Reviews & Analysis

This Perspective discusses that generative AI aligns with generative linguistics by showing that neural language models (NLMs) are formal generative models. Furthermore, generative linguistics offers a framework for evaluating and improving NLMs.

Large language models remain largely unexplored is the design of cities. In this Perspective, the authors discuss the potential opportunities brought by these models in assisting urban planning.

Large language models are increasingly important in social science research. The authors provide guidance on how best to validate and use these models as rigorous tools to further scientific inference.

This Perspective highlights the potential integrations of large language models (LLMs) in chemical research and provides guidance on the effective use of LLMs as research partners, noting the ethical and performance-based challenges that must be addressed moving forward.

Physical computing, particularly photonic computing, offers a promising alternative by directly encoding data in physical quantities, enabling efficient probabilistic computing. This Perspective discusses the challenges and opportunities in photonic probabilistic computing and its applications in artificial intelligence.

The inverse design of functional crystalline materials via generative models is a rapidly growing field, but one that faces challenges in representation and generation architectures. This Perspective systematically examines these limitations and explores strategies for future improvement.

This Perspective highlights the vital role of physics-based modeling in computational enzyme engineering, exploring key advances, challenges and future steps. By integrating machine learning, these approaches can enhance each other, unlocking the full potential of enzyme design and discovery.