https://twitter.com/maxhkw/status/1635693113560772633
Humans start by continuing conversations as well, but then are able to process facts to deduct new, previously unseen conclusions (sometimes wrong), and so do LLMs.
It's "continuing a conversation" in that it's predicting output token-by-token based upon training and the context window. I'm not trying to be dismissive, I actually don't think it's much different from how humans do it.
Some may not want to call it thinking, but that's ahead of thinking ability of many an adult human with voting rights.
If your model wasn't trained on tons of text about how great eugenics is, then it learns that words expressing positive sentiment in the context of eugenics are extremely low probability.
This is also the "cause" of "woke" models: it turns out that the majority of us recognize that being an asshole is bad, and this is reflected in the majority of the training corpus, which comes from us collectively.
They had to plug it. Why?
Also see above, they are word prediction machines similarly to how humans are word prediction machines.
…but we disagree about what “being an asshole” looks like. Reasonable people think hating people for their sex/race is bad, no matter which one it is.
So why are wokeists constantly complaining about what companies allow on their platforms?
> Discrimination is when people are treated differently because of attributes they had no influence
Yes, so why would that exclude men or white people?
I don't know, but maybe because they want to make sure minorities are being treated fairly, so they advocate for removing language they think is insulting people or belittling them. Again, this has nothing to do with censorship.
> Yes, so why would that exclude men or white people?
It wouldn't
Discrimination is what you said with no exceptions for overrepresentation.
People are asking for the raw output of the underlying LLM, which is a pretty clear ask that doesn't require deconstructing the sentience and model-building capacity of an LLM.