Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval-augmented reasoning represents a promising approach. However, significant challenges still persist, including inaccurate and insufficient retrieval for complex questions, as well as difficulty in integrating multi-source knowledge. To address this, we propose Beam Aggregation Reasoning, BeamAggR, a reasoning framework for knowledge-intensive multi-hop QA. BeamAggR explores and prioritizes promising answers at each hop of question. Concretely, we parse the complex questions into trees, which include atom and composite questions, followed by bottom-up reasoning. For atomic questions, the LLM conducts reasoning on multi-source knowledge to get answer candidates. For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory. Extensive experiments on four open-domain multi-hop reasoning datasets show that our method significantly outperforms SOTA methods by 8.5%. Furthermore, our analysis reveals that BeamAggR elicits better knowledge collaboration and answer aggregation.

Appropriately regulating artificial intelligence is an increasingly urgent policy challenge. Legislatures and regulators lack the specialized knowledge required to best translate public demands into legal requirements. Overreliance on industry self-regulation fails to hold producers and users of AI systems accountable to democratic demands. Regulatory markets, in which governments require the targets of regulation to purchase regulatory services from a private regulator, are proposed. This approach to AI regulation could overcome the limitations of both command-and-control regulation and self-regulation. Regulatory market could enable governments to establish policy priorities for the regulation of AI, whilst relying on market forces and industry R&D efforts to pioneer the methods of regulation that best achieve policymakers' stated objectives.

As we speak--and browse, and post photos, and move about--artificial intelligence is transforming the fabric of our lives. It is making life easier, better informed, healthier, more convenient. It also threatens to crimp our freedoms, worsen social disparities, and gives inordinate powers to unseen forces. Both AI's virtues and risks have been on vivid display during this moment of global turmoil, forcing a deeper conversation around its responsible use and, more importantly, the rules and regulations needed to harness its power for good. This is a vastly complex subject, with no easy conclusions.

Artificial Intelligence ("AI") technology is transforming the way humans interact and society operates. As the development of AI accelerates, many of us see the need for a movement that ensures we harness AI's benefits and mitigate its risks. To explore these questions and start identifying solutions, The Rockefeller Foundation has designed A Month of AI in Bellagio that kicks off this week. The Bellagio Center is a remarkable conference and residency center in Italy celebrating 60 years of enabling groundbreaking ideas, partnerships, and commitments. After months of consultations with a range of experts, we have curated a month-long residency program and three week-long conferences that will bring together leading experts and innovators from technology, the public sector, social sciences, and the arts.

This is the weekend roundup of The WorldPost, of which Nathan Gardels is the editor in chief. In The WorldPost this week, we examine and evaluate two key developments of the digital age: the emergent geopolitics of artificial intelligence and Facebook's recent move toward "reputational scores" as a means to signal trustworthy information to users. For Lee, who is based in Beijing, the world of AI has become a "duopoly" in which China and America competitively drive each other's innovations forward while dominating the rest of the world. Lee argues that China's "scrappy" days of stealing intellectual property to get ahead are behind it. Rather, its rapid advance in AI today is due to the superior business model of its tech entrepreneurs.

At a computational level, language processing tasks are traditionally processed in a language-only space/context, isolated from perception and action. However, at a cognitive level, language processing has been shown experimentally to be embodied, i.e. to inform and be informed by perception and action. In this paper, we argue that embodied cognition dictates the development of a new generation of language processing tools that bridge the gap between the symbolic and the sensorimotor representation spaces. We describe that tasks and challenges such tools need to address and provide an overview of the first such suite of processing tools developed in the framework of the POETICON project.

The KDD conference series is the primary venue for presenting and publishing the highest quality advances in the field.

They perform many different tasks, from giving tours to collecting trash. Many have distinguished themselves (usually with first-or second-place finishes) at various indoor and outdoor mobile robot competitions. Each case study is self-contained and includes detailed descriptions of important algorithms, including pseudo-code. Thus this volume serves as a recipe book for the design of successful mobile robot appli cations. Common themes include navigation and mapping, computer vision, and architecture.