CIA Can Anticipate Social Unrest ‘Three to Five Days’ Out in Some Cases

The agency, Hallman said, has significantly improved its “anticipatory intelligence,” using a mesh of sophisticated algorithms and analytics against complex systems to better predict the flow of everything from illicit cash to extremists around the globe. Deep learning and other forms of machine learning can help analysts understand how seemingly disparate data sets might be linked or lend themselves to predicting future events with national security ramifications.While intelligence analysts have access to CIA’s own classified data stores to sift through, they’re also increasingly turning to open data sets, which Brennan has said this summer have turned into a “tremendous advantage” for the agency.“We have, in some instances, been able to improve our forecast to the point of being able to anticipate the development of social unrest and societal instability some I think as near as three to five days out,” said Hallman, speaking Tuesday at The Next Tech event hosted by Government Executive and Nextgov

Source: CIA Can Anticipate Social Unrest ‘Three to Five Days’ Out in Some Cases – Nextgov.com

AI Machine-learning models vulnerable to reverse engineering

In a paper [PDF] presented in August at the 25th Annual Usenix Security Symposium, researchers at École Polytechnique Fédérale de Lausanne, Cornell University, and The University of North Carolina at Chapel Hill showed that machine learning models can be stolen and that basic security measures don’t really mitigate attacks.

Machine learning models may, for example, accept image data and return predictions about what’s in the image.

Taking advantage of the fact that machine learning models allow input and may return predictions with percentages indicating confidence of correctness, the researchers demonstrate “simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees.”

That’s a polite way of saying such models can be reverse engineered. The researchers tested their attack successfully on BigML and Amazon Machine Learning, both of which were told of the findings in February.

Source: How to steal the mind of an AI: Machine-learning models vulnerable to reverse engineering

Unintended consequences of AI: Amazon Echo seems to condition kids to be rude

Alexa will put up with just about anything. She has a remarkable tolerance for annoying behavior, and she certainly doesn’t care if you forget your please and thank yous.

But while artificial intelligence technology can blow past such indignities, parents are still irked by their kids’ poor manners when interacting with Alexa, the assistant that lives inside the Amazon Echo.

“I’ve found my kids pushing the virtual assistant further than they would push a human,” says Avi Greengart, a tech analyst and father of five who lives in Teaneck, New Jersey. “[Alexa] never says ‘That was rude’ or ‘I’m tired of you asking me the same question over and over again.’”
[…]
The syntax is generally simple and straightforward, but it doesn’t exactly reward niceties like “please.” Adding to this, extraneous words can often trip up the speaker’s artificial intelligence. When it comes to chatting with Alexa, it pays to be direct—curt even. “If it’s not natural language, one of the first things you cut away is the little courtesies,” says Dennis Mortensen, who founded a calendar-scheduling startup called x.ai.
[…]
this is a box you speak to as if it were a person who does not require social graces.”

It’s this combination that worries Hunter Walk, a tech investor in San Francisco. In a blog post, he described the Amazon Echo as “magical” while expressing fears it’s “turning our daughter into a raging asshole.”

Source: Parents are worried the Amazon Echo is conditioning their kids to be rude

Unintended consequences of AI!

Swarm A.I. Correctly Predicts the Kentucky Derby, Accurately Picking all Four Horses of the Superfecta at 540 to 1 Odds – showing that humans can swarm

Until recently, the human species has been unable to take advantage of this fundamental biological technique, for we didn’t evolve the ability to swarm. Enter Unanimous A.I., a Silicon Valley startup founded in 2014 by serial entrepreneur and researcher Dr. Louis Rosenberg. The core question Rosenberg set out to answer was: Can humans swarm, and if so can we amplify our intelligence beyond the ability of individuals? The answer appears to be a resounding yes.

Unanimous spent the last two years building a swarm intelligence platform called UNU that enables groups to get together as online swarms — combining their thoughts, opinions, and intuitions in real-time to answer questions, make predictions, reach decisions, and even play games as a unified collective intelligence. To quantify how smart these UNU swarms really are, researchers at Unanimous regularly convene swarms and ask them to make predictions on high profile events, testing whether or not many minds are truly better than one.

UNU has made headlines in recent months by predicting the Oscars better than the experts, even besting the renowned forecasters at FiveThirtyEight. UNU also surprised the sports world by predicting the NCAA college bowl games with 70% accuracy against the spread, earning +34% return on Vegas odds. But still, the fact that average people could use UNU to amplify their collective intelligence so dramatically was met with cautious resistance.

Enter Hope Reese, a reporter from TechRepublic. Two weeks ago, she challenged Unanimous A.I. to use UNU to predict the winners of the Kentucky Derby.

Source: Swarm A.I. Correctly Predicts the Kentucky Derby, Accurately Picking all Four Horses of the Superfecta at 540 to 1 Odds – Yahoo Finance

Fathom – AI Neural Network learning accelerator on a USB stick

Movidius is also introducing the Fathom Neural Compute Stick — the first product of its kind — a modular deep learning accelerator in the form of a standard USB stick. Featuring a full-fledged Myriad 2 VPU, the Fathom Neural Compute Stick not only enables rapid prototyping, but also delivers high levels of neural network compute to existing devices via a USB port.

Source: Fathom | Machine Vision Technology | Movidius

Machine Learning Inspired by Human Learning  – AI can learn handwriting using a single example

Taking inspiration from the way humans seem to learn, scientists have created AI software capable of picking up new knowledge in a far more efficient and sophisticated way.

The new AI program can recognize a handwritten character about as accurately as a human can, after seeing just a single example. The best existing machine-learning algorithms, which employ a technique called deep learning, need to see many thousands of examples of a handwritten character in order to learn the difference between an A and a Z.

Source: Machine Learning Inspired by Human Learning | MIT Technology Review

How Ashley Madison Hid Its Fembot Con From Users and Investigators

The developers at Ashley Madison created their first artificial woman sometime in early 2002. Her nickname was Sensuous Kitten, and she is listed as the tenth member of Ashley Madison in the company’s leaked user database. On her profile, she announces: “I’m having trouble with my computer … send a message!”

Source: How Ashley Madison Hid Its Fembot Con From Users and Investigators

AI starts here!

Evolving swarm intelligence in robots

The Lausanne university in Switserland has moved from the software to the reality: they’ve managed to get robots to evolve and learn behaviours, as well as the behaviour to decieve and cooperate through communication (flashing lights) and movement. It’ s a very interesting experiment, showing that robots are getting smarter every day and are now showing some very very lifelike traits.

Darwin’s Robots | h+ Magazine.