An LLM is a stack of transformer layers (N=96 for GPT-2). Each word/token output is the result of exactly N sentence transformations from input to output. Clea

An LLM is a stack of transformer layers (N=96 for GPT-2). Each word/token output is the result of exactly N sentence transformations from input to output. Clearly there are many things (specifically things requiring deliberation and planning) that require an open ended number of thought steps, and therefore could only, even in theory, we done by an LLM in "think step by step" fashion where it breaks the problem down into pieces (maybe recursively) each simple enough to be done in < N of these primitive steps. In practice it seems that LLM's ability (esp. given their lack of any internal working memory) to maintain long term focus for an extended task is poor, so the theoretical benefit of using their own memory as a scratchpad for planning doesn't seem to be realized. The notion of scale it up, and/or train it right, and a transformer can implement AGI, seems to be more wishful thinking than based on any analysis of what the architecture is and is not capable of. The lack of support for continuous incremental learning seems to be a deal-breaker for intelligence that people just ignore.