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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."

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

Early A.I. product offerings are making it more efficient to plan meetings and provide customer service. Artificial intelligence (A.I.) has become one of the hottest topics in business recently, with developers building the technology into everything from cars to refrigerators. Some have expressed skepticism about AI's potential, but early product offerings have revealed technology that appears to actually enhance the work professionals do rather than replace it. Jamie Domenici, Salesforce's VP of Small Business, is excited about the way A.I. is affecting sales and marketing. I sat down with her at this year's Dreamforce in San Francisco, where attendees got a close up look at the company's new A.I. tool Einstein, which the company is embedding into its products and services.

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.

Who here remembers taking computer programming in school? Whether you learned programming by punching holes in a never ending series of cards, or by writing simple DOS or other computer language commands, the fact remained that computers needed an incredibly precise set of instructions to accomplish a task. The more complicated the task, the more complicated your instructions had to be. Machine learning is inherently different. Rather than telling a computer exactly how to solve a problem, the programmer instead tells it how to go about learning to solve the problem for itself.

IBM Hosted a Big Party for Watson With its CEO as the Star By Darryl K. Taft Posted 2016-10-28 Print NEWS ANALYSIS: IBM CEO Ginni Rometty keynoted the company's World of Watson conference in Las Vegas and drove home that Watson is the AI platform for business. LAS VEGAS–IBM held a big party for its Watson cognitive computing technology here this week in the form of the IBM World of Watson (IBM WoW) 2016 conference. Originally known as IBM Insight, the company augmented the name of the event to focus on Watson and the importance of cognitive technology to the company's future. Indeed, IBM CEO Ginni Rometty said hundreds of millions of users interact with Watson-powered systems today and, by the end of next year, she expects there to be more than 1 billion Watson users. If nothing else, the World of Watson event was a paean to the continuing adoption of Watson across a broad variety of industries and technology areas, including medicine and health care, financial services, retail, manufacturing and travel, as well as technology areas such as cloud computing, the internet of things (IoT), software development and more.

It's not about what it can do, but the effects of its prioritization As buzzwords become ubiquitous they become easier to tune out. We've finely honed this defense mechanism, for good purpose. It's better to focus on what's in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn't help you. VR could eat all media, but it's hardware requirements keep it many years away from common use.

AI -- looms on the horizon as the next game-changer for business operations, including sales. Salespeople tend to adopt technologies as consumers first and then expand use into their professional lives, so sales leaders thinking about AI will play may be focusing on assistive intelligence like Amazon Echo, Cortana and Siri. There is strong consumer demand: Strategy Analytics predicts that the use of voice-activated AI assistants will rise to almost 350 million by 2020. It's also easy to imagine the ways a voice-enabled assistant like Siri could become a great sales sidekick. Picture AI providing a spoken-worded guided selling system in CPQ, interacting with sales management systems while the salesperson is behind the wheel on a road trip, producing custom reports based on the salesperson's spoken requests and then displaying the results on a screen.

Recent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenia brain. However, most of the studies focused on the correlation value to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered functional connectivity in the schizophrenia brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenia brain relative to healthy brain with various similarity measures using time-frequency domain analysis, while participants performing the working memory task in the MRI scanner. To assess the distinctiveness of the BOLD connectivity in the schizophrenia, recognition performances of pattern classifier machine trained by each similarity measure were compared. Interestingly, classifier machine trained by time-lagging patterns of low-frequency fluctuation (LFF) produced highest classifying accuracy than the machines trained by other measures. Also, classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern. These results indicate that characteristics of altered functional network in the schizophrenia brain can hardly defined with single aspect of relationship across the multiple brain regions.