Two weeks after Twitter acquired Magic Pony to advance its machine learning smarts for improving users' experience of photos and videos on its platform, Google is following suit. Today, the maker of Android and search giant announced that it has acquired Moodstocks, a startup based out of Paris that develops machine-learning based image recognition technology for smartphones whose APIs for developers have been described as "Shazam for images." Moodstocks' API and SDK will be discontinued "soon", according to an announcement on the company's homepage. "Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription," the company noted. Terms of the deal were not disclosed and it's not clear how much Moodstocks had raised: CrunchBase doesn't note any VC money, although when we first wrote about the company back in 2010 we noted that it had raised 500,000 in seed funding from European investors.

Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says. Machine learning is a type of artificial intelligence that allows computer programs to learn when exposed to new data without being programmed. "With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," Meije Wink explained.

Everything we know about the face recognition systems the FBI and police use suggests the software has a built-in racial bias. That isn't on purpose--it's an artifact of how the systems are designed, and the data they are trained on. Law enforcement agencies are relying more and more on such tools to aid in criminal investigations, increasing the risk that something could go wrong. Law enforcement agencies haven't provided many details on how they use facial recognition systems, but in June the Government Accountability Office issued a report saying that the FBI has not properly tested the accuracy of its face matching system, nor that of the massive network of state-level face matching databases it can access. And while state-of-the-art face matching systems can be nearly 95 percent accurate on mugshot databases, those photos are taken under controlled conditions with generally coöperative subjects.

Google's autonomous cars may look cute, like a yuppie cross between a Little Tikes Cozy Coupe and a sheet of flypaper, but to make it in the real world they're going to have to act like calculating predators. At least, that's what a handful of scientists at the Institute of Neuroinformatics at the University of Zurich in Switzerland believe. They recently taught a robot to act like a predator and hunt its prey--which was a human-controlled robot--using a specialized camera and software that allowed the robot to essentially teach itself how to find its mark. The end goal of the work is arguably more beneficial to humanity than creating a future robot bloodsport, however. The researchers aim to design software that would allow a robot to assess its environment and find a target in real time and space.

CSNE researchers work on neural recording. Parallels have been drawn between the human brain and the computer since technology's earliest days. One day, however, computing could be used to help brains damaged by traumatic events like a stroke to work once again. Like a computer, the brain requires huge numbers of connections to work, allowing messages to be passed from one part of the brain to another, or from the brain to the body. If any of those connections are blocked or broken, the messages can't get through.

It is Wednesday, July 6. A 300-pound robot is the new security guard at Uber's inspection lot in San Francisco. Here's what else is happening in the Golden State: When undercover CHP officers shot at suspects in a moving car this weekend, they used a tactic that's been outlawed in many major cities because experts believe it's too dangerous. They fired at a moving car. "Only a fool thinks a … bullet is going to stop a 3,800-pound car. Nobody is really shooting at the vehicle, they're shooting at the driver," said Sid Heal, a retired Los Angeles sheriff's commander and chairman of strategy development for the National Tactical Officers Assn.

By rolling out self-driving technology to consumers more aggressively than its competitors, Tesla Motors secured a spot in the forefront of a coming industry. But that strategy could expose the company to a risk it has sought to avoid: liability in crashes. Tesla in 2015 activated its autopilot mode, which automates steering, braking and lane switching. Tesla asserts the technology doesn't shift blame for accidents from the driver to the company. But Google, Zoox and other firms seeking to develop autonomous driving software say it's dangerous to expect people in the driver's seat to exercise any responsibility. Drivers get lulled into acting like passengers after a few minutes of the car doing most of the work, the companies say, so relying on them to suddenly brake when their cars fail to spot a hazard isn't a safe bet.

Alleged criminals might not be the only ones to benefit from pleading the Fifth. By falling silent during the Turing test, artificial intelligence (AI) systems can fool human judges into believing they're human, according to a study by machine intelligence researchers from Coventry University. Alan Turing, considered the father of theoretical computer science and AI, devised the Turing test in an attempt to outline what it means for a thing to think. In the test, a human judge or interrogator has a conversation with an unseen entity, which might be a human or a machine. The test posits that the machine can be considered to be "thinking" or "intelligent" if the interrogator is unable to tell whether or not the machine is a human.

Amazon's annual competition for picking perfect robots that could one day work in the company warehouses crowned the latest champion in its robotic picking challenge. Competitors were asked to handle a wide range of products from toiletries to clothes and then evaluated on the basis of speed and precision in stocking shelves. The contest was won by a joint team from the TU Delft Robotics Institute in the Netherlands and the company Delft Robotics. The robot developed by Team Delft managed to pick items from a mock Amazon warehouse shelf at a rate of 100 per hour. The failure rate noted was 16.7%.

WIMBLEDON fans are being served a mash up of machine learning and advanced analytics in a bid to capture viewers' attention on social media and digital platforms. Statistics and analytics have been a big feature of grand slam tennis for some years now. But what's new this year is Watson. IBM's flagship AI-driven analytics platform has been tasked with crunching through the hundreds of thousands of social media and online posts which the event generates. It's mission will be to find the stories that fans are most engaged with, and drive creation of the sort of content that they most want to see.