Musk becomes world's first half-trillionaire Tesla boss Elon Musk has become the first person ever to achieve a net worth of more than $500bn (£370.9bn), The tech magnate's net worth briefly reached $500.1bn on Wednesday afternoon New York time, before dipping slightly to just over $499bn later in the day, the Forbes billionaires index reported. Alongside Tesla, valuations of his other ventures, including the artificial intelligence start-up xAI and rocket company SpaceX, have also reportedly climbed in recent months. According to Forbes' billionaires index, Oracle founder Larry Ellison is the world's second richest person, with a fortune of about $350.7bn. Mr Ellison briefly overtook Musk last month after shares in Oracle soared by more than 40%, boosted by the firm's surprisingly rosy outlook for its cloud infrastructure business and artificial intelligence (AI) deals.

In this paper, we propose a game-theoretic framework for studying attacks and defenses which exist in equilibrium.

We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices.

Neural Information Processing Systems http://nips.cc/

The majority of LiRP A-based methods focus on simple feed-forward networks and need particular manual derivations and implementations when extended to other architectures. In this paper, we develop an automatic framework to enable perturbation analysis on any neural network structures, by generalizing existing LiRP A algorithms such as CROWN to operate on general computational graphs.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Artificial intelligence is set to revolutionize social and political life in unpredictable ways, raising questions about the principles that ought to guide its development and regulation. By examining digital advertising and social media algorithms, this article highlights how artificial intelligence already poses a significant threat to the republican conception of liberty -- or freedom from unaccountable power -- and thereby highlights the necessity of protecting republican liberty when integrating artificial intelligence into society. At an individual level, these algorithms can subconsciously influence behavior and thought, and those subject to this influence have limited power over the algorithms they engage. At the political level, these algorithms give technology company executives and other foreign parties the power to influence domestic political processes, such as elections; the multinational nature of algorithm-based platforms and the speed with which technology companies innovate make incumbent state institutions ineffective at holding these actors accountable. At both levels, artificial intelligence has thus created a new form of unfreedom: digital domination. By drawing on the works of Quentin Skinner, Philip Pettit, and other republican theorists, this article asserts that individuals must have mechanisms to hold algorithms (and those who develop them) accountable in order to be truly free.

Autonomous inspection systems are essential for ensuring the performance and longevity of industrial assets. Recently, agentic frameworks have demonstrated significant potential for automating inspection workflows but have been limited to digital tasks. Their application to physical assets in real-world environments, however, remains underexplored. In this work, our contributions are two-fold: first, we propose a hierarchical agentic framework for autonomous drone control, and second, a reasoning methodology for individual function executions which we refer to as ReActEval. Our framework focuses on visual inspection tasks in indoor industrial settings, such as interpreting industrial readouts or inspecting equipment. It employs a multi-agent system comprising a head agent and multiple worker agents, each controlling a single drone. The head agent performs high-level planning and evaluates outcomes, while worker agents implement ReActEval to reason over and execute low-level actions. Operating entirely in natural language, ReActEval follows a plan, reason, act, evaluate cycle, enabling drones to handle tasks ranging from simple navigation (e.g., flying forward 10 meters and land) to complex high-level tasks (e.g., locating and reading a pressure gauge). The evaluation phase serves as a feedback and/or replanning stage, ensuring actions align with user objectives while preventing undesirable outcomes. We evaluate the framework in a simulated environment with two worker agents, assessing performance qualitatively and quantitatively based on task completion across varying complexity levels and workflow efficiency. By leveraging natural language processing for agent communication, our approach offers a novel, flexible, and user-accessible alternative to traditional drone-based solutions, enabling autonomous problem-solving for industrial inspection without extensive user intervention.