Macron's G7 legacy hangs on fickle AI funding and data centers With less than a year left in office, Emmanuel Macron wants to be remembered as the French president who put Europe back in the technology race. His decade-old ambition to turn France into a "startup nation" never fully delivered. Now Macron sees a second chance by positioning France as Europe's artificial intelligence powerhouse, leveraging the nation's abundant supply of nuclear energy for data centers. He convinced SoftBank Group to invest as much as €75 billion ($87 billion) in French projects. His advisers have dubbed the AI effort "Project Marengo," a reference to Napoleon Bonaparte's victory over an Austrian army in 1800 at the battle of the same name, won through speed and decisive action. Marengo was also a political victory, securing Bonaparte's hold on power.

Crypto token's 50% wipeout shows magnitude of AI-hacking threat The same artificial intelligence tools helping developers audit code in cryptocurrency are also lowering the barriers for attackers, creating an arms race across the industry, researchers say. When Eli Ben-Sasson helped create the Zcash cryptocurrency nearly a decade ago, the cryptographer worried about human adversaries. He didn't expect that machine intelligence would one day expose a flaw that had eluded years of expert human judgment. That reality rattled investors recently after a security researcher working with Zcash used Anthropic's Claude Opus 4.8 to uncover a critical vulnerability that had gone undetected for more than four years. After Zcash disclosed the flaw on June 4, the token -- which traded at far higher levels just weeks earlier -- tumbled about 50% as traders reassessed the security of one of crypto's most prominent privacy networks. The exploit struck at the heart of Zcash's value proposition.

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

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

In collective decision making, members of a group need to coordinate their actions in order to achieve a desirable outcome. When there is no direct communication between group members, one should decide based on inferring others' intentions from their actions. The inference of others' intentions is called theory of mind and can involve different levels of reasoning, from a single inference on a hidden variable to considering others partially or fully optimal and reasoning about their actions conditioned on one's own actions (levels of "theory of mind"). In this paper, we present a new Bayesian theory of collective decision making based on a simple yet most commonly observed behavior: conformity. We show that such a Bayesian framework allows one to achieve any level of theory of mind in collective decision making. The viability of our framework is demonstrated on two different experiments, a consensus task with 120 subjects and a volunteer's dilemma task with 29 subjects, each with multiple conditions.

The promise of autonomous scientific discovery (ASD) hinges not only on answering questions, but also on knowing which questions to ask. Most recent works in ASD explore the use of large language models (LLMs) in goal-driven settings, relying on human-specified research questions to guide hypothesis generation. However, scientific discovery may be accelerated further by allowing the AI system to drive exploration by its own criteria. The few existing approaches in open-ended ASD select hypotheses based on diversity heuristics or subjective proxies for human interestingness, but the former struggles to meaningfully navigate the typically vast hypothesis space, and the latter suffers from imprecise definitions. This paper presents AutoDiscovery -- a method for open-ended ASD that instead drives scientific exploration using Bayesian surprise. Here, we quantify the epistemic shift from the LLM's prior beliefs about a hypothesis to its posterior beliefs after gathering experimental results. To efficiently explore the space of nested hypotheses, our method employs a Monte Carlo tree search (MCTS) strategy with progressive widening using surprisal as the reward function. We evaluate AutoDiscovery in the setting of data-driven discovery across 21 real-world datasets spanning domains such as biology, economics, finance, and behavioral science. Our results demonstrate that under a fixed budget, AutoDiscovery substantially outperforms competitors by producing 5-29% more discoveries deemed surprising by the LLM. Our human evaluation further reveals that two-thirds of discoveries made by our system are surprising to domain experts as well, suggesting this is an important step towards building open-ended ASD systems.

This study investigates whether the opinions of robotic agents can influence human decision-making when robots display value awareness (i.e., the capability of understanding human preferences and prioritizing them in decision-making). We designed an experiment in which participants interacted with two Furhat robots - one programmed to be Value-Aware and the other Non-Value-Aware - during a labeling task for images representing human values. Results indicate that participants distinguished the Value-Aware robot from the Non-Value-Aware one. Although their explicit choices did not indicate a clear preference for one robot over the other, participants directed their gaze more toward the Value-Aware robot. Additionally, the Value-Aware robot was perceived as more loyal, suggesting that value awareness in a social robot may enhance its perceived commitment to the group. Finally, when both robots disagreed with the participant, conformity occurred in about one out of four trials, and participants took longer to confirm their responses, suggesting that two robots expressing dissent may introduce hesitation in decision-making. On one hand, this highlights the potential risk that robots, if misused, could manipulate users for unethical purposes. On the other hand, it reinforces the idea that social robots could encourage reflection in ambiguous situations and help users avoid scams.

Large language models (LLMs) such as ChatGPT are increasingly integrated into high-stakes decision-making, yet little is known about their susceptibility to social influence. We conducted three preregistered conformity experiments with GPT-4o in a hiring context. In a baseline study, GPT consistently favored the same candidate (Profile C), reported moderate expertise (M = 3.01) and high certainty (M = 3.89), and rarely changed its choice. In Study 1 (GPT + 8), GPT faced unanimous opposition from eight simulated partners and almost always conformed (99.9%), reporting lower certainty and significantly elevated self-reported informational and normative conformity (p < .001). In Study 2 (GPT + 1), GPT interacted with a single partner and still conformed in 40.2% of disagreement trials, reporting less certainty and more normative conformity. Across studies, results demonstrate that GPT does not act as an independent observer but adapts to perceived social consensus. These findings highlight risks of treating LLMs as neutral decision aids and underline the need to elicit AI judgments prior to exposing them to human opinions.

The inference of others' intentions is called "theory of mind"