Posted in a Substack conversation here:
An alternative view of what is happening is that we have been passing through three different phases of LLM-based development.
In Phase 1, "scaling is all you need" was the dominant view. As data, network size, and compute scaled, new capabilities (especially in-context learning) emerged. But each increment in performance required exponentially more data and compute.
In Phase 2, "scaling + external resources is all you need" became dominant. It started with RAG and toolformer, but has rapidly moved to include invoking python interpreters and external problem solvers (plan verifiers, wikipedia fact checking, etc.).
In Phase 3, "scaling + external resources + inference compute is all you need". I would characterize this as the realization that the LLM only provides part of what is needed for a complete cognitive system. OpenAI doesn't call it this, but we could view o1 as adopting the impasse mechanism of SOAR-style architectures. If the LLM has high uncertainty after a single forward pass through the model, it decides to conduct some form of forward search combined with answer checking/verification to find the right answer. In SOAR, this generates a new chunk in memory, and perhaps in OpenAI, they will salt this away as a new training example for periodic retraining. The cognitive architecture community has a mature understanding of the components of the human cognitive architecture and how they work together to achieve human general intelligence. In my view, they give us the best operational definition of AGI. If they are correct, then building a cognitive architecture by combining LLMs with the other mechanisms of existing cognitive architectures is likely to produce "AGI" systems with capabilities close to human cognitive capabilities.
