzing gravitational lenses in distant space. The method is some 10 million times faster than current strategies, offering a key upgrade to our ability to make sense from observations of light from far-flung galaxies… ‘Neural networks will help us identify interesting objects and analyze them quickly. This will give us more time to ask the right questions about the universe,’ study co-author Perreault Levasseur told Physics World.— Michael Byrne, Editor Learn More on Motherboard >

#Not Awesome
“For years we’ve been recorded in public on security cameras, police bodycams, livestreams, other people’s social media posts, and on and on. But even if there’s a camera in our face, there’s always been a slight assurance that strangers wouldn’t really be able to do anything that affects us with the footage…. [Today,] given a large dataset of images or video, [deep learning] systems can be trained to learn what a person’s face looks like, and reliably identify it again and again. So what’s a privacy-minded, law-abiding citizen to do when surveillance becomes the norm? Not much.” -Dave Gershgorn, Reporter Learn More on Quartz >

What we’re reading.

1/ Researchers built a machine learning system to identify ISIS operatives and ban them from Twitter, but fear authoritarian governments could abuse it to suppress peaceful dissent. Learn More on MIT Sloan >

2/ Cultural issues, not technological ones, may be the reason it takes longer than expected for self-driving cars to be the primary mode of transportation. Learn More on Nautilus >

3/ Science fiction depictions of superhuman robots put the fear of god in us, but maybe we should be more wary of the simple systems that are busy reshaping the workplace. Learn More on Tim Harford >

4/ From automating mundane tasks like grading tests to creating immersive visual learning experiences, artificial intelligence systems are set to shake up higher education. Learn More on World Economic Forum >

5/ Every new algorithm we bring online will have total access to all knowledge from previous algorithms, similar to the way humans are born with instincts encoded in their DNA. Learn More on Occam’s Razor >

6/ If we don’t have inclusive conversations about the ideal society we want to build with AI, we’re unlikely to make it a reality. Learn More on The Verge >

7/ Researchers using machine learning systems to generate automated product reviews risk completely de-legitimizing the reviews we’ve come to trust on Amazon and other sites we depend on. Learn More on Business Insider >

Links from the community.

“Build Algorithms Like You Give a Damn” submitted by Avi Eisenberger. Learn More on Mode Analytics >

“Self Driving Cars, The Most Hyped Thing Since…The Segway?” submitted by Li Jiang. Learn More on Medium >

“iOS 11: Are Apple’s new NLP capabilities game changers?” submitted by Clément Delangue. Learn More on Medium >

“The new rules of Build vs. Buy in an AI-first world” by Michael Vaccarino. Learn More on LinkedIn >

“ is a robot that can plan your schedule in Slack” submitted by Jarret Alberti. Learn More on TechCrunch >

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Identifying ISIS operatives with machine learning 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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