In recent years we observed rapid and significant advancements in artificial intelligence (A.I.). So much so that many wonder how close humanity is to developing an A.I. model that can achieve human level of intelligence, also known as artificial general intelligence (A.G.I.). In this work we look at this question and we attempt to define the upper bounds, not just of A.I., but rather of any machine-computable process (a.k.a. an algorithm). To answer this question however, one must first precisely define A.G.I. We borrow prior work's definition of A.G.I. [1] that best describes the sentiment of the term, as used by the leading developers of A.I. That is, the ability to be creative and innovate in some field of study in a way that unlocks new and previously unknown functional capabilities in that field. Based on this definition we draw new bounds on the limits of computation. We formally prove that no algorithm can demonstrate new functional capabilities that were not already present in the initial algorithm itself. Therefore, no algorithm (and thus no A.I. model) can be truly creative in any field of study, whether that is science, engineering, art, sports, etc. In contrast, A.I. models can demonstrate existing functional capabilities, as well as combinations and permutations of existing functional capabilities. We conclude this work by discussing the implications of this proof both as it regards to the future of A.I. development, as well as to what it means for the origins of human intelligence.

翻译：近年来，我们见证了人工智能领域的快速且显著的进步。这种进展如此迅猛，以至于许多人开始思考人类距离开发出能够达到人类智能水平的人工智能模型还有多远，这种智能通常被称为人工通用智能。在本研究中，我们审视这一问题，并尝试定义不仅是人工智能，而且是任何机器可计算过程（即算法）的上限。然而，要回答这个问题，首先必须精确定义人工通用智能。我们借鉴了先前研究中对人工通用智能的定义[1]，该定义最准确地描述了人工智能领域领先开发者所使用这一术语的内涵：即在某一研究领域中，以创造性方式创新，从而解锁该领域中新的、先前未知的功能性能力。基于这一定义，我们重新划定了计算极限的边界。我们正式证明，任何算法都无法展示出初始算法本身不具备的新功能性能力。因此，任何算法（以及任何人工智能模型）都无法在科学研究、工程、艺术、体育等任何领域中真正具有创造性。相比之下，人工智能模型可以展示现有的功能性能力，以及现有功能性能力的组合与排列。最后，我们讨论了这一证明对人工智能未来发展的影响，以及它对人类智能起源的意义。