Meta AI Has Built A Neural Theorem Prover That Has Solved 10 International Math Olympiad (IMO) Problems — 5x More Than Any Previous Artificial Intelligence AI System

The scientific world has long recognized that the introduction of mathematical theorems is an important first step in the development of artificial intelligence. To prove the truth or falsehood of a guess, one must use symbolic thinking and sort through an unlimited number of options. These tasks are beyond the reach of even the most sophisticated AI systems.

The art form of artificial intelligence today is to create machines that can “solve immediately” or create a whole solution to a problem at once. However, this is not how most individuals approach difficult situations. Mathematical reasoning is a greater challenge in formal identification and measurement.

Meta AI has led to significant developments at the intersection of artificial intelligence and mathematics. The neural theorem developed by this team completed five times more IMO problems than any other AI system before it totaled ten. Regarding miniF2F, a popular math test, the AI ​​model runs 20% beyond the state of the art and 10% of the Metamath process.

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The team-developed HyperTree Proof Search (HTPS) method is taught to generalize from a set of accurate mathematical evidence to a whole new challenge. By using some arithmetic reduction to a limited number of examples, it is possible to reduce the valid evidence for an IMO problem.

Neurological theories suggest that the “situation” needs to be linked to an existing (incomplete) understanding of the problem so that it can behave like a human. Initially, researchers used reinforcement learning strategies that integrated with existing verification tools such as Lean.

The “current state” of the (unfinished) evidence is treated as a node in the graph, and each subsequent step is an edge. This allows to see the whole evidence. Evidence-giving assistants use the subtraction argument process to make this approach possible.

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AI chess requires a similar mechanism: the ability to assess the strength of a given chess position in this case, the evidence status. To achieve this, the team adopted a reminiscent strategy of finding the Monte Carlo tree (MCTS), which is repeated in the following estimates:

  1. A set of credible arguments to use in a given evidence situation
  2. The result of the evidence after a fixed number of objections is presented.

This allows for online training procedures that greatly improve the performance of the initially trained model on specific issues.

As a result, the proposed method is 20% more efficient than the current edition of Art on Minif2f validation accuracy and solves ten previously unresolved IMO issues. The team hopes that their work will help the community build their work so that we can all move forward faster in this exciting field.

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Tanushree Shenwai is an intern at MarktechPost. She is currently pursuing a B.Tech from the Indian Institute of Technology (IIT), Bhubaneswar. She is a lover of data science and has a strong interest in the scope of artificial intelligence in various fields. She is passionate about discovering new advances in technology and their real-life applications.


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