Describing their findings, Prof. Geordie Williamson said that AI is an “extraordinary tool” for pure mathematics.
Sydney: Some of the world’s leading mathematicians have joined forces with a team of computer scientists to use artificial intelligence (AI) to develop new theorems and solve seemingly impenetrable questions in the field of node theory and representation theory.
Their report, published in the scientific journal Nature on Thursday, was in collaboration with the director of the Mathematical Research Institute at the University of Australia in Sydney, Prof. Geordie Williamson.
Describing their findings, Williamson said that AI is an “extraordinary tool” for pure mathematics.
“Intuition can take us a long way, but AI can help us find connections that the human mind may not always be able to easily identify,” he said.
“Working to prove or disprove long-standing conjectures in my field sometimes involves taking into account infinite space and extremely complex sets of multidimensional equations.”
Williamson, who has worked with computer scientists at DeepMind, a UK-based AI research company and mathematicians at Oxford University in England, said the use of AI brought him closer to demonstrating a conjecture called the Kazhdan-Lusztig polynomials. Xinhua News Agency reported.
The complex mathematical expression, which has been unresolved for 40 years, deals with symmetry problems in higher dimensional algebra.
Oxford professors and co-authors of the reports Marc Lackeby and Andras Juhasz took the use of AI a step further, discovering a connection between the algebraic and geometric invariants of nodes that they say established a new theorem in mathematics.
Node theory, which is the study of three-dimensional closed curves, has many scientific applications, including the understanding of DNA strands, fluid dynamics, and the interaction of forces in the Sun’s corona.
“We have shown that when guided by mathematical intuition, machine learning provides a powerful framework that can reveal interesting and demonstrable assumptions in areas where a large amount of data is available or where objects are too large to be studied with methods. classics. ” said Juhasz.
The authors say they hope that their work can serve as a model for deepening collaboration between the mathematical fields of study and AI.
“My hope is that AI can give us another axis of intelligence to work with and that this new axis will deepen our understanding of the mathematical world,” Williamson said.
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