It provides operating system software for other manufacturers who make their phones. Google, in a way, is recognizing something that Apple has really based its business on for a long time, which is the close integration of hardware and software really has some big advantages. In what looked like a challenge to Apple, Google unveiled Pixel earlier this month and bragged its camera was superior to the iPhone's.

After years in the labs, artificial intelligence (AI) is being unleashed at last. Google, Microsoft and Facebook have all made their own AI APIs open source in recent months, while IBM has opened Watson (pictured above) for business and Amazon has purchased AI startup Orbeus. These announcements have not drawn much media attention, but are hugely significant. "In the long run, I think we will evolve in computing from a mobile-first world to an AI-first world," says Google CEO Sundar Pichai. What does the appearance of AI bots and machine learning on the open market mean for business, IT, big data, and for sellers of physical hardware? The AI APIs now opening up are essentially free platforms on which companies can build incredibly powerful analytics tools.

Amazon has crowned the latest champion in its robotic picking challenge -- an annual competition that looks for robots that could one day work in the company's warehouses. It's basically American Idol, but for robotic arms that can grab items off a shelf and put them back again. Competitors are asked to handle a range of products, from toiletries to clothes, and then scored on speed and accuracy in stocking shelves. This year's contest was won by a joint team from the TU Delft Robotics Institute in the Netherlands and the company Delft Robotics (both named after the city of Delft). The team's robot managed to pick items from a mock Amazon warehouse shelf at a speed of around 100 an hour, reports TechRepublic, with a failure rate of 16.7 percent.

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Machine learning will drop the cost of making predictions, but raise the value of human judgement. To really understand the impact of artificial intelligence in the modern world, it's best to think beyond the mega-research projects like those that helped Google recognize cats in photos. According to professor Ajay Agrawal of the University of Toronto, humanity should be pondering how the ability of cutting edge A.I. techniques like deep learning--which has boosted the ability for computers to recognize patterns in enormous loads of data--could reshape the global economy. Making his comments at the Machine Learning and the Market for Intelligence conference this week by the Rotman School of Management at the University of Toronto, Agrawal likened the current boom of A.I. to 1995, when the Internet went mainstream. Gaining enough mainstream traction, the Internet ceased to be seen as a new technology.

To really understand the impact of artificial intelligence in the modern world, it's best to think beyond the mega-research projects like those that helped Google recognize cats in photos. According to professor Ajay Agrawal of the University of Toronto, humanity should be pondering how the ability of cutting edge A.I. techniques like deep learning--which has boosted the ability for computers to recognize patterns in enormous loads of data--could reshape the global economy. Making his comments at the Machine Learning and the Market for Intelligence conference this week by the Rotman School of Management at the University of Toronto, Agrawal likened the current boom of A.I. to 1995, when the Internet went mainstream. Gaining enough mainstream traction, the Internet ceased to be seen as a new technology. Instead, it was a new economy where businesses could emerge online.

Adversarial input can fool a machine-learning algorithm into misperceiving images. Over the past five years, machine learning has blossomed from a promising but immature technology into one that can achieve close to human-level performance on a wide array of tasks. In the near future, it is likely to be incorporated into an increasing number of technologies that directly impact society, from self-driving cars to virtual assistants to facial-recognition software. Yet machine learning also offers brand-new opportunities for hackers. Malicious inputs specially crafted by an adversary can "poison" a machine learning algorithm during its training period, or dupe it after it has been trained.

Machine Learning (ML) tasks are becoming pervasive in a broad range of applications, and in a broad range of systems (from embedded systems to data centers). As computer architectures evolve toward heterogeneous multi-cores composed of a mix of cores and hardware accelerators, designing hardware accelerators for ML techniques can simultaneously achieve high efficiency and broad application scope. While efficient computational primitives are important for a hardware accelerator, inefficient memory transfers can potentially void the throughput, energy, or cost advantages of accelerators, that is, an Amdahl's law effect, and thus, they should become a first-order concern, just like in processors, rather than an element factored in accelerator design on a second step. In this article, we introduce a series of hardware accelerators (i.e., the DianNao family) designed for ML (especially neural networks), with a special emphasis on the impact of memory on accelerator design, performance, and energy. We show that, on a number of representative neural network layers, it is possible to achieve a speedup of 450.65x over a GPU, and reduce the energy by 150.31x on average for a 64-chip DaDianNao system (a member of the DianNao family).

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. I really don't know much about this beyond what's in the video, but we don't see a lot of Russian robots around here, so: It looks to be a project from the Russian equivalent of DARPA, designed to go into space by 2021. This video of Agile Justin feeling up different kinds of rods was a finalist for both "IROS Best Paper on Cognitive Robotics" and "IROS Best Student Paper": This is cool because until this point, building pneumatic robots required making molds and casting custom parts.

Department of Transportation Secretary Anthony Foxx recently released a 116-page policy document that aims to guide automakers and technologists on best-practices when it comes to the manufacturing and deployment of autonomous vehicle features. Apple, which has been rumored to be building a car, recently laid off employees of its automotive project and pivoted from making a car to creating autonomous software, according to reports. Another aftermarket self-driving tech company recently completed a successful 120-mile beer delivery without anyone at the wheel. A big rig cab equipped with sensors made by Otto, a startup bought by Uber recently for $670 million, made the delivery of Budweiser beer while its driver rested in the sleeper berth during most of the trip down Colorado's Interstate 25.