> 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 end

> 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. 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.