Stealthy MedyMatch emerged in February with plans to improve emergency room care using cognitive analysis and artificial intelligence. Now, in its first collaboration with a U.S. hospital, the company is developing its first real-time decision-support tool using data from New Jersey-based Capital Health. Under the agreement, Capital Health will supply Israel-based MedyMatch with anonymized data to help it develop the tool, which will target stroke patients. It will analyze medical images and provide the ER radiologist with information to help him or her determine the course of treatment. It combines "deep vision, advanced cognitive analytics and artificial intelligence" to analyze images and identify anomalies that may be invisible to the human eye.

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PRO: Chris Bishop, director of Microsoft Research in Cambridge, said earlier this year that he believes the hyperbole around the AI risks could jeopardise any future developments that may in fact assist humanity. "Any scenario in which AI is an existential threat to humanity is not just around the corner," he told the Guardian. Referring to the views of high-profile cynics like professor Stephen Hawking, Bishop said: "I think they must be talking decades away for those comments to make any sense. Right now we are in control of that technology and we can make lots of choices about the paths that we follow." Oren Etzioni, chief executive of the Allen Institute for AI and professor of computer science at the University of Washington, meanwhile says the popular dystopian vision of AI is wrong because it "equates intelligence with autonomy".

The first death has been reported in self-driving cars. Our worst fears about artificial intelligence are already beginning to come true. A test man working with Tesla motors was killed after the autopilot feature failed to see a bright-white 18-wheeler which was poorly contrast against the bright lit sky. It is unfortunate, and spooky, since white is the most common color for tractor trailers, and the big rigs account for the most deadly accidents. One wonders how a tiny, smart car making decisions of a computer would stand up to such a vehicle. Not "sophisticated enough to overcome blindness from bright or low contrast light."

The popular name for Convolutional Neural Network (CNN) these days is "Deep Learning" (DL) or Deep Neural Network (DNN). Researchers at Google, Facebook, IBM, Nvidia, and potentially a number of other companies are experimenting with their own variations of DL algorithms based on CNN. Twitter recently acquired a company called Magic Pony to increase focus on DL. Applications for DL range from vision processing for self-driving car applications to natural language processing and face/object recognition, for potential applications in security, advertising, gaming, VR/AR, etc. A neural network is a computational algorithm that is loosely based on mechanics of the learning process in animals.

Ashok Goel wants to expand high-quality education to "millions" more people over the internet. It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities -- and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider.

Artificial intelligence (AI) that can understand and answer any work-related question it is asked has been made available in the UK for the first time. The computer software, called Starmind, uses machine learning to understand queries, then finds answers from previous staff conversations on a subject or tracks down experts in the company who are able to help. Its creators refer to it as "brain technology", adding its aim is to become a central knowledge bank within any company, an instant database of information that can be accessed by anyone. Starmind co-founder Pascal Kaufmann said of the technology: "Thousands of human brains connected can outsmart any machine today. "But if you can find ways for humans and AI-inspired technologies to autonomously collaborate rather than focusing on ways for them to compete, you can bring out the best in both." The algorithm within the system, which was developed in Switzerland, becomes more powerful the more it is used and is able to build a map of the people in a business and the areas in which all of them are experts, or are able to provide relevant information. "Starmind acts like an artificial hyperbrain that seamlessly exists at the core of a company," Mr Kaufmann added. "The algorithm is then fuelled by the know-how stored inside the brains of everyone that engages with the system." Several major companies in Europe, including UBS and Bayer, are using the system. A new version of the software - called Starmind NOW - has also been launched. It enables the software to be accessed outside a company intranet for the first time. Starmind says that makes the technology more "intuitive and seamless" to use. Former Microsoft executive Peter Waser has also joined the company as CEO. "It's a new technology that has never been available on the market in this form," he said. "Brain technology is the latest technology in the megatrend of machine learning and artificial intelligence.

Look, Chrome is a great browser. It's feature-packed, overstuffed with fun extensions, and keeps your digital life organized across multiple devices. But maybe its insatiable resource-lust is bringing your laptop down. Maybe Google gives you the privacy willies. Or hey, it first showed up on Macs in 2009--maybe you've just got a seven-year itch.

Input: a) A set of labelled examples where every is the input vector, and is the corresponding output label. Output: The set of expected labels for all instances in . There are two ways (or rather, two philosophies) you could use, to solve this problem. Induction, in the context of learning, is the attempted discovery of rules/generalizations based on analysis of collected data. 'Attempted discovery' is the key term here – the generalizations are not facts, but approximations based on evidence you have gathered.