Nvidia unveiled its Jetson TX2 platform to power drones with good artificial intelligence. The platform includes the Jetson TX2 "embedded AI supercomputer," a chip and its surruonding hardware that can power 4K video drones that consume only about 7.5 watts of power. Drones with the TX2 solution can operate two cameras simultaneously. The Jetson 3.0 platform was designed for AI "at the edge" of the network, rather than in the cloud, or Internet-connected data center. Drones with cameras can capture a huge amount of data. This means Jetson has to handle a lot of the processing of data at the edge, in the device itself, rather than transferring all of that data to the cloud, said Deepu Talla, vice president and general manager of Nvidia's Tegra business unit, at a press event in San Francisco.

A super computer the size of a credit card that can power artificial intelligence (AI) such as robots, drones and smart cameras, has been unveiled by computer graphics firm Nvidia. Revealed at an event in San Francisco, the super intelligent yet tiny device is called the Jetson TX2 and is part of an Internet of Things (IoT) platform that looks to make the world's cities smarter and safer by enabling a "new class of intelligent machines" across manufacturing and retail industries. In what could help kick start the robot revolution, the Jetson TX2 is aimed at anyone developing some form of AI, from researchers and start-up companies to schools and enterprises. It is said to deliver better AI computing "on the edge" to run larger, deeper neural networks, meaning devices that it powers will be smarter and run with higher accuracy and faster response times. This could improve tasks in current technology such as image classification, navigation and speech recognition, Nvidia said.

The Raspberry Pi may be the most widely known board computer being sold, but Nvidia's Jetson TX2 is one of the fastest. The Jetson TX2, unveiled Tuesday, is a full Linux computer on a tiny board the size of a Raspberry Pi. It's designed to help make robots, drones and other devices that rely on computer vision applications. The board's main attraction is a GPU based on Nvidia's latest Pascal architecture, which is also in the company's fastest GPUs, like the Tesla P100. The Pascal GPU brings computer vision to robots and drones, allowing them to recognize objects and navigate around obstacles.

Nvidia has a new generation of its Jetson embedded computing platform for devices at the edge of a network, including things like traffic cameras, manufacturing robotics, smart sensors and more. The Jetson TX2 has twice the performance of its predecessor, the TX1, or it can also redirect efficiency to power savings, using less than half the power consumption of the original to achieve the same processing abilities. The TX2 uses a Pascal-based GPU, as well as two 64-bit Nvidia quad-core ARM chips, with 8GB of RAM on board and 32GB of fast flash storage. It also features built-in 802.11ac Wi-Fi networking, Bluetooth connectivity and 1GB Ethernet for wired connections.

Germany and Japan, March 8, 2017 --- Deutsches Forschungszentrum für Künstliche Intelligenz (German Research Center for Artificial Intelligence, 'DFKI') and Hitachi, Ltd. (Hitachi) today announced the joint development of AI (artificial intelligence) technology for human activity recongnition of workers using wearable devices. The AI technology performs real-time recognition of workers' activities by integrating technology in eye-tracking glasses*1to recognize gazed objects with technology in armband devices to recognize action. The recognition ability of each activity is achieved by having the AI understand the tools or parts used at the production site as well as anticipated actions through Deep Learning*2. DFKI and Hitachi will use this newly developed AI technology to assist operations and prevent human error, to contribute to enhancing quality and efficiency on the front line of manufacturing. In line with initiatives such as Industry 4.0*3in Germany and Society 5.0*4in Japan, the manufacturing industry is accelerating steps towards innovating production using AI and robotics, and the automation of menial tasks.

"Jetson TX2 brings powerful AI capabilities at the edge, making possible a new class of intelligent machines. These devices will enable intelligent video analytics that keep our cities smarter and safer, new kinds of robots that optimize manufacturing, and new collaboration that makes long-distance work more efficient." The chipmaker has also revealed that around a dozen partner companies are already using the TX2 in different ways. Cisco is using the platform for an all-in-one collaboration device that enables screen sharing, interactive whiteboarding and video conferencing. A company called Fellow Robots relies on TX2 to record on-shelf inventory and to detect items and availability in store.

First they figured out how to play checkers and backgammon. Then they mastered chess, Go, "Jeopardy!" and even a few Atari video games. Now computers can challenge humans at the poker table -- and win. DeepStack, a software program developed at the University of Alberta's Computer Poker Research Group, took on 33 professional poker players in more than 44,000 hands of Texas hold'em. Overall, the program won by a significantly higher margin than if it had simply folded in each round, according to a new study in Science.

We saw a transition in the financial ecosystem from the Keynesian "animal spirits" of desk traders and investors to "microbe spirits" by computer algorithms simpler than animals or humans -- but just much, much, much faster. To understand these new phenomena we need a complexity approach. Econophysicists and financial gurus in complexity are no more in the fringe. What our work suggests is that since this war is already under way, and because from our paper it seems to correlate very well with observable crashes in the markets over timescales such as months, then any government watchdog/regulator charged with controlling their financial system also ought to have its own'troops on the ground'.» Not that this is wrong, but you can see that this level of competition would be very ferocious -- which is what happens in the markets.»

It works rather like the BB-8 robot from Star Wars, and could change the way we drive. Goodyear has revealed a radical new spherical tire powered by AI and linked to the car by magnetic force so it can rotate on any axis in any direction. The firm says it will be able to sense road conditions and adapt accordingly, turning itself into either a wet or dry weather configuration instantly. Working like human muscles, the smart tire can re-shape the individual sections of the tire's tread design, adding'dimples' for wet conditions (left) or smoothing the tread for dry conditions (right) Made of super-elastic polymer, the tire's bionic skin has a flexibility similar to that of human skin, allowing it to expand and contract. This outer layer covers a foam-like material that is strong enough to remain flexible despite the weight of a vehicle.

The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage. In this work, we model the order of accumulation of such mutations during the progression, which eventually leads to the disease, by means of probabilistic graphic models, i.e., Bayesian Networks (BNs). We investigate how to perform the task of learning the structure of such BNs, according to experimental evidence, adopting a global optimization meta-heuristics. In particular, in this work we rely on Genetic Algorithms, and to strongly reduce the execution time of the inference -- which can also involve multiple repetitions to collect statistically significant assessments of the data -- we distribute the calculations using both multi-threading and a multi-node architecture. The results show that our approach is characterized by good accuracy and specificity; we also demonstrate its feasibility, thanks to a 84x reduction of the overall execution time with respect to a traditional sequential implementation.