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.

After many years of research, misfires and frightening Hollywood plotlines, artificial intelligence (AI) is finally coming into its own and beginning to demonstrate significant business value. The combined forces of big data, human expertise and AI are being used across industries as diverse as healthcare and manufacturing, as well as within all aspects of business. IT operations is one area that AI is beginning to contribute to enormously. IT infrastructures are changing rapidly today, particularly hybrid cloud environments. While they are increasingly dynamic and agile, they are also extraordinarily complex. Humans are no longer able to sift through the variety, volume and velocity of Big Data streaming out of IT infrastructures in real time, making AI especially machine learning a powerful and necessary tool for automating analysis and decision making.

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.

How DOES YouTube choose those 3 candidate thumbnail images for your video?

At MyOxygen we always like to stay ahead of the tech curve. We are always investigating and experimenting with new technologies such as Virtual Reality and Wearables and more recently our experiments have taken us into the world of Artificial intelligence (AI), machine learning and chatbots. We wanted to share with you what we've found so far. Chatbots allow users to interact with services through widely used messaging platforms such as Skype, Facebook Messenger and Slack. Bots can also be embedded in existing apps as a way of offering assistance when doing things like searching through documents.

In last month's blog I talked about keeping your team innovative. One of the key tenets of an innovative team is the concept of moonshots or "10x Thinking" or audacious goals. In a "moonshot economy," while a breakthrough can mean the start of a new industry leader, it can also mean the death of an industry stalwart. First, let's take a look at some previous moonshots and how they've impacted various industries and the world. The first is, of course, the great Space Race.

Google has announced a deal to buy Moodstock, a French startup that builds technology which helps smartphone devices recognize images and objects. Moodstock's work in computer vision and machine learning and its technology behind smartphones being able to recognize images and objects was the reason behind Google's interest. Google's technology has been including machine learning, such as in its free translation and photo services. Google's inclination towards this as well as their interest in improving machine vision is what prompted the US technology' giant's decision to buy Moodstock. The Moodstocks team, described as a small group of researchers and engineers, will join a Google research and development centre in Paris.

At betaworks, we are very excited about the opportunities conversational interfaces offer for building new kinds of products, services, distribution channels and user experiences. We have been investing and building in this space for quite some time now. Given our curiosity and experience in this space, we introduced Botcamp back in April this year and we were blown away by the quality and quantity of applications in this space. We received over 400 applications from early stage bot companies from all over the world. These developers are building conversational services in various verticals from enterprise, finance, and commerce to travel, analytics and news on different platforms. Some products were thin bots, some had taken a human-assisted approach and some were a result of over a year's work on language analysis and machine learning in specific verticals.