Have you tried video editing? You can do a lot with a good song and curiosity.

an eight-year-old girl was among those killed

I think it was Perplexity. Moved to using Flux since that cuddly monstrosity.

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I think this is accurate. But I’d like to restate it.

The Left (as the apparent big tent party full of literal minorities) has been learning to deal with disenfranchisement and the feeling “that their anguish is belittled as a personal failure, and often downright mocked” for its entire existence. Because of a huge variety of factors, the Right is losing some of its influence. They are not handling this well. The Left (being well acquainted with feeling unheard) should have been able to help the Right through this transition. Due to deep seated insecurities on both sides, we are no longer able to help one another as a people. Buckle up.

Is it Elmo?

Calling what attention transformers do memorization is wildly inaccurate.

*affected

small win

It honestly blows my mind that people look at a neutral network that’s even capable of recreating short works it was trained on without having access to that text during generation… and choose to focus on IP law.

The issue is that next to the transformed output, the not-transformed input is being in use in a commercial product.

Are you only talking about the word repetition glitch?

How do you imagine those works are used?

It’s called learning, and I wish people did more of it.

This is an inaccurate understanding of what’s going on. Under the hood is a neutral network with weights and biases, not a database of copyrighted work. That neutral network was trained on a HEAVILY filtered training set (as mentioned above, 45 terabytes was reduced to 570 GB for GPT3). Getting it to bug out and generate full sections of training data from its neutral network is a fun parlor trick, but you’re not going to use it to pirate a book. People do that the old fashioned way by just adding type:pdf to their common web search.

You’ve made a lot of confident assertions without supporting them. Just like an LLM! :)

Just taking GPT 3 as an example, its training set was 45 terabytes, yes. But that set was filtered and processed down to about 570 GB. GPT 3 was only actually trained on that 570 GB. The model itself is about 700 GB. Much of the generalized intelligence of an LLM comes from abstraction to other contexts.

Equating LLMs with compression doesn’t make sense. Model sizes are larger than their training sets. if it requires “hacking” to extract text of sufficient length to break copyright, and the platform is doing everything they can to prevent it, that just makes them like every platform. I can download © material from YouTube (or wherever) all day long.

Aye, flux [pro] via glif.app, though it’s funny, sometimes I get better results from the smaller [schnell] model, depending on the use case.