The UK will ban people from flying into the country with large electronic devices by Saturday. The Department for Transport said that it will implement the ban on laptops and tablets from a range of countries this week. Any large gadgets flying from a range of affected countries will have to be put into the luggage hold, and can't be taken onto the plane. Airlines are being told to implement the rules "over the coming days and no later than 25 March", a DfT spokesperson said. Passengers "should go to the airport with the expectation that the measures are already in effect", she said.

NEW HAVEN – The idea of a tax on robots was raised last May in a draft report to the European Parliament prepared by MEP Mady Delvaux from the Committee on Legal Affairs. Emphasizing how robots could boost inequality, the report proposed that there might be a "need to introduce corporate reporting requirements on the extent and proportion of the contribution of robotics and AI to the economic results of a company for the purpose of taxation and social security contributions." The public reaction to Delvaux's proposal has been overwhelmingly negative, with the notable exception of Bill Gates, who endorsed it. But we should not dismiss the idea out of hand. In just the past year, we have seen the proliferation of devices such as Google Home and Amazon Echo Dot (Alexa), which replace some aspects of household help. Likewise, the Delphi and nuTonomy driverless taxi services in Singapore have started to replace taxi drivers.

Technology's unrelentingly advance is transforming work and the workplace. McKinsey Global Institute (MGI), using data from U.S. Bureau of Labor Statistics and the Occupational Information Network (O*Net), examined automation's potential effect on the workplace by looking at over 2,000 activities comprising more than 800 occupations. MGI's intriguing results tell an important story about the change that leaders must look for, manage and guide. MGI quantified the time spent on specific activities comprising a range of work and analyzed the feasibility of automating them. The automation potential of various types of work depends on technical feasibility, the availability of skills that automation may replace, cost to automate relative to human wages, and considerations of social-acceptance and regulation.

Experts say robots will be able to do'most jobs' within 30 years. That kind of prediction makes current international trade spats seem paltry in comparison to the brewing demise of humanity's jobs market. China's Changying Precision Technology recently replaced 90% of its employees with robotics systems and saw a boost in productivity in doing so. Japan even has a hotel run by robots. When it comes to the implications of the impending Johnny 5 era, Mady Delvaux is one of the European Parliament's most vocal MEPs.

DARPA is working on a new machine learning technology that could let a future artificial intelligence grow up, learning from its experiences over the entirety of its unnatural life. Essentially, DARPA wants to turn every interaction the A.I. has into an opportunity to collect data. The Lifetime Learning Machines (L2M) initiative's goal wouldn't be surveillance, though that's certainly a concern, but rather improving artificial intelligence by exposing it to new data and experiences and letting it learn from them -- just like a biological brain. The project's leaders call this a "new computing paradigm" that could supplant classical A.I. coding, in which knowledge and behaviors must all be specified in advance. Though the four-year program to let A.I.s become "responsive and adaptive collaborators" with humans is still just getting started, the machine learning goals for its contributors are clear: to allow A.I.s to learn from incidental experiences.

Stocks sank deeper into negative territory on the Tokyo Stock Exchange Wednesday, with investor sentiment hurt by an overnight sell-off on Wall Street and the yen's sharp appreciation against the dollar. The 225-issue Nikkei average tumbled 414.50 The key price indicator suffered the biggest closing loss since Donald Trump's victory in the U.S. presidential election in November last year. On Tuesday, the Nikkei average shed 65.71 points. The Topix index of all first-section issues closed down 33.22 points, or 2.12 percent, at 1,530.20, after losing 2.43 points the previous day.

Machine learning is increasingly being seen as the solution, dealing - or at least appearing to deal - with a number of the problems organizations are having implementing their cybersecurity initiatives. Former Department of Defense Chief Information Officer, Terry Halvorsen, believes that'within the next 18-months, AI will become a key factor in helping human analysts make decisions about what to do.' This point of view is being reinforced by significant investment in the field by the world's largest technology companies. MIT has been experimenting with it for some years, while IBM is training its AI-based Watson in security protocols and has now made it available to customers. Amazon also recently acquired AI-based cyber-security company Harvest.ai,

We are at the cusp of the next industrial revolution--or maybe it's in full swing already. Artificial intelligence, Internet of Things, cloud computing, smartphones, and a slew of other technologies that were unknown or sci-fi before the turn of the century are redefining and disrupting different aspects of life as we know it today. As with every industrial revolution, most of the changes overcoming our lives are pleasant. These are just some of the advantages brought by these technologies. But the same trends drag in tow some less appreciated disruptions, namely the upheaval of the socio-economic landscape.

As a general rule of thumb, the law and government are slow-moving and deliberate. That's really handy for important things that you have to get right, but the trouble is that disruptive technology tends to move much faster. That's bad enough if the disruptive technology you're talking about is (say) the sharing economy, but it's more serious when it's something that Elon Musk once described as "potentially more dangerous than nukes". That thing is artificial intelligence. And while it's hard to feel too threatened when it's your Amazon Echo failing to understand you saying "Play REM" for the tenth time in a row, the threat – potentially – is a real one.

Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vector. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analysis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization.