Artificial Intelligence in Mathematics
From Transformers to Large Language Model Assistants
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Beschreibung
Artificial Intelligence is rapidly transforming mathematics, reshaping how problems are explored, solved, verified, and even discovered. In Artificial Intelligence in Mathematics: From Transformers to Large Language Model Assistants, Miquel Noguer i Alonso and Daniel Bloch provide a comprehensive and forward-looking examination of this emerging frontier.
Bridging the worlds of modern AI and mathematical reasoning, the book explores how transformer architectures, large language models, neural-symbolic systems, and formal verification tools are changing the practice of mathematics. Readers will discover how AI can tackle symbolic computation, theorem proving, differential equations, linear algebra, optimization, geometric reasoning, scientific machine learning, and mathematical discovery.
Moving beyond headlines and hype, the authors present a rigorous framework for understanding the strengths, limitations, and reliability of AI-powered mathematical systems. They introduce a verification-centered approach in which generation, search, and pattern recognition are combined with formal proof, symbolic validation, and computational certification to produce trustworthy mathematical results.
Drawing on the latest developments in large language models, reinforcement learning, theorem provers, and AI-assisted discovery systems, this book offers both conceptual foundations and practical methodologies for researchers, educators, students, quantitative professionals, and anyone interested in the future of mathematical intelligence.
At the intersection of mathematics, computer science, and artificial intelligence, this timely volume reveals how machines are evolving from computational tools into collaborative partners in mathematical reasoning and discovery.
First verification-centered guide to AI in mathematics, linking LLMs, proofs, search, and formal verification Unifies transformers, theorem proving, symbolic computation, and scientific ML in one framework Bridges theory and practice with AI-mathematician workflows, reliability protocols, and research standards
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Miquel Noguer i Alonso
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