Linguistics experts will have a fun time untangling the pre-AI language from the post-AI era.
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As it happens, I’m now teaching an undergraduate course at the University of Tokyo titled “The Meaning of Language in the Age of AI.” The students—who come from various countries and majors—and I are discussing how theories of human language will have to change now that humans interact not only with each other but also with AI.
I don’t think we’ll have any full-fledged theories ready by the end of the semester, but some points to consider are emerging. One is that aspects of language use that are affected by the social status, personal identities, and consciousness of the speakers and listeners—politeness, formality, forms of address, pronoun reference, etc.—will have to be rethought now that people are increasingly conversing with entities that have no social status, personal identity, or consciousness (not yet, at least).
And, yes, thinking about all this is a lot of fun.
Not sure if I am completely off-topic here, but I was recently reading this essay by Bruce Schneier: https://www.schneier.com/blog/archives/2023/12/ai-and-trust...., where he mentions a related risk: because we now can "talk" to those LLMs in a similar way that we talk to people, it makes it harder for us to realize that they are not people. And so it's easier to abuse our trust.
I guess my question here would be: will human language have to change because of interactions with LLMs, or is the whole point of LLMs that it does not have to change (and therefore humans will have to learn not to be abused by the machines)? Because we already have languages to talk to machines: those are programming languages, which are designed to be unambiguous. The problem I see is not that we don't know how to talk to machines, but rather that we now have machines that are really good at pretending they are not machines.
Not sure if I am making any sense at all :-).
You are making a lot of sense to me. Thanks for the link to that essay, too.
The point about programming-language nonambiguity is a good one. After I started using ChatGPT a year ago, it took me a while to realize that I didn’t have to be careful about my spelling, capitalization, punctuation, etc. It turned out to be good at interpreting the intention of sloppily written prompts. And it never pointed out or complained about my mistakes, either—another difference from humans.
> And it never pointed out or complained about my mistakes, either—another difference from humans.
Yeah that is a big difference indeed: a human has some notion of confidence about what they know. Not every human expresses it the same way: a scientist will say "I am pretty sure that climate change is a consequence of human activity", meaning "well, maybe we all live in the Matrix and gravity is an illusion, but from what we know, we are pretty sure". On the other end of the spectrum, some will say "I know for a fact that God exists, because it is written in a very old book". But both have a consistent notion of confidence about what they (think they) know and what they don't know.
An LLM does not have that. An LLM is not critical. It cannot conclude stuff like "I have to take into account that this text was written in a period of war and the author is not neutral, so there is probably a bias". It can generate text that pretends it does, but it remains a generated text, it is not a critical analysis. Also it cannot be offended by something you write, even though it could generate text that pretends it is.
So yeah, it can probably be engineered to never/always point out or complain about mistakes, with the caveat that it does not have an "idea" of what a mistake is. It just generates text that pretends it does.
> It turned out to be good at interpreting the intention of sloppily written prompts.
In the context of a conversation with a human (you), who is very good at navigating ambiguous communications. But if you tried to describe an algorithm with an ambiguous natural language, you may quickly have problems. And errors accumulate, so it gets worse when the task becomes more complex. Which is an interesting thought: it may make simple tasks simpler, but it's not at all a given that it scales.