Tag Archives: AI

Artificial Intelligence Has Found an Unknown ‘Ghost’ Ancestor in The Human Genome

by Peter Dockrill, ScienceAlert

Nobody knows who she was, just that she was different: a teenage girl from over 50,000 years ago of such strange uniqueness she looked to be a ‘hybrid’ ancestor to modern humans that scientists had never seen before.

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Can artificial intelligence tell a polar bear from a can opener?

by Stuart Wolpert, UCLA Newsroom

UCLA psychologists’ experiments demonstrate severe limitations of ‘deep learning’ machines

How smart is the form of artificial intelligence known as deep learning computer networks, and how closely do these machines mimic the human brain? They have improved greatly in recent years, but still have a long way to go, a team of UCLA cognitive psychologists reports in the journal PLOS Computational Biology.

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AI is Fueling Smarter Prosthetics Than Ever Before

by Andrea Powell, Wired

THE DISTANCE BETWEEN prosthetic and real is shrinking. Thanks to advances in batteries, brain-controlled robotics, and AI, today’s mechanical limbs can do everything from twist and point to grab and lift. And this isn’t just good news for amputees. “For something like bomb disposal, why not use a robotic arm?” says Justin Sanchez, manager of Darpa’s Revolutionizing Prosthetics program. Well, that would certainly be handy.

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AI is better at bluffing than professional gamblers

by Andrew Tarantola, Engadget

AI and ML systems are turning the professional gambling world on its head.

The act of gambling on games of chance has been around for as long as the games themselves. For as long as there’s been money to be made wagering on the uncertain outcomes of these events, bettors have been leveraging mathematics to give them an edge on the house. As gaming has moved from bookies and casinos into the digital realm, gamblers are beginning to use modern computing techniques, especially AI and machine learning (ML), to increase their odds of winning.

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This clever AI hid data from its creators to cheat at its appointed task

by Devin Coldewey, TechCrunch

Depending on how paranoid you are, this research from Stanford and Google  will be either terrifying or fascinating. A machine learning agent intended to transform aerial images into street maps and back was found to be cheating by hiding information it would need later in “a nearly imperceptible, high-frequency signal.” Clever girl!

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AI can generate fake faces now. Here’s how to spot them

by Dave Gershgorn, Quartz

Earlier this month, a research paper from Nvidia, maker of graphics processing units, showed that the company is able to generate photo-realistic images of people who never existed. It marked another step toward a world in which any media can be easily and believably generated from scratch, a looming crisis for truth online.

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This free online tool uses AI to quickly remove the background from images

by James Vincent, The Verge

A handy utility if you don’t have access to Photoshop

If you’ve ever needed to quickly remove the background of an image you know it can be tedious, even with access to software like Photoshop. Well, Remove.bg is a single-purpose website that uses AI to do the hard work for you. Just upload any image and the site will automatically identify any people in it, cut around the foreground, and let you download a PNG of your subject with a transparent background. Easy.

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37% of Tech Experts Worry Artificial Intelligence Will Make Humanity Worse by 2030

by Kevin Kelleher, MSN News

More than a third of AI experts surveyed by Pew Research said they are concerned that artificial intelligence will leave humanity worse off in 2030 than they are now, with the majority optimistic that the benefits will make life better for individuals.

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AI reveals hidden objects in the dark

by Jon Fingas, engadget

It could lead to safer medical scans and other breakthroughs.

You might not see most objects in near-total darkness, but AI can. MIT scientists have developed a technique that uses a deep neural network to spot objects in extremely low light. The team trained the network to look for transparent patterns in dark images by feeding it 10,000 purposefully dark, grainy and out-of-focus pictures as well as the patterns those pictures are supposed to represent. The strategy not only gave the neural network an idea of what to expect, but highlighted hidden transparent objects by producing ripples in what little light was present.

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