It kind of is. The first self-learning algorithm I was taught adjusted a single variable in order to aproximate a linear function. Now, being continuous and unb

It kind of is. The first self-learning algorithm I was taught adjusted a single variable in order to aproximate a linear function. Now, being continuous and unbounded, it is mathematically impossible to memoize such a function, however you can memoize the function calls as they happen. The results would be indistinguishable. However real machine learning tries to approximate functions in n-dimensions and that is really, really, really hard to do. Currently no one really has a lookup table. There are some inputs where the error level is acceptably low, and others where the model just isn't optimized enough and the errors are ridiculous. The only question that remains to be answered, is whether any of these high-dimensional functions can actually be found by machine learning, or if we are just stuck with these endless approximations. Also I suppose you could ask if any such functions actually exist; maybe certain phenomena are just pure chaos.