Brain in a Vat: On Missing Pieces Towards Artificial General
  Intelligence in Large Language Models
Yuxi Ma Chi Zhang Song-Chun Zhu
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.