Deep Learning Algorithms For Solving Advanced Mathematical Problems

Deep Learning Algorithms For Solving Advanced Mathematical Problems

Neural networks are getting better at math. Neural networks are getting better at math. A seq2seq transformer model can solve advanced math equations using symbolic reasoning.

Ever tried building a neural network model to solve simple math problems? Like multiplication of two numbers or square of a number? Then you would have probably realized neural networks are not designed to solving these simple problems. You would need a comparatively complex model just to approximate the square of a number. It wouldn't be perfect either.

Isn’t this a real problem? Today precision and accuracy in numbers are significant in any cutting-edge technology. A small variation in results can cause an extreme failure to the system where the AI is deployed. I didn’t mean that it is impossible to construct a near-perfect end-to-end neural network model that gives direct answers to math problems. But that is totally unnecessary.

Instead, we can use AI to understand the math problem or the mathematical part of a given problem and then use the machine’s arithmetic unit _to solve it. This, according to me, will be the easiest and the most reliable way to solve mathematical problems. Or even embedding an _arithmetic unit inside a neural network model might be a good idea. I’m not sure if any such model exists. But today we’re going to dive into a model which is able to translate complex problems into simple solutions.

Facebook AI’s sequence-to-sequence (seq2seq) transformer model can solve, actually simplify advanced math equations using symbolic reasoning. They claim their model is the first of its kind and it performs better than traditional computation systems at solving integration problems and differential equations.

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