Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
As the subway roared into Tokyo's Tsukishima Station a gust of wind tossed up a stray face mask, sending it sailing above the platform. Hisashi Taniguchi watched the piece of fabric fluttering about. He immediately pictured in his mind a microscopic view in which the wind dispersed -- in the air he was breathing -- countless viral particles that had been trapped between the mask's filters. There needs to be an efficient system to disinfect these public spaces, he thought. This was back in March, when the spread of COVID-19 was just starting to pick up speed in the capital.
Researchers from MIT, Stanford University, and the University of Pennsylvania have devised a method for predicting failure rates of safety-critical machine learning systems and efficiently determining their rate of occurrence. Safety-critical machine learning systems make decisions for automated technology like self-driving cars, robotic surgery, pacemakers, and autonomous flight systems for helicopters and planes. Unlike AI that helps you write an email or recommends a song, safety-critical system failures can result in serious injury or death. Problems with such machine learning systems can also cause financially costly events like SpaceX missing its landing pad. Researchers say their neural bridge sampling method gives regulators, academics, and industry experts a common reference for discussing the risks associated with deploying complex machine learning systems in safety-critical environments. In a paper titled "Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems," recently published on arXiv, the authors assert their approach can satisfy both the public's right to know that a system has been rigorously tested and an organization's desire to treat AI models like trade secrets.
After months of staying home, perhaps you've done enough sourdough baking to last a lifetime. As we head into winter, it's time to mix it up. These cool DIY kits, which are all an extra 20% off through September 21 with the coupon code VIPSALE20, will help you build cool gadgets and models like programmable cars, game consoles, and more. Take on the challenge of constructing a game console yourself with the MAKERbuino Educational DIY Game Console. Usually $79.99, this unique product is an extra 20% off with code VIPSALE20, bringing the price to about $64.
George Konidaris still remembers his disheartening introduction to robotics. "When you're a young student and you want to program a robot, the first thing that hits you is this immense disappointment at how much you can't do with that robot," he says. Most new roboticists want to program their robots to solve interesting, complex tasks -- but it turns out that just moving them through space without colliding with objects is more difficult than it sounds. Fortunately, Konidaris is hopeful that future roboticists will have a more exciting start in the field. That's because roughly four years ago, he co-founded Realtime Robotics, a startup that's solving the "motion planning problem" for robots.
With the rise of AI at the edge comes a whole host of new requirements for memory systems. Can today's memory technologies live up to the stringent demands of this challenging new application, and what do emerging memory technologies promise for edge AI in the long-term? The first thing to realize is that there is no standard "edge AI" application; the edge in its broadest interpretation covers all AI-enabled electronic systems outside the cloud. That might include "near edge," which generally covers enterprise data centers and on-premise servers. Further out are applications like computer vision for autonomous driving.
We're hearing more and more about Industry 4.0, but do we know what it is, how does it help our company, what are its advantages and drawbacks? The concept of Industry 4.0 was first mentioned at the Hannover fair (fair dedicated to industrial technology) of 2011 with the intention of starting a project that would carry out the design and development of the intelligent factory associated with the fourth industrial revolution. A vision of computerized manufacturing with all its processes interconnected with each other making use of the Internet of Things (IoT), today called the Industrial Internet of Things (IIoT). This revolution is marked by the emergence of new technologies such as robotics, analytics, artificial intelligence, cognitive technologies, and nanotechnology, among others. Organizations must identify the technologies that best meet their needs to invest in them.
Here we discuss the five technology megatrends of our times and how their adoption was accelerated by the reality of trying to sustain business as well as live and cope in a COVID-19-impacted world. While many were juggling at-home schooling of children and professional responsibilities amid stay-at-home orders because of COVID-19, the digital transformation accelerated. Here are five of the biggest technology trends of our times and how the coronavirus pandemic accelerated their adoption. This acceleration will change how businesses operate and compete as they emerge out of the pandemic.