OpenAI disclosed Sunday that the company has new methods to tell if the content you're reading has been authored by ChatGPT, though they haven't been released. The method of watermarking its own AI-generated content apparently works, according to OpenAI. But it also can be apparently easily duped. The company is also considering how releasing and applying the tool might be used to stigmatize groups that use AI as an actual writing tool, including those who can't speak the language well enough to generate polished content. A second method, metadata, holds more promise.

Elon Musk is once again suing OpenAI and its chief executive, Sam Altman, resurrecting a legal battle against his former partners with a case that now claims they manipulated him into co-founding the artificial intelligence company. Months after abruptly withdrawing a similar lawsuit without explanation, Musk filed a new lawsuit on Monday in a northern California federal court. OpenAI denied the allegations in a statement to the Guardian, pointing to its previous blogposts about Musk's initial lawsuit earlier this year. Musk's latest complaint claims the case is a "textbook tale of altruism versus greed", repeating allegations in his previous suit that his former co-founders in OpenAI betrayed him by turning the company from a non-profit into a largely for-profit enterprise. "The perfidy and deceit is of Shakespearean proportions," it states.

Elon Musk has filed another lawsuit against OpenAI and the company's CEO Sam Altman, two months after withdrawing a previous one. Musk once again alleges that OpenAI breached its founding commitments by putting commercial concerns ahead of the public good. This time around, though, the suit has been filed in federal court rather than in a state court. That's because the new filing alleges that OpenAI violated federal racketeering laws by conspiring to defraud Musk, according to his lawyer, Marc Toberoff. "The previous suit lacked teeth -- and I don't believe in the tooth fairy," Toberoff told The New York Times. "This is a much more forceful lawsuit."

Worries about AI often imagine doomsday scenarios where systems escape control or even understanding. But there are nearer-term harms we should take seriously: that AI could jeopardize public discourse; cement biases in loan decisions, judging or hiring; or disrupt creative industries. However, we foresee a different, but no less urgent, class of risks: those stemming from relationships with nonhuman agents. AI companionship is no longer theoretical--our analysis of a million ChatGPT interaction logs reveals that the second most popular use of AI is sexual role-playing. We are already starting to invite AIs into our lives as friends, lovers, mentors, therapists, and teachers.

Motorcycle accidents pose significant risks, particularly when riders and passengers do not wear helmets. This study evaluates the efficacy of an advanced vision-language foundation model, OWLv2, in detecting and classifying various helmet-wearing statuses of motorcycle occupants using video data. We extend the dataset provided by the CVPR AI City Challenge and employ a cascaded model approach for detection and classification tasks, integrating OWLv2 and CNN models. The results highlight the potential of zero-shot learning to address challenges arising from incomplete and biased training datasets, demonstrating the usage of such models in detecting motorcycles, helmet usage, and occupant positions under varied conditions. We have achieved an average precision of 0.5324 for helmet detection and provided precision-recall curves detailing the detection and classification performance. Despite limitations such as low-resolution data and poor visibility, our research shows promising advancements in automated vehicle safety and traffic safety enforcement systems.

Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs). This study investigates whether such constraints on generation space impact LLMs' abilities, including reasoning and domain knowledge comprehension. Specifically, we evaluate LLMs' performance when restricted to adhere to structured formats versus generating free-form responses across various common tasks. Surprisingly, we observe a significant decline in LLMs' reasoning abilities under format restrictions. Furthermore, we find that stricter format constraints generally lead to greater performance degradation in reasoning tasks.

