False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.

The prospect of collusive agreements being stabilized via the use of pricing algorithms is widely discussed by antitrust experts and economists. However, the literature is often lacking the perspective of computer scientists, and seems to regularly overestimate the applicability of recent progress in machine learning to the complex coordination problem firms face in forming cartels. Similarly, modelling results supporting the possibility of collusion by learning algorithms often use simple market simulations which allows them to use simple algorithms that do not produce many of the problems machine learning practitioners have to deal with in real-world problems, which could prove to be particularly detrimental to learning collusive agreements. After critically reviewing the literature on algorithmic collusion, and connecting it to results from computer science, we find that while it is likely too early to adapt antitrust law to be able to deal with self-learning algorithms colluding in real markets, other forms of algorithmic collusion, such as hub-and-spoke arrangements facilitated by centralized pricing algorithms might already warrant legislative action.

In the future, if armies deploy autonomous robot soldiers and they fire on the wrong targets, who will we hold responsible - the general who deployed them or their designer, Singapore law professor Simon Chesterman asks in We, the Robots.

On September 2, 2021, the US District Court for the Eastern District of Virginia granted the United States Patent and Trademark Office's (USPTO's) motion for summary judgement, finding that an artificial intelligence (AI) system cannot be named as an inventor on a patent. The action concerned two patent applications that Stephen Thaler had filed with the USPTO, which he alleged should not have been rejected by the Office. The USPTO had rejected the applications on the basis that no natural person was identified as an inventor. Thaler argued that a patent application for an AI-generated invention should list the AI system as the inventor when the AI system has met the invention criteria. Thaler alleged that he developed and applied advanced AI systems that are capable of generating patentable output under conditions where no natural person traditionally meets inventorship criteria. Thaler is the owner of "DABUS," an AI machine that "invented" a light beacon that flashes in a new and inventive manner to attract attention, and a beverage container based on fractal geometry.

READY FOR ITS CLOSE-UP: Artificial intelligence has long been hyped as a game changer in health care: Remember this 2012 prediction that computers will replace 80 percent of doctors? But it's been much harder to get a sense of the real-world scale of the phenomenon. Is AI a perpetual technology of the future? Or is it starting to get a toehold? A recently released Food and Drug Administration database starts to get at that question.

The UK has outlined a 10-year plan today to make the country a leader in Artificial Intelligence (AI), in a bid to draw in foreign investment and shape the way it is regulated globally. In the country's first AI strategy, which comes amid London Tech Week, the UK has proposed a new white paper on AI regulation. There are also plans to launch a new national programme to support research and development – which the government said it would raise investment in to 2.4 per cent of GDP by 2027 in its AI Sector Deal earlier this year. With the UK's ambition to use technology as one of its post-Brexit selling points, a fresh approach to AI could help stop online banking fraud, speed up disease diagnosis and unlock the potential for driverless cars. "The UK already punches above its weight internationally and we are ranked third in the world behind the USA and China in the list of top countries for AI," Department for Digital, Culture, Media and Sport (DCMS) Minister Chris Philip said, adding that the strategy will help the UK "seize the potential of artificial intelligence and play a leading role in shaping the way the world governs it." It comes ahead of the upcoming National Cyber Strategy which is set to continue pushing for building security into the development of AI.

The European Commission announced first-of-its-kind legislation regulating the use of artificial intelligence in April. This unleashed criticism that the regulations could slow AI innovation, hamstringing Europe in its competition with the U.S. and China for leadership in AI. For example, Andrew McAfee wrote an article titled "EU proposals to regulate AI are only going to hinder innovation." Anticipating this criticism and mindful of the example of GDPR, where Europe's thought-leadership position didn't necessarily translate into data-related innovation, the EC has tried to address AI innovation directly by publishing a new Coordinated Plan on AI. Released in conjunction with the proposed regulations, the plan is full of initiatives intended to help the EU become a leader in AI technology.

Top science advisers to President Joe Biden are calling for a new "bill of rights" to guard against powerful new artificial intelligence technology. The White House's Office of Science and Technology Policy on Friday launched a fact-finding mission to look at facial recognition and other biometric tools used to identify people or assess their emotional or mental states and character. Biden's chief science adviser, Eric Lander, and the deputy director for science and society, Alondra Nelson, also published an opinion piece in Wired magazine detailing the need to develop new safeguards against faulty and harmful uses of AI that can unfairly discriminate against people or violate their privacy. "Enumerating the rights is just a first step," they wrote. "What might we do to protect them? Possibilities include the federal government refusing to buy software or technology products that fail to respect these rights, requiring federal contractors to use technologies that adhere to this'bill of rights,' or adopting new laws and regulations to fill gaps."

As the WEF mentioned, the growth of AI as an adversarial is already happening. Artificially Intelligent viruses and malware are already fighting against their AI cybersecurity counterparts. The initial application of AI in cyber-threat tended to focus on parsing through mountains of seemingly unconnected data to find patterns and relationships that help an attacker find a weak spot. That weak spot could be a way to attack a corporation's network, a pattern of human behaviour, or simply password cracking. However, this is already evolving into more sophisticated methods, as Dark Reading mentions here.

Stephen Thaler and Ryan Abbott plan to bring a light beacon, a beverage container, and a machine called Dabus into court, along with a simple question: Does an inventor need to be human? Depending on how they respond, a panel of judges on the U.S. Court of Appeals for the Federal Circuit could open the door to another significant question: What is "obvious" to a machine? A basic tenet of U.S. law is that patents aren't awarded for inventions that are obvious. The standard of obviousness in patent law is measured against a hypothetical person of ordinary skill in the art. Putting artificial intelligence, with its potential for near omnipotent capabilities, on equal footing as human inventors could have a significant impact on patent law's obviousness standard, attorneys and patent professionals say.