Also human can reason, LLMs currently can't do this in useful way and is very limited by their context in all the trials to make it do that. Not to mention their ability to make new things if they do not exist (and not complete made up stuff that are non-sense) is very limited.
1. The vast majority of people never come up with a truly new idea. those that do are considered exceptional and their names go down in history books.
2. Most 'new ideas' are rehashes of old ones.
3. If you set the temperature up on an LLM, it will absolutely come up with new ideas. Expecting an LLM to make a scientific discover a la einstein is ... a bit much, don't you think [1]? When it comes to 'everyday' creativity, such as short poems, songs, recipes, vacation itineraries, etc. ChatGPT is more capable than the vast majority of people. Literally, ask ChatGPT to write you a song about _____, and it will come up with something creative. Ask it for a recipe with ridiculous ingredients and see what it does. It'll make things you've never seen before, generate an image for you and even come up with a neologism if you ask it too. It's insanely creative.
[1] Although I have walked chatgpt through various theoretical physics scenarios and it will create new math for you.
I don’t need to finetune on five hundred pictures of rabbits to know one. I need one look and then I’ll know for life and can use this in unimaginable and endless variety.
This is a simplistic example which you can naturally pick apart but when you do I’ll provide another such example. My point is, learning at human (or even animal) speeds is definitely not solved and I’d say we are not even attempting that kind of learning yet. There is “in context learning” and “finetuning” and both are not going to result in human level intelligence judging from anything I’ve had access to.
I think you are anthropomorphizing the clever text randomization process. There is a bunch of information being garbled and returned in a semi-legible fashion and you imbue the process behind it with intelligence that I don’t think it has. All these models stumble over simple reasoning unless specifically trained for those specific types of problems. Planning is one particularly famous example.
Time will tell, but I’m not betting on LLMs. I think other forms of AI are needed. Ones that understand substance, modality, time and space and have working memory, not just the illusion of it.
So if you do use in-context learning and give chatGPT a few images of your novel class, then it will correctly classify usually. Finetuning is so you an save on token cost.
Moreover, you don't typically need that many pictures to fine tune. The studies show that the models successfully extrapolate once they've been 'pre-trained'. This is similar to how my toddler insists that a kangaroo is a dog. She's not been exposed to enough data to know otherwise. Dog is a much more fluid category for her than in real life. If you talk with her for a while about it, she will eventually figure out kangaroo is kangaroo and dog is dog. But if you ask her again next week, she'll go back to saying they're dogs. Eventually she'll learn.
> All these models stumble over simple reasoning unless specifically trained for those specific types of problems. Planning is one particularly famous example.
We have extremely expensive programs called schools and universities designed to teach little humans how to plan and execute. If you look at cultures without American/Western biases (and there's not very many left, so we really have to look to history), we see that the idea of planning the way we do it is not universal.