Toyota Motor North America said Saturday it will work with DEKA Research and Development -- the company founded by Segway inventor Dean Kamen -- to help launch the next-generation iBOT motorized wheelchairs that are designed to navigate stairs and lift users to stand face-to-face with companions. For its part, Toyota will license balancing technologies held by DEKA and its affiliate for medical rehabilitative therapy and potentially other purposes. The companies said they continue to engage in ongoing discussions about how Toyota can further support DEKA and its mobility assistance technology. DEKA stopped selling the iBOT specialized wheelchair in 2009 but its next generation is poised for a comeback due in part to Toyota's involvement, Kamen said. Financial details were not immediately available.

Robots can paint, but when it comes to writing, they shouldn't quit their day jobs. That's the combined conclusion from results of two contests announced this week. On the upside, artificial intelligence created some pretty impressive works for RobotArt.org's The contest challenged artists and engineers to create a robot that painted like a real artist. Essentially, the aim was to get "as many teams as possible to set up a robot that can do any sort of painting," the contest site explains.

The goal in this competition is to take an image of a handwritten single digit, and determine what that digit is. As the competition progresses, we will release tutorials which explain different machine learning algorithms and help you to get started. The data for this competition were taken from the MNIST dataset. The MNIST ("Modified National Institute of Standards and Technology") dataset is a classic within the Machine Learning community that has been extensively studied.

On Thursday, we asked Google about its new custom-made chip for artificial intelligence called a Tensor Processing Unit, or TPU. Google politely declined to answer Recode's questions, saying only that "more information is coming later." Later in the day, after we posted our questions, Google changed its mind and gave us some short answers to a few -- but not all -- of our questions, via an email from a spokesperson. This is a big one that Google did not answer. It probably is pre-trained, but we don't know for sure.

Today, Google convened 7,000 developers and journalists at a popular outdoor concert venue in Silicon Valley and gave a two-hour state of the union on the future of arguably the most powerful and ambitious company in the world. In front of a packed crowd and tens of thousands watching via live stream, the company brought its best and brightest minds on stage and unveiled to the world that Google is playing catch-up with a slew of products and services we've already seen before from other companies. There's Google Assistant, a conversational AI chat and search bot (Facebook's M/Microsoft's bot projects/Apple's Siri/Viv/literally everyone has a bot these days); Google Home, a voice-powered home entertainment and task hub (Amazon's dreadfully popular and beloved Echo); Allo and Duo, two mobile messaging and mobile video apps (Facebook's Messenger goliath with nearly 1 billion users); and Daydream, Google's Android-powered virtual reality platform, headset, and multimedia content hub (Oculus). But make no mistake, Google, led by its fresh-faced, immaculately tailored blue jacket–wearing CEO, Sundar Pichai, didn't tiptoe around the stage today. Quite the opposite, Google's 2016 I/O keynote address was confident, enthusiastic, and more than just a little impressive.

In a recent Scientific American article entitled "Springtime for AI: The Rise of Deep Learning," computer scientist Yoshua Bengio explains why complex neural networks are the key to true artificial intelligence as people have long envisioned it. It seems logical that the way to make computers as smart as humans is to program them to behave like human brains. However, given how little we know of how the brain functions, this task seems more than a little daunting. So how does deep learning work? Evidently, these so-called "hidden layers" play a key role in breaking down visual components to decode the image as a whole.

A successful new product delivers a compelling user experience for an unmet need. I think this is why there is such a natural marriage between technology and product development; as technology progresses it opens up feasibility of meeting previously unmet or poorly met needs. When something big happens, like the web, or the advent of pocket supercomputers aka smart phones, we all get to dream up ways these things can be leveraged to deliver value to users. New technology is particularly exciting for entrepreneurs and underdogs. Established companies eventually get a lock on new markets. For example, it is increasingly difficult for a solo developer to build a profitable app, but when the iPhone and Android first launched, there was a window there where those who quickly saw ways smartphones could meet previously poorly met needs, like easily sharing photos or finding nearby places to eat, could make a killing.

Between the University of Washington, a thriving tech community, and strong research institutions, like the Allen Institute for Artificial Intelligence (AI2), many of the rapid developments in AI are playing out in Seattle. Perhaps that's why the White House has selected the Emerald City for its first public workshop on artificial intelligence. The Office of Science and Technology Policy will co-host the first of four events on artificial intelligence at the University of Washington May 24. The workshop, put on by the UW's Tech Policy Lab and School of Law, will explore issues such as policy, logistical applications, and safety, as they relate to AI. Speakers include AI2 CEO and UW Professor of Computer Science and Engineering Oren Etzioni, White House Deputy U.S. CTO Edward Felten, Microsoft Principal Researcher Kate Crawford, and other industry experts. The workshops are intended "to spur public dialogue on artificial intelligence and machine learning and identify challenges and opportunities related to this emerging technology," writes Felton in a White House blog post.

January 28, 2016 Abstract: GT Watson is a set of cognitive systems designed, developed and deployed at Georgia Tech on top of IBM's Watson cognitive system. It includes cognitive systems for question answering in support of biologically inspired design, ecological modeling, and education and training in artificial intelligence. GT Watson adds layers of semantic processing to Watson and explores the use of external data sources. For example, in the project on ecological modeling, it exploits Smithsonian Institution's Encyclopedia of Life, the world's largest database of biological species. In this team talk, we will present several instances of GT Watson .

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