LLMs are statistical models trained on human-generated text. They aren't the perfectly logical "machine brains" that Asimov and others imagined.
The upshot of this is that LLMs are quite good at the stuff that he thinks only humans will be able to do. What they aren't so good at (yet) is really rigorous reasoning, exactly the opposite of what 20th century people assumed.
> LLMs are statistical models trained on human-generated text.
I mean, not only human-generated text. Also, human brains are arguably statistical models trained on human-generated/collected data as well...
> Also, human brains are arguably statistical models trained on human-generated/collected data as well...
I'd say no, human brains are "trained" on billions of years of sensory data. A very small amount of that is human-generated.
Almost everything we learn in schools, universities, most jobs, history, news, hackernews, etc is literally human-generated text. Our brains have an efficient structure to learn language, which has evolved over time, but the processes of actually learning languages happens after you are born, based on human-generated text/voice. Things like balance/walking, motion control, speaking (physical voice control), other physical things are trained on sensory data, but there's no reason LLMs/AIs can't be trained on similar data (and in many cases they already are).
What we generate is probably a function of our sensory data + what we call creativity. At least humans still have access to the sensory data, so we can separate the two (with various success).
LLMs have access to what we generate, but not the source. So it embed how we may use words, but not why we use this word and not others.
One can look at creativity as discovery of a hitherto unknown pattern in a very large space of patterns.
No reason to think an LLM (a few generations down the line if not now) cannot do that