We will need a combination of technologies we have in order to really achieve emergent intelligence. Humans are comprised of various "subnets" modelling aspect

We will need a combination of technologies we have in order to really achieve emergent intelligence. Humans are comprised of various "subnets" modelling aspects which, in unison, produce self-conciousness and real intelligence. What is missing in the current line of approaches is that we only rely on auto-alignment of subnetworks by machine learning, which scales only up to a point. If we would produce a model which has * something akin a LLM as we know it today, which is able to * store or fetch facts to a short- ("context") or longterm ("memory") storage * if not in the current "context", query the longterm context ("memory") by keywords for associations, which are one-by-one inserted into the current "context" * repeat as required until fulfilling some self-defined condition ("thinking") To me, this is mostly mechanical plumbing work and lots of money. Also, if we get rid of the "word-boundedness" of LLMs - which we already did to some degree, as shown by the multi-language capabilities - LLMs would be free to roam in the domain of thoughts /s :) This approach could be further improved by meta-LLMs governing the longterm memory access, providing an "intuition" which longterm memory suits the provided context best. Apply recursion as needed to improve results (paying by exponential training time, but this meta-NN will quite probably be independent of actual training, as real life / brain organization shows).