优化方法无法实现真正的人工认知，因其固有缺陷难以克服。
Optimisation Is Not What You Need
- 证明优化方法存在先天缺陷，无法解决认知问题
- 实验证明世界建模方法可避免遗忘与过拟合
- 呼吁跳出机器学习，寻找新认知范式
人工智能领域长期依赖优化方法解决各类问题，甚至超越图灵测试。然而研究发现，这些方法存在根本性缺陷，阻碍其发展为真正的通用智能。本文正式证明，灾难性遗忘是优化方法的固有属性，将始终限制以优化为核心的人工通用智能路径。同时讨论了过拟合及其他次要问题。实验表明，世界建模方法能有效规避上述困境。因此，人工智能需突破机器学习框架，探索新的认知构建方式。
原文摘要 · Abstract (English)
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 Turing Test. 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.
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