Most improvements in machine learning has been made by increasing the data (and by using models that can generalize larger data better).
Perfect data isn’t needed as the errors will “even out”. Although now there’s the problem that most new content on the Internet is low quality AI garbage.
Perfect data isn’t needed as the errors will “even out”.
That is an assumption.
I do not think that it is a correct assumption.
now there’s the problem that most new content on the Internet is low quality AI garbage.
This reminds me about a recommendation from some philosopher - I forgot who it was - he said that you should read only such books that are at least 100 years old.
15 years ago people made fun of AI models because they could mistake some detail in a bush for a dog. Over time the models became more resistant against those kinds of errors. The change was more data and better models.
It’s the same type of error as hallucination. The model is overly confident about a thing it’s wrong about. I don’t see why these types of errors would be any different.
I don’t see why these types of errors would be any different.
Well it is easy to see when you understand what LLMs actually do and how it is different from what humans do. Humans have multiple ways to correct errors and we do it all the time, intuitively. LLMs have none of these ways, they can only repeat their training (and not even hope for the best, because to hope is human again)
Most improvements in machine learning has been made by increasing the data (and by using models that can generalize larger data better).
Perfect data isn’t needed as the errors will “even out”. Although now there’s the problem that most new content on the Internet is low quality AI garbage.
That is an assumption.
I do not think that it is a correct assumption.
This reminds me about a recommendation from some philosopher - I forgot who it was - he said that you should read only such books that are at least 100 years old.
I’m extrapolating from history.
15 years ago people made fun of AI models because they could mistake some detail in a bush for a dog. Over time the models became more resistant against those kinds of errors. The change was more data and better models.
It’s the same type of error as hallucination. The model is overly confident about a thing it’s wrong about. I don’t see why these types of errors would be any different.
Well it is easy to see when you understand what LLMs actually do and how it is different from what humans do. Humans have multiple ways to correct errors and we do it all the time, intuitively. LLMs have none of these ways, they can only repeat their training (and not even hope for the best, because to hope is human again)