Nov 14, 2015
Self-parallel parking car
Posted by Shailesh Prasad in categories: robotics/AI, transportation
Mitsubishi Self-Driving Car
This new Mitsubishi self-driving car is so advanced it can parallel park itself.
Mitsubishi Self-Driving Car
This new Mitsubishi self-driving car is so advanced it can parallel park itself.
Could the planet’s next catastrophe be averted by R2-D2? That’s the idea behind the DARPA Robotics Challenge, a robot Olympiad designed to create autonomous machines that can go where no man can or should go—nuclear disaster sites, minefields, Montauk over Labor Day weekend—and fix all the toxic messes we make. The stakes are $3.5 million. Oh, and possibly the future of mankind.
In the past couple of years, Google has been trying to improve more and more of its services with artificial intelligence. Google also happens to own a quantum computer — a system capable of performing certain computations faster than classical computers.
It would be reasonable to think that Google would try running AI workloads on the quantum computer it got from startup D-Wave, which is kept at NASA’s Ames Research Center in Mountain View, California, right near Google headquarters.
Google is keen on advancing its capabilities in a type of AI called deep learning, which involves training artificial neural networks on a large supply of data and then getting them to make inferences about new data.
No driver? No ticket.
That, at least, was the result when a police officer pulled over one of Google’s self-driving cars Thursday in Mountain View, California.
It might not be too long before a trip to the grocery store involves dodging Tally, a new robot designed to tootle from aisle to aisle while taking note of stock levels.
Tally’s Silicon Valley creators, Simbe Robotics, point out that most retailers currently rely on IT systems and manual labor to manage inventory, but call this method “costly and inaccurate.” Tally can apparently do full-store audits in a fraction of the usual time, keeping staff up to date on what items are running low so that shelves can be quickly refilled.
Simbie says Tally’s ability to carry out such “repetitive and laborious” auditing tasks means human staff can get on with serving customers directly.
Michio Kaku on A.I.
“Why compete with robots when we can take the best attributes of robots and incoporate it into our body?” — Dr. Michio Kaku. Do you agree?
Shape — Shifting Robotic Snake
It can morph into a lamp stand as quick as it provides email notifications: There’s not much this shapeshifting snake can’t do. http://voc.tv/1P6L9zh
A new story out in Breitbart that’s about AI, transhumanism, and politics: http://www.breitbart.com/tech/2015/11/08/trans-humanist-pres…overlords/
Zoltan Istvan is running for President in 2016, and hoping he might be one of the last humans to hold the job.
SANTA CLARA, California — Robotic spacecraft may ride the solar wind toward interstellar space at unprecedented speeds a decade or so from now.
Researchers are developing an “electric sail” (e-sail) propulsion system that would harness the solar wind, the stream of protons, electrons and other charged particles that flows outward from the sun at more than 1 million mph (1.6 million kilometers per hour).
“It looks really, really promising for ultra-deep-space exploration,” Les Johnson, of NASA’s Marshall Space Flight Center in Huntsville, Alabama, said of the e-sail concept here at the 100-Year Starship Symposium on Oct. 30. [Superfast Spacecraft Propulsion Concepts (Images)].
Posted by Jeff Dean, Senior Google Fellow, and Rajat Monga, Technical Lead.
Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.
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