Amazon has announced a full range of new spherical Echo devices, new motorised smart display, a camera drone that flies around your house, a game-streaming service and more. In a streaming presentation, the firm showed off a smorgasbord of new devices from its various brands, including Ring, Eero Fire and Echo. The new standard Echo ditches its cylindrical shape for a fabric-covered ball design with Amazon's characteristic light-ring in the base to indicate when it is listening to you. It has a new 3in woofer and two tweeters with Dolby processing for stereo sound and automatic adjustment to the acoustics of your room. It also has Amazon's new AZ1 artificial intelligence chip for greater local processing of voice and other actions for increased privacy and speed.

EU countries are already strong in digital industry and business-to-business applications. With a high-quality digital infrastructure and a regulatory framework that protects privacy and freedom of speech, the EU could become a global leader in the data economy and its applications. AI could help people with improved health care, safer cars and other transport systems, tailored, cheaper and longer-lasting products and services. It can also facilitate access to information, education and training. The need for distance learning became more important because of the Covid-19 pandemic. AI can also make workplace safer as robots can be used for dangerous parts of jobs, and open new job positions as AI-driven industries grow and change.

Regina Barzilay, a professor at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), is the first winner of the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, a new prize recognizing outstanding research in AI. Barzilay started her career working on natural-language processing. After surviving breast cancer in 2014, she switched her…

The full-self driving car is about to take a step closer to reality. Tesla CEO Elon Musk said Tuesday that the test version of company's Autopilot system will be released in "a month or so." While he didn't describe its capabilities, Musk said that once it's out, "you'll see what it's like. It's clearly going to work." A number of startups and established automakers have been racing to develop self-driving technology.

RAILWAY AGE, SEPTEMBER 2020 ISSUE: Whether it's the track structure or the equipment that operates on it, there are many things that the naked eye cannot readily see. Increasingly, machine vision technology is becoming the best way to identify potential flaws before they lead to failures. "The various machine vision technologies deployed detect thousands of conditions each year that could potentially lead to accidents," says Robert Coakley, Director of Business Development, ENSCO Rail. Compared to manual visual inspections, he says, autonomous machine vision offers advantages of speed, reduced track occupancy, inspection frequency and consistency. The equipment is installed on revenue service trains, can perform inspections at track speed and does not require the additional occupancy of a hi-rail vehicle.

UNESCOs Member States have announced there has been'major progress' in the development of a global normative instrument for the ethics of artificial intelligence (AI). In November 2019, the United Nations secretary-general Antonio Guterres congratulated the organisation for taking up this challenge, declaring that AI is a critical frontier issue for the whole UN system and the whole world. In March this year, UNESCO asked 24 experts with multidisciplinary experience in the ethics of artificial intelligence to develop a draft recommendation on the ethics of AI. UNESCO then launched a wide process of consultations to obtain the many points of view of stakeholders. This involved experts from 155 countries, members of the public (through a global online survey), United Nations agencies, major stakeholders from the sector such as Google, Facebook and Microsoft, and the world of academe with the University of Stanford and the Chinese Academy of Sciences.

Researchers at the University of Pittsburgh School of Medicine and Carnegie Mellon University College of Engineering have created a machine-learning algorithm that can detect subtle signs of osteoarthritis--too abstract to register in the eye of a trained radiologist--on an MRI scan taken years before symptoms even begin. These results will publish this week in PNAS. With this predictive approach, patients could one day be treated with preventative drugs rather than undergoing joint replacement surgery. "The gold standard for diagnosing arthritis is X-ray. As the cartilage deteriorates, the space between the bones decreases," said study co-author Kenneth Urish, M.D., Ph.D., associate professor of orthopaedic surgery at Pitt and associate medical director of the bone and joint center at UPMC Magee-Womens Hospital.

Facial-recognition tech can see around hoodies or big shades, so pair them with a face covering. Plus, you'll get protection against coronavirus particles and tear gas. There are makeup tutorials online for edgy face paint intended to trick face-recognizing algorithms, but these designs are unproven. Also, it's probably easier for humans to track you if you look like a member of Insane Clown Posse. Make yourself less memorable to both humans and machines by wearing clothing as dark and pattern-free as your commitment to privacy.

Next year, a squad of souped-up Dallara race cars will reach speeds of up to 200 miles per hour as they zoom around the legendary Indianapolis Motor Speedway to discover whether a computer could be the next Mario Andretti. The planned Indy Autonomous Challenge--taking place in October 2021 in Indianapolis--is intended for 31 university computer science and engineering teams to push the limits of current self-driving car technology. There will be no human racers sitting inside the cramped cockpits of the Dallara IL-15 race cars. Instead, onboard computer systems will take their place, outfitted with deep-learning software enabling the vehicles to drive themselves. In order to win, a team's autonomous car must be able to complete 20 laps--which equates to a little less than 50 miles in distance--and cross the finish line first in 25 minutes or less.

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.