This work was truly a team effort, so please check out the credits of the repo and give everyone there a warm e-hug. Earlier today, I released instructions for compiling TensorFlow on the Raspberry Pi 3, as well as a pre-built Python wheel that can be used to install it directly. I'm hoping that this will enable some really cool projects involving both portable GPIO device-based learning and experimentation with TensorFlow's distributed runtime. This has been an effort that has gone on since TensorFlow was open-sourced, and I'm really happy to be part of the group of people that made it happen. There are two main attractions to the repository: a pre-built Python wheel that can be used to easily install TensorFlow on a Raspberry Pi 3, and a step-by-step guide to building TensorFlow yourself.

We'll Train Them Like Dogs Jason Tanz WIRED "Whether you're a member of the coding elite or someone who barely feels competent to futz with the settings on your phone--don't get used to it. Our machines are starting to speak a different language now, one that even the best coders can't fully understand...The neural network's operations are largely opaque and inscrutable. It is, in other words, a black box. And as these black boxes assume responsibility for more and more of our daily digital tasks, they are not only going to change our relationship to technology--they are going to change how we think about ourselves, our world, and our place within it." Drew Endy and Laurie Zoloth Cosmos Magazine "In a world where human reproduction has already become a competitive marketplace, with eggs, sperm and embryos carrying a price, it is easy to make up far stranger uses of human genome synthesis capacities...Given that human genome synthesis is a technology that can completely redefine the core of what now joins all of humanity together as a species, we argue that discussions of making such capacities real, like today's Harvard conference, should not take place without open and advance consideration of whether it is morally right to proceed."

Japan's Sony said it plans to build up its artificial intelligence (AI) business and eventually turn it into a major revenue source, beginning with an investment in a U.S. startup. The electronics maker has invested an undisclosed sum in California-based Cogitai. The year-old firm, founded by three researchers, focuses on technology that allows machines to learn continually and autonomously from interaction in the real world. The move comes a time when major technology companies such as Facebook, Apple, and Alphabet's Google are spending aggressively on AI ventures. "From an objective perspective, we are lagging behind," Hiroaki Kitano, chief executive of Sony Computer Science Laboratories, said in an interview.

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.