With only a few seconds of audio, the'Deep Voice' software developed by China's Baidu is able to clone a human voice - raising fears about the security of biometrics. Baidu has been working on Deep Voice for over a year, and had already managed to reproduce speaker identities with about half an hour of training data. With new developments, it has lowered that time to 3.7 seconds. A believable, if low-quality, false voice can now be produced from a only single sentence of speech. Of course, more training leads to higher-quality results, especially if there is more than one sample to learn from.

Any mention of Artificial Intelligence, or AI, inspires some trepidation in many people, including designers, but those most familiar with it say that designers should be excited about the advent of AI. "I'm very excited about the future with AI, but we do need to make sure we are responsible with it, as with any other technological development,'' Silka Miesnieks, head of the design lab at Adobe, told the Brainstorm Design conference in Singapore on Wednesday. "Our superpower as humans is our creativity, and we are all creative," she said, adding that AI can aid human creativity but not replace it. "We spend a lot of time thinking about unintended consequences and trying to avoid them. And we urge all designers to do that too," she said. Rod Farmer leads organizational transformation by design for McKinsey & Company. As digital expert associate partner, he helps clients bridge the worlds between design, digital, and business impact. He took a similar view to Miesnieks. "In most cases, AI only affects a percentage of a job a person does, but rarely the whole job.

TOKYO (Reuters) - Japanese auto parts supplier Denso Corp 6902.T said on Friday it has agreed to acquire an additional 4.5 percent stake in chip maker Renesas Electronics Corp 6723.T, as carmakers accelerate development of self-driving vehicles and other technologies.

In 2010 I wrote that there were three sponsored research projects to solve the problem of safely inspecting and maintaining high voltage transmission lines using robotics. Existing 2010 methods ranged from humans crawling the lines, to helicopters flying close-by and scanning, to cars and jeeps with people and binoculars attempting to scan with the human eye. In 2014 I described the progress from 2010 including the Japanese start-up HiBot and their inspection robot Expliner which seemed promising. This project got derailed by the Fukushima disaster which took away the funding and attention from Tepco which was forced to refocus all its resources on the disaster. HiBot later sold their IP to Hitachi High-Tech which, thus far, hasn't reported any progress or offered any products.

Human players handily beat robots with artificial intelligence (AI) in a curling game on Thursday, despite the machines demonstrating evolving skill levels. At a competition hosted by the Ministry of Science and ICT, two robots named Curly exhibited considerable skill in placing stones on target and showed the ability to formulate strategy. The robots were developed by some 60 researchers from eight institutions, including Korea University and the Ulsan National Institute of Science and Technology (UNIST), the ministry said. The machines were embedded with "CurlBrain" software that instructs the robots to throw stones in a way to win the game, the ministry said. "There are more factors that Curly has to take into account, such as the number of stones and ice, when making strategy, compared with AlphaGo," said a researcher.

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the surface of water, Ripple Network stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that Ripple Network achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the objective function. We assume that only noisy gradient and Hessian information of the smooth part of the objective function is available via calling stochastic first and second order oracles. The proposed method can be seen as a hybrid approach combining stochastic semismooth Newton steps and stochastic proximal gradient steps. Two inexact growth conditions are incorporated to monitor the convergence and the acceptance of the semismooth Newton steps and it is shown that the algorithm converges globally to stationary points in expectation. Moreover, under standard assumptions and utilizing random matrix concentration inequalities, we prove that the proposed approach locally turns into a pure stochastic semismooth Newton method and converges r-superlinearly with high probability. We present numerical results and comparisons on $\ell_1$-regularized logistic regression and nonconvex binary classification that demonstrate the efficiency of our algorithm.

It's easy for us to forget that the vast majority of the world doesn't really care about (or even know about) robots. With that in mind, it's understandable why most roboticists consider robots operating "in the wild" to be "anywhere that isn't the controlled environment of my lab." But there are "real world" environments, and then there's the actual wild, and we almost never hear about research happening there. This is too bad, because we don't have nearly enough appreciation for how robots can potentially be used to mitigate problems throughout the developing world. There's also very little research into how different cultures react to robots with a social component--most human-robot interaction (HRI) studies rely on local participants who are easy (and cheap) to recruit, and are consequently full of students, which is a terrible representation of most of the rest of the world.

The world is full of connected devices – and more are coming. In 2017, there were an estimated 8.4 billion internet-enabled thermostats, cameras, streetlights and other electronics. By 2020 that number could exceed 20 billion, and by 2030 there could be 500 billion or more. Because they'll all be online all the time, each of those devices – whether a voice-recognition personal assistant or a pay-by-phone parking meter or a temperature sensor deep in an industrial robot – will be vulnerable to a cyberattack and could even be part of one. Today, many "smart" internet-connected devices are made by large companies with well-known brand names, like Google, Apple, Microsoft and Samsung, which have both the technological systems and the marketing incentive to fix any security problems quickly. But that's not the case in the increasingly crowded world of smaller internet-enabled devices, like light bulbs, doorbells and even packages shipped by UPS.

Earlier this year, Alibaba CEO Jack Ma made headlines for proclaiming the imminent arrival of the robot CEO. He told an audience at a conference in China that we are only decades away from having robots run our companies. He backed that claim up shortly after via a television interview with CNN, predicting that, in 30 years, a robot would grace the cover of Time Magazine. As implausible as that scenario might seem to some, he's not isolated in his thinking. Earlier this year, SoftBank CEO Masayoshi Son spoke at Mobile World Conference 2017 about the concept of'singularity' – the point at which machine intelligence will surpass our own and start improving itself at an exponential rate – which he predicts will happen as soon as 2047.