Artificial intelligence (A.I.) and machine learning have emerged as key tools in the armory of many major tech companies, but can it be harnessed to solve some of the world's greatest challenges? IBM and Xprize want to find out. First announced back in February, the 5 million IBM Watson AI Xprize is a competition from Xprize, an initiative launched in 1995 to help solve "the world's Grand Challenges" through incentive-based prizes. Registrations for the four-year global competition are now open, with entrants asked to show how humans and A.I. can tackle issues in education, energy and the environment, health care, exploration, and global development. Xprize has given birth to numerous notable competitions in the past, one of the most recent being the Google-sponsored Lunar Xprize that's setting out to send a private, unmanned aircraft to the moon.

SITTING IN AN office in San Francisco, Igor Barani calls up some medical scans on his screen. He is the chief executive of Enlitic, one of a host of startups applying deep learning to medicine, starting with the analysis of images such as X-rays and CT scans. It is an obvious use of the technology. Deep learning is renowned for its superhuman prowess at certain forms of image recognition; there are large sets of labelled training data to crunch; and there is tremendous potential to make health care more accurate and efficient. Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.

"I think there's recognition it makes sense to have some people thinking about [AI safety] now," says Oxford University philosophy professor Nick Bostrom. Over the past year, Oxford University philosophy professor Nick Bostrom has gained visibility for warning about the potential risks posed by more advanced forms of artificial intelligence. He now says that his warnings are earning the attention of companies pushing the boundaries of artificial intelligence research. Many people working on AI remain skeptical of or even hostile to Bostrom's ideas. But some prominent technologists and scientists--including Elon Musk, Stephen Hawking, and Bill Gates--have echoed some of his concerns.

Promobot RI77 escaped its high-tech lab and evaded Russian scientists for the i second /i time this month. LOS ANGELES, CA (Catholic Online) - Promobot, short for "promotional robot," was equipped with artificial intelligence, allowing it to learn through ... continue reading In another sign the U.S. is slipping internationally, China has unveiled the world's fastest supercomputer, that is five times more powerful that the fastest U.S. supercomputer. It is also built with all Chinese microprocessors. LOS ANGELES, CA (California Network) - ... continue reading Security robots have gone to work in a Silicon Valley mall and designers are shocked at the reactions they're getting from the public. The mixed response means the robots have to be capable of protecting themselves if needed.

Steven Levy is in characteristic excellent form in a long piece on Medium about the internal vogue for machine learning at Google; drawing on the contacts he made with In the Plex, his must-read 2012 biography of the company, Levy paints a picture of a company that's being utterly remade around newly ascendant machine learning techniques. Machine learning had humble beginnings in the company as a class given by and for engineers, which quickly captivated key technical staff around the world, blossoming into something like a full-fledged internal MOOC. Fast-forward to today and the company has moved its head of machine learning to be head of Search, Google's flagship product. Today, machine learning is "involved in every query" and affects the rankings in not "every query but in a lot of queries," with machine learning being the third-most important "signal" in how Google ranks its results. The company even produces its own machine learning-optimized chips, the Tensor Processing Unit, which take the place of the graphics cards that have been pressed into service across the industry for all kinds of parallel computation (from Bitcoin mining to AI), thanks to the thousands of small independent processors incorporated into their designs.

America's tax enforcement agency, the Inland Revenue Service, is pretty sure Paulo Marques is an international tax evader. For the last five years, without fail, he's been summoned by the authorities to spend hours explaining the ins and outs of his revenue streams. But the Inland Revenue's computer doesn't know that, Marques tells the audience WIRED Money 2016. In fact, when it comes to stopping fraud, machines get it wrong far too often. "If you want to stop fraud, you really need to understand human behaviour," says Marques, who founded Feedzai, a company that uses big data to combat fraud.

Here's the worst case scenario: By 2045, 70 million additional car-bound people choke American highways. Bridges, tunnels, and freeways continue to crumble, risking lives and more traffic delays. Luckily, solutions are on the way, many already accessible at the tap of an iPhone. Uber, Lyft, Zipcar, bike share, drone grocery delivery: Technology has repainted the picture of American mobility, and especially in cities. Early adopters are those with the social capital, money, and time to play with radical new mobility options.

As buzzwords become ubiquitous they become easier to tune out. We've finely honed this defense mechanism, for good purpose. It's better to focus on what's in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn't help you. VR could eat all media, but it's hardware requirements keep it many years away from common use.

A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. This project is hosted at https://github.com/rhiever/tpot

Computational linguists and computer scientists, among them University of Texas professor Jason Baldridge, have been working for over fifty years toward algorithmic understanding of human language. They are, however, doing a pretty good job with important tasks such as entity recognition, relation extraction, topic modeling, and summarization. These tasks are accomplished via natural language processing (NLP) technologies, implementing linguistic, statistical, and machine learning methods. Voice response and personal assistants -- Siri, Google Now, Microsoft Cortana, Amazon Alexa -- rely on NLP to interpret requests and formulate appropriate responses. Search and recommendation engines apply NLP, as do applications ranging from pharmaceutical drug discovery to national security counter-terrorism systems.