AI 中文总结
本文驳斥了通用人工智能可行的两类论证，指出其核心论点为开放复杂环境下的认知模型无法实现，论证了机器不会统治世界。
AI 中文摘要
在我们的著作《为什么机器永远不会统治世界》[13,14]中，我们提出通用人工智能在数学上是不可能的，因为表现出智能的人类及相关过程是复杂系统，其行为无法被我们借助或不借助计算机生成的模型所捕捉。当代机器智能的支持者提出两类反驳：一是基于神经网络通用近似定理和丘奇-图灵-多伊奇原理的理论论证；二是基于标准化基准测试分数快速提升的经验论证。本文中我们对这两类反驳进行考察并予以驳斥：首先，我们指出基于丘奇-图灵-多伊奇原理的物理主义反驳存在严重问题；其次，我们梳理近期证据，表明知名基准测试存在训练数据污染、测试构建缺陷及策略优化问题。我们的核心论点依然成立：在开放、热力学复杂且非遍历环境中执行认知行为所需的模型，目前不存在且未来也无法实现。
英文摘要
In our book Why machines will never rule the world [13, 14] we argue that arti- ficial general intelligence is mathematically impossible. This is because the human beings and the processes which exhibit intelligence are complex systems whose be- haviour cannot be captured by the kinds of models that we can generate with or without computers. Proponents of contemporary machine intelligence respond with two lines of argument: a theoretical one, grounded in the universal approximation theorems for neural networks and the Church-Turing-Deutsch principle; and an em- pirical one, grounded in rapidly rising scores on standardized benchmarks. In this communication we examine and reject both responses. First, we show serious issues in the physicalist counter-argument based on the Church-Turing-Deutsch principle. Second, we review recent evidence to the effect that prominent benchmarks are compromised by training-data contamination, flawed test construction, and strate- gic optimization. Our central argument remains: That models required to perform cognitive behaviour in open-ended, thermodynamically complex and non-ergodic environments are not and will not become achievable.
