John Charles Martin "Johnny" Nash was a teen when he first started hearing a voice in his head. A born-again Christian, he interpreted this voice as God speaking to him. Once, he walked into the middle of a busy highway because the voice said he should. He was an accomplished chess player and math whiz, but playing and calculating became increasingly hard. It wasn't long until a psychiatrist diagnosed him with schizophrenia.

Underwater explorers no longer need to get their feet wet to uncover some of history's most notable shipwrecks. Oxygen tanks and flippers are being replaced by underwater robots. Sam Macdonald is the president of Deep Trekker, a Canadian company that makes underwater bots. Since Deep Trekker's creation in 2010, the company's robots have become a standard in the aquaculture industry, but, Macdonald says, that wasn't the original intent. "One night I dropped a flashlight off my boat and I started thinking about having an underwater robot that we could do things with including retrieving lost items, but also for exploring all of these ship wrecks," Macdonald said.

There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps! Deep learning is a branch of machine learning, employing numerous similar, yet distinct, deep neural network architectures to solve various problems in natural language processing, computer vision, and bioinformatics, among other fields. Deep learning has experienced a tremendous recent research resurgence, and has been shown to deliver state of the art results in numerous applications. In essence, deep learning is the implementation of neural networks with more than a single hidden layer of neurons.

Honda Motor Co. will spearhead its artificial intelligence efforts out of a new lab in Tokyo so that researchers can work closely with its engineers to commercialize the technology. Honda, based in Tokyo, will start the r&d center next year and combine existing AI teams in Silicon Valley, Europe and Japan at the downtown location, according to Yoshiyuki Matsumoto, president of the automaker's largely independent research arm. In choosing Tokyo over Silicon Valley, the carmaker is betting closer interaction between its scientists and developers will lead to AI-enabled products consumers want, he said in an interview. Advances in artificial intelligence are sprouting like "bamboo shoots after rain," so it's time to find commercial uses for the technology by marrying research with Japan's traditional strength in hardware, Matsumoto said. "We won't make much difference if we did the same things as everyone else in Silicon Valley. And not everyone has succeeded there."

GPU's Role in Artificial Intelligence Advances Featured at Conference By Wayne Rash Posted 2016-10-26 Print NEWS ANALYSIS: The confluence of big data, massively powerful computing resources and advanced algorithms is bringing new artificial intelligence capabilities to scientific research. WASHINGTON, D.C.--Massively parallel supercomputing hardware and advanced artificial intelligence algorithms are being harnessed to deliver powerful new research tools in science and medicine, according to Dr. France A. Córdova, director of the National Science Foundation. Córdova spoke Oct. 26 at the GPU Technology Conference organized by Nvidia, a company that got its start making video cards for PCs and gaming systems and now manufactures advanced graphics processors for high-performance servers and supercomputers. Córdova, who is directing long-term research in AI at the NSF, said the research there is being used already in the Cancer Moonshot project currently spearheaded by Vice President Joe Biden, whose son Beau Biden died of brain cancer in 2015 at the age of 46. The Cancer Moonshot is a major effort to focus resources and funding on the fight to cure cancer on a scale similar to the original mission by NASA to land on the moon.

JAPANESE carmaker Honda Motor Co. has chosen to headquarter its artificial intelligence (AI) research hub in Tokyo, saying its own home ground will enable closer interactions between scientists and researchers compared to Silicon Valley. According to Bloomberg, the research and development (R&D) center will launch in 2017 and will consolidate all the company's current AI teams from Silicon Valley, Europe, and Japan in Tokyo. Yoshiyuki Matsumoto, president of Honda's research arm, said in an interview that the carmakers chose Tokyo due to saturation in the San Francisco Bay Area, which is home to thousands of tech companies and startups. Honda chooses Tokyo over Silicon Valley for AI research centre – THE BUSINESS TIMES https://t.co/r25wCfNHKy Matsumoto was quoted saying: "We won't make much difference if we did the same things as everyone else in Silicon Valley. And not everyone has succeeded there."

Until recently, [Europe's] contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO. London is Europe's startup centre, mixing capital, proximity to markets, and world-class research hubs.

Earth experienced a near miss on Sunday night as an asteroid passed near our planet. While the object posed no threat, it did get close, dodging us by only 310,000 miles. Astronomers spotted this the Near Earth Object (NEO) thanks to an experimental "intruder alert" NASA program to detect and track potentially harmful space rocks passing close to our planet. The asteroid is one of many recent discoveries of NEOs as NASA and other international programs continue to refine techniques for discovering these potential threats from space. In recent years, scientists have become increasingly concerned with the question of protecting Earth from potential collisions.

Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics.

The analysis of nonstationary time series is of great importance in many scientific fields such as physics and neuroscience. In recent years, Gaussian process regression has attracted substantial attention as a robust and powerful method for analyzing time series. In this paper, we introduce a new framework for analyzing nonstationary time series using locally stationary Gaussian process analysis with parameters that are coupled through a hidden Markov model. The main advantage of this framework is that arbitrary complex nonstationary covariance functions can be obtained by combining simpler stationary building blocks whose hidden parameters can be estimated in closed-form. We demonstrate the flexibility of the method by analyzing two examples of synthetic nonstationary signals: oscillations with time varying frequency and time series with two dynamical states. Finally, we report an example application on real magnetoencephalographic measurements of brain activity.