> >Any number of systems might deliver the empirical result you measured > Like what ? What exactly would give similar empirical results over the wide number o

> >Any number of systems might deliver the empirical result you measured > Like what ? What exactly would give similar empirical results over the wide number of tests... The GP gave a single trivial example and my reply was highlighting how that example didn't inform the question at hand in any way. That example might be just as well be exhibited by a lookup table, a lucky random output, a modified markov chain, a Chinese Box, a trivial small language model trained on the right corpus, etc. That certain LLM's or chatbots might also be able to deliver on more sophisticated examples that those systems cannot is not what they or I were talking about here. It was a discussion about obviousness from trivial samples, and about the deeper semantic dependencies hidden the GP's definition. Such trivial samples don't hold up to scrutiny as their definition recurses down through concepts that are not at all demonstrated in these trivial cases. Their attempt at rhetoric just fails. > Empirically assessing the result after the fact is how we determine intelligence and reasoning in.. anything humans included. No. We project intelligence onto humans inductively by identifying our own experience as intelligence and assuming that other things that are almost identical to us are very likely to be working in the same way. In recent decades, there's been growing acceptance that other systems approximately like us (animals of certain kinds) exhibit it to various degrees as well, but even this itself was a formally rejected concept during the early 20th century when "intelligence" first became treated as something measurable and quantifiable at all. The concept of intelligence is a wholly cultural one and its definition and applicability moves around over time. Yet there are currently very few people who would apply the word to a lookup table, a random output, a Chinese Room, a modified markov chain, etc and a smattering-but-growing number of people who are comfortable applying it to LLM's and chatbots as we see them today. As this happens and its use expands, the sense of the word changes, as it has been doing for hundreds of years. At this point, we mostly can't yet rely on chatbots to fulfill roles that require interesting human intelligence. They can pass some benchmark tests to greater or less degrees, but also happen to be excellent benchmark optimizers (that's the essence of their design), so it remains hard to know what that really means. If and when they become reliable substitutes for human intelligence in general tasks, or become interestingly independent in the way we see certain animals, the prior-yet-modern senses of intelligence will be easier to apply. But we're not there yet.