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."

MIT's Nightmare Machine puts Halloween scares in hands of AI

"In real-world applications, sometimes people want to know why the model makes the predictions it does," said graduate student Tao Lei. "One major reason that doctors don't trust machine-learning methods is that there's no evidence." "You may not want to just verify that the model is making the prediction in the right way; you might also want to exert some influence in terms of the types of predictions that it should make," commented Tommi Jaakkola, an MIT professor of electrical engineering and computer science. The researchers address neural nets trained on textual data. To enable interpretation of a neural net's decisions, the group divide the net into two modules.

The idea of artificial intelligence (AI) -- autonomous computers that can learn independently -- makes some people extremely uneasy, regardless of what the computers in question might be doing. Those individuals probably wouldn't find it reassuring to hear that a group of researchers is deliberately training computers to get better at scaring people witless. The project, appropriately enough, is named "Nightmare Machine." Digital innovators in the U.S. and Australia partnered to create an algorithm that would enable a computer to understand what makes certain images frightening, and then use that data to transform any photo, no matter how harmless-looking, into the stuff of nightmares. Images created by Nightmare Machine are unsettling, to say the least.

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MIT's Nightmare Machine puts Halloween scares in hands of AI

Since the inception of networked video surveillance, many companies have worked to develop a variety of different analytics to enhance the value of the systems to end-users. Some vendors have been more successful than others in being able to provide reliable video analytics to their customers and, after a period in which the technology was greeted with a healthy amount of skepticism, it has now become commonplace in many surveillance installations across a wide range of vertical markets. While the use cases for analytics have changed, the technology itself has remained relatively the same – algorithms are created to search for certain pre-defined actions within a camera's field-of-view. However, the evolution of artificial intelligence (AI) means that the future of analytics will lie not in the creation of static algorithms but on the ability of machines to learn what operators should and should not be alerted to. For example, for those installations that use virtual trip wires for notification of perimeter breaches, many analytics cannot decipher between a human coming onto the property, which would obviously be the primary concern, vs. an animal, which would be of little interest.

At the inaugural O'Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities. There is no better place to imbibe the most up-to-date tech zeitgeist than at an O'Reilly Media event as has been proven again and again ever since the company put together the first Web-related meeting (WWW Wizards Workshop in July 1993). The conference was organized by Ben Lorica and Roger Chen, with Peter Norvig and Tim O'Reilly acting as honorary program chairs. Here's a summary of what I heard there, embellished with a few references to recent AI news and commentary: In contrast to traditional software, explained Peter Norvig, Director of Research at Google, "what is produced [by machine learning] is not code but more or less a black box--you can peak in a little bit, we have some idea of what's going on, but not a complete idea." Tim O'Reilly recently wrote in "The great question of the 21st century: Whose black box do you trust?": Because many of the algorithms that shape our society are black boxes… because they are, in the world of deep learning, inscrutable even to their creators – [the] question of trust is key.

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.

GPUs are highly specialized computer chips that were originally designed to accelerate the processing of video information in a computer. The conversation we had led to a discussion about the future of computer hardware, and I became more excited than ever. Our new friend had us over to the Silicon Valley headquarters of his company, where we walked into a room that had the very latest demonstrations for their GPU processors. We saw video games with graphics so realistic and impressive that it made everything I had seen previously look simplistic and cartoonish. Just as impressively, the games reacted to my control button pushes instantaneously.