Nick Clegg has compared the clamour over artificial intelligence to the 80s-era "moral panic" over video games, firing a warning shot to international politicians and regulators as they gather for a two-day summit on AI safety. The former UK deputy prime minister who is now president of global affairs at Mark Zuckerberg's Meta said AI was caught in a "great hype cycle" but warned that new technologies inspired a mixture of excessive zeal and excessive pessimism. British officials are hoping to use the summit, which starts on Wednesday at Bletchley Park, to kickstart a regulatory process that could mirror international attempts to combat the climate crisis. But Clegg's comments show they are likely to encounter resistance from some of the industry's most powerful companies. "New technologies always lead to hype," he said.

The emergence of AI tools in cybersecurity creates many opportunities and uncertainties. A focus group with advanced graduate students in cybersecurity revealed the potential depth and breadth of the challenges and opportunities. The salient issues are access to open source or free tools, documentation, curricular diversity, and clear articulation of ethical principles for AI cybersecurity education. Confronting the "black box" mentality in AI cybersecurity work is also of the greatest importance, doubled by deeper and prior education in foundational AI work. Systems thinking and effective communication were considered relevant areas of educational improvement. Future AI educators and practitioners need to address these issues by implementing rigorous technical training curricula, clear documentation, and frameworks for ethically monitoring AI combined with critical and system's thinking and communication skills.

Finding optimal adversarial attack strategies is an important topic in reinforcement learning and the Markov decision process. Previous studies usually assume one all-knowing coordinator (attacker) for whom attacking different recipient (victim) agents incurs uniform costs. However, in reality, instead of using one limitless central attacker, the attacks often need to be performed by distributed attack agents. We formulate the problem of performing optimal adversarial agent-to-agent attacks using distributed attack agents, in which we impose distinct cost constraints on each different attacker-victim pair. We propose an optimal method integrating within-step static constrained attack-resource allocation optimization and between-step dynamic programming to achieve the optimal adversarial attack in a multi-agent system. Our numerical results show that the proposed attacks can significantly reduce the rewards received by the attacked agents.

We investigate MT evaluation metric performance on adversarially-synthesized texts, to shed light on metric robustness. We experiment with word- and character-level attacks on three popular machine translation metrics: BERTScore, BLEURT, and COMET. Our human experiments validate that automatic metrics tend to overpenalize adversarially-degraded translations. We also identify inconsistencies in BERTScore ratings, where it judges the original sentence and the adversarially-degraded one as similar, while judging the degraded translation as notably worse than the original with respect to the reference. We identify patterns of brittleness that motivate more robust metric development.

Proper enforcement of hate speech laws is key for protecting groups of people against harmful and discriminatory language. However, determining what constitutes hate speech is a complex task that is highly open to subjective interpretations. Existing works do not align their systems with enforceable definitions of hate speech, which can make their outputs inconsistent with the goals of regulators. This research introduces a new perspective and task for enforceable hate speech detection centred around legal definitions, and a dataset annotated on violations of eleven possible definitions by legal experts. Given the Figure 1: A visualization of our proposed method to challenge of identifying clear, legally enforceable ground hate speech to specialized legal definitions. A instances of hate speech, we augment the legal professional reads external legal resources and dataset with expert-generated samples and an makes a judgement on some hate speech input, then automatically mined challenge set. We experiment identifies offences according to our definitions and with grounding the model decision in makes a judgement on violations.

Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled texts belong to novel classes, thereby limiting the practicality of these methods. We argue that the OpenRE setting should be more aligned with the characteristics of real-world data. Specifically, we propose two key improvements: (a) unlabeled data should encompass known and novel classes, including hard-negative instances; and (b) the set of novel classes should represent long-tail relation types. Furthermore, we observe that popular relations such as titles and locations can often be implicitly inferred through specific patterns, while long-tail relations tend to be explicitly expressed in sentences. Motivated by these insights, we present a novel method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data. Experimental evaluations on several Open-world RE benchmarks demonstrate that KNoRD consistently outperforms other existing methods, achieving significant performance gains.

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, and then extend the construction to random field data via the Karhunen-Loeve expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model.

China claims terrorists have'armed' themselves with AI-created genetic weapons that target specific races. Ministry of State Security released a statement on WeChat announcing that'certain' non-governmental organizations recruited Chinese'volunteers' to collect biodiversity distribution data under the guise of biological species research. The statement noted that these foreign nations are engineering weapons that hunt out genetic differences associated with ethnicity or race. It comes after Independent presidential hopeful RFK Jr claimed Covid-19 was'ethnically targeted' to not effect Jewish or Chinese people in a bizarre rant. China's Ministry of State Security alleges these foreign nations could attack its people as the organization used Chinese people to steal species data, which was uploaded to a smartphone app (stock photo) 'Compared with traditional biological weapons and chemical weapons, genetic weapons are more concealable, deceptive, easy to spread and harmful in the long-term, and are difficult to prevent, difficult to isolate, and low-cost.

What if The Atlantic owned a train car? Amtrak, I had just learned on the internet, allows owners of private railcars to lash onto runs along the Northeast Corridor, among other routes. "We should have a train car," I slacked an editor. Moments later, it appeared on my screen, bright red with our magazine's logo emblazoned in white, just like I'd ordered. It's an old logo, and misspelled, but the effect was the same: A momentary notion--one unworthy of relating to someone in private, let alone executing--had been realized, thanks to DALL-E 3, an artificial-intelligence image generator now built into Microsoft Bing's Image Creator website.

The costumed children celebrating Halloween with President Biden weren't there for the unveiling of a sweeping new executive order on artificial intelligence. Yet as the US government digests its lengthy, new to-do list and Vice President Kamala Harris heads to a UK summit on AI to sell the president's vision, leaders in Congress and nations around the world may be asking themselves, trick or treat? While this White House is bullish on the power of the president's pen, executive orders have limited power domestically--and none overseas. Behind the White House's rosy PR push about setting a new course for AI lurk the scary but very real monsters of congressional dysfunction and international rivals. Without overcoming both, Biden's AI vision could struggle to take root as his administration hopes it will.