Human mind can perform actual reasoning, while ChatGPT only mirrors the output of reasoning and when it gets output correctly it's due to mixture of luck and closeness to training material.
Human mind or even something like Wolfram Alpha can perform reasoning.
When a model "reasons through" a problem its just outputting text that is statistically likely to appear in the context of "reasoning through" things. There is no intent, consideration of the options available, the implications, possible outcomes.
However, the result often looks the same, which is neat
"thinking" and reasoning can be done by toddlers with a dataset a fraction of a fraction of the size that even the simplest language models are trained on.
I don't understand this thinking that it's x because it looks like x(thinking, artistic creativity, etc.). I can prompt Google for incrementally more correct answers to a problem, does that mean there's no difference between "google" and "thought"?
It definitely means that it was thinking wrongly if at all. Just talk to GPT about math. You'll quickly change your mind about the possibility of it thinking.
LLMs are bad at arithmetic due to tokenization limitations but they're actually pretty decent at mathematical reasoning. You don't know what you're talking about I'm afraid.
Please just try. It's horrible at mathematical reasoning. Use just words to avoid problems with tokenization. Alternatively just read through the link you provided. It has many examples of failures of GPT and gabage it produces when talked to about math.
The provided example directly shows ability in mathematical reasoning by coming up with a novel concept and example case, it is just poor in arithmetic.
Math is not simply arithmetic abilities, you seem unable to comprehend this.
"I want it to come up with a new idea. Its first attempt was to just regurgitate the definition of the set of zero-divisors (a very basic concept), and (falsely) asserted that they formed an ideal (among other false claims about endomorphism rings)."
"I tried a few more times, and it gave a few more examples of ideas that are well-known in ring theory (with a few less-than-true modifications sometimes), insisting that they are new and original."
"This in particular is quite an interesting failure. "
"So there we have it. A new definition. One example (of a 4-cohesive ring) extracted with only mild handholding, and another example (of a 2-cohesive ideal) extracted by cherry-picking, error-forgiveness, and some more serious handholding."
"Some errors (being bad at arithmetic) will almost certainly be fixed in the fairly near future." - and this opinion is based on absolutely nothing.
“ There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely; but they are the same old pieces of colored glass that have been in use through all the ages.”
I’d argue ChatGPT can indeed be creative, as it can combine ideas in new ways.
The important difference is that humans are trained on a lot less data than ChatGPT. This implies that the human brain and LLMs are very different, the human brain likely has a lot of language faculties pre-encoded (this is the main argument of Universal Grammar). OpenAI's GPT 4 is now trained on visual data.
Anyway, I think a lot of ongoing conversations have orthogonal arguments. ChatGPT can be both impressive and generate topics broader than the average human while not giving us deeper insight into how human language works.
Based on the current advances, in about a year we should see the first real-world interaction robot that learns from its environment (probably Tesla or OpenAI).
I'm curious (just leaving it here to see what happens in the future), what will be the excuse of Google this time.
This is again the same situation: Google has supposedly superior tech but not releasing it (or maybe it's as good as Bard...)
Thats assuming modern humans, I was talking about ancient humans, before civilisation. You could argue thats where the creative mind shows up most, as there are very few humans to imitate.
ChatGPT and similar do seem to make new things, arguably they do it more freely than the average adult human.
Art generators are the most obvious example to me. They regularly create depictions of entirely new animals that may look like a combination of known species.
People got a kick out of art AIs struggling to include words as we recognize them. How can we say what looked like gibberish to us wasn't actually part of a language the AI invented as part of the art piece, like Tolkien inventing elvish for a book?
Plenty of examples of it coming up with new languages or ideas. And it’s very hard for a person to come up with a new language completely independent of reference to other known languages.
What experiment can you do to confirm this? If I ask ChatGPT to come up with a new language, it will do it. How do I distinguish that from what a human comes up with?
By not giving them any examples of language. I would expect humans to come up with a language, if not vocal, without guidance. I doubt GPT would do anything without training data to imitate.
Just try to talk with it about math. You'll quickly see that it's as if you talked to a person who doesn't understand anything about math. Just read some books about it and attempts to mimic their style to appear to be smart and knowledgable.
In your message you say it is gibberish, but I have completely different results and get very good Base64 on super long and random strings.
I frequently use Base64 (both ways) to bypass filters in both GPT-3 and 4/Bing so I'm sure it works ;)
It sometimes make very small mistakes but overall amazing.
At this stage if it can work on random data that never appeared in the training set it's not just luck, it means it has acquired that skill and learnt how to generalise it.
It could when I tested 1 week after the first version of chatGPT was in private beta. It's always been able to convert base64 both ways.
It sometimes gets some of the conversion wrong or converts a related word instead of the word you actually asked it to convert. This strongly suggests that it's the actual LLM doing the conversion (and there's no reason to believe it wouldn't be).
This behavior will likely be replicated in open source LLMs soon.
Human mind or even something like Wolfram Alpha can perform reasoning.