Since we have little idea (with lots of noise) of what intelligence is, how cells develop and use it, Large Language Models, Generative Diffusion, and all the o

Since we have little idea (with lots of noise) of what intelligence is, how cells develop and use it, Large Language Models, Generative Diffusion, and all the other architectures could move away from AGI and we have no benchmark to check it. A certain thing is that the current architectures are giving rise to artificial savants: an amazing ability to generate/hallucinate a story, some lines of code, or an image from a few words, but hilariously unable to distinguish between inside/outside and similar concepts which the simplest single-cell "knows" how to handle. It's not clear whether these architectures could give rise to an agent, nothing extra-ordinary, but merely able to explore various spaces without getting stuck [1]. One other aspect is the business side: in the following years lots of buzzwords will be flying around and most to all of the entities involved in statistical learning applications will get stuck in a local minimum worth somewhere between $10 billion to $10 trillion: shareholders care about, well, shares, not AGI. Another invariant in the technological race is that the worst technology will always win in the short to medium term: in Ancient Greece slavery was preferred to the steam engine [2], in 1900 the internal combustion engine won, not the more logical electric vehicle, today we have Windows and MacOS as major operating systems, not Plan9, and so forth. [1] Michael Levin, From physics to mind, timestamp discussing Xenobots, https://youtu.be/_QICRPFWDpg?t=1343 https://youtu.be/_QICRPFWDpg?t=1343 [2] https://en.wikipedia.org/wiki/Aeolipile https://en.wikipedia.org/wiki/Aeolipile