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This book presents a comprehensive framework for analyzing, evaluating, and guiding Artificial Intelligence (AI) for Sciences (AI4Sci) research, offering a unified approach that facilitates analysis across various academic fields through a shared set of dimensions and indicators. It provides a systematic overview of recent AI4Sci advances in various disciplines and offers insights into the latest issues in and prospects of AI4Sci. The book is based on the theory of Parallel Intelligence (PI), which forms the foundation for the general AI4Sci framework. By analyzing multiple cases in various academic fields, this framework integrates key elements of AI4Sci, such as real scientific problems, datasets, virtual systems, AI methods, human roles, and organizational mechanisms, from a multidimensional perspective. It also assesses and summarizes the limitations of AI4Sci, incorporating the latest advances in AI for fundamental models. Lastly, it explores the impact of DeSci and DAO, as well as TAO, on AI4Sci ecosystem development and prospects. Through its balanced approach, the book offers readers a goal-oriented perspective, focusing on a concise presentation of the core ideas and reducing detailed descriptions of specific AI4Sci cases to a minimum.
As Artificial Intelligence (AI) rapidly advances in recent years, its application in various scientific fields such as physics, chemistry, biology, astronomy, and others is also emerging. This technology, known as AI for Sciences (AI4S), is revolutionizing scientific research by providing new tools and techniques for analyzing data, discovering patterns, and making predictions, etc. AI4S helps scientists process large amounts of data more efficiently, identify new research directions, and accelerate the pace of discovery. It also has the potential to address complex scientific challenges that may be difficult or time-consuming for humans to solve alone.
The article “Advancing mathematics by guiding human intuition with AI” introduces examples of mathematicians discovering new conjectures and theorems in foundational areas of mathematics with the assistance of Machine Learning. The intuition of mathematicians plays an extremely important role in mathematical discoveries, as “It is only with a combination of both rigorous formalism and good intuition that one can tackle complex mathematical problems.” The AI for mathematics methods described in this article is based on the core idea of using Machine Learning to guide the intuition of mathematicians and propose conjectures. A relatively basic AI model was used in this chapter, that is, a fully connected feed-forward neural network with multiple hidden layers, using the sigmoid function as the activation function. The task is constructed as a multi-class classification problem, with different signature values as categories, cross-entropy loss as the optimizable loss function, and test classification accuracy as the performance metric. The framework aids mathematicians in two key ways: first, by confirming the hypothesized presence of structure or patterns in mathematical objects using supervised machine learning, and second, by facilitating comprehension of these patterns through attribution techniques