The California Public Utilities Commission (CPUC) has given Waymo permission to expand its robotaxi operations to Los Angeles and more locations in the San Francisco Peninsula despite opposition from local groups and government agencies. "Waymo may begin fared driverless passenger service operations in the specified areas of Los Angeles and the San Francisco Peninsula, effective today," the regulator wrote in its decision (PDF). As CNBC notes, Waymo has been testing its driverless vehicles in those locations for a while now, but this decision will allow it to charge passengers for their robotaxi rides. In the CPUC's decision, it admitted that it received letters of protests regarding Waymo's expansion from the City of South San Francisco, the County of San Mateo, the Los Angeles Department of Transportation, the San Francisco County Transportation Authority and the San Francisco Taxi Workers Alliance. And, it received those letters before the agency suspended Waymo's expansion efforts in February for up to 120 days following the Alphabet-owned company's revelation that it had issued a recall for its vehicles.

State regulators on Friday gave the green light for Waymo to expand into Los Angeles and San Mateo counties, clearing the way for the driverless taxi service to launch in the coming months. Exactly when Waymo services will be available in Los Angeles is still to be determined, but the decision by the California Public Utilities Commission will open the streets of America's second-largest city to a fleet of autonomous vehicles -- even as self-driving cars continue to be the subject of safety concerns and some public criticism. Waymo, formerly known as the Google self-driving car project, is owned by Google's parent company, Alphabet, and already operates in parts of San Francisco. Waymo's driverless taxi launch in Santa Monica attracted both excited enthusiasts and concerned critics. The company is allowed to operate fully autonomous vehicles and carry public passengers as part of its testing and promotion, and has been testing its driverless white Jaguars in Los Angeles for more than a year.

Despite extensive pre-training and fine-tuning in moral alignment to prevent generating harmful information at user request, large language models (LLMs) remain vulnerable to jailbreak attacks. In this paper, we propose AutoDefense, a response-filtering based multi-agent defense framework that filters harmful responses from LLMs. This framework assigns different roles to LLM agents and employs them to complete the defense task collaboratively. The division in tasks enhances the overall instruction-following of LLMs and enables the integration of other defense components as tools. AutoDefense can adapt to various sizes and kinds of open-source LLMs that serve as agents. Through conducting extensive experiments on a large scale of harmful and safe prompts, we validate the effectiveness of the proposed AutoDefense in improving the robustness against jailbreak attacks, while maintaining the performance at normal user request. Our code and data are publicly available at https://github.com/XHMY/AutoDefense.

In today's digital world, social media plays a significant role in facilitating communication and content sharing. However, the exponential rise in user-generated content has led to challenges in maintaining a respectful online environment. In some cases, users have taken advantage of anonymity in order to use harmful language, which can negatively affect the user experience and pose serious social problems. Recognizing the limitations of manual moderation, automatic detection systems have been developed to tackle this problem. Nevertheless, several obstacles persist, including the absence of a universal definition for harmful language, inadequate datasets across languages, the need for detailed annotation guideline, and most importantly, a comprehensive framework. This study aims to address these challenges by introducing, for the first time, a detailed framework adaptable to any language. This framework encompasses various aspects of harmful language detection. A key component of the framework is the development of a general and detailed annotation guideline. Additionally, the integration of sentiment analysis represents a novel approach to enhancing harmful language detection. Also, a definition of harmful language based on the review of different related concepts is presented. To demonstrate the effectiveness of the proposed framework, its implementation in a challenging low-resource language is conducted. We collected a Persian dataset and applied the annotation guideline for harmful detection and sentiment analysis. Next, we present baseline experiments utilizing machine and deep learning methods to set benchmarks. Results prove the framework's high performance, achieving an accuracy of 99.4% in offensive language detection and 66.2% in sentiment analysis.

