Optimisation Is Not What You Need

--The Artificial Intelligence field has focused on developing optimisation methods to solve multiple problems, specifically problems that we thought to be only solvable through cognition. The obtained results have been outstanding, being able to even surpass the T uring T est. However, we have found that these optimisation methods share some fundamental flaws that impede them to become a true artificial cognition. Specifically, the field have identified catastrophic forgetting as a fundamental problem to develop such cognition. This paper formally proves that this problem is inherent to optimisation methods, and as such it will always limit approaches that try to solve the Artificial General Intelligence problem as an optimisation problem. Additionally, it addresses the problem of overfitting and discuss about other smaller problems that optimisation methods pose. Finally, it empirically shows how world-modelling methods avoid suffering from either problem. As a conclusion, the field of Artificial Intelligence needs to look outside the machine learning field to find methods capable of developing an artificial cognition. HERE is a common goal in the Artificial Intelligence field: approaching the achievement of an artificial cognition by producing results similar to those produced by a natural cognition (i.e. a human). That is, the efforts in such field have been focused on mimicking the effects of cognition. This approach has produced a plethora of optimisation methods that try to solve problems that are considered solvable only by humans. The underlying assumption was that, if some algorithm is able to solve these problems, it will be due to the emergence of cognition (or at least some kind of cognition-like reasoning).