Artificial Intelligence for Science (AI4S)

Frontiers and Perspectives Based on Parallel Intelligence

Authors: Qinghai Miao, Fei-Yue Wang

Publisher: Springer Nature Switzerland

Book Series : SpringerBriefs in Service Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Table of Contents

About this book

This book presents a comprehensive framework for analyzing, evaluating, and guiding 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.

Frontmatter

Chapter 1. AI4S Based on Parallel Intelligence

Abstract

AI for Sciences (AI4S) has made significant achievements in recent years. To tackle challenges and advance future research, this chapter introduces the Parallel Intelligence perspectives for analyzing, comparing, and developing AI4S within a unified framework called HANOI-AI4S. First, a systematic introduction to Parallel Intelligence is provided, followed by the perspectives of Parallel Science, characterized by “three worlds, three ITs, three scientists, and three modes.” The challenges of AI4S and benefits of a unified AI4S framework are then analyzed. Finally, the details of HANOI-AI4S are described, with an emphasis on human roles, organizations, and AI methods.

Qinghai Miao, Fei-Yue Wang

Chapter 2. AI for Mathematics

Abstract

AI has made notable strides in the field of mathematics. One area of focus is the development of AI systems that can assist mathematicians in proving theorems and solving complex mathematical problems. These systems use techniques such as reinforcement learning, symbolic computation, genetic programming, and large language models to explore mathematical spaces and discover new patterns and relationships. AI algorithms and paradigms, like AlphaZero, are also being used to analyze mathematical texts and extract useful information, such as key concepts and problem-solving strategies. As representative examples, this chapter provides brief introductions to selected advancements in AI for mathematics, including guiding mathematicians’ intuition for conjectures, searching for new programs for combinatorial optimization, proving geometric theorems, and discovering faster algorithms for matrix multiplication and sorting.

Qinghai Miao, Fei-Yue Wang

Chapter 3. AI for Physics

Abstract

AI has had a significant impact on physics research in recent years. One area where AI is making a difference is in the analysis of complex data from experiments, such as those conducted at particle accelerators or telescopes. Machine learning techniques are being used to help physicists sift through massive amounts of data to identify patterns and anomalies that may lead to new discoveries. AI is also being applied to problems in theoretical physics, such as optimizing quantum algorithms or predicting the behavior of complex systems. Additionally, AI is helping physicists simulate and model physical processes more efficiently, leading to advances in areas like materials science and fluid dynamics. As representative examples, this chapter provides brief introductions to selected advancements in AI for physics, including unfolding observables from the Large Hadron Collider, magnetic control of tokamak plasmas, and finding evidence for intrinsic charm quarks, based on AI methods such as neural networks, genetic algorithms, reinforcement learning, and more.

Qinghai Miao, Fei-Yue Wang

Chapter 4. AI for Biology

Abstract

AI has had a transformative impact on biology in recent years. One of the significant advancements in biology, particularly in the field of structural biology, is the application of AI to protein research. Among the numerous frontiers, this chapter covers topics from accurate protein structure prediction with AlphaFold to the innovative design of functional proteins with RFdiffusion and advanced single-cell analysis with scGPT. These advancements not only enhance our understanding of biological processes but also pave the way for new therapeutics and applications in medicine and beyond.

Qinghai Miao, Fei-Yue Wang

Chapter 5. AI for Health and Medicine

Abstract

AI has made significant advancements in health and medicine in recent years. It is being used for a variety of applications, including disease diagnosis, personalized treatment planning, drug discovery, and healthcare management. This chapter covers various AI advancements in health and medicine, including Swarm Learning for decentralized diagnosis, and advanced models like IRENE and EVE for clinical diagnostics. These innovations aim to enhance data confidentiality, improve diagnostic accuracy, streamline patient triaging, and clinical decision-making.

Qinghai Miao, Fei-Yue Wang

Chapter 6. AI for Chemistry

Abstract

Chemistry provides the foundational knowledge and tools for many fields such as drug discovery, materials science, and molecular design. Among the numerous advancements on the topic of AI for chemistry, this chapter introduces AlphaFlow, a reinforcement learning (RL)-guided self-driving lab (SDL) that integrates machine learning with automated experimentation. Additionally, it highlights the role of large language models (LLMs) in chemical research. Coscientist, an intelligent agent powered by GPT-4, demonstrates the potential of LLMs in autonomously designing, planning, and executing complex scientific experiments. This integration of laboratory automation with LLMs showcases AI’s ability to revolutionize chemical research by enhancing chemical synthesis planning, data analysis, and experiment execution.

Qinghai Miao, Fei-Yue Wang

Chapter 7. AI for Material Science

Abstract

In recent years, AI has made significant contributions to materials science. AI algorithms are being used to accelerate materials discovery and development processes. One area of focus is the prediction of new materials with desirable properties, such as high strength or conductivity, by analyzing large datasets of material properties and structures. AI is also being used to optimize material synthesis processes, helping researchers identify the best conditions for producing materials with specific properties. Additionally, AI is aiding in the design of new materials for applications in areas such as energy storage, catalysis, and electronics. As representative examples, this chapter provides brief introductions to selected advancements in AI for materials, including discovering materials via Bayesian Active Learning, Graph Networks, and designing autonomous laboratories by utilizing Active Learning with robots.

Qinghai Miao, Fei-Yue Wang

Chapter 8. AI for Astronomy

Abstract

Artificial intelligence has had a significant impact on astronomy over the years. One key area is in the analysis of large astronomical datasets, such as those generated by sky surveys and telescopes. AI algorithms are being used to sift through this data to identify celestial objects, classify galaxies, and detect rare events such as gravitational waves. AI is also being used to improve the accuracy and efficiency of astronomical simulations, helping researchers model complex phenomena such as galaxy formation and the evolution of the universe. Additionally, AI is aiding in the development of autonomous telescopes and observatories, which can automatically prioritize observations based on scientific goals and environmental conditions. Among a large number of AI-aided advancements, this chapter covers three areas including locating exoplanets, estimating dark matter distribution, and analyzing stellar light curves.

Qinghai Miao, Fei-Yue Wang

Chapter 9. Toward a Sustainable AI4S Ecosystem

Abstract

The preceding chapters give an overview on the advancements of AI in fields such as mathematics, physics, biology, chemistry, materials science, medicine, and astronomy. While these selected works represent only a portion of the progress made in AI for Sciences (AI4S) in recent years, these breakthroughs are truly thrilling. On the other hand, as a rapidly evolving field, AI4S encounters numerous challenges. Hence, the establishment of a robust and sustainable ecosystem is paramount. Despite its significance, the AI4S ecosystem often remains overlooked. This chapter delves into AI ecology and sustainable development issues, examining them through the lenses of Decentralized Science (DeSci), Decentralized Autonomous Organizations (DAOs), Blockchain Technology, and Foundational Intelligence.

Qinghai Miao, Fei-Yue Wang

Title: Artificial Intelligence for Science (AI4S)
Authors: Qinghai Miao
Fei-Yue Wang
Publisher: Springer Nature Switzerland
Electronic ISBN: 978-3-031-67419-8
Print ISBN: 978-3-031-67418-1
DOI: https://doi.org/10.1007/978-3-031-67419-8