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.

While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication of attacks, has prompted the need for a data driven solution, with machine learning forming the core of many cybersecurity systems. Machine learning was not designed with security in mind, and the essential assumption of stationarity, requiring that the training and testing data follow similar distributions, is violated in an adversarial domain. In this paper, an adversary's view point of a classification based system, is presented. Based on a formal adversarial model, the Seed-Explore-Exploit framework is presented, for simulating the generation of data driven and reverse engineering attacks on classifiers. Experimental evaluation, on 10 real world datasets and using the Google Cloud Prediction Platform, demonstrates the innate vulnerability of classifiers and the ease with which evasion can be carried out, without any explicit information about the classifier type, the training data or the application domain. The proposed framework, algorithms and empirical evaluation, serve as a white hat analysis of the vulnerabilities, and aim to foster the development of secure machine learning frameworks.

NASA is developing a robot that could go where no robot has gone before: over and under difficult terrain on Mars or another planet. The PUFFER -- Pop-Up Flat Folding Explorer Robot -- could traverse ground that a standard rover would get caught on, making a helpful travel companion. The space administration says its Jet Propulsion Laboratory in California is working on the little trooper, which will be lightweight and "was inspired by origami." A NASA video (below) shows how it's done: The robot can fold into itself to squeeze underneath short obstacles or use its tailfin as leverage to propel itself up and over them. And it could last a long time because it has solar panels on its underside so it can recharge. PUFFER wouldn't go on a solo journey to Mars, but would be a teammate that could tag in when a larger rover faces a topographical blockade.

Ten minutes from downtown Holland, Michigan, there is a massive $300 million dollar plant that manufactures advanced battery cells for electric vehicles. When it was first announced in 2009 that LG Chem would be setting up a subsidiary in the small town of 34,000 people, government officials planned for an economic revitalization. They spared no expense to woo the Korean giant. The local, state, and federal government awarded over $250 million in tax subsidies, grants, and concessions, promising that Michigan would become "the world capital for advanced batteries." The plan amounted to spending $750,000 for a job that paid only $54,000 a year.