When will this AI agents be able to do great math?
Feb 25, 2024 - news.ycombinator.com
The article discusses the capabilities of different machine learning models, specifically comparing the AlphaGo model to transformer-based models. It highlights that unlike transformer-based models, AlphaGo retains symbolic inference of the Q states/map it has learned. The ability of these models to learn complex functions, such as f(x) = 123xe^3 + 12x^2 + f2(x), is dependent on the number of neurons in each hidden layer.
The article also discusses the potential of these models to learn integrals, similar to LSTMs, based on the detail of the boosting method used. However, the level of detail in the results is constrained by the implementation, the number of neurons, the number of hidden layers, and the boosting method.
Key takeaways:
The AlphaGo model retains symbolic inference of the Q states/map it learned, unlike transformer based models.
The ability of a model to learn the coordinates of a complex function depends on the number of neurons in each hidden layer.
With a detailed boosting method, a model can learn integrals, similar to LSTMs.
The detail of the result is limited by the implementation, the number of neurons, hidden layers, and the boosting method.
