The generation of complex, large-scale code projects using generative AI models presents challenges due to token limitations, dependency management, and iterative refinement requirements. This paper introduces the See-Saw generative mechanism, a novel methodology for dynamic and recursive code generation. The proposed approach alternates between main code updates and dependency generation to ensure alignment and functionality. By dynamically optimizing token usage and incorporating key elements of the main code into the generation of dependencies, the method enables efficient and scalable code generation for projects requiring hundreds of interdependent files. The mechanism ensures that all code components are synchronized and functional, enabling scalable and efficient project generation. Experimental validation demonstrates the method's capability to manage dependencies effectively while maintaining coherence and minimizing computational overhead.

Several GenAI coding assistants, including GitHub's Copilot [45], Tabnine [119], Codeium [24], and Cody [25], as well as general purpose tools such as ChatGPT [100], Claude [11], and Gemini [42], have become readily accessible, either as IDE extensions or standalone applications, enabling developers to perform many coding tasks with little effort, including automated code completion, summarization, and debugging.

Bluesky, which has surged in the days following the US election, said on Friday that it won't train on its users' posts for generative AI. The declaration stands in stark contrast to the AI training policies of X (Twitter) and Meta's Threads. Probably not coincidentally, Bluesky's announcement came the same day X's new terms of service, allowing third-party partners to train on user posts, went into effect. "A number of artists and creators have made their home on Bluesky, and we hear their concerns with other platforms training on their data," Bluesky posted (via The Verge) on Friday. "We do not use any of your content to train generative AI, and have no intention of doing so."

Elon Musk has expanded his lawsuit against the ChatGPT maker OpenAI, adding federal antitrust and other claims and adding OpenAI's largest financial backer, Microsoft, as a defendant. Musk's amended lawsuit, filed on Thursday night in federal court in Oakland, California, said Microsoft and OpenAI illegally sought to monopolize the market for generative artificial intelligence and sideline competitors. Like Musk's original August complaint, it accused OpenAI and its chief executive, Samuel Altman, of violating contract provisions by putting profits ahead of the public good in the push to advance AI. "Never before has a corporation gone from tax-exempt charity to a 157bn for-profit, market-paralyzing gorgon – and in just eight years," the complaint said. It seeks to void OpenAI's license with Microsoft and force them to divest "ill-gotten" gains. OpenAI in a statement said the latest lawsuit "is even more baseless and overreaching than the previous ones".

OpenAI was founded in 2015 with the aim of building an artificial general intelligence (AGI) - generally taken to mean AI that can perform any task a human being is capable of. In 2019, the firm announced a new "capped profit" structure allowing it to raise money. Microsoft made an initial 1bn investment into OpenAI shortly thereafter - increasing this to a multi-year, multi-billion dollar partnership in 2023. The lawsuit also accuses boss Sam Altman - a named defendant in the lawsuit - of "rampant self-dealing". Mr Musk's initial legal action filed in March argued the agreement had transformed it into "a closed-source de facto subsidiary" of the PC giant.

Elon Musk has amended his lawsuit against OpenAI, adding more anti-trust claims against the company and including Microsoft as a defendant. He also added his company, xAI, as well as Shivon Zilis, a former OpenAI board member and mother to three of his children, as plaintiffs. Musk originally sued OpenAI in March, accusing founders Sam Altman and Greg Brockman of violating the organization's non-profit mission by teaming up with Microsoft. He withdrew the state court lawsuit in June before suing OpenAI and Altman again in federal court. Musk was one OpenAI's earliest backers, and one of his arguments was that he was "betrayed by Mr. Altman and his accomplices."

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

As the boundaries of human computer interaction expand, Generative AI emerges as a key driver in reshaping user interfaces, introducing new possibilities for personalized, multimodal and cross-platform interactions. This integration reflects a growing demand for more adaptive and intuitive user interfaces that can accommodate diverse input types such as text, voice and video, and deliver seamless experiences across devices. This paper explores the integration of generative AI in modern user interfaces, examining historical developments and focusing on multimodal interaction, cross-platform adaptability and dynamic personalization. A central theme is the interface dilemma, which addresses the challenge of designing effective interactions for multimodal large language models, assessing the trade-offs between graphical, voice-based and immersive interfaces. The paper further evaluates lightweight frameworks tailored for mobile platforms, spotlighting the role of mobile hardware in enabling scalable multimodal AI. Technical and ethical challenges, including context retention, privacy concerns and balancing cloud and on-device processing are thoroughly examined. Finally, the paper outlines future directions such as emotionally adaptive interfaces, predictive AI driven user interfaces and real-time collaborative systems, underscoring generative AI's potential to redefine adaptive user-centric interfaces across platforms.

The demands for accurate and representative generative AI systems means there is an increased demand on participatory evaluation structures. While these participatory structures are paramount to to ensure non-dominant values, knowledge and material culture are also reflected in AI models and the media they generate, we argue that dominant structures of community participation in AI development and evaluation are not explicit enough about the benefits and harms that members of socially marginalized groups may experience as a result of their participation. Without explicit interrogation of these benefits by AI developers, as a community we may remain blind to the immensity of systemic change that is needed as well. To support this provocation, we present a speculative case study, developed from our own collective experiences as AI researchers. We use this speculative context to itemize the barriers that need to be overcome in order for the proposed benefits to marginalized communities to be realized, and harms mitigated.

This paper explores how Large Language Models (LLMs) can automate consensus-seeking in supply chain management (SCM), where frequent decisions on problems such as inventory levels and delivery times require coordination among companies. Traditional SCM relies on human consensus in decision-making to avoid emergent problems like the bullwhip effect. Some routine consensus processes, especially those that are time-intensive and costly, can be automated. Existing solutions for automated coordination have faced challenges due to high entry barriers locking out SMEs, limited capabilities, and limited adaptability in complex scenarios. However, recent advances in Generative AI, particularly LLMs, show promise in overcoming these barriers. LLMs, trained on vast datasets can negotiate, reason, and plan, facilitating near-human-level consensus at scale with minimal entry barriers. In this work, we identify key limitations in existing approaches and propose autonomous LLM agents to address these gaps. We introduce a series of novel, supply chain-specific consensus-seeking frameworks tailored for LLM agents and validate the effectiveness of our approach through a case study in inventory management. To accelerate progress within the SCM community, we open-source our code, providing a foundation for further advancements in LLM-powered autonomous supply chain solutions.