Concepts is All You Need: A More Direct Path to AGI
This work addresses the fundamental problem of achieving AGI for the AI research community, but it appears incremental as it builds on existing cognitive theories without demonstrating major breakthroughs.
The paper argues that current statistical AI approaches lack a clear path to AGI and proposes a cognitive AI approach centered on concepts as a more direct route, outlining an architecture and preliminary results.
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.