get_flat_ccs_offset() reads the base of the flat CCS storage from the
hardware, scales it by the number of enabled L3 nodes, and rounds the
result up to 128K. Everything below that offset is then ...
No, that’s not actually how it works in the real world. You’re equating the human process of learning and practicing art to scraping and feeding a bunch of stuff into a machine learning algorithm, but they couldn’t be more different.
Of course a human artist learns from studying the works of other artists, but not at all in the same way that AI does. A human artist has a lifetime of lived experiences which contain a perspective that is unique to their place and time, as well as direct sensory observations and a worldview than informs how they learn and create. Human artists also have individual tastes and personal agency that inform the kinds of work that they like and want to study from.
In other words, when a person draws a cat, for example, they aren’t just recycling images of cats that they’ve seen in other people’s artwork, they are also imbuing their artwork with their own understanding of what a cat is, how it looks and acts, etc.
People don’t function like statistical models. They don’t experience the universe in the same way, they don’t learn in the same way, and they certainly don’t create or interact with artistic media in the same way. Even a digital painting made by a human is not analogous to generative AI output despite the fact that they are both pixels in the end. It’s an entirely different process from start to finish. Would anyone seriously try to argue that what Suno is doing with music in any way equivalent to a person learning how to play the piano or the guitar?
On top of that, a human is obviously not going to have the same potential impact on the market that generative AI would have. Even the most experienced writer can only thoughtfully write a book so quickly, while an LLM can be used to generate countless books in a single day. The market impact of a use is one of the key pillars to making a determination of “fairness” in a legal/copyright sense, and it’s abundantly clear that human learning and “machine learning” are not comparable in terms of their market impact.
In other words, when people equate AI to human learning, it is nothing more than anthropomorphization and personification. What is fair for a human to do one way is not necessarily fair for a corporate computer system to do in an entirely different way. It’s comparing apples to oranges.
If we’re going to talk about the fairness of using copyrighted works for AI training, we have to talk about that specific process, as it really is, and how it affects society. People who equate a person trying to learn how to manga with a corporate image generation machine strip mining every image on the internet are doing nothing but providing cover for some of the biggest, richest corporations on Earth while they exploit the work of unpaid creative labor.
People don’t function like statistical models. They don’t experience the universe in the same way, they don’t learn in the same way, and they certainly don’t create or interact with artistic media in the same way
Why is this relevant to ethics. Why do you need to learn like a human does, in order for it to be ethical. Why is it only exploitation when a machine does it.
You’re lumping all of machine learning into the bucket of tech bros, ChatGPT, Facebook and such.
There was a thriving, small scale world of machine learning stuff that existed before they hijacked the public’s attention, and it never went away. It still exists. It will continue to exist when this bubble at least deflates some.
That’s not my intent. Machine learning as a method of computation isn’t the problem.
Among other things, the problem is how the business of AI is sourcing their input training data, how these outputs are being used to manipulate people and markets, etc.
ML is sometimes the best tool for certain computing tasks and succeeds where algorithmic programming fails. Sometimes traditional logic and algorithms is preferable for it’s determinism and easily understandable behavior.
The AI industry however is built on exploitation of human and natural resources, and it also built on top of a massive economic bubble and is hurting the market for jobs and human made things.
Comparing AI to human learning is neglecting to acknowledge the problems that we currently face with how AI is currently being trained and deployed by the biggest players in the industry.
Okay, well, what about Nvidia Nemotron’s series as a counter example?
It’s built on an open dataset; its training regime isn’t secret, it’s reproducible. It’s Apache licensed. It’s not published to manipulate anyone; it’s mostly presented as a research tool, or a starting point for users to customize for mundane text processing tasks. It runs reasonably efficiently on a large variety of software and hardware, with a standardized architecture.
Of course AI, as it is mostly presented to the public, is an existential problem.
But you’re trying to fundamentally tie text-based machine learning to the tech bros as a root for its ethical issues. It’s not that simple. Even if Sam Altman and all his kind keel over tomorrow, the complications the existence of LLMs and other generative models present is not going away, and we will have to deal with models that can do unethical things without anything unethical in their training regime.
If you were a painter who who makes art for a living, does it really seem “fair” for someone else to take your work, without any sort of consent or compensation, and use it to produce other works that are in direct competition (for time, money and attention) with you? How is that “fair” in any sense of the word?
