Google DeepMind Used a Large Language Model to Solve an Unsolvable Math Problem

 

Google DeepMind recently made a groundbreaking achievement by using a large language model to solve an unsolved math problem. This remarkable feat has sparked significant interest and raised several questions. In this blog, we will explore the implications of this achievement and address some of the major questions related to this topic.

The Achievement

Google DeepMind, in a paper published in Nature, revealed that they used a large language model to crack a famous unsolved problem in pure mathematics. The researchers emphasized that this is the first time a large language model has been used to discover a solution that was not known before. The model, named FunSearch, was able to generate entirely new and accurate solutions to highly complex math problems, which were previously unattainable by humans.

Implications

This achievement has significant implications for the field of artificial intelligence and mathematics. It demonstrates the potential of large language models to not only provide existing information but also to generate entirely new and valid solutions. The use of FunSearch to solve the unsolvable math problem has opened up new possibilities for the application of artificial intelligence in pure mathematics and problem-solving.

Addressing Major Questions

1. How Did the Model Solve an "Unsolvable" Math Problem?

The model, FunSearch, was trained on computer code and fine-tuned to reject incorrect or nonsensical answers while retaining and improving valid solutions. The researchers at Google DeepMind were initially unsure about the effectiveness of this approach but were astonished by the model's ability to discover a previously unknown solution to the unsolved math problem. The model's capability to provide new and accurate solutions has challenged the notion of certain problems being "unsolvable".

2. What Sets FunSearch Apart from Generative AI Models?

Unlike traditional generative AI models, FunSearch is designed to adhere to mathematical truths and provide solutions that are not mere fabrications but entirely new and verifiable. This distinguishes it from other generative AI models that are known for producing fictitious outputs. The accuracy and novelty of the solutions generated by FunSearch make it a significant advancement in the field of artificial intelligence.

3. How Does This Achievement Impact the Future of Mathematics and AI?

The successful use of FunSearch to solve an unsolvable math problem has the potential to revolutionize the way mathematicians approach complex problems. It presents a new avenue for integrating large language models into mathematical research and problem-solving. While mathematicians are still exploring the best ways to incorporate such models into their work, this achievement offers a promising path forward and highlights the transformative impact of AI on the field of mathematics.

4. What Are the Limitations of This Approach?

Despite its remarkable success, the researchers at Google DeepMind acknowledge that they are still working to understand the underlying mechanisms that enable FunSearch to provide such groundbreaking solutions. The model's ability to solve complex problems faster than human-devised methods is impressive, but there is ongoing research to determine the best practices for integrating large language models into mathematical research and to identify any potential limitations of this approach.In conclusion, Google DeepMind's use of a large language model to solve an unsolvable math problem represents a significant leap forward in the capabilities of artificial intelligence and its potential impact on the field of mathematics. This achievement has raised important questions and opened up new possibilities for the application of AI in pure mathematics and problem-solving.