The European Union's executive arm proposed a bill that would limit police use of facial-recognition software in public and ban the marketing or use of certain kinds of AI systems, in one of the broadest efforts yet to regulate high-stakes applications of artificial intelligence. The bill proposed on Wednesday would also create a list of so-called high-risk uses of AI that would be subject to new supervision and standards for their development and use, such as critical infrastructure, college admissions and loan applications. Regulators could fine a company up to 6% of its annual world-wide revenue for the most severe violations, though in practice EU officials rarely if ever mete out their maximum fines. The bill is one of the broadest of its kind to be proposed by a Western government, and part of the EU's expansion of its role as a global tech enforcer. In recent years, the EU has sought to take a global lead in drafting and enforcing new regulations aimed at taming the alleged excesses of big tech companies and curbing potential dangers of new technologies, in areas ranging from digital competition to online-content moderation.

BRUSSELS (AFP) - The European Union unveils a plan on Wednesday (April 21) to regulate the sprawling field of artificial intelligence, aimed at making Europe a leader in the new tech revolution while reassuring the public against Big Brother-like abuses. "Whether it's precision farming in agriculture, more accurate medical diagnosis or safe autonomous driving, artificial intelligence will open up new worlds for us. But this world also needs rules," European Commission President Ursula von der Leyen said in her state-of-the-union speech in September last year. "We want a set of rules that puts people at the centre." The Commission, the EU's executive arm, has been preparing the proposal for over a year and a debate involving the European Parliament and 27 member states is to go on for months more before a definitive text is in force.

The European Union on Wednesday unveiled strict regulations to govern the use of artificial intelligence, a first-of-its-kind policy that outlines how companies and governments can use a technology seen as one of the most significant, but ethically fraught, scientific breakthroughs in recent memory. Presented at a news briefing in Brussels, the draft rules would set limits around the use of artificial intelligence in a range of activities, from self-driving cars to hiring decisions, school enrollment selections and the scoring of exams. It would also cover the use of artificial intelligence by law enforcement and court systems -- areas considered "high risk" because they could threaten people's safety or fundamental rights. Some uses would be banned altogether, including live facial recognition in public spaces, though there would be some exemptions for national security and other purposes. The rules have far-reaching implications for major technology companies including Amazon, Google, Facebook and Microsoft that have poured resources into developing artificial intelligence, but also scores of other companies that use the technology in health care, insurance and finance.

In just the last two years, artificial intelligence has become embedded in scores of medical devices that offer advice to ER doctors, cardiologists, oncologists, and countless other health care providers. The Food and Drug Administration has approved at least 130 AI-powered medical devices, half of them in the last year alone, and the numbers are certain to surge far higher in the next few years. Several AI devices aim at spotting and alerting doctors to suspected blood clots in the lungs. Some analyze mammograms and ultrasound images for signs of breast cancer, while others examine brain scans for signs of hemorrhage. Cardiac AI devices can now flag a wide range of hidden heart problems.

The European Union's executive arm proposed a bill that would limit police use of facial-recognition software in public and ban the marketing or use of certain kinds of AI systems, in one of the broadest efforts yet to regulate high-stakes applications of artificial intelligence. The bill proposed on Wednesday would also create a list of so-called high-risk uses of AI that would be subject to new supervision and standards for their development and use, such as critical infrastructure, college admissions and loan applications. Regulators could fine a company up to 6% of its annual world-wide revenue for the most severe violations, though in practice EU officials rarely if ever mete out their maximum fines. The bill is one of the broadest of its kind to be proposed by a Western government, and part of the EU's expansion of its role as a global tech enforcer. In recent years, the EU has sought to take a global lead in drafting and enforcing new regulations aimed at taming the alleged excesses of big tech companies and curbing potential dangers of new technologies, in areas ranging from digital competition to online-content moderation.

