[borzunov]

Chat bot interfaces are only a small part of what can be done with large LMs.

You can use and fine-tune them to solve almost all existing natural language processing tasks: machine translation, recommendation/search, text classification and summarization, code generation, etc.

[thomastjeffery]

False.

You can use them to transform already existing text and code (the training datasets); but you can never do more than that.

There is no room in the ML algorithm to introduce logic. It's doomed to forever be a guessing game; and the resulting guesses will always be limited by the information it is fed to begin with.

The only reason chatGPT is so impressive is that it is transforming human conversation that itself is impressive (except that we were already aware of it). The code generation, literature, and definitions, etc. it outputs are all just rephrasing the written code, literature, and definitions that it was given as training data.

It's effectively no more than a sleight-of-hand. Flashy and impressive, but never anything more.

[stolsvik]

You should read this: https://ai.googleblog.com/2022/11/characterizing-emergent-ph... .. and probably also the paper.

I find these emergent phenomena pretty interesting.

[thomastjeffery]

They are missing the forest for the trees.

The "emergent phenomena" can be trivially explained by the input they are giving it.

They are not using a dataset that contains an equal amount of "correct" and "incorrect" responses. They are using datasets of human communication, which are obviously filtering for "correct" data. We get things wrong occasionally, but that is quite rare relative to what we get right. We can't even structure a sentence without getting something correct!

If you feed a dog good food, is it really a surprise that dog is healthy? You never fed it poison!

The language model is only returning semantic relationships. The "emergent phenomena" is that most semantic relationships in human communication just happen to also be logical relationships.

But the language model doesn't know that. In no way does it interact with logic. It only interacts with semantics.

If anyone actually bothered to train an instance of GPT or whatever on poisoned data, (i,e nonsensical stories) then you would see that emergent phenomena disappear. But no one is writing the nonsensical stories in the first place, so such a dataset does not exist.