Try, "ChatGPT, what do you think about this song"...
LLMs do not constitute "AI" let alone the more rigorous AGI. They are a GREAT statistical parlor trick for people that don't understand statistics though.
> LLMs do not constitute "AI" let alone the more rigorous AGI.
I have a textbook, "Artificial Intelligence: A Modern Approach," which covers Language Models in Chapter 23 (page 824) and the Transformer architecture in the following chapter. In any field technical terms emerge to avoid ambiguity. Laymen often adopt less accurate definitions from popular culture. LLMs do qualify as AI, even if not according to the oversimplified "AI" some laymen refer to.
It has been argued for the last several decades that every advance which was an AI advance according to AI researchers and AI textbooks was not in fact AI. This is because the laymen have a stupid definition of what constitutes an AI. It isn't because the field hasn't made any progress, but instead because people outside the field lack the sophistication to make coherent statements when discussing the field because their definitions are incoherent nonsense derived from fiction.
> They are a GREAT statistical parlor trick for people that don't understand statistics though.
The people who believe that LLMs constitute AI in a formal sense of the word aren't statistically illiterate. AIMA covers statistics extensively: chapter 12 is on Quantifying Uncertainty, 13 on Probabilistic Reasoning, 14 on Probabilistic Reasoning Over Time, 15 on Probabilistic Programming, and 20 on Learning Probabilistic Models.
Notably, in some of these chapters probability is proven to be optimal and sensible; far from being a parlor trick it can be shown with mathematical rigor that failing to abide by its strictures is not optimal. The ontological commitments of probability theory are quite reasonable; they're the same commitments logic makes. That we model accordingly isn't a parlor trick, but a reasonable and rational choice with ledger arguments proving that failing to do so would lead to regret.
I suspect that part of the OP's point was that ChatGPT happily parrots the aggregate critical opinion as its "thoughts" despite never having heard the song (or parsed its MP3 file or stems or sheet music)
If you ask me what I think of a song I've never heard, I'm general enough to want to listen to it...
So? Do you think we can’t make an LLM which picks a favourite song and writes about it with the gusto a person does? What is this example supposed to illustrate?
We clearly can, but not with gusto. LLM can't feel gusto about anything. If the point is that it doesn't matter as long as a person reading it is convinced there is gusto, then my point is that the 'opinion' of such a thing is irrelevant.
This is circular reasoning. LLMs can't feel gusto because they can't feel gusto. You have any way to measure "gusto" that we're all aware of ?
If other humans ascribing the quality of something you can't properly define isn't enough then you clearly don't care about what a LLM does or does not have, only what you are convinced it doesn't have.
Maybe, but 1. the point isn’t to describe, but to explain an abstract and potentially novel idea (thought) on the song, and 2. we can’t train this kind of thing in a generic way right now.
I have a textbook, "Artificial Intelligence: A Modern Approach," which covers Language Models in Chapter 23 (page 824) and the Transformer architecture in the following chapter. In any field technical terms emerge to avoid ambiguity. Laymen often adopt less accurate definitions from popular culture. LLMs do qualify as AI, even if not according to the oversimplified "AI" some laymen refer to.
It has been argued for the last several decades that every advance which was an AI advance according to AI researchers and AI textbooks was not in fact AI. This is because the laymen have a stupid definition of what constitutes an AI. It isn't because the field hasn't made any progress, but instead because people outside the field lack the sophistication to make coherent statements when discussing the field because their definitions are incoherent nonsense derived from fiction.
> They are a GREAT statistical parlor trick for people that don't understand statistics though.
The people who believe that LLMs constitute AI in a formal sense of the word aren't statistically illiterate. AIMA covers statistics extensively: chapter 12 is on Quantifying Uncertainty, 13 on Probabilistic Reasoning, 14 on Probabilistic Reasoning Over Time, 15 on Probabilistic Programming, and 20 on Learning Probabilistic Models.
Notably, in some of these chapters probability is proven to be optimal and sensible; far from being a parlor trick it can be shown with mathematical rigor that failing to abide by its strictures is not optimal. The ontological commitments of probability theory are quite reasonable; they're the same commitments logic makes. That we model accordingly isn't a parlor trick, but a reasonable and rational choice with ledger arguments proving that failing to do so would lead to regret.