Software's effect upon the world hinges upon the hardware that interprets it. This tends not to be an issue, because we standardise hardware. AI is typically conceived of as a software ``mind'' running on such interchangeable hardware. This formalises mind-body dualism, in that a software ``mind'' can be run on any number of standardised bodies. While this works well for simple applications, we argue that this approach is less than ideal for the purposes of formalising artificial general intelligence (AGI) or artificial super-intelligence (ASI). The general reinforcement learning agent AIXI is pareto optimal. However, this claim regarding AIXI's performance is highly subjective, because that performance depends upon the choice of interpreter. We examine this problem and formulate an approach based upon enactive cognition and pancomputationalism to address the issue. Weakness is a measure of plausibility, a ``proxy for intelligence'' unrelated to compression or simplicity. If hypotheses are evaluated in terms of weakness rather than length, then we are able to make objective claims regarding performance (how effectively one adapts, or ``generalises'' from limited information). Subsequently, we propose a definition of AGI which is objectively optimal given a ``vocabulary'' (body etc) in which cognition is enacted, and of ASI as that which finds the optimal vocabulary for a purpose and then constructs an AGI.

翻译：软件对世界的影响取决于解释它的硬件。这通常不是问题，因为我们将硬件标准化。人工智能通常被设想为运行在这种可互换硬件上的软件“大脑”。这形式化了心身二元论，即软件“大脑”可以在任意数量的标准化身体上运行。虽然这种方法在简单应用中效果良好，但我们认为，在形式化通用人工智能（AGI）或超级人工智能（ASI）的目标上，这种方案并不理想。通用强化学习智能体AIXI是帕累托最优的。然而，这一关于AIXI性能的论断具有高度主观性，因为其性能依赖于解释器的选择。我们审视这一问题，并基于能动认知和泛计算主义提出一种方法来解决该问题。弱度是一种合理性的度量，是“智能的代理指标”，与压缩性或简洁性无关。如果假设基于弱度而非长度进行评估，那么我们就能对性能（即如何从有限信息中有效地适应或“泛化”）做出客观论断。随后，我们提出一个AGI的定义，该定义在认知得以实现的“词汇表”（如身体等）中客观最优；同时，ASI被定义为能针对特定目的找到最优词汇表并构建AGI的系统。