This is why the European Commission has proposed a set of actions to boost excellence in AI, and rules to ensure that the technology is trustworthy. The Regulation on a European Approach for Artificial Intelligence and the update of the Coordinated Plan on AI will guarantee the safety and fundamental rights of people and businesses, while strengthening investment and innovation across EU countries. Once the AI system is on the market, authorities are in charge of the market surveillance, users ensure human oversight and monitoring, while providers have a post-market monitoring system in place. Providers and users will also report serious incidents and malfunctioning. In 2018, the Commission and EU Member States took the first step by joining forces through a Coordinated Plan on AI that helped lay the ground for national strategies and policy developments.

One of the victims killed in last week's Tesla car crash in Texas, which police suspect to have involved the vehicle's autopilot mode, was William Varner, a 58-year-old anaesthesiologist, his employer said. In the incident on Saturday, two men were killed after their 2019 Tesla Model S, travelling at a high speed, failed to negotiate a curve and crashed into a tree, catching fire, police reports noted. According to the police, one of the victims was found in the passenger seat and the other in the back seat, while nobody was at the driving seat at the time of impact, raising doubts on the involvement of the car's autopilot mode. However, Tesla CEO Elon Musk tweeted on Monday saying that data logs retrieved from the crashed car by the company ruled out the use of the autopilot system. "Data logs recovered so far show Autopilot was not enabled ... Moreover, standard Autopilot would require lane lines to turn on, which this street did not have," he tweeted.

BERKELEY, California – The fatal crash of a Tesla with no one apparently behind the wheel has cast a new light on the safety of semiautonomous vehicles and the nebulous U.S. regulatory terrain they navigate. Police in Harris County, Texas, said a Tesla Model S smashed into a tree on Saturday at high speed after failing to negotiate a bend and burst into flames, killing one occupant found in the front passenger seat and the owner in the back seat. Tesla Chief Executive Elon Musk tweeted on Monday that preliminary data downloaded by Tesla indicate the vehicle was not operating on Autopilot, and was not part of the automaker's "Full Self-Driving" (FSD) system. Tesla's Autopilot and FSD, as well as the growing number of similar semi-autonomous driving functions in cars made by other automakers, present a challenge to officials responsible for motor vehicle and highway safety. U.S. federal road safety authority, the National Highway Traffic Safety Administration (NHTSA), has yet to issue specific regulations or performance standards for semi-autonomous systems such as Autopilot, or fully autonomous vehicles (AVs).

The Center for Connected Autonomy and Artificial Intelligence is housed in the state-of-the-art Engineering East building on the Boca Raton campus. Artificial intelligence technologies are quickly evolving and changing every aspect of industry in the United States and globally. Artificial intelligence enables autonomy by robotic mobility and control learned through examples and computational decision-making and estimation from data using past training data experience. It has the ability to process large amounts of data much faster and make predictions more accurately than humanly possible. To rapidly advance the field of artificial intelligence and autonomy, Florida Atlantic University's College of Engineering and Computer Science recently unveiled its "Center for Connected Autonomy and Artificial Intelligence" (CCA-AI), a cutting-edge center designed to accelerate the development of innovative artificial intelligence and autonomy solutions.

To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximization problem presenting the first-order adversaries embedded within the outer minimization of the training loss. To generalize the adversarial robustness over different perturbation types, the adversarial training method has been augmented with the improved inner maximization presenting a union of multiple perturbations e.g., various $\ell_p$ norm-bounded perturbations. However, the improved inner maximization only enjoys limited flexibility in terms of the allowable perturbation types. In this work, through a gating mechanism, we assemble a set of expert networks, each one either adversarially trained to deal with a particular perturbation type or normally trained for boosting accuracy on clean data. The gating module assigns weights dynamically to each expert to achieve superior accuracy under various data types e.g., adversarial examples, adverse weather perturbations, and clean input. In order to deal with the obfuscated gradients issue, the training of the gating module is conducted together with fine-tuning of the last fully connected layers of expert networks through adversarial training approach. Using extensive experiments, we show that our Mixture of Robust Experts (MoRE) approach enables flexible integration of a broad range of robust experts with superior performance.