AI models can produce poems that rate well on certain ‘metrics’. But the event of reading poetry is not one in which we arrive at standardised outcomes.
Humans think there’s some magic to us that can’t be reproduced by machines. But there really isn’t.
We’re a pattern matching machine with a conscious interface thrown over the top.
Neural networks are really good at finding patterns. Train it on the positions of the planets over a couple decades and it will internally come up with the equations to calculate them and apply those to future and past inputs.
It can come up with equations we don’t even know as long as input and output is available for training. Somewhere deep in it’s black box it has them.
Art is no different. There’s a hidden pattern to art. Something that triggers our brains to react a certain way. You can literally get degrees in the study of it.
Give it enough data and it will learn the patterns.
There’s definitely nothing about humans that is irreproducible - but the machines aren’t even close yet. You clearly didn’t read the article.
The entire point here was that the poetry written by LLMs is valid, shallow and has appeal only to the uninformed.
Article:
In my view, the results of the study are less testaments to the “quality” of machine poetry than to the wider difficulty of giving life to poetry. It takes reading and rereading to experience what literary critic Derek Attridge has called the “event” of literature, where “new possibilities of meaning and feeling” open within us. In the most significant kinds of literary experiences, “we feel pulled along by the work as we push ourselves through it”.
[ … ]
When readers say they prefer AI poetry, then, they would seem to be registering their frustration when faced with writing that does not yield to their attention. If we do not know how to begin with poems, we end up relying on conventional “poetic” signs to make determinations about quality and preference.
This is also true of AI art.
The pattern matching machines will give us more of the same: glossy and regurgitated.
You will not find a machine that will output excellent work that when found 20 years later will become a sensation. They produce gold painted dross.
This is another condemnation of art and cultural education in the West.
Current LLMs generate poems that people prefer to human-written poetry. Current image generators win art contests. They don’t need to get better to produce more appealing art than humans. Maybe not every time, maybe the people writing the prompts and filtering results are inherent to producing quality results, but there’s not some extra trick needed for people to find their outputs aesthetically appealing.
This research didn’t use a million poems, it used 5 human and 5 generated poems. The 5 generated poems were simply the first five generated, they did not use a human to curate from a larger population.
It’s “infinite monkeys on infinite typewriters” because a million would be far too small a sample size to expect Shakespeare. The monkeys aren’t trying to make anything, they’re just randomly hitting keys. For Shakespeare to come out, there would likely need to be more Monkeys than there are atoms in the universe. Conversely, we’re getting something people enjoy from AI right now. No need to approach infinity. It’s not what most people wanted AI to be used for, but it’s succeeding at it, and current models have only been around for a few years. This isn’t random chance happening upon something we like - this is a pattern-recognizing machine getting progressively better at recognizing the patterns we enjoy.
Yes, because ‘these monkeys’ have been reading all of available content humans created, not really fair comparison to infinite scale of pure randomness.
I would argue against pattern machines getting better at recognizing patterns better, but I don’t think it would change any minds.
Yes, I agree it’s a bad comparison. That’s why I said as such in my response to your comment that brought it up.
Though the current models have only been around for a few years, pattern recognition programs have been around for a long time. The latest ones are just a better model …because they are getting better.
The monkeys are just random chance - if you don’t yet have Shakespeare, you’re no more likely to get it than when you started - but pattern recognition software is steadily improving. If it’s not at some benchmark you want it to be at, it’s at least closer than it was 10 years ago, and will continue getting closer over time.
Humans think there’s some magic to us that can’t be reproduced by machines. But there really isn’t.
We’re a pattern matching machine with a conscious interface thrown over the top.
Neural networks are really good at finding patterns. Train it on the positions of the planets over a couple decades and it will internally come up with the equations to calculate them and apply those to future and past inputs.
It can come up with equations we don’t even know as long as input and output is available for training. Somewhere deep in it’s black box it has them.
Art is no different. There’s a hidden pattern to art. Something that triggers our brains to react a certain way. You can literally get degrees in the study of it.
Give it enough data and it will learn the patterns.
There’s definitely nothing about humans that is irreproducible - but the machines aren’t even close yet. You clearly didn’t read the article.
The entire point here was that the poetry written by LLMs is valid, shallow and has appeal only to the uninformed.
Article:
[ … ]
This is also true of AI art.
The pattern matching machines will give us more of the same: glossy and regurgitated.
You will not find a machine that will output excellent work that when found 20 years later will become a sensation. They produce gold painted dross.
This is another condemnation of art and cultural education in the West.
Yes, technically you are correct.
But current technology will not do that. LLMs are not going to get much better, we need more complex setups.
Current LLMs generate poems that people prefer to human-written poetry. Current image generators win art contests. They don’t need to get better to produce more appealing art than humans. Maybe not every time, maybe the people writing the prompts and filtering results are inherent to producing quality results, but there’s not some extra trick needed for people to find their outputs aesthetically appealing.
It’s like the old ‘million monkeys on million typewriters will eventually write Shakespear’.
This research didn’t use a million poems, it used 5 human and 5 generated poems. The 5 generated poems were simply the first five generated, they did not use a human to curate from a larger population.
It’s “infinite monkeys on infinite typewriters” because a million would be far too small a sample size to expect Shakespeare. The monkeys aren’t trying to make anything, they’re just randomly hitting keys. For Shakespeare to come out, there would likely need to be more Monkeys than there are atoms in the universe. Conversely, we’re getting something people enjoy from AI right now. No need to approach infinity. It’s not what most people wanted AI to be used for, but it’s succeeding at it, and current models have only been around for a few years. This isn’t random chance happening upon something we like - this is a pattern-recognizing machine getting progressively better at recognizing the patterns we enjoy.
Yes, because ‘these monkeys’ have been reading all of available content humans created, not really fair comparison to infinite scale of pure randomness.
I would argue against pattern machines getting better at recognizing patterns better, but I don’t think it would change any minds.
Yes, I agree it’s a bad comparison. That’s why I said as such in my response to your comment that brought it up.
Though the current models have only been around for a few years, pattern recognition programs have been around for a long time. The latest ones are just a better model …because they are getting better.
The monkeys are just random chance - if you don’t yet have Shakespeare, you’re no more likely to get it than when you started - but pattern recognition software is steadily improving. If it’s not at some benchmark you want it to be at, it’s at least closer than it was 10 years ago, and will continue getting closer over time.