Deep neural network-based Synthetic Aperture Radar (SAR) target recognition models are susceptible to adversarial examples. Current adversarial example generation methods for SAR imagery primarily operate in the 2D digital domain, known as image adversarial examples. Recent work, while considering SAR imaging scatter mechanisms, fails to account for the actual imaging process, rendering attacks in the three-dimensional physical domain infeasible, termed pseudo physics adversarial examples. To address these challenges, this paper proposes SAR-AE-SFP-Attack, a method to generate real physics adversarial examples by altering the scattering feature parameters of target objects. Specifically, we iteratively optimize the coherent energy accumulation of the target echo by perturbing the reflection coefficient and scattering coefficient in the scattering feature parameters of the three-dimensional target object, and obtain the adversarial example after echo signal processing and imaging processing in the RaySAR simulator. Experimental results show that compared to digital adversarial attack methods, SAR-AE-SFP Attack significantly improves attack efficiency on CNN-based models (over 30\%) and Transformer-based models (over 13\%), demonstrating significant transferability of attack effects across different models and perspectives.

We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs) capable of generating high-quality text. While these LLMs have revolutionized text generation across various domains, they also pose significant risks to the information ecosystem, such as the potential for generating convincing propaganda, misinformation, and disinformation at scale. This paper offers a review of AI-generated text forensic systems, an emerging field addressing the challenges of LLM misuses. We present an overview of the existing efforts in AI-generated text forensics by introducing a detailed taxonomy, focusing on three primary pillars: detection, attribution, and characterization. These pillars enable a practical understanding of AI-generated text, from identifying AI-generated content (detection), determining the specific AI model involved (attribution), and grouping the underlying intents of the text (characterization). Furthermore, we explore available resources for AI-generated text forensics research and discuss the evolving challenges and future directions of forensic systems in an AI era.

Exploring molecular spaces is crucial for advancing our understanding of chemical properties and reactions, leading to groundbreaking innovations in materials science, medicine, and energy. This paper explores an approach for active learning in molecular discovery using Deep Kernel Learning (DKL), a novel approach surpassing the limits of classical Variational Autoencoders (VAEs). Employing the QM9 dataset, we contrast DKL with traditional VAEs, which analyze molecular structures based on similarity, revealing limitations due to sparse regularities in latent spaces. DKL, however, offers a more holistic perspective by correlating structure with properties, creating latent spaces that prioritize molecular functionality. This is achieved by recalculating embedding vectors iteratively, aligning with the experimental availability of target properties. The resulting latent spaces are not only better organized but also exhibit unique characteristics such as concentrated maxima representing molecular functionalities and a correlation between predictive uncertainty and error. Additionally, the formation of exclusion regions around certain compounds indicates unexplored areas with potential for groundbreaking functionalities. This study underscores DKL's potential in molecular research, offering new avenues for understanding and discovering molecular functionalities beyond classical VAE limitations.

Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering questions related to common knowledge, LLMs encounter difficulties in learning about uncommon long-tail knowledge (tail entities). Since manually constructing QA datasets demands substantial human resources, the types of existing QA datasets are limited, leaving us with a scarcity of datasets to study the performance of LLMs on tail entities. In this paper, we propose an automatic approach to generate specialized QA datasets for tail entities and present the associated research challenges. We conduct extensive experiments by employing pretrained LLMs on our newly generated long-tail QA datasets, comparing their performance with and without external resources including Wikipedia and Wikidata knowledge graphs.

Paid autonomous vehicle service is coming to Los Angeles, thanks to a decision by California regulators today to allow Alphabet subsidiary Waymo to operate in the city. Under the new ruling, Waymo is also permitted to launch service in a large section of the San Francisco Peninsula. The decision by the California Public Utilities Commission will likely prove controversial. It comes over the protest of local governments and agencies, including the Los Angeles Department of Transportation, the San Francisco County Transportation Authority, the city of South San Francisco, and the County of San Mateo. All argued that local government and citizens should have more input and oversight over the expanded autonomous taxi service.

A disc-shaped object longer than the height of the Empire State Building emerged from the horizon of Ukraine's embattled Donetsk province last Friday, hovering eerily still a mile off the ground, a soldier has told DailyMail.com That soldier, a drone operator, had cautiously guided his infrared quadcopter 500-feet for a reconnaissance mission, struggling against high winds, when he suddenly spotted the flat, 1,300-foot-long UFO, which stood motionless despite those winds. In an interview from the warzone, the soldier, who is with the Ukrainian army's 406th Battalion, said he and his fellow servicemen had'never seen things like this before.' 'Initially, I thought that it was something new invented by the Russians,' he added, 'but then I understood... 'No! It might be [a] UFO.''