Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.

Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-term trajectory prediction remains a major challenge due to accumulated errors and uncertainties. Additionally, balancing accuracy with interpretability in the prediction is another challenging issue in predicting vehicle trajectory. To address these challenges, this paper proposes a Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP). The GNP model simplifies vehicle trajectory prediction into a two-stage process: determining the vehicle's goal and then choosing the appropriate trajectory to reach this goal. The GNP model contains two sub-modules to achieve this process. The first sub-module employs a multi-head attention mechanism to accurately predict goals. The second sub-module integrates a deep learning model with a physics-based social force model to progressively predict the complete trajectory using the generated goals. The GNP demonstrates state-of-the-art long-term prediction accuracy compared to four baseline models. We provide interpretable visualization results to highlight the multi-modality and inherent nature of our neural physics framework. Additionally, ablation studies are performed to validate the effectiveness of our key designs.

Ridgeless regression has garnered attention among researchers, particularly in light of the ``Benign Overfitting'' phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an $\ell_0$-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an $l_q$-norm analysis technique (with $0

The United States Department of Justice is quietly prosecuting a novel Espionage Act case involving a drone, a Chinese national, and classified nuclear submarines. The case is such a rarity that it appears to be the first known prosecution under a World War II–era law that bans photographing vital military installations using aircraft, showing how new technologies are leading to fresh national security and First Amendment issues. "This is definitely not something that the law has addressed to any significant degree," Emily Berman, a law professor at the University of Houston who specializes in national security, tells WIRED. "There's definitely no reported cases." On January 5, 2024, Fengyun Shi flew to Virginia while on leave from his graduate studies at the University of Minnesota and rented a Tesla at the airport.

Ukraine on Wednesday presented an AI-generated spokesperson called Victoria who will make official statements on behalf of its foreign ministry. The ministry said it would "for the first time in history" use a digital spokesperson to read its statements, which will still be written by humans. Dressed in a dark suit, the spokesperson introduced herself as Victoria Shi, a "digital person", in a presentation posted on social media. The figure gesticulates with her hands and moves her head as she speaks. The foreign ministry's press service said that the statements given by Shi would not be generated by AI but "written and verified by real people".

Ukraine has introduced an AI spokesperson to provide information about its ongoing war efforts against Russia's invasion of the country. The AI spokesperson, named Victoria Shi – after'victory' and the Ukrainian abbreviation of'AI' – is based on the likeness of Ukrainian singer and influencer Rosalie Nombre who agreed to participate pro bono. The avatar is dressed in all black with aa Ukranian flag pin, hair pulled back and she's wearing studded earrings - but officials stressed the digital person and Nombre'are two different people.' In a video released by the Ministry of Foreign Affairs (MFA), Shi introduced herself and described her role and job functions, saying she was built to protect'the rights and interests of Ukrainian citizens abroad.' Victoria Shi, an AI spokesperson for Ukraine's Ministry of Foreign Affairs (pictured) will provide information about the governments ongoing war efforts against Russia's invasion The decision to add an AI MFA spokesperson was'not a whim,' but is a requirement of wartime efforts, the Minister of Foreign Affairs of Ukraine, Dmytro Kuleba, said in a Google-translated statement.

In this work, we provide insight into one important limitation of large language models (LLMs), i.e. false attribution, and introduce a new hallucination metric - Simple Hallucination Index (SHI). The task of automatic author attribution for relatively small chunks of text is an important NLP task but can be challenging. We empirically evaluate the power of 3 open SotA LLMs in zero-shot setting (LLaMA-2-13B, Mixtral 8x7B, and Gemma-7B), especially as human annotation can be costly. We collected the top 10 most popular books, according to Project Gutenberg, divided each one into equal chunks of 400 words, and asked each LLM to predict the author. We then randomly sampled 162 chunks for human evaluation from each of the annotated books, based on the error margin of 7% and a confidence interval of 95% for the book with the most chunks (Great Expectations by Charles Dickens, having 922 chunks). The average results show that Mixtral 8x7B has the highest prediction accuracy, the lowest SHI, and a Pearson's correlation (r) of 0.737, 0.249, and -0.9996, respectively, followed by LLaMA-2-13B and Gemma-7B. However, Mixtral 8x7B suffers from high hallucinations for 3 books, rising as high as an SHI of 0.87 (in the range 0-1, where 1 is the worst). The strong negative correlation of accuracy and SHI, given by r, demonstrates the fidelity of the new hallucination metric, which is generalizable to other tasks. We publicly release the annotated chunks of data and our codes to aid the reproducibility and evaluation of other models.

We study how to design learning-based adaptive controllers that enable fast and accurate online adaptation in changing environments. In these settings, learning is typically done during an initial (offline) design phase, where the vehicle is exposed to different environmental conditions and disturbances (e.g., a drone exposed to different winds) to collect training data. Our work is motivated by the observation that real-world disturbances fall into two categories: 1) those that can be directly monitored or controlled during training, which we call "manageable", and 2) those that cannot be directly measured or controlled (e.g., nominal model mismatch, air plate effects, and unpredictable wind), which we call "latent". Imprecise modeling of these effects can result in degraded control performance, particularly when latent disturbances continuously vary. This paper presents the Hierarchical Meta-learning-based Adaptive Controller (HMAC) to learn and adapt to such multi-source disturbances. Within HMAC, we develop two techniques: 1) Hierarchical Iterative Learning, which jointly trains representations to caption the various sources of disturbances, and 2) Smoothed Streaming Meta-Learning, which learns to capture the evolving structure of latent disturbances over time (in addition to standard meta-learning on the manageable disturbances). Experimental results demonstrate that HMAC exhibits more precise and rapid adaptation to multi-source disturbances than other adaptive controllers.

Frightened of a future where artificial intelligence replaces the real thing? Brace yourself, because here comes quantum computing. The developing technology -- which relies on subatomic, quantum mechanics -- could accelerate the advancement of AI to lightning speed, experts say. Such a powerful upgrade could lead to amazing things -- or terrible ones. "We could cure cancer with quantum computing combined with AI," Lisa Palmer, chief AI strategist for the consulting firm AI Leaders told The Post. "There is a huge upside here … like upgrading from a bicycle to a high speed sports car."

Artificial intelligence is here, and it's coming for your job. So promising are the tool's capabilities that Microsoft -- amid laying off 10,000 people -- has announced a "multiyear, multibillion-dollar investment" in the revolutionary technology, which is growing smarter by the day. And the rise of machines leaves many well-paid workers vulnerable, experts warn. "AI is replacing the white-collar workers. I don't think anyone can stop that," said Pengcheng Shi, an associate dean in the department of computing and information sciences at Rochester Institute of Technology.