At the end of September, amidst its usual flurry of fall hardware announcements, Amazon debuted two especially futuristic products within five days of each other. The first is a small autonomous surveillance drone, Ring Always Home Cam, that waits patiently inside a charging dock to eventually rise up and fly around your house, checking whether you left the stove on or investigating potential burglaries. The second is a palm recognition scanner, Amazon One, that the company is piloting at two of its grocery stores in Seattle as a mechanism for faster entry and checkout. Both products aim to make security and authentication more convenient--but for privacy-conscious consumers, they also raise red flags. Amazon's latest data-hungry innovations are not launching in a vacuum.
Powerful local processors can remove the need for a device to have a cloud connection. Along the coastline of Australia's New South Wales (NSW) state hovers a fleet of drones, helping to keep the waters safe. Earlier this year, the drones helped lifeguards at the state's Far North Coast rescue two teenagers who were struggling in heavy surf. The drones are powered by artificial-intelligence (AI) and machine-vision algorithms that constantly analyze their video feeds and highlight items that need attention: say, sharks, or stray swimmers. This is the same kind of technology that enables Google Photos to sort pictures, a home security camera to detect strangers, and a smart fridge to warn you when your perishables are close to their expiration dates.
Ring's Always Home Cam is an indoor security camera drone. Ring on Thursday introduced a new product to its growing roster of smart home devices -- the Ring Always Home Cam. Unlike the Amazon company's other security cameras, the Always Home Cam is a flying camera drone that docks when it isn't in use. The Ring Always Home Cam will be available in 2021 for $250. Along with this hardware announcement, Ring says you'll be able to turn on end-to-end encryption in the Ring app's Control Center "later this year" in an effort to improve the security of its devices.
I always know a new product is excellent when its makers describe it as "next-level." I hear you moan, on seeing the new, wondrous Ring Always Home Cam. Also: When is Prime Day 2020? Oh, how can you be such a killjoy? When Amazon's Ring describes it as "Next-Level Compact, Lightweight, Autonomously Flying Indoor Security Camera," surely you leap toward your ceiling and exclaim: "Finally, something from Amazon I actually want! A drone that flies around my living room!"
I actually had to double-check my calendar to make sure today wasn't April Fool's. Because watching the intro video of an indoor surveillance drone operated by Amazon seemed like just the sort of geeky joke you'd expect on April 1. But it isn't April Fools, and besides, Google has always been the one with the twisted sense of humor. Amazon has always been the one with the twisted sense of world domination. This was a serious press briefing.
Consumer drones are notorious for being hard to fly at first, before you learn what you're doing, and the odds are, you will crash it. So how about a drone that flies automatically, in the home as a roaming security camera? One the manufacturer promises won't crash into a ceiling fan or a flower pot, because it has obstacle avoidance technology. And flies back into its cradle when the flight is complete. Jamie Siminoff, the founder of the Amazon Ring subsidiary, insists that it will because there's an app for it.
When discussing Artificial Intelligence (AI), a common debate is whether AI is an existential threat. The answer requires understanding the technology behind Machine Learning (ML), and recognizing that humans have the tendency to anthropomorphize. We will explore two different types of AI, Artificial Narrow Intelligence (ANI) which is available now and is cause for concern, and the threat which is most commonly associated with apocalyptic renditions of AI which is Artificial General Intelligence (AGI). To understand what ANI is you simply need to understand that every single AI application that is currently available is a form of ANI. These are fields of AI which have a narrow field of specialty, for example autonomous vehicles use AI which is designed with the sole purpose of moving a vehicle from point A to B. Another type of ANI might be a chess program which is optimized to play chess, and even if the chess program continuously improves itself by using reinforcement learning, the chess program will never be able to operate an autonomous vehicle.
Autonomous things (AuT), or the Internet of autonomous things (IoAT), is a rising term for the innovative advancements that are expected to carry computers into the physical environment as autonomous entities without human guidance, openly moving and collaborating with people and different items. AuTs are stocked with sensors, AI and analytical abilities to improve the things they can do. With that impact, each machine can settle on its own choice and complete tasks autonomously. These gadgets are fit for working independently. Autonomous devices need to interface with their environmental surroundings to stay away from accidents.
A fleet of 40 autonomous robots has been deployed on Arizona State University's Tempe campus, making it the latest institution to implement robot food-delivery from Starship Technologies, according to a university release. ASU's food-service provider, Aramark, has partnered with the delivery robot's creator, Starship, to provide the nearly four dozen robots that will serve ASU's on-campus community. According to the release, the robots will retrieve food and drinks from "on-campus retailers to be delivered anywhere on campus, within minutes." Starship is already providing the food-delivery services to over 10 campuses across the country. The robots rolled out to Northern Arizona University's campus in 2019.
Recent researches on robotics have shown significant improvement, spanning from algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged robots, drones, and autonomous vehicles, are now widely applied in diverse scenarios. However, the high computation and data complexity of robotic algorithms pose great challenges to its applications. On the one hand, CPU platform is flexible to handle multiple robotic tasks. GPU platform has higher computational capacities and easy-touse development frameworks, so they have been widely adopted in several applications. On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios. With specialized designed hardware logic and algorithm kernels, FPGA-based accelerators can surpass CPU and GPU in performance and energy efficiency. In this paper, we give an overview of previous work on FPGA-based robotic accelerators covering different stages of the robotic system pipeline. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications, to serve as a guide for future work. Therefore, the computation and storage complexity, as well as real-time and power constraints of the robotic system, Over the last decade, we have seen significant progress hinders its wide application in latency-critical or power-limited in the development of robotics, spanning from algorithms, scenarios . Various robotic systems, like Therefore, it is essential to choose a proper compute platform manipulators, legged robots, unmanned aerial vehicles, selfdriving for the robotic system. CPU and GPU are two widely cars have been designed for search and rescue , , used commercial compute platforms. CPU is designed to exploration , , package delivery , entertainment , handle a wide range of tasks quickly and is often used to  and more applications and scenarios. These robots are develop novel algorithms. A typical CPU can achieve 10-on the rise of demonstrating their full potential. Take drones, 100 GFLOPS with below 1GOP/J power efficiency . In a type of aerial robots, for example, the number of drones contrast, GPU is designed with thousands of processor cores has grown by 2.83x between 2015 and 2019 based on the running simultaneously, which enable massive parallelism. The typical GPU can perform up to 10 TOPS performance and registered number has reached 1.32 million in 2019, and the become a good candidate for high-performance scenarios. Recently, FFA expects this number will come to 1.59 billion by 2024.