Meta wowed Wall Street with improvements in ad targeting fueled by AI alongside huge investment. Big tech earnings so far this week have sent a clear warning: investors are willing to overlook soaring spending on artificial intelligence if it fuels strong growth, but are quick to punish companies that fall short. The contrast was clear in Thursday's stock market reaction to earnings from Microsoft and Meta, highlighting how dramatically the stakes have changed since the launch of ChatGPT started the AI boom more than three years ago. Shares of the Instagram parent surged more than 9% on strong sales, while those of Microsoft slumped 10% after its cloud business failed to impress. "The market appears to be questioning whether these massive capital expenditure hikes will generate sufficient returns," said Jesse Cohen, senior analyst at Investing.com.

EU and US unshackle regulations in quest for growth, and is the AI bubble about to burst? In tech, the European Union is deregulating artificial intelligence; the United States is going even further. The AI bubble has not popped, thanks to Nvidia's astronomical quarterly earnings, but fears persist. And Meta has avoided a breakup for a similar reason as Google. The hundreds of billions of dollars being spent on AI are overwhelming Europe's commitment to digital privacy and stringent tech regulation.

The Association for the Advancement of Artificial Intelligence's 2025 Spring Symposium Series was held in Burmingame, California, March 31-April 2, 2025. There were eight symposia in the spring program: AI for Engineering and Scientific Discoveries, AI for Health Symposium: Leveraging Artificial Intelligence to Revolutionize Healthcare, Current and Future Varieties of Human-AI Collaboration, GenAI@Edge: Empowering Generative AI at the Edge, Human-Compatible AI for Well-being: Harnessing Potential of GenAI for AI-Powered Science, Machine Learning and Knowledge Engineering for Trustworthy Multimodal and Generative AI, Symposium on Child-AI Interaction in the Era of Foundation Models, Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation. This report contains summaries of the workshops, which were submitted by some, but not all, of the workshop chairs. This symposium aims to advance and diversify the application of AI in emerging engineering and scientific discovery domains. Inspired by progress in large language models, generative AI, and AI-assisted scientific computing, we seek to foster new collaborations between industry and academia to tackle challenging problems in materials, manufacturing, and life sciences. We also plan to explore new directions in human-machine interaction for accelerating knowledge discovery and address related ethical considerations. Through invited speakers, panel discussions, and contributions from researchers with cross-disciplinary expertise, we hoped to cultivate partnerships that drive transformative advances in both AI and scientific research. No formal report was filed by the organizers for this symposium.

Urban profiling aims to predict urban profiles in unknown regions and plays a critical role in economic and social censuses. Existing approaches typically follow a two-stage paradigm: first, learning representations of urban areas; second, performing downstream prediction via linear probing, which originates from the BERT era. Inspired by the development of GPT style models, recent studies have shown that novel self-supervised pretraining schemes can endow models with direct applicability to downstream tasks, thereby eliminating the need for task-specific fine-tuning. This is largely because GPT unifies the form of pretraining and inference through next-token prediction. However, urban data exhibit structural characteristics that differ fundamentally from language, making it challenging to design a one-stage model that unifies both pretraining and inference. In this work, we propose Urban In-Context Learning, a framework that unifies pretraining and inference via a masked autoencoding process over urban regions. To capture the distribution of urban profiles, we introduce the Urban Masked Diffusion Transformer, which enables each region' s prediction to be represented as a distribution rather than a deterministic value. Furthermore, to stabilize diffusion training, we propose the Urban Representation Alignment Mechanism, which regularizes the model's intermediate features by aligning them with those from classical urban profiling methods. Extensive experiments on three indicators across two cities demonstrate that our one-stage method consistently outperforms state-of-the-art two-stage approaches. Ablation studies and case studies further validate the effectiveness of each proposed module, particularly the use of diffusion modeling.

