In this perspective paper, we first comprehensively review existing
evaluations of Large Language Models (LLMs) using both standardized tests and
ability-oriented benchmarks. We pinpoint several problems with current
evaluation methods that tend to overstate the capabilities of LLMs. We then
articulate what artificial general intelligence should encompass beyond the
capabilities of LLMs. We propose four characteristics of generally intelligent
agents: 1) they can perform unlimited tasks; 2) they can generate new tasks
within a context; 3) they operate based on a value system that underpins task
generation; and 4) they have a world model reflecting reality, which shapes
their interaction with the world. Building on this viewpoint, we highlight the
missing pieces in artificial general intelligence, that is, the unity of
knowing and acting. We argue that active engagement with objects in the real
world delivers more robust signals for forming conceptual representations.
Additionally, knowledge acquisition isn't solely reliant on passive input but
requires repeated trials and errors. We conclude by outlining promising future
research directions in the field of artificial general intelligence.

我们首先综述了对大型语言模型（LLMs）的现有评估，包括标准化测试和面向能力的基准测试。然后，我们明确了当前评估方法存在的几个问题，倾向于夸大 LLMs 的能力。我们进一步阐述了人工通用智能应该超越 LLMs 能力的几个特征。我们提出了通用智能代理的四个特征：1）能够执行无限的任务；2）能够在特定环境中生成新的任务；3）基于支撑任务生成的价值体系进行操作；4）拥有反映现实的世界模型，影响其与世界的交互。在这一观点基础上，我们强调了人工通用智能中缺失的部分，即认知与行动的统一。我们认为与现实世界中的对象进行积极互动可以提供更强大的形成概念表示的信号。此外，知识获取不仅依赖于被动输入，还需要反复尝试和错误。最后，我们概述了人工通用智能领域未来研究的有希望的方向。