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In this perspective paper, we first comprehensively review existingevaluations of Large Language Models (LLMs) using both standardized tests andability-oriented benchmarks. We pinpoint several problems with currentevaluation methods that tend to overstate the capabilities of LLMs. We thenarticulate what artificial general intelligence should encompass beyond thecapabilities of LLMs. We propose four characteristics of generally intelligentagents: 1) they can perform unlimited tasks; 2) they can generate new taskswithin a context; 3) they operate based on a value system that underpins taskgeneration; and 4) they have a world model reflecting reality, which shapestheir interaction with the world. Building on this viewpoint, we highlight themissing pieces in artificial general intelligence, that is, the unity ofknowing and acting. We argue that active engagement with objects in the realworld delivers more robust signals for forming conceptual representations.Additionally, knowledge acquisition isn't solely reliant on passive input butrequires repeated trials and errors. We conclude by outlining promising futureresearch directions in the field of artificial general intelligence.

Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models