> So, as usual, ChatGPT fails to answer the modified riddle and gives the plagiarized stock answer and explanation to the original one. No intelligence here.
Or, fails in the same way any human would, when giving a snap answer to a riddle told to them on the fly - typically, a person would recognize a familiar riddle half of the first sentence in, and stop listening carefully, not expecting the other party to give them a modified version.
It's something we drill into kids in school, and often into adults too: read carefully. Because we're all prone to pattern-matching the general shape to something we've seen before and zoning out.
I'm curious what you think is happening here as your answer seems to imply it is thinking (and indeed rushing to an answer somehow). Do you think the generative AI has agency or a thought process? It doesn't seem to have anything approaching that to me, nor does it answer quickly.
It seems to be more like a weighing machine based on past tokens encountered together, so this is exactly the kind of answer we'd expect on a trivial question (I had no confusion over this question, my only confusion was why it was so basic).
It is surprisingly good at deceiving people and looking like it is thinking, when it only performs one of the many processes we use to think - pattern matching.
My thinking is that LLMs are very similar, perhaps structurally the same, as a piece of human brain that does the "inner voice" thing. The boundary between the subconscious and conscious, that generates words and phrases and narratives pretty much like "feels best" autocomplete[0] - bits that other parts of your mind evaluate and discard, or circle back, because if you were just to say or type directly what your inner voice says, you'd sound like... a bad LLM.
In my own experience, when I'm asked a question, my inner voice starts giving answers immediately, following associations and what "feels right"; the result is eerily similar to LLMs, particularly when they're hallucinating. The difference is, you see the immediate output of an LLM; with a person, you see/hear what they choose to communicate after doing some mental back-and-forth.
So I'm not saying LLMs are thinking - mostly for the trivial reason of them being exposed through low-level API, without built-in internal feedback loop. But I am saying they're performing the same kind of thing my inner voice does, and at least in my case, my inner voice does 90% of my "thinking" day-to-day.
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[0] - In fact, many years before LLMs were a thing, I independently started describing my inner narrative as a glorified Markov chain, and later discovered it's not an uncommon thing.
Interesting perspective, thanks. I can’t help but feel they are still missing a major part of cognition though which is having a stable model of the world.
> Or, fails in the same way any human would, when giving a snap answer to a riddle told to them on the fly
The point of o1 is that it's good at reasoning because it's not purely operating in the "giving a snap answer on the fly" mode, unlike the previous models released by OpenAI.
Or, fails in the same way any human would, when giving a snap answer to a riddle told to them on the fly - typically, a person would recognize a familiar riddle half of the first sentence in, and stop listening carefully, not expecting the other party to give them a modified version.
It's something we drill into kids in school, and often into adults too: read carefully. Because we're all prone to pattern-matching the general shape to something we've seen before and zoning out.