In a world full of things that are much, much bigger than they are, insects manage to do pretty well with getting around. Some of the most successful insects are the social and cooperative ones, like ants, which can do incredible things such as using their bodies to create structures to get themselves across rivers. In Ron Fearing's lab at UC Berkeley, Carlos Casarez was inspired by behaviors like these to modify some VelociRoACHes to help each other climb up and over obstacles with the aid of an adorable little magnetic tether system. Robots like UC Berkeley's VelociRoACH (Velocity Robotic Autonomous Crawling Hexapod) are, relative to most other robots, very simple and inexpensive to manufacture, with high speed and good maneuverability. They'd be just the thing you might want to send a swarm of into disaster areas to search for survivors, except that they're absolutely terrible at climbing over obstacles.

This study suggests a new prediction model for chaotic time series inspired by the brain emotional learning of mammals. We describe the structure and function of this model, which is referred to as BELPM (Brain Emotional Learning-Based Prediction Model). Structurally, the model mimics the connection between the regions of the limbic system, and functionally it uses weighted k nearest neighbors to imitate the roles of those regions. The learning algorithm of BELPM is defined using steepest descent (SD) and the least square estimator (LSE). Two benchmark chaotic time series, Lorenz and Henon, have been used to evaluate the performance of BELPM. The obtained results have been compared with those of other prediction methods. The results show that BELPM has the capability to achieve a reasonable accuracy for long-term prediction of chaotic time series, using a limited amount of training data and a reasonably low computational time.

We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech. Some of the key advancements that contribute to our system include: a nearest-neighbor discriminant analysis (NDA) approach (as opposed to LDA) for intersession variability compensation in the i-vector space, the application of speaker and channel-adapted features derived from an automatic speech recognition (ASR) system for speaker recognition, and the use of a DNN acoustic model with a very large number of output units ( 10k senones) to compute the frame-level soft alignments required in the i-vector estimation process. We evaluate these techniques on the NIST 2010 SRE extended core conditions (C1-C9), as well as the 10sec-10sec condition. To our knowledge, results achieved by our system represent the best performances published to date on these conditions. For example, on the extended tel-tel condition (C5) the system achieves an EER of 0.59%. To garner further understanding of the remaining errors (on C5), we examine the recordings associated with the low scoring target trials, where various issues are identified for the problematic recordings/trials. Interestingly, it is observed that correcting the pathological recordings not only improves the scores for the target trials but also for the nontarget trials.

In the last few years, an increasing amount of public-private initiatives have adopted IoT solutions, ranging from smart grids to energy efficiency applications. The unprecedented growth of the urban population, meanwhile, highlights the importance of increased public-private cooperation in smart cities and the circular economy to deliver more scalable low-carbon development models. For example, IBM's China Research Lab is working with the Beijing Environmental Protection Bureau (BEPB) and other municipal authorities in China to scale-up its air quality forecasting system, as part of IBM's Green Horizons Initiative. The system uses pollution data from a network of sensors spread throughout Beijing. Through complex modeling and machine learning techniques, it returns increasingly precise forecasts of air pollution levels in different neighborhoods.

The rise of the chatbot and the fall of the app-based economy have recently taken centre stage, thanks in large part to Mark Zuckerberg's announcement that Facebook was opening up Messenger to third-party bots. The potential for brands to interact with users at the same level they do with their peers is immense. As Kik's Ivar Chan says, "In a world where messenger apps have surpassed social networks, companies need to expand their digital presence to these greenfield pastures." OK, he's a partner at a leading chatbot specialist, so he does have a dog in the fight… but he's also right. This isn't about bots and artificial intelligence, this is about ubiquity of messaging as platform.

Baidu, China's largest search engine, is under investigation after college student with a rare form of cancer said it promoted a fraudulent treatment center. Baidu, China's largest search engine, is under investigation after college student with a rare form of cancer said it promoted a fraudulent treatment center. Chinese health and Internet authorities have launched an investigation into Baidu, the country's largest search engine, following the death of a college student who accused Baidu of misleading him to a fraudulent cancer treatment. Experts believe the scandal will damage the credibility of Baidu's search results, and its long-term economic prospects. On Monday, news of the government investigation caused Baidu's stock to tumble by nearly 8% on the NASDAQ.

The building site of the future is going to look very different to the one we are all used to today. Instead of men in high-visibility jackets and hard hats, there are going to be drones buzzing overhead, robotic bulldozers and 3D printers churning out new structures. That at least is the hope of those making technological solutions. But first they have to convince the traditionally risk-averse construction industry that such change is necessary. US start-up Skycatch is using drones on some high-profile building projects - although it cannot name them because of commercial sensitivity.

When Tor.com published Daniel H. Wilson's short story "The Nostalgist" in 2009, the author wasn't yet a leading voice on robotics in fiction. Sure, the Carnegie Mellon Ph.D. had won a Rave Award from WIRED in 2006 for his book How To Survive A Robot Uprising, but his bestselling novel Robopocalypse was still a few years off. So when filmmaker Giacomo Cimini wanted to adapt Wilson's story into a film, it wasn't a big ask. After Wilson's novel hit big, though--with Stephen Spielberg and Drew Goddard tapped to direct and write the movie adaptation--the writer had to take more time to think about it. "I was just this Italian director living in London who wanted to adapt one of his short stories," says Cimini.

About one in four owners of premium cars would buy a self-driving vehicle, according to Volvo's chief executive, who has vowed to make the technology affordable. Håkan Samuelsson said Volvo had had a deluge of interest in its "Drive me" trial in London next year, when 100 drivers will test its new autonomous driving technology on motorways and major roads. The Swedish carmaker plans to start selling vehicles equipped with the technology as early as 2020. Volvo will test the technology in Gothenburg this year. It is also looking into conducting a trial in China, where congestion and road safety are major issues.

Think of the gear you can't live without: The smartphone you constantly check. The camera that goes with you on every vacation. The TV that serves as a portal to binge-watching and -gaming. Each owes its influence to one model that changed the course of technology for good. Some of these, like Sony's Walkman, were the first of their kind. Others, such as the iPod, propelled an existing idea into the mainstream. Some were unsuccessful commercially, but influential nonetheless. And a few represent exciting but unproven new concepts (looking at you Oculus Rift). Rather than rank technologies--writing, electricity, and so on--we chose to rank gadgets, the devices by with consumers let the future creep into their present. The list--which is ordered by influence--was assembled and deliberated on at (extreme) length by TIME's technology and business editors, writers and reporters.