Large language models and the transformer architecture are just the ALGOL 60 [1] of the search for general synthetic competence (intelligence is too finicky, co

Large language models and the transformer architecture are just the ALGOL 60 [1] of the search for general synthetic competence (intelligence is too finicky, competence is being given a task and fulfilling it successfully, in time, on budget, optimizing along the way). ChatGPT + Wolfram or other plugins are great for making transformers mathy, but the plugins must also be end-to-end machine learning architectures. To continue the high-level programming language analogy, we will probably see the C-level neural architecture in a few years once we get more embedded data (competence obtained through correlation physical object ↔ concept) with feedback loops forcing algorithms to operate under energy-restrictions (unlike the current architectures, more of a proof of concept, being careless about their energy). [1] https://en.wikipedia.org/wiki/ALGOL_60 https://en.wikipedia.org/wiki/ALGOL_60