LLMs are far more training data-intensive, hardware-intensive, and energy-intensive than a human brain. They’re still very much a brute-force method of getting computers to work with language.
This is what I never understood about the whole training on AI thing.
When a human creates an artwork, they don’t do it out of a vacuum. They’ve had a lifetime of inspiration from artwork they’ve discovered that inspires then to create something wholly new. AI does the same thing
The AIs we are talking about are large language models. They take human work as input and produce facsimiles. They are owned by individuals or companies that have no permission to exploit in this way intellectual property tied to other people’s livelihoods to copy them.
LLMs are not sentient, they don’t have inspiration, they are not creative and therefore do not create in the sense an artist would. They are an elaborate mathematical equation.
“Training” an AI has nothing to do with training an actual living being. It’s just tuning: adjusting an algorithm incrementally until the operator is satisfied with the result. I think it’s defendable to amount this form of extraction to plagiarism.
Dude, tell me, why do u think they have being doing this only with books and art but no music?
Thats because music really has people protecting their assets. U can have ur opinion about it, but that’s the only reason they haven’t ABUSED companies and people’s work in music.
It’s not reading, it’s the equivalent of me taking a movie, making a function, charge for it, and then be displeased when the creators demand an explanation.
There are a few reasons why music models haven’t exploded the way that large-language models and generative image models have. Maybe the strength of the copyright-holders is part of it, but I think that the technical issues are a bigger obstacle right now.
Generative models are extremely data-inefficient. The Internet is loaded with text and images, but there isn’t as much music.
Language and vision are the two problems that machine learning researchers have been obsessed with for decades. They built up “good” datasets for these problems and “good” benchmarks for models. They also did a lot of work on figuring out how to encode these types of data to make them easier for machine learning models. (I’m particularly thinking of all of the research done on word embeddings, which are still pivotal to large language models.)
Even still, there are fairly impressive models for generative music.
‘Reading my book infringes on my copyright.’ say confused writers.
This is a strawman.
You cannot act as though feeding LLMs data is remotely comparable to reading.
Why not?
Because reading is an inherently human activity.
An LLM consuming data from a training model is not.
LLMs forcing us to take a look at ourselves and see if we’re really that special.
I don’t think we are.
For now, we’re special.
LLMs are far more training data-intensive, hardware-intensive, and energy-intensive than a human brain. They’re still very much a brute-force method of getting computers to work with language.
This is what I never understood about the whole training on AI thing.
When a human creates an artwork, they don’t do it out of a vacuum. They’ve had a lifetime of inspiration from artwork they’ve discovered that inspires then to create something wholly new. AI does the same thing
AIs are trained for the equivalent of thousands of human lifetimes (if not more). There’s no precedent for anything like this.
The AIs we are talking about are large language models. They take human work as input and produce facsimiles. They are owned by individuals or companies that have no permission to exploit in this way intellectual property tied to other people’s livelihoods to copy them.
LLMs are not sentient, they don’t have inspiration, they are not creative and therefore do not create in the sense an artist would. They are an elaborate mathematical equation.
“Training” an AI has nothing to do with training an actual living being. It’s just tuning: adjusting an algorithm incrementally until the operator is satisfied with the result. I think it’s defendable to amount this form of extraction to plagiarism.
Dude, tell me, why do u think they have being doing this only with books and art but no music?
Thats because music really has people protecting their assets. U can have ur opinion about it, but that’s the only reason they haven’t ABUSED companies and people’s work in music.
It’s not reading, it’s the equivalent of me taking a movie, making a function, charge for it, and then be displeased when the creators demand an explanation.
What is the meaning of “making a function” in your sentence?
There are a few reasons why music models haven’t exploded the way that large-language models and generative image models have. Maybe the strength of the copyright-holders is part of it, but I think that the technical issues are a bigger obstacle right now.
Generative models are extremely data-inefficient. The Internet is loaded with text and images, but there isn’t as much music.
Language and vision are the two problems that machine learning researchers have been obsessed with for decades. They built up “good” datasets for these problems and “good” benchmarks for models. They also did a lot of work on figuring out how to encode these types of data to make them easier for machine learning models. (I’m particularly thinking of all of the research done on word embeddings, which are still pivotal to large language models.)
Even still, there are fairly impressive models for generative music.