The co creativity community is making significant progress in developing more sophisticated and tailored systems to support and enhance human creativity. Design considerations from prior work can serve as a valuable and efficient foundation for future systems. To support this effort, we conducted a systematic literature review of 62 papers on co-creative systems. These papers cover a diverse range of applications, including visual arts, design, and writing, where the AI acts not just as a tool but as an active collaborator in the creative process. From this review, we identified several key dimensions relevant to system design: phase of the creative process, creative task, proactive behavior of the system, user control, system embodiment, and AI model type. Our findings suggest that systems offering high user control lead to greater satisfaction, trust, and a stronger sense of ownership over creative outcomes. Furthermore, proactive systems, when adaptive and context sensitive, can enhance collaboration. We also extracted 24 design considerations, highlighting the value of encouraging users to externalize their thoughts and of increasing the system's social presence and transparency to foster trust. Despite recent advancements, important gaps remain, such as limited support for early creative phases like problem clarification, and challenges related to user adaptation to AI systems.

In typical multi-agent reinforcement learning (MARL) problems, communication is important for agents to share information and make the right decisions. However, due to the complexity of training multi-agent communication, existing methods often fall into the dilemma of local optimization, which leads to the concentration of communication in a limited number of channels and presents an unbalanced structure. Such unbalanced communication policy are vulnerable to abnormal conditions, where the damage of critical communication channels can trigger the crash of the entire system. Inspired by decentralization theory in sociology, we propose DMAC, which enhances the robustness of multi-agent communication policies by retraining them into decentralized patterns. Specifically, we train an adversary DMAC\_Adv which can dynamically identify and mask the critical communication channels, and then apply the adversarial samples generated by DMAC\_Adv to the adversarial learning of the communication policy to force the policy in exploring other potential communication schemes and transition to a decentralized structure. As a training method to improve robustness, DMAC can be fused with any learnable communication policy algorithm. The experimental results in two communication policies and four multi-agent tasks demonstrate that DMAC achieves higher improvement on robustness and performance of communication policy compared with two state-of-the-art and commonly-used baselines. Also, the results demonstrate that DMAC can achieve decentralized communication structure with acceptable communication cost.

The traditional techniques for extracting polycrystalline grain structures from microscopy images, such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM), are labour-intensive, subjective, and time-consuming, limiting their scalability for high-throughput analysis. In this study, we present an automated methodology integrating edge detection with generative diffusion models to effectively identify grains, eliminate noise, and connect broken segments in alignment with predicted grain boundaries. Due to the limited availability of adequate images preventing the training of deep machine learning models, a new seven-stage methodology is employed to generate synthetic TEM images for training. This concept-oriented synthetic data approach can be extended to any field of interest where the scarcity of data is a challenge. The presented model was applied to various metals with average grain sizes down to the nanoscale, producing grain morphologies from low-resolution TEM images that are comparable to those obtained from advanced and demanding experimental techniques with an average accuracy of 97.23%.

A number of recent studies have focused on developing surgical simulation platforms to train machine learning (ML) agents or models with synthetic data for surgical assistance. While existing platforms excel at tasks such as rigid body manipulation and soft body deformation, they struggle to simulate more complex soft body behaviors like cutting and suturing. A key challenge lies in modeling soft body fracture and splitting using the finite-element method (FEM), which is the predominant approach in current platforms. Additionally, the two-way suture needle/thread contact inside a soft body is further complicated when using FEM. In this work, we use the material point method (MPM) for such challenging simulations and propose new rigid geometries and soft-rigid contact methods specifically designed for them. We introduce CRESSim-MPM, a GPU-accelerated MPM library that integrates multiple MPM solvers and incorporates surgical geometries for cutting and suturing, serving as a specialized physics engine for surgical applications. It is further integrated into Unity, requiring minimal modifications to existing projects for soft body simulation. We demonstrate the simulator's capabilities in real-time simulation of cutting and suturing on soft tissue and provide an initial performance evaluation of different MPM solvers when simulating varying numbers of particles.