One of the decisive moments in my understanding of #LLMs and their limitations was when, last autumn, @emilymbender walked me through her Thai Library thought experiment.
She's now written it up as a Medium post, and you can read it here. The value comes from really pondering the question she poses, so take the time to think about it. What would YOU do in the situation she outlines?
@joncounts
Interesting thought experiment - but LLMs don't learn that way. They use *labeled* data:
https://labelyourdata.com/articles/data-annotation-for-training-chatgpt
In general, when a neural network is trained, you need data to process where you already know the correct "meaning", this data is called labeled data.
Labeled data would mean some books with pictures, or even instructional books.
Also, afaik LMM don't learn from peoples answers. You cannot re-configure them "on the fly".
@andreasgoebel @ct_bergstrom @emilymbender @benjamingeer Thanks for those responses. That makes sense.
Whenever machines *can* learn this way, by adjusting to human responses, I still expect it's going to be quite a challenge to figure out the meaning behind words. It feels like we're still at the very start of a long road to get to real artificial intelligence.
@joncounts
I agree that (at the moment) AI doesn't really understand language, the general conclusion is right, . the thought experiment is (sadly) not completely acurate.
But yes, we're only at the beginning.
@ct_bergstrom @emilymbender @benjamingeer
Carl T. Bergstrom
Jon Sullivan
Andreas