> On two occasions I have been asked, – "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

He clearly didn't know enough about vector embeddings.

This is so similar to human decision-making though. First I my innate experience to approximate to what I expect is right, then I map that to the truth.

It's the same for so many things:

- reading documentation (what do I expect this function to be called?)

- finding clothes in a shop (something long-sleeved and light)

- picking the fridge for dinner

- finding a book in the library...

so many analogues where I'm not coming cold to a choice.

If information is totally wrong then all you have to do is invert it to get the truth. What was it that Sherlock Holmes said? The problem ends up being that it often takes a tremendous number of counterexamples to eliminate everything that is impossible.

Worse is when you don't know whether the answers you have are totally wrong.

the trick is that it's not totally wrong to start with

Eh? “Generate an approximation and refine it algorithmically” is a well-known technique.

This been the case for a long time!

The entire problem of search is that the user has the wrong data and wants to use it to receive the correct data. That was the start, not the state we’ve ended up at - it is unironically how we got to LLMs.

I can't believe people spend their lives finding lazier ways to classify a bunch of objects that will end up heaped in dormitory dumpsters across the US next spring.