alt Hacker News1. Not true. People have been trying to analyze whether mechanical/formal processes can "think" since at least 18th century. E.g. Leibniz wrote:
> if we could find characters or signs appropriate for expressing all our thoughts as definitely and as exactly as arithmetic expresses numbers or geometric analysis expresses lines, we could in all subjects in so far as they are amenable to reasoning accomplish what is done in arithmetic and geometry
2. You're missing the fact that meaning of words is defined through their use. It's an obvious fact that if people call certain phenomenon "thinking" then they call that "thinking".
3. The normal process is to introduce more specific terms and keep more general terms general. E.g. people doing psychometrics were not satisfied with "thinking", so they introduced e.g. "fluid intelligence" and "crystallized intelligence" as different kinds of abilities. They didn't have to redefine what "thinking" means.
re #2: Do people call it thinking, or is it just clever marketing from AI companies, that whenever you ask a question and it repeatedly prints out "...thinking...", as well as offering various modes with the word "thinking" written somewhere.
The AI companies obviously want the masses to just assume these are intelligent beings who think like humans and so we can just trust their output as being truthful.
I have an intelligent IT colleague who doesn't follow the AI news at all and who has zero knowledge of LLMs, other than that our company recently allowed us limited Copilot usage (with guidelines as to what data we are allowed to share). I noticed a couple weeks ago that he was asking it various mathematical questions, and I warned him to be wary of the output. He asked why, so I asked him to ask copilot/chatGPT "how many r letters are in the word strawberry". Copilot initially said 2, then said after thinking about it, that actually it was definitely 3, then thought about it some more then said it can't say with reasonable certainty, but it would assume it must be 2. We repeated the experiment with completely different results, but the answer was still wrong. On the 3rd attempt, it got it right, though the "thinking" stages were most definitely bogus. Considering how often this question comes up in various online forums, I would have assumed LLM models would finally get this right but alas, here we are. I really hope the lesson instilled some level of skepticism to just trust the output of AI without first double-checking.