The AlphaGo Blueprint Behind Advanced Math Problem-Solving AI

In 2016, DeepMind's AlphaGo beat the world champion in the complex game of Go using a smart combination of different AI techniques:

1. A neural network that recommends the next best move to make

2. A neural network that evaluates which player is most likely winning from any board position

3. An advanced search algorithm that models many possible future moves and picks the most promising option

4. A clear feedback signal on who is winning, allowing the system to improve itself through practice.

By bringing together learning components and search, AlphaGo was able to outperform humans by constantly getting better through self-play.

Now there is lots of hype around a rumored AI system named "Q*" for solving complex math problems. While details are unclear, it likely combines:

1. A large language model to generate step-by-step reasoning

2. Additional AI components to check each reasoning step

3. Innovative search algorithms to efficiently explore the space of possible solutions

4. Clear feedback such as known answers to practice problems

The key insight is that combining learning and search elements in a "perpetual motion machine" allows AI systems to bootstrap their own capabilities through practice, instead of just imitating human examples. This is what made AlphaGo superhuman at Go.

While the specifics behind Q* remain a mystery, the basic principles likely apply to create an AI that can solve advanced math problems. However, whether such a narrow system will match more general human abilities like creativity remains doubtful.