Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Tokyo-based start-up Tsubame Industries has developed a 14.8-feet, four-wheeled robot that looks like "Mobile Suit Gundam" from the wildly popular Japanese animation series, and it can be yours for $3 million. Called ARCHAX after the avian dinosaur archaeopteryx, the robot has cockpit monitors that receive images from cameras hooked up to the exterior so that the pilot can manoeuvre the arms and hands with joysticks from inside its torso. The 3.5-ton robot, which will be unveiled at the Japan Mobility Show later this month, has two modes: the upright'robot mode' and a'vehicle mode' in which it can travel up to 6 miles per hour.
If you grew up loving the Transformers franchise, you may have dreamed of the day technology would bring your favourite robots to life. Now, a Japanese engineering company has turned that childhood fantasy into a reality, with its 3.5-ton robot that transforms into a car within moments. The Archax, designed by Tsubame Industries, is a 15ft-tall piloted mech able to go from a standing'robot mode' into a streamlined'vehicle mode' with the press of a button. Incredible footage shows a pilot climbing in through the cockpit hatch and manipulating the robot's arms and hands before switching modes and driving off. Tsubame Industries has currently made five of the giant robots and plans to sell them for $3 million (£2.46m) each.
The Tokyo Institute of Technology, also known as Tokyo Tech, has revealed that the TSUBAME 3.0 supercomputer scheduled to be installed this summer will provide 47 half precision (16-bit) petaflops of performance, making it one of the most powerful machines on the planet for artificial intelligence computation. For Tokyo Tech, the use of NVIDIA's latest P100 GPUs is a logical step in TSUBAME's evolution. The original 2006 system used ClearSpeed boards for acceleration, but was upgraded in 2008 with the Tesla S1040 cards. In 2010, TSUBAME 2.0 debuted with the Tesla M2050 modules, while the 2.5 upgrade included both the older S1050 and S1070 parts plus the newer Tesla K20X modules. Bringing the P100 GPUs into the TSUBAME lineage will not only help maintain backward compatibility for the CUDA applications developed on the Tokyo Tech machines for the last nine years, but will also provide an excellent platform for AI/machine learning codes.
The H-IIA rocket took off from Tanegashima Space Center in Kagoshima Prefecture at 10:26 a.m., carrying a Shikisai climate research satellite and a low-altitude test satellite named Tsubame. The Japan Aerospace Exploration Agency (JAXA), working in conjunction with Mitsubishi Heavy Industries Ltd., hopes the success will allow it to orbit multiple satellites using one rocket in the future. Until now, each JAXA satellite had been launched individually. The H-IIA rocket released the Shikisai first before decelerating and dropping to an altitude of around 480 km to release the Tsubame. Shikisai will travel on a path that will see it return to the same orbit after a certain period, allowing it to investigate changes in water circulation and the mechanisms involved in climate change over a set period.
The Global Scientific Information and Computing Center at the Tokyo Institute of Technology has been at the forefront of accelerated computing, and well before GPUs came along and made acceleration not only cool but affordable and normal. But its latest system, Tsubame 3.0, being installed later this year, the Japanese supercomputing center is going to lay the hardware foundation for a new kind of HPC application that brings together simulation and modeling and machine learning workloads. The hot new idea in HPC circles is not just being able to run machine learning workloads side by side with simulations, but to use machine learning to further accelerate the simulation, and we have a future feature story underway, based on conversations with researchers at TiTech and at Oak Ridge National Laboratory, where the "Summit" hybrid CPU-GPU system is being built for the US Department of Energy, about this very topic. Suffice it to say, the idea is to integrate machine learning into the simulation, to do some of the computationally intensive stuff in a new way. So, as part of a climate model, you teach the system using machine learning to predict the weather by watching movies of the weather, or in astronomy, you use machine learning to remove the noise from the signal to find the interesting bits of a star field.