Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Wisconsin Gov. Tony Evers on Thursday vetoed Republican proposals that would have allowed election observers to get closer to poll workers and required a new post-election audit, while signing into law a bill requiring that political TV ads using artificial intelligence come with a disclaimer. Evers, a Democrat, also signed a bipartisan bill exempting purchases of precious metal, such as gold and silver, from the state sales tax. The exemption does not apply to jewelry and other personal property, including works of art and scrap metal.

This study offers an in-depth analysis of the application and implications of the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) within the domain of surveillance technologies, particularly facial recognition technology. Given the inherently high-risk and consequential nature of facial recognition systems, our research emphasizes the critical need for a structured approach to risk management in this sector. The paper presents a detailed case study demonstrating the utility of the NIST AI RMF in identifying and mitigating risks that might otherwise remain unnoticed in these technologies. Our primary objective is to develop a comprehensive risk management strategy that advances the practice of responsible AI utilization in feasible, scalable ways. We propose a six-step process tailored to the specific challenges of surveillance technology that aims to produce a more systematic and effective risk management practice. This process emphasizes continual assessment and improvement to facilitate companies in managing AI-related risks more robustly and ensuring ethical and responsible deployment of AI systems. These insights contribute to the evolving discourse on AI governance and risk management, highlighting areas for future refinement and development in frameworks like the NIST AI RMF. Surveillance technologies are increasingly widespread in both public and private spaces, often being developed and deployed with little engagement from relevant stakeholders. Most notably, the individuals subject to the surveillance technology are rarely included in creating that technology. As an illustration of both prominence and controversy, one may consider the AI system developed by Clearview AI Inc. to monitor and record the activities of individuals and groups, including rapid face identification. Their system has come under close scrutiny for the ways that the organization scraped images and training data from the Internet; the company is currently under investigation in multiple jurisdictions for scraping billions of images from social media sites without users' consent [1, 2], and other companies like Facebook, Twitter, Venmo, and Google have issued cease and desist letters citing violations of their terms of service [3].

Potential malicious misuse of civilian artificial intelligence (AI) poses serious threats to security on a national and international level. Besides defining autonomous systems from a technological viewpoint and explaining how AI development is characterized, we show how already existing and openly available AI technology could be misused. To underline this, we developed three exemplary use cases of potentially misused AI that threaten political, digital and physical security. The use cases can be built from existing AI technologies and components from academia, the private sector and the developer-community. This shows how freely available AI can be combined into autonomous weapon systems. Based on the use cases, we deduce points of control and further measures to prevent the potential threat through misused AI. Further, we promote the consideration of malicious misuse of civilian AI systems in the discussion on autonomous weapon systems (AWS).

This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restless bandits, so that it can model both the internal state transitions of each arm and the influence of external global environmental contexts. Using the dual decomposition method, we develop a scalable index policy algorithm for solving the CRB problem, and theoretically analyze the asymptotical optimality of this algorithm. In the case when the arm models are unknown, we further propose a model-based online learning algorithm based on the index policy to learn the arm models and make decisions simultaneously. Furthermore, we apply the proposed CRB framework and the index policy algorithm specifically to the demand response decision-making problem in smart grids. The numerical simulations demonstrate the performance and efficiency of our proposed CRB approaches.

Advancements in autonomous vehicle (AV) technologies necessitate precise estimation of perceived risk to enhance user comfort, acceptance and trust. This paper introduces a novel AV-Occupant Risk (AVOR) model designed for perceived risk estimation during AV cut-in scenarios. An empirical study is conducted with 18 participants with realistic cut-in scenarios. Two factors were investigated: scenario risk and scene population. 76% of subjective risk responses indicate an increase in perceived risk at cut-in initiation. The existing perceived risk model did not capture this critical phenomenon. Our AVOR model demonstrated a significant improvement in estimating perceived risk during the early stages of cut-ins, especially for the high-risk scenario, enhancing modelling accuracy by up to 54%. The concept of the AVOR model can quantify perceived risk in other diverse driving contexts characterized by dynamic uncertainties, enhancing the reliability and human-centred focus of AV systems.

Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. We propose a principled boundary-aware safe stochastic planning framework with promising results. Our method generates a value function that can strictly distinguish the state values between free (safe) and non-navigable (boundary) spaces in the continuous state, naturally leading to a safe boundary-aware policy. At the core of our solution lies a seamless integration of finite elements and kernel-based functions, where the finite elements allow us to characterize safety-critical states' borders accurately, and the kernel-based function speeds up computation for the non-safety-critical states. The proposed method was evaluated through extensive simulations and demonstrated safe navigation behaviors in mobile navigation tasks. Additionally, we demonstrate that our approach can maneuver safely and efficiently in cluttered real-world environments using a ground vehicle with strong external disturbances, such as navigating on a slippery floor and against external human intervention.

Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed dataset by augmenting additional data that can be used for fine-tuning on a specific task. LLM2LLM (1) fine-tunes a baseline student LLM on the initial seed data, (2) evaluates and extracts data points that the model gets wrong, and (3) uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data. This approach amplifies the signal from incorrectly predicted data points by the LLM during training and reintegrates them into the dataset to focus on more challenging examples for the LLM. Our results show that LLM2LLM significantly enhances the performance of LLMs in the low-data regime, outperforming both traditional fine-tuning and other data augmentation baselines. LLM2LLM reduces the dependence on labor-intensive data curation and paves the way for more scalable and performant LLM solutions, allowing us to tackle data-constrained domains and tasks. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime using a LLaMA2-7B student model.

This paper makes two main contributions towards this goal. First, we formalize the problem of fair energy resource Distributed energy resources (DERs), such as small-scale allocation, providing a framework for studying fairness in the solar and wind generation, electric vehicles, and batteries, context of energy systems. This framework allows aggregators are crucial components of the clean energy transition; they to trace out a portion of the Pareto front and explore the enable end-users to actively participate in the energy market optimal trade-offs between efficiency and fairness. Second, by generating, storing, and potentially selling electricity back we generalize the resource allocation problem to involve to the grid [1]. However, individual users often cannot jointly optimizing the total resources to allocate and the directly interact with the larger electricity market, facing allocation to individual users. This generalization leads to barriers because of the complexity of energy markets, lack of new theoretical and computational challenges.

A ship with a firing turret, mobile air-defense system, surveillance aircraft, and military vehicles with directed sensors are all examples of moving pursuers with rotating platforms installed onboard. The rotating platforms can be a turret, a missile launcher, or a gimballed camera or sensor. Various aspects of such systems have been studied in works such as [1-4]. Recently, such systems have become increasingly fast and autonomous, thereby driving advancements in guidance and control of such vehicles. Classical guidance laws such as pure-pursuit, proportional navigation (PN), and line-of-sight guidance implement an underlying geometrical rule that is guaranteed to lead to interception.

This thesis investigates how natural language understanding and generation with transformer models can benefit from grounding the models with knowledge representations. Currently, the most prevailing paradigm for training language models is through pre-training on abundant raw text data and fine-tuning on downstream tasks. Although language models continue to advance, especially the recent trend of Large Language Models (LLMs) such as ChatGPT, there seem to be limits to what can be achieved with text data alone and it is desirable to study the impact of applying and integrating rich forms of knowledge representation to improve model performance. The most widely used form of knowledge for language modelling is structured knowledge in the form of triples consisting of entities and their relationships, often in English. This thesis explores beyond this conventional approach and aims to address several key questions: Can knowledge of entities extend its benefits beyond entity-centric tasks such as entity linking? How can we faithfully and effectively extract such structured knowledge from raw text, especially noisy web text? How do other types of knowledge, beyond structured knowledge, contribute to improving NLP tasks?