Artificial intelligence laws and regulations are still a new concept for most countries, and the current state of rules is neither universal nor inclusive. It's crystal clear that AI's constructive and negative roles in every sector require a call for agreed-upon rules. Artificial intelligence (AI), the creation of computer systems that can learn and make decisions without the need for human intelligence, has the potential to revolutionize and foster innovation in business and government. More goods and services are entering the market as AI research and technology continue to advance. For instance, businesses are creating AI to help people manage their houses and let the elderly live in their homes for longer. AI is applied in many aspects of modern life, including self-driving cars, digital assistants, and healthcare technology. However, initiatives to look into and create standards have been motivated by worries about potential abuse or unforeseen repercussions of AI. Already, AI is enhancing healthcare, connecting people in new ways, and drastically increasing productivity. But when applied incorrectly or irresponsibly, AI might result in employment losses, prejudiced or racist outcomes, etc.

Abstract: Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. Here we provide a vision for the future 6G industrial DT ecosystem, which shall bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios. Subsequently, we explore the technical challenges that are brought by such ambitions, and identify the key enabling technologies that may help tackle down these issues.

US officials are considering new export restrictions on China in the fields of quantum computing and AI software, Bloomberg reported last week, citing people familiar with the matter. Discussions are still at an early stage, according to the unnamed source, and there are no further details on what shape any potential bans might take. Major Chinese tech firms including Baidu and Alibaba have invested in quantum computing and released related products in recent years, interests which could take a direct hit if the US introduces restrictions. In terms of AI, major machine learning frameworks such as Google's TensorFlow and PyTorch are open-source and it is not immediately clear how the US would be able to place effective controls on them, but it is another indication of the Biden administration's intention to attempt to limit China's access to advanced computing technologies.

"The significance of drone swarms is that they can be conceivably applied to virtually any mission." Many nations are working on such swarms, including China, Russia, India, the UK, Turkey, and Israel, which in 2021 became the first nation to use swarming drones in combat. The US Navy has always been a leader in this field, and while they did not respond to requests to discuss their work, the budget documents that MIT Technology Review has read reveal ambitious plans for swarms vastly bigger than anything yet seen. Buried in hundreds of pages of budget numbers are details of several projects not previously revealed, which involve drone boats and submarines as well as uncrewed air vehicles. Together they fall under a project named Super Swarm.

In the discussion paper, the UK financial supervisory authorities have not provided a new legal framework or their intended future approaches for regulating the use of AI and machine learning in financial services. However, they have assessed the benefits, risks and harms related to the use of AI, and the current legal framework that applies to AI in financial services. The UK financial services regulators, the Bank of England (BoE), the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) (together Supervisory Authorities) jointly published a discussion paper (DP5/22) on artificial intelligence (AI) and machine learning on 11 October 2022. The purpose of the discussion paper was to facilitate a public debate on the safe and responsible adoption of AI in UK financial services. The Supervisory Authorities have also raised discussion questions for stakeholder input, with the aim of understanding whether the current regulatory framework is sufficient to address the potential risks and harms associated with AI and how any additional intervention may support the safe and responsible adoption of AI in UK financial services.

How would an artificial intelligence (AI) decide what to do? One common approach in AI research is called "reinforcement learning". Reinforcement learning gives the software a "reward" defined in some way, and lets the software figure out how to maximise the reward. This approach has produced some excellent results, such as building software agents that defeat humans at games like chess and Go, or creating new designs for nuclear fusion reactors. However, we might want to hold off on making reinforcement learning agents too flexible and effective.

The lack of wide coverage datasets annotated with everyday metaphorical expressions for languages other than English is striking. This means that most research on supervised metaphor detection has been published only for that language. In order to address this issue, this work presents the first corpus annotated with naturally occurring metaphors in Spanish large enough to develop systems to perform metaphor detection. The presented dataset, CoMeta, includes texts from various domains, namely, news, political discourse, Wikipedia and reviews. In order to label CoMeta, we apply the MIPVU method, the guidelines most commonly used to systematically annotate metaphor on real data. We use our newly created dataset to provide competitive baselines by fine-tuning several multilingual and monolingual state-of-the-art large language models. Furthermore, by leveraging the existing VUAM English data in addition to CoMeta, we present the, to the best of our knowledge, first cross-lingual experiments on supervised metaphor detection. Finally, we perform a detailed error analysis that explores the seemingly high transfer of everyday metaphor across these two languages and datasets.

This paper presents an adaptive autopilot for fixed-wing aircraft and compares its performance with a fixed-gain autopilot. The adaptive autopilot is constructed by augmenting the autopilot architecture with adaptive control laws that are updated using retrospective cost adaptive control. In order to investigate the performance of the adaptive autopilot, the default gains of the fixed-gain autopilot are scaled to degrade its performance. This scenario provides a venue for determining the ability of the adaptive autopilot to compensate for the degraded fixed-gain autopilot. Next, the performance of the adaptive autopilot is examined under failure conditions by simulating a scenario where one of the control surfaces is assumed to be stuck at an unknown angle. The adaptive autopilot is also tested in physical flight experiments under degraded-nominal conditions, and the resulting performance improvement is examined.

While machine translation evaluation metrics based on string overlap (e.g., BLEU) have their limitations, their computations are transparent: the BLEU score assigned to a particular candidate translation can be traced back to the presence or absence of certain words. The operations of newer learned metrics (e.g., BLEURT, COMET), which leverage pretrained language models to achieve higher correlations with human quality judgments than BLEU, are opaque in comparison. In this paper, we shed light on the behavior of these learned metrics by creating DEMETR, a diagnostic dataset with 31K English examples (translated from 10 source languages) for evaluating the sensitivity of MT evaluation metrics to 35 different linguistic perturbations spanning semantic, syntactic, and morphological error categories. All perturbations were carefully designed to form minimal pairs with the actual translation (i.e., differ in only one aspect). We find that learned metrics perform substantially better than string-based metrics on DEMETR. Additionally, learned metrics differ in their sensitivity to various phenomena (e.g., BERTScore is sensitive to untranslated words but relatively insensitive to gender manipulation, while COMET is much more sensitive to word repetition than to aspectual changes). We publicly release DEMETR to spur more informed future development of machine translation evaluation metrics

Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by exploiting certain side information, such as class labels, in addition to independence. However, most existing work is constrained by functional form assumptions such as independent sources or further with linear transitions, and distribution assumptions such as stationary, exponential family distribution. It is unknown whether the underlying latent variables and their causal relations are identifiable if they have arbitrary, nonparametric causal influences in between. In this work, we establish the identifiability theories of nonparametric latent causal processes from their nonlinear mixtures under fixed temporal causal influences and analyze how distribution changes can further benefit the disentanglement. We propose \textbf{\texttt{TDRL}}, a principled framework to recover time-delayed latent causal variables and identify their relations from measured sequential data under stationary environments and under different distribution shifts. Specifically, the framework can factorize unknown distribution shifts into transition distribution changes under fixed and time-varying latent causal relations, and under observation changes in observation. Through experiments, we show that time-delayed latent causal influences are reliably identified and that our approach considerably outperforms existing baselines that do not correctly exploit this modular representation of changes. Our code is available at: \url{https://github.com/weirayao/tdrl}.