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.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Artificial intelligence is either the bright shining future of technology or an insidious threat that could endanger all of mankind, depending on your point of view. Now Google, one of the companies leading the development of AI systems, has set out five key challenges that need to be overcome with the technology - but they are somewhat more mundane than robots rising up to take over the world. Instead, the company sees one of the key problems as being how to stop negative side effects, such as a cleaning robot that knocks over a precious vase to get its job done faster. Google has published a new research paper highlight five challenges it sees as needing to be overcome to prevent AI and robots causing unintended harm. It also says robots need to be programmed in a way so they do not'game the system' – such as simply covering mess in a room with a sheet it cannot see through rather than tidying up. Avoiding Negative Side Effects: How can we ensure that an AI system will not disturb its environment in negative ways while pursuing its goals, e.g. a cleaning robot knocking over a vase because it can clean faster by doing so?

Artificial intelligence is a 15 billion dollar industry and growing. With more than 2,600 companies developing intelligent technology, the value of AI is expected to rise to more than 70 billion by 2020. And it's not just attracting the tech giants: USAA is using AI to protect its users from identity theft, and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. For designers, that represents a major business opportunity. But AI is also a challenge requiring every strength and skill they've learned and many they haven't.