Japan's AI gamble: Can technology offset the cost of an ageing society? Beneath the business towers of Tokyo's Otemachi district, a test is taking place to see whether artificial intelligence can help solve one of Japan's biggest economic challenges: a shrinking workforce. Deep underground, Marunouchi Heat Supply Company operates a 30km (18.6-mile) network of heating and cooling pipelines serving offices, commercial buildings and transport facilities in one of Japan's most important business areas. The company is now using AI to manage this complex infrastructure with the goal of moving towards more automated operations by 2027. Developed with the Tokyo-based AI company Preferred Networks, the project uses PlantPilot, an AI system trained on historical operating data and the expertise of experienced engineers.

As artificial intelligence pushes semiconductors and data centers to new physical limits, advances in materials science are becoming essential to sustaining the pace of innovation. The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every increase in computing performance increases the physical demands placed on the systems that make and run AI. Delivering these gains depends not only on advances in chip design and system architecture, but on advances in the materials that enable them to perform under extreme conditions. As AI continues to push the physical limits of semiconductors and data center infrastructure, advanced materials are no longer simply supporting innovation in this area; they are defining the limits of what is possible.

Discover four foundational elements of AI architecture that will endure as models continue to advance: data quality, context engineering, governance, and human expertise. With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture--the structural framework required for deploying and managing reliable, integrated AI systems at scale--allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems. The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves. Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.

The claim that the AI community, or society at large, should'democratize AI' has attracted considerable critical attention and controversy. Two core problems have arisen and remain unsolved: conceptual disagreement persists about what democratizing AI means; normative disagreement persists over whether democratizing AI is ethically and politically desirable. We identify eight common AI democratization traps: democratization-skeptical arguments that seem plausible at first glance, but turn out to be misconceptions. We develop arguments about how to resist each trap. We conclude that, while AI democratization may well have drawbacks, we should be cautious about dismissing AI democratization prematurely and for the wrong reasons. We offer a constructive roadmap for developing alternative conceptual and normative approaches to democratizing AI that successfully avoid the traps.

Machine learning competitions (MLCs) play a pivotal role in advancing artificial intelligence (AI) by fostering innovation, skill development, and practical problem-solving. This study provides a comprehensive analysis of major competition platforms such as Kaggle and Zindi, examining their workflows, evaluation methodologies, and reward structures. It further assesses competition quality, participant expertise, and global reach, with particular attention to demographic trends among top-performing competitors. By exploring the motivations of competition hosts, this paper underscores the significant role of MLCs in shaping AI development, promoting collaboration, and driving impactful technological progress. Furthermore, by combining literature synthesis with platform-level data analysis and practitioner insights a comprehensive understanding of the MLC ecosystem is provided. Moreover, the paper demonstrates that MLCs function at the intersection of academic research and industrial application, fostering the exchange of knowledge, data, and practical methodologies across domains. Their strong ties to open-source communities further promote collaboration, reproducibility, and continuous innovation within the broader ML ecosystem. By shaping research priorities, informing industry standards, and enabling large-scale crowdsourced problem-solving, these competitions play a key role in the ongoing evolution of AI. The study provides insights relevant to researchers, practitioners, and competition organizers, and includes an examination of the future trajectory and sustained influence of MLCs on AI development.

As the key component in multimodal large language models (MLLMs), the ability of the visual encoder greatly affects MLLM's understanding on diverse image content. Although some large-scale pretrained vision encoders such as vision encoders in CLIP and DINOv2 have brought promising performance, we found that there is still no single vision encoder that can dominate various image content understanding, e.g., the CLIP vision encoder leads to outstanding results on general image understanding but poor performance on document or chart content. To alleviate the bias of CLIP vision encoder, we first delve into the inherent behavior of different pre-trained vision encoders and then propose the MoVA, a powerful and novel MLLM, adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism. In the coarse-grained stage, we design a context-aware expert routing strategy to dynamically select the most suitable vision experts according to the user instruction, input image, and expertise of vision experts.

High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained

As a result, these and other large conferences must rely on automated systems to decide what members of the impaneled reviewer pool will review each paper.

Receiving fine-grained instruction from these specialized teachers can often be non-uniform, costly, and limited by their availability.