In a test kitchen in a corner building in downtown Pasadena, Flippy the robot grabbed a fryer basket full of chicken fingers, plunged it into hot oil -- its sensors told it exactly how hot -- then lifted, drained and dumped maximally tender tenders into a waiting hopper. A few feet away, another Flippy eyed a beef patty sizzling on a griddle. With its camera eyes feeding pixels to a machine vision brain, it waited until the beef hit the right shade of brown, then smoothly slipped its spatula hand under the burger and plopped it on a tray. The product of decades of research in robotics and machine learning, Flippy represents a synthesis of motors, sensors, chips and processing power that wasn't possible until recently. Now, Flippy's success -- and the success of the company that built it, Miso Robotics -- depends on simple math and a controversial hypothesis of how robots can transform the service economy.
Self-driving cars are one of the high-risk artificial intelligence applications the European Union wants to regulate. The European Commission today unveiled its plan to strictly regulate artificial intelligence (AI), distinguishing itself from more freewheeling approaches to the technology in the United States and China. The commission will draft new laws--including a ban on "black box" AI systems that humans can't interpret--to govern high-risk uses of the technology, such as in medical devices and self-driving cars. Although the regulations would be broader and stricter than any previous EU rules, European Commission President Ursula von der Leyen said at a press conference today announcing the plan that the goal is to promote "trust, not fear." The plan also includes measures to update the European Union's 2018 AI strategy and pump billions into R&D over the next decade.
CUJO AI is using artificial intelligence (AI) and machine learning (ML) for its new online privacy and tracking platform, which is called Incognito. CUJO AI offers "digital life protection" through its AI solutions that are in use by network service providers and their customers. CUJO AI provides network, mobile and public Wi-Fi operators with a full-stack set of cloud and edge software that captures, processes, curates and acts on device-level network data. With Incognito, CUJO AI uses its AI engine, ML analysis and real-time traffic classification to help broadband users and service providers evaluate privacy threats in the data flows and then block elements to provide privacy protection. Incognito uses machine learning to analyze website requests and upstream responses, looking for third-party trackers such as cookies, browser fingerprinting techniques and tracking ads.
SAN FRANCISCO – As companies quickly adopt machine learning systems, cybercriminals are close behind scheming to compromise them. That worries legal experts who say a lack of laws swing open the door for bad guys to attack systems. During a panel session at RSA Conference 2020 this week, Cristin Goodwin, the assistant general counsel with Microsoft, said the number of machine learning related U.S. court cases is a mere 52. She noted most were related to patents, workplace discrimination and even gerrymandering. Few court cases addressed actual cyberattacks on machine learning systems – demonstrating a dangerous dearth in legal precedent around the technology.
The U.S. Energy Department earlier this month appointed former 3M Co. artificial-intelligence leader Cheryl Ingstad as the first director of its Artificial Intelligence and Technology Office, where she will oversee the DOE's AI activities. The mission of the AITO, which was formed in September 2019, is to coordinate the department's artificial-intelligence activities, which includes scaling AI projects across the DOE, sharing best practices and reducing duplicate projects. The office also is charged with facilitating partnerships...
Chris Smith and Phil Sansom delve into the world of artificial Intelligence (AI) to find out how this emerging technology is changing the way we practise medicine... Mike - I think this is an area where AI stands a really good chance of making quite dramatic improvements to very large numbers of people's lives. Carolyn - Save lives and reduce medical complications. Beth - That's a concern - when machine-learning algorithms learn the wrong things. Andrew - Frankly revolutionary productivity that we are now starting to see from these AI approaches in drug design. Lee - It will replace all manual labor in all research laboratories. And then suddenly everyone can collaborate. Phil - But what was previously sci-fi is now closer to reality. AI technology exists, and there's a brand new frontier where it's being applied to the world of healthcare. Chris - But this isn't the AI you see in the movies.
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The AI-Base Module will be implemented across 5,000 Atal Tinkering Labs (ATL), empowering 2.5 million students. The module is likely to be introduced to ATL students on 27 February. The module contains activities, videos and experiments that enable students to work through and learn the various concepts of AI. Niti Aayog CEO Amitabh Kant said the AI module was critical since it was targeted at young children. "This is path breaking, it combines playing and academics and our job is to make things very interesting. We want to make artificial intelligence a great fun, so that children can enjoy it, they can evolve and learn and take India forward," said Kant. "This is the first ever industry government academia initiative on such a scale to keep the school students abreast of latest technologies," said Mission Director Atal Innovation Mission, NITI Aayog R Ramanan.
Denis Shiryaev specializes in using AI-powered neural networks to upscale historical footage, and his latest work is this short clip of Moscow, Tverskaya Street in 1896 that was originally captured by Charles Moisson from the Lumière company. The neural networks were used upscale to 60FPS, boost image resolution with ESRGAN (general dataset), resort video sharpness, remove blur, remove compression artifacts, and colorization. Neural networks are basically networks of artificial neurons, or mathematical functions that transform a set of input values into an output value. The key feature is that they can be trained, so if you have lots of example inputs whose "correct" outputs are known, you can tune the parameters of the network to make it more likely to produce correct answers. The goal is that this training will generalize, in which once you've trained the neural networks to produce the right answer for inputs the network has seen before, it will produce good answers for inputs it hasn't seen as well.
Far more people in the US may have died from opioids in the past two decades than previously reported, according to a new analysis of unclassified drug deaths carried out using machine-learning algorithms. Elaine Hill and her colleagues at the University of Rochester, New York, were examining data on drug overdose deaths when they realised that 22 per cent of such cases reported between 1999 and 2016 were listed on death certificates as overdoses without specifying the substance involved. "We found that remarkable, given the scale of the issue," says team member Andrew Boslett. The team tried to estimate what percentage of these unclassified deaths were due to opioids by analysing the coroners' and medical reports from opioid overdoses and unclassified overdoses. First, the researchers used machine-learning algorithms to analyse deaths that had been recorded as being due to opioid overdose.