Holacracy is an innovative management model proposed by Brian Robertson, the founder of the software company. It is a democratic and open organizational structure with shared governance for all, aiming at the decentralized management of an organization by breaking the authoritarianism of the leadership through the assumption of work by roles[1]. Such a management model is better to give employees the freedom to be more creative; however, at the same time, it also creates conflicts between roles and teams, resulting in many organizational practices ending in failure [2]. Although some static influence mechanisms have been explored in the past [3, 4], the dynamic operation of the system, like autority delegation, is not well understood. In this paper, based on the simulation capacity of Large Language Model (LLM) [5, 6], we built CareerAgent, an organizational behavior simulation framework based on LLM Agents, as shown in Figure 1, to simulate the operation of organizations under the holacracy framework, and found some interesting phenomena. One of the characteristics of the holacracy is that the leaders delegate their authority to the employees at the lower level.

Artificial Intelligence (AI) is increasingly acknowledged as a vital component for the advancement and competitiveness of modern organizations, including small and medium enterprises (SMEs). However, the adoption of AI technologies in SMEs faces significant barriers, primarily related to cost, lack of technical skills, and employee acceptance. This study proposes a comprehensive, phased framework designed to facilitate the effective adoption of AI in SMEs by systematically addressing these barriers. The framework begins with raising awareness and securing commitment from leadership, followed by the adoption of low-cost, general-purpose AI tools to build technical competence and foster a positive attitude towards AI. As familiarity with AI technologies increases, the framework advocates for the integration of task-specific AI tools to enhance efficiency and productivity. Subsequently, it guides organizations towards the in-house development of generative AI tools, providing greater customization and control. Finally, the framework addresses the development of discriminative AI models to meet highly specific and precision-oriented tasks. By providing a structured and incremental approach, this framework ensures that SMEs can navigate the complexities of AI integration effectively, driving innovation, efficiency, and competitive advantage. This study contributes to the field by offering a practical, prescriptive framework tailored to the unique needs of SMEs, facilitating the successful adoption of AI technologies and positioning these organizations for sustained growth in a competitive landscape.

Locating and editing knowledge in large language models (LLMs) is crucial for enhancing their accuracy, safety, and inference rationale. We introduce ``concept editing'', an innovative variation of knowledge editing that uncovers conceptualisation mechanisms within these models. Using the reverse dictionary task, inference tracing, and input abstraction, we analyse the Multi-Layer Perceptron (MLP), Multi-Head Attention (MHA), and hidden state components of transformer models. Our results reveal distinct patterns: MLP layers employ key-value retrieval mechanism and context-dependent processing, which are highly associated with relative input tokens. MHA layers demonstrate a distributed nature with significant higher-level activations, suggesting sophisticated semantic integration. Hidden states emphasise the importance of the last token and top layers in the inference process. We observe evidence of gradual information building and distributed representation. These observations elucidate how transformer models process semantic information, paving the way for targeted interventions and improved interpretability techniques. Our work highlights the complex, layered nature of semantic processing in LLMs and the challenges of isolating and modifying specific concepts within these models.

This paper introduces a novel integration of Large Language Models (LLMs) with Conformal Geometric Algebra (CGA) to revolutionize controllable 3D scene editing, particularly for object repositioning tasks, which traditionally requires intricate manual processes and specialized expertise. These conventional methods typically suffer from reliance on large training datasets or lack a formalized language for precise edits. Utilizing CGA as a robust formal language, our system, shenlong, precisely models spatial transformations necessary for accurate object repositioning. Leveraging the zero-shot learning capabilities of pre-trained LLMs, shenlong translates natural language instructions into CGA operations which are then applied to the scene, facilitating exact spatial transformations within 3D scenes without the need for specialized pre-training. Implemented in a realistic simulation environment, shenlong ensures compatibility with existing graphics pipelines. To accurately assess the impact of CGA, we benchmark against robust Euclidean Space baselines, evaluating both latency and accuracy. Comparative performance evaluations indicate that shenlong significantly reduces LLM response times by 16% and boosts success rates by 9.6% on average compared to the traditional methods. Notably, shenlong achieves a 100% perfect success rate in common practical queries, a benchmark where other systems fall short. These advancements underscore shenlong's potential to democratize 3D scene editing, enhancing accessibility and fostering innovation across sectors such as education, digital entertainment, and virtual reality.