Little demonstrable progress has been made toward AGI (Artificial General
Intelligence) since the term was coined some 20 years ago. In spite of the
fantastic breakthroughs in Statistical AI such as AlphaZero, ChatGPT, and
Stable Diffusion none of these projects have, or claim to have, a clear path to
AGI. In order to expedite the development of AGI it is crucial to understand
and identify the core requirements of human-like intelligence as it pertains to
AGI. From that one can distill which particular development steps are necessary
to achieve AGI, and which are a distraction. Such analysis highlights the need
for a Cognitive AI approach rather than the currently favored statistical and
generative efforts. More specifically it identifies the central role of
concepts in human-like cognition. Here we outline an architecture and
development plan, together with some preliminary results, that offers a much
more direct path to full Human-Level AI (HLAI)/ AGI.

自从 20 年前 AGI（人工通用智能）一词诞生以来，对其几乎没有取得可观的进展。尽管统计人工智能方面取得了极大突破，如 AlphaZero、ChatGPT 和 Stable Diffusion，但这些项目都没有明确的 AGI 路径。为了加快 AGI 的发展，了解和确定人类智能与 AGI 的核心要求至关重要。通过这样可以梳理出实现 AGI 所需的特定开发步骤以及哪些是分散注意力的因素。这种分析强调了对认知人工智能方法的需求，而不是目前受青睐的统计和生成式方法。具体而言，它确定了概念在类人认知中的核心角色。在这里，我们概述了一种架构和开发计划，以及一些初步结果，提供了一条更直接的实现全面人类级 AI（HLAI）/ AGI 的路径。