[lelanthran]

> That's the difference between innovating and copying/distilling someone else's innovation.

Aren't the models from Anthropic and OpenAI simply the distilled work of everyone else who ever put their work online, or in books?

Why is their distillation okay, but other distillations not ok?

[rmunn]

The difference I was referring to was economic; I was not making an ethical judgment. So many people seem to be reading my comment as making an ethical judgment (based on their reactions); maybe I should edit it to clarify.

Nope, seems I'm past the edit window. Oh well.

[lelanthran]

> The difference I was referring to was economic; I was not making an ethical judgment

What is the economic difference? LLMs have been trained on the results of billions of dollars worth of time, research, investment and expenditure. When you ask an LLM a question, they are giving you the results of those billions, or hundreds of billions, effort.

Those things weren't free; they cost money to produce! If anything, the OpenAI and Anthropics of the world got more economic value for free than the people distilling them did.

[rmunn]

It takes more computing power, and money, to pay for training an LLM, compared to distilling an LLM that someone else has already trained. That's the economic difference.

[lelanthran]

> It takes more computing power, and money, to pay for training an LLM, compared to distilling an LLM that someone else has already trained.

And that is still less money than it took to create that data in the first place, which the AI companies then gladly took to use for training.

[rmunn]

Which is true, but also completely irrelevant to the point of this discussion. Which was about the U.S. companies spending trillions and the Chinese companies spending billions and matching them in quality. I made the point that the U.S. companies and Chinese companies were doing different things: training on raw data vs. distilling the model that someone else had trained. That difference explains the difference in spending. (The secondary point is that the Chinese companies would not have been able to get to the point they have, while spending as little as they have, without Claude, GPT, et al to copy from).

This is probably the last reply I'll make to you. I'm getting a little tired of repeating myself. Whether you're just not getting it, or refusing to get it, either way it's starting to feel like a waste of time to try to rephrase the same thing again and again. Please read more carefully in the future.

[Tadpole9181]

Can you please stop being coy and intentionally obtuse? Just have a discussion in good faith, I'm so beyond sick of this kind of rhetoric.

Yes, AI training uses human data and a lot of it was not compensated. But that has absolutely nothing to do with the thing this thread is about.

Distilling models costs less money than a really procuring quality data and training a model yourself. If you disagree, debate that.

[lelanthran]

> But that has absolutely nothing to do with the thing this thread is about.

That's how this thread started:

>>>> That's the difference between innovating and copying/distilling someone else's innovation.

How is distillation by one party okay but distillation by another not okay, even though in the second case the other party is paying the asking fees?

[rmunn]

And, as I pointed out elsewhere, the difference I was pointing out was an economic difference. You keep on ignoring that fact and thinking I'm talking about ethics, but I'm not. I'm saying the difference is between spending trillions on training from raw data vs. spending billions on distilling that trained-from-raw-data model.

[lelanthran]

> And, as I pointed out elsewhere, the difference I was pointing out was an economic difference. You keep on ignoring that fact and thinking I'm talking about ethics, but I'm not.

I am talking about money now;

> . I'm saying the difference is between spending trillions on training from raw data vs. spending billions on distilling that trained-from-raw-data model.

Firstly, they didn't spend trillions on training.

I am pointing out that literal trillions were spent to assemble the data that the AI corps then spent dozens of billions training from.

You ignored that fact completely.

[Tadpole9181]

First of all, this is actually how this part of the conversation started:

> If the US companies need trillions to barely beat Chinese companies spending billions, despite a multi year head start...

Because they're talking about the cost difference of distilled model development and ground-up trained model development.

And second, the answer is that OpenAI and Anthropic had to do all the research into how to train models. Then they had to acquire all the data, curate and filter it. Then they had to design all the ways to iterate on training and antagonize it to be better - because there's not actually an enormous corpus of aligned, human stream-of-thought data. Then over half a decade they've been refining these methods.

There's no way you don't understand that if it was not easier and cheaper to distill a model, the institutions in question would be training their own models from scratch.

[mike_hearn]

Not at all. At this point, a large amount of the work is in reinforcement learning where they are effectively generating their own data.

[justacrow]

Ah, so it's okay to copy and distill all human-produced works evee, except for reinforcment-learning data generated by OpenAI/Anthropic etc

[mike_hearn]

I didn't take any position on the morality of it, just pointing out that the data they're training on isn't all taken from the rest of the world.