In ‘Computing Machinery and Intelligence’, Turing, sceptical of the question ‘Can machines think?’, quickly replaces it with an experimentally verifiable test: the imitation game. I suggest that for such a move to be successful the test needs to be relevant, expansive, solvable by exemplars, unpredictable, and lead to actionable research. The Imitation Game is only partially successful in this regard and its reliance on language, whilst insightful for partially solving the problem, has put AI progress on the wrong foot, prescribing a top-down approach for building thinking machines. I argue that to fix shortcomings with modern AI systems a nonverbal operationalisation is required. This is provided by the recent Animal-AI Testbed, which translates animal cognition tests for AI and provides a bottom-up research pathway for building thinking machines that create predictive models of their environment from sensory input.

中文翻译：

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通过解决动物认知任务构建思维机器

在“计算机与智能”一书中，图灵对“机器能思考吗？”这个问题持怀疑态度，很快用一个实验验证的测试代替了它：模仿游戏。我建议，为了让这样的举措取得成功，测试必须是相关的、广泛的、可通过范例解决的、不可预测的，并导致可操作的研究。模仿游戏在这方面只取得了部分成功，它对语言的依赖，虽然在部分解决问题方面很有洞察力，但却把人工智能的进步放在了错误的位置，规定了一种自上而下的方法来构建思维机器。我认为，要解决现代 AI 系统的缺点，需要非语言操作。这是由最近的 Animal-AI Testbed 提供的，