You’re equating the human process of learning and practicing art to scraping and feeding a bunch of stuff into a machine learning algorithm
You were not talking at all about LLMs in your rhetoric: you were talking about artists painting and I responded to what you wrote. You made the analogy linking LLM output to an artists output; I did not.
No, that’s not actually how it works in the real world. You’re equating the human process of learning and practicing art to scraping and feeding a bunch of stuff into a machine learning algorithm, but they couldn’t be more different.
Of course a human artist learns from studying the works of other artists, but not at all in the same way that AI does. A human artist has a lifetime of lived experiences which contain a perspective that is unique to their place and time, as well as direct sensory observations and a worldview than informs how they learn and create. Human artists also have individual tastes and personal agency that inform the kinds of work that they like and want to study from.
In other words, when a person draws a cat, for example, they aren’t just recycling images of cats that they’ve seen in other people’s artwork, they are also imbuing their artwork with their own understanding of what a cat is, how it looks and acts, etc.
People don’t function like statistical models. They don’t experience the universe in the same way, they don’t learn in the same way, and they certainly don’t create or interact with artistic media in the same way. Even a digital painting made by a human is not analogous to generative AI output despite the fact that they are both pixels in the end. It’s an entirely different process from start to finish. Would anyone seriously try to argue that what Suno is doing with music in any way equivalent to a person learning how to play the piano or the guitar?
On top of that, a human is obviously not going to have the same potential impact on the market that generative AI would have. Even the most experienced writer can only thoughtfully write a book so quickly, while an LLM can be used to generate countless books in a single day. The market impact of a use is one of the key pillars to making a determination of “fairness” in a legal/copyright sense, and it’s abundantly clear that human learning and “machine learning” are not comparable in terms of their market impact.
In other words, when people equate AI to human learning, it is nothing more than anthropomorphization and personification. What is fair for a human to do one way is not necessarily fair for a corporate computer system to do in an entirely different way. It’s comparing apples to oranges.
If we’re going to talk about the fairness of using copyrighted works for AI training, we have to talk about that specific process, as it really is, and how it affects society. People who equate a person trying to learn how to manga with a corporate image generation machine strip mining every image on the internet are doing nothing but providing cover for some of the biggest, richest corporations on Earth while they exploit the work of unpaid creative labor.
Why is this relevant to ethics. Why do you need to learn like a human does, in order for it to be ethical. Why is it only exploitation when a machine does it.
You’re lumping all of machine learning into the bucket of tech bros, ChatGPT, Facebook and such.
There was a thriving, small scale world of machine learning stuff that existed before they hijacked the public’s attention, and it never went away. It still exists. It will continue to exist when this bubble at least deflates some.
That’s not my intent. Machine learning as a method of computation isn’t the problem.
Among other things, the problem is how the business of AI is sourcing their input training data, how these outputs are being used to manipulate people and markets, etc.
ML is sometimes the best tool for certain computing tasks and succeeds where algorithmic programming fails. Sometimes traditional logic and algorithms is preferable for it’s determinism and easily understandable behavior.
The AI industry however is built on exploitation of human and natural resources, and it also built on top of a massive economic bubble and is hurting the market for jobs and human made things.
Comparing AI to human learning is neglecting to acknowledge the problems that we currently face with how AI is currently being trained and deployed by the biggest players in the industry.
Okay, well, what about Nvidia Nemotron’s series as a counter example?
It’s built on an open dataset; its training regime isn’t secret, it’s reproducible. It’s Apache licensed. It’s not published to manipulate anyone; it’s mostly presented as a research tool, or a starting point for users to customize for mundane text processing tasks. It runs reasonably efficiently on a large variety of software and hardware, with a standardized architecture.
Of course AI, as it is mostly presented to the public, is an existential problem.
But you’re trying to fundamentally tie text-based machine learning to the tech bros as a root for its ethical issues. It’s not that simple. Even if Sam Altman and all his kind keel over tomorrow, the complications the existence of LLMs and other generative models present is not going away, and we will have to deal with models that can do unethical things without anything unethical in their training regime.
You were not talking at all about LLMs in your rhetoric: you were talking about artists painting and I responded to what you wrote. You made the analogy linking LLM output to an artists output; I did not.
Please engage in good faith or self-ban.