Google DeepMind used an LLM to solve an unsolvable math problem

Dec 16, 2023 ·

Google's DeepMind has announced a breakthrough in large-scale language modeling by using their AI to solve a previously unsolvable math problem known as the “Cap Set” problem. This is a problem that requires complex algorithms and powerful computers to solve, and until now, no solution had been found. The AI used by Google's DeepMind was able to come up with a novel approach to the problem, which allowed it to outperform traditional methods of solving the problem.

The Cap Set problem is a mathematical problem involving the intersection of two sets, with the goal being to determine whether one set is a subset of another. This is a difficult problem to solve because there are no general rules about how to go about solving it. This makes it difficult to develop an algorithm that works for all cases, so the only method available has been brute force calculations.

Google's AI was able to solve the Cap Set problem using a new technique called 'neural architecture search'. This technique involves training an AI on a specific task, such as the Cap Set problem, and then giving it the ability to search through different solutions and find the most efficient one for solving the problem. It is estimated that the AI was able to solve the problem in around 1000 times less time than a human would have taken to solve the same problem.

This breakthrough has huge implications for the future of AI technology. With more powerful computation and better algorithms, AI could be used to solve even more complex problems in the future. In addition, this breakthrough could lead to improvements in other fields such as healthcare and finance, where large amounts of data can be processed quickly and accurately.

Overall, Google's breakthrough in large-scale language modeling is a major achievement in AI research, and could pave the way for more advanced applications in the future. With the successful implementation of this technique, AI could be used to solve more complex problems that have not yet been solved. Additionally, this could lead to improved performance in areas like healthcare and finance, allowing data to be processed quickly and accurately.