Movidius breakthrough puts artificial intelligence on a USB stick

#artificialintelligence

Irish chip maker Movidius has created the world's first deep learning USB stick that can add artificial intelligence (AI) to future products from self-driving cars to robots, and drones that will learn to think for themselves. Entitled the Fathom Neural Compute Stick, the device will sell for less than 100 and will allow powerful neural networks to be moved out of the cloud and deployed on new products like robots and drones. It is the latest breakthrough for the Dublin company, which has been winning major multi-million dollar deals with Google and drone maker DJI. 'With Fathom, every robot, big and small, can now have state-of-the-art vision capabilities' – DR YANN LECUN, NEW YORK UNIVERSITY "Any organisation can now add deep learning or machine intelligence to devices using the USB stick and create products that will be accessible to broader markets," Movidius co-founder David Moloney told Siliconrepublic.com. "We've already seen how the auto industry has been outflanked by Tesla and this is also starting to affect other industries.



RFID Camera Lock Smart Mailbox

IEEE Spectrum Robotics

A self-locking mailbox could someday flag down delivery drones and intelligently screen your driveway for intruders. Columbus State University computer scientist Lydia Ray presented the technology, called the ADDSMART project, during a 20 October session at the annual IEEE Ubiquitous Computing, Electronics, and Mobile Communication Conference in New York City. The project aims to achieve two goals: clearly marking addresses for autonomous vehicles, and reducing the energy and data storage costs of home surveillance systems. An early prototype mailbox attachment suggests that the trick, in both cases, may be radio-frequency identification. Powered by an Arduino Yun processor, one component of the ADDSMART device controls a high-frequency 13.56-MHz RFID reader, USB camera, passive-infrared motion sensor, solenoid lock, and an onboard Wi-Fi module.


Facebook's Mark Zuckerberg savaged by politicians from around the world for avoiding them

The Independent - Tech

Facebook has been savaged by politicians from across the globe after boss Mark Zuckerberg once again refused to answer questions. The company was accused of undermining democratic institutions and failing to take responsibility for the damage it had done to the world. But the hearing was marked by the fact that Mr Zuckerberg had not arrived, despite a request that came from a coalition of lawmakers from around the world. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.


Decentralized dynamic task allocation for UAVs with limited communication range

arXiv.org Artificial Intelligence

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workloadbased solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the bestcase scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem. Keywords: task allocation, unmanned aerial vehicles, max-sum, decentralized 1. Introduction Unmanned Aerial Vehicles (UAVs) are an attractive technology for largearea surveillance [1]. Today, there are readily available UAVs that are reasonably cheap, have many sensing abilities, exhibit a long endurance and can communicate using radios. UAVs have traditionally been controlled either remotely or by following externally-designed flight plans. Requiring human operators for each UAV implies a large, specialized and expensive human workforce. Likewise, letting UAVs follow externally prepared plans introduces a single point of failure (the planner) and requires UAVs with expensive (satellite) radios to maintain continuous communication with a central station. These constraints are acceptable in some application domains, other applications require more flexible techniques. For instance, consider a force of park rangers tasked with the surveillance of a large natural park. Upon reception of an emergency notification, the rangers must assess the situation as quickly as possible.