Identity theft involving stolen driver's licenses creates lasting legal exposure unlike credit card fraud, as license numbers cannot be changed and require extensive cleanup efforts.

Large Language Models (LLMs) excel in various domains but pose inherent privacy risks. Existing methods to evaluate privacy leakage in LLMs often use memorized prefixes or simple instructions to extract data, both of which well-alignment models can easily block. Meanwhile, Jailbreak attacks bypass LLM safety mechanisms to generate harmful content, but their role in privacy scenarios remains underexplored. In this paper, we examine the effectiveness of jailbreak attacks in extracting sensitive information, bridging privacy leakage and jailbreak attacks in LLMs. Moreover, we propose PIG, a novel framework targeting Personally Identifiable Information (PII) and addressing the limitations of current jailbreak methods. Specifically, PIG identifies PII entities and their types in privacy queries, uses in-context learning to build a privacy context, and iteratively updates it with three gradient-based strategies to elicit target PII. We evaluate PIG and existing jailbreak methods using two privacy-related datasets. Experiments on four white-box and two black-box LLMs show that PIG outperforms baseline methods and achieves state-of-the-art (SoTA) results. The results underscore significant privacy risks in LLMs, emphasizing the need for stronger safeguards. Our code is availble at https://github.com/redwyd/PrivacyJailbreak.

Santa Monica Police spotted and stopped a man who was burglarizing vehicles in a parking lot near the pier by using a drone. On July 6, a Santa Monica police officer was directing the department's drone back to the station from a radio call when the officer decided to survey the Fourth of July weekend crowd near the pier and the nearby parking lots. As the drone flew over Lot 1 North, the parking lot next to the pier, he noticed a man wandering the lot, according to a video the department posted on their YouTube account. "As [the pilot] watched, the subject approached an unoccupied parked vehicle, pulled out tools from his sweatshirt and quickly punched open the lock of the driver's side door," the department said in the video. The drone footage shows the suspected burglar break the lock of the driver's side door of a black SUV then climb into the car.

'Gutfeld!' panelists weigh in on dating app Bumble's Opening Moves feature where women won't have to make the first move amid the app's plunging stock price. The birds and the bees bring Bumble to its knees. I refer to the Bumble dating app, which launched a decade ago, described as the feminist version of Tinder -- but maybe it should have been called Hinder, because that's what these feminists did to women trying to meet men. Bumble's big innovation was that only female users could make the first move to contact a potential match. But that was Bumble's brand: The women get to ask, and the men don't.

Dilemma zones at signalized intersections present a commonly occurring but unsolved challenge for both drivers and traffic operators. Onsets of the yellow lights prompt varied responses from different drivers: some may brake abruptly, compromising the ride comfort, while others may accelerate, increasing the risk of red-light violations and potential safety hazards. Such diversity in drivers' stop-or-go decisions may result from not only surrounding traffic conditions, but also personalized driving behaviors. To this end, identifying personalized driving behaviors and integrating them into advanced driver assistance systems (ADAS) to mitigate the dilemma zone problem presents an intriguing scientific question. In this study, we employ a game engine-based (i.e., CARLA-enabled) driving simulator to collect high-resolution vehicle trajectories, incoming traffic signal phase and timing information, and stop-or-go decisions from four subject drivers in various scenarios. This approach allows us to analyze personalized driving behaviors in dilemma zones and develop a Personalized Transformer Encoder to predict individual drivers' stop-or-go decisions. The results show that the Personalized Transformer Encoder improves the accuracy of predicting driver decision-making in the dilemma zone by 3.7% to 12.6% compared to the Generic Transformer Encoder, and by 16.8% to 21.6% over the binary logistic regression model.

Vehicle-based voice assistants are the next great frontier, incorporating artificial intelligence into the driving experience. At CES 2024, Amazon and BMW announced a partnership to significantly improve the pre-existing experience, marrying a large language model (LLM) with Alexa and the actual driver's manual. You can ask the Alexa-powered chatbot anything about your car and receive accurate real-time information. That thick and unwieldy manual can stay in the glovebox, for good. Amazon says this tool offers a "more natural way of getting to know your new car."

Jefferson County Sheriff Jeff Bullard said after one year in effect, the SAFE-T Act is having the "intended result" and damaging the policing profession in Illinois. With the calendar-page turn to 2024 on Monday comes 320 new state laws that Illinois residents will need to navigate. Some will have a widespread effect, including a law banning semi-automatic rifles and another requiring paid time off. But others won't have an immediate or noticeable impact, including a law that lets county governments consider a potential contractor's participation in an approved apprenticeship program in determining the winning low bid for a project. One law that took effect in 2019 but is still impacting tens of thousands of workers is an increase in the minimum wage.

Semi-autonomous driving, as it is already available today and will eventually become even more accessible, implies the need for driver and automation system to reliably work together in order to ensure safe driving. A particular challenge in this endeavour are situations in which the vehicle's automation is no longer able to drive and is thus requesting the human to take over. In these situations the driver has to quickly build awareness for the traffic situation to be able to take over control and safely drive the car. Within this context we present a software and hardware framework to asses how aware the driver is about the situation and to provide human-centred assistance to help in building situation awareness. The framework is developed as a modular system within the Robot Operating System (ROS) with modules for sensing the environment and the driver state, modelling the driver's situation awareness, and for guiding the driver's attention using specialized Human Machine Interfaces (HMIs). A particular focus of this paper is on an Answer Set Programming (ASP) based approach for modelling and reasoning about the driver's interpretation and projection of the scene. This is based on scene data, as well as eye-tracking data reflecting the scene elements observed by the driver. We present the overall application and discuss the role of semantic reasoning and modelling cognitive functions based on logic programming in such applications. Furthermore we present the ASP approach for interpretation and projection of the driver's situation awareness and its integration within the overall system in the context of a real-world use-case in simulated as well as in real driving.

Large language models such as Open AI's Generative Pre-trained Transformer (GPT) models are proficient at answering questions, but their knowledge is confined to the information present in their training data. This limitation renders them ineffective when confronted with questions about recent developments or non-public documents. Our research proposes a method that enables GPT models to answer questions by employing context from an information source not previously included in their training data. The methodology includes preprocessing of contextual information, the embedding of contexts and queries, constructing prompt through the integration of context embeddings, and generating answers using GPT models. We applied this method in a controlled test scenario using the California Driver's Handbook as the information source. The GPT-3 model achieved a 96% passing score on a set of 50 sample driving knowledge test questions. In contrast, without context, the model's passing score fell to 82%. However, the model still fails to answer some questions correctly even with providing library of context, highlighting room for improvement. The research also examined the impact of prompt length and context format, on the model's performance. Overall, the study provides insights into the limitations and potential improvements for GPT models in question-answering tasks.

Digital transformation (DX) is not just a differentiator but a matter of survival in the modern business world. And while a maelstrom of technologies, including cloud computing, mobility, IoT, and AR-VR, are delivering value and creating better experiences for customers, studies show that Artificial Intelligence (AI) will play the most crucial role in driving businesses through DX. That said, while leading tech companies such as Amazon, Netflix, and Google use AI and Machine Learning (ML) at scale in their core business processes, small and medium-sized enterprises often struggle to expand their machine learning projects beyond a small pilot scope. In a Deloitte survey involving 2,875 executives from 11 top economies, 79% of leading digital transformers (as against 49% of the starters) stated that their AI initiatives are essential to their market competitiveness. As a catalyst for digital transformation, AI helps companies become more data-driven, accelerate innovation, strengthen decision-making and increase productivity with better resource utilization in crucial business functions and domains.