I see great potential for the TrueNorth chip as we migrate towards Quantum & Singularity. TrueNorth is an interim chip that assists researchers, engineers, etc. in their efforts to mimic the human brain’s nuero sensors and processing for robotics, BMI technology, etc.
The new IBM supercomputer chip mimics the human brain by using an architecture with 1 million neurons. Nevertheless, its true purpose remains in question for a project with massive public funding.
Interesting; however, I can not wait to see Nividia’s new car especially with their new GPU chip & DGX-1 technology.
While companies such as Google chase the fully autonomous car, Toyota is taking a more measured approach toward a “guardian angel” car that would seize control only when an accident is imminent.
But as starkly different as those approaches are, they both will require a wide range of data-intensive technologies, according to Gill Pratt (pictured), chief executive officer of the Toyota Research Institute, a research center focused on AI and robotics. He spoke at the GPU Technology Conference in San Jose today.
Toyota has made a huge bet– a billion dollars over five years, in fact–not only on semiautonomous cars but robots that could help older people with indoor mobility. The Toyota Research Institute, which will have facilities near Stanford University and the Massachusetts Institute of Technology, is intended to focus both on what Toyota calls outdoor mobility (cars) as well as indoor mobility (robots).
The theoretical results of a piece of international research published in Nature, whose first author is Ion Errea, a researcher at the UPV/EHU and DIPC, suggest that the quantum nature of hydrogen (in other words, the possibility of it behaving like a particle or a wave) considerably affects the structural properties of hydrogen-rich compounds (potential room-temperature superconducting substances). This is in fact the case of the superconductor hydrogen sulphide: a stinking compound that smells of rotten eggs, which when subjected to pressures a million times higher than atmospheric pressure, behaves like a superconductor at the highest temperature ever identified. This new advance in understanding the physics of high-temperature superconductivity could help to drive forward progress in the search for room-temperature superconductors, which could be used in levitating trains or next-generation supercomputers, for example.
Superconductors are materials that carry electrical current with zero electrical resistance. Conventional or low-temperature ones behave that way only when the substance is cooled down to temperatures close to absolute zero (−273 °C o 0 degrees Kelvin). Last year, however, German researchers identified the high-temperature superconducting properties of hydrogen sulphide which makes it the superconductor at the highest temperature ever discovered: −70 °C or 203 K.
Beautiful future lays ahead in QC.
Quantum physics not only explains how matter behaves at the subatomic level, but is also used to create many devices in our everyday lives, from lasers and transistors to GPS and mobile phones. The next wave of innovation could lead to unbreakable encryption and computers that are up to one million times faster. On 6 April, Parliament’s Science and Technology Options Assessment (STOA) unit organised a workshop to discuss with experts the potential of these new quantum technologies.
Exploiting the quirks of the quantum world
Quantum theory looks at matter at the subatomic level — down to electrons. And that behaviour, compared to our everyday world, is very strange. For example, an electron can be in different places at the same time, a phenomenon known as superposition. Or it can interact with another particle at a large distance thanks to an effect called “entanglement”.
Could we see race car driver careers become all AI? Nvidia is testing the concept.
Formula E is going completely autonomous with the all-new Roborace series slated for the upcoming race season. At its GTC developer conference, Nvidia announced these autonomous, electric race cars will be powered by Nvidia Drive PX 2, a supercomputer built for self-driving cars.
Drive PX 2 is powered by 12 CPU cores and four Pascal GPUs that provides eight teraflops of computer power. The supercomputer-in-a-box is vital to deep learning and trains artificial intelligence to adapts to different driving conditions, including asphalt, rain and dirt.
At a time when PCs have become rather boring and the market has stagnated, the Graphics Processing Unit (GPU) has become more interesting and not for what it has traditionally done (graphical user interface), but for what it can do going forward. GPUs are a key enabler for the PC and workstation market, both for enthusiast seeking to increase graphics performance for games and developers and designers looking to create realistic new videos and images. However, the traditional PC market has been in decline for several years as consumer shift to mobile computing solutions like smartphones. At the same time, the industry has been working to expand the use of GPUs as a computing accelerator because of the massive parallel compute capabilities, often providing the horsepower for top supercomputers. NVIDIA has been a pioneer in this GPU compute market with its CUDA platform, enabling leading researchers to perform leading edge research and continue to develop new uses for GPU acceleration.
Now, the industry is looking to leverage over 40 years of GPU history and innovation to create more advanced computer intelligence. Through the use of sensors, increased connectivity, and new learning technique, researchers can enable artificial intelligence (AI) applications for everything from autonomous vehicles to scientific research. This, however, requires unprecedented levels of computing power, something the NVIDIA is driven to provide. At the GPU Technology Conference (GTC) in San Jose, California, NVIDIA just announced a new GPU platform that takes computing to the extreme. NVIDIA introduced the Telsa P100 platform. NVIDIA CEO Jen-Hsun Huang described the Tesla P100 as the first GPU designed for hyperscale datacenter applications. It features NVIDIA’s new Pascal GPU architecture, the latest memory and semiconductor process, and packaging technology – all to create the densest compute platform to date.
