Emmanuel Macron told delegates at the AI summit: 'Europe is not blindly focused on regulation.' Emmanuel Macron told delegates at the AI summit: 'Europe is not blindly focused on regulation.' Macron defends EU AI rules and vows crackdown on child'digital abuse' Emmanuel Macron has hit back at US criticism of Europe's efforts to regulate AI, vowing to protect children from "digital abuse" during France's presidency of the G7. Speaking at the AI Impact summit in Delhi, the French president called for tougher safeguards after global outrage over Elon Musk's Grok chatbot being used to generate tens of thousands of sexualised images of children, and amid mounting concern about the concentration of AI power in a handful of companies. His remarks were echoed by António Guterres, the UN secretary general, who told delegates - including several US tech billionaires - that "no child should be a test subject for unregulated AI". "The future of AI cannot be decided by a few countries or left to the whims of a few billionaires," Guterres said. "AI must belong to everyone".

A technician works at an Amazon Web Services AI datacenter in New Carlisle, Indiana, on 2 October 2025. A technician works at an Amazon Web Services AI datacenter in New Carlisle, Indiana, on 2 October 2025. At Davos, tech CEOs laid out their vision for AI's world domination Tech chiefs waxed poetic about AI to delegates at Davos. Plus, the'human' drama of AI startups and why Tesla is thriving in Texas This week's edition is a team effort: my colleague Heather Stewart reports on the plans for AI's world domination at Davos; I examine how huge investments have followed AI companies with little to their names but drama and dreams; and Nick Robins-Early spotlights how lax regulation of autonomous driving in Texas allowed Tesla to thrive. When they weren't discussing Donald Trump, delegates at the World Economic Forum last week were being dazzled by the prospects for artificial intelligence.

Georgieva said that AI would wipe out many roles traditionally taken up by younger workers. Georgieva said that AI would wipe out many roles traditionally taken up by younger workers. Young will suffer most when AI'tsunami' hits jobs, says head of IMF Artificial intelligence will be a "tsunami hitting the labour market", with young people worst affected, the head of the International Monetary Fund warned the World Economic Forum on Friday. Kristalina Georgieva told delegates in Davos that the IMF's own research suggested there would be a big transformation of demand for skills, as the technology becomes increasingly widespread. "We expect over the next years, in advanced economies, 60% of jobs to be affected by AI, either enhanced or eliminated or transformed - 40% globally," she said.

We will clarify that'policy hierarchy' in no way refers to HRL.

At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. 'Obedient, yielding and happy to follow': the troubling rise of AI girlfriends E leanor, 24, is a Polish historian and lecturer at a university in Warsaw; Isabelle, 25, is a detective serving with the NYPD; Brooke, 39, is an American housewife who enjoys an opulent Miami lifestyle financed by her frequently absent husband. All three women will flirt and chat and send nude photographs and explicit videos via one of a soaring number of new adult dating websites that offer an increasingly realistic selection of AI girlfriends for subscribers willing to pay a monthly fee. At the TES adult industry conference in Prague last month, delegates noted a sharp increase in new websites offering users the chance to form relationships with AI-generated girlfriends, who will remove their clothes in exchange for tokens purchased by bank transfer.

In an era of increasing societal fragmentation, political polarization, and erosion of public trust in institutions, representative deliberative assemblies are emerging as a promising democratic forum for developing effective policy outcomes on complex global issues. Despite theoretical attention, there remains limited empirical work that systematically traces how specific ideas evolve, are prioritized, or are discarded during deliberation to form policy recommendations. Addressing these gaps, this work poses two central questions: (1) How might we trace the evolution and distillation of ideas into concrete recommendations within deliberative assemblies? (2) How does the deliberative process shape delegate perspectives and influence voting dynamics over the course of the assembly? To address these questions, we develop LLM-based methodologies for empirically analyzing transcripts from a tech-enhanced in-person deliberative assembly. The framework identifies and visualizes the space of expressed suggestions. We also empirically reconstruct each delegate's evolving perspective throughout the assembly. Our methods contribute novel empirical insights into deliberative processes and demonstrate how LLMs can surface high-resolution dynamics otherwise invisible in traditional assembly outputs.

As AI technologies improve, people are increasingly willing to delegate tasks to AI agents. In many cases, the human decision-maker chooses whether to delegate to an AI agent based on properties of the specific instance of the decision-making problem they are facing. Since humans typically lack full awareness of all the factors relevant to this choice for a given decision-making instance, they perform a kind of categorization by treating indistinguishable instances -- those that have the same observable features -- as the same. In this paper, we define the problem of designing the optimal algorithmic delegate in the presence of categories. This is an important dimension in the design of algorithms to work with humans, since we show that the optimal delegate can be an arbitrarily better teammate than the optimal standalone algorithmic agent. The solution to this optimal delegation problem is not obvious: we discover that this problem is fundamentally combinatorial, and illustrate the complex relationship between the optimal design and the properties of the decision-making task even in simple settings. Indeed, we show that finding the optimal delegate is computationally hard in general. However, we are able to find efficient algorithms for producing the optimal delegate in several broad cases of the problem, including when the optimal action may be decomposed into functions of features observed by the human and the algorithm. Finally, we run computational experiments to simulate a designer updating an algorithmic delegate over time to be optimized for when it is actually adopted by users, and show that while this process does not recover the optimal delegate in general, the resulting delegate often performs quite well.

However, deliberative forums such as citizens' assemblies have shown promise in bypassing party polarization and fostering productive discussions on contentious political issues [3]. Unfortunately, most deliberations do not take place in carefully structured settings with nationally representative participants. Instead, they often occur within homogeneous groups [17]. When this happens, deliberation can lead to group polarization, where individuals become more extreme in their initial positions rather than engaging with opposing viewpoints [22]. This can be problematic if the goal of deliberation is to build common ground and consensus within a pluralistic electorate. Given that large language models (LLMs) have demonstrated some fidelity in accurately responding to opinion surveys [1, 20] and adopting different personas [12], we explore whether an LLM-powered tool can help introduce missing perspectives in group deliberation.

As thousands of delegates from across China arrive in Beijing this week to participate in the annual parliamentary session, there is a barely perceptible shift in the mood in the capital. Though few ordinary Chinese pay much attention to goings-on inside the Great Hall of the People, the imposing 1950s modernist building that flanks the western edge of Tiananmen Square, the ripple effects of this week's conclave can be felt across the city. Extra uniformed personnel have been deployed to stand guard on Beijing's bridges – lest anyone attempt a stunt inspired by Peng Lifa's protest at Sitong Bridge ahead of the 20th party congress in 2022. Virtual private networks – apps used to tunnel through the firewall of internet censorship – slow down, as the authorities try to tighten their grip on the exchange of information with the outside world. It is imperative to the Communist party that the parallel sessions of the "Chinese People's Political Consultative Conference", an advisory body, and the National People's Congress (NPC), China's rubber-stamp parliament, run smoothly.