Doing the Lord’s work in the Devil’s basement
Now if I want to win the annoying Lemmy bingo I just need to shill extra hard for more restrictive copyright law!
Reasoning has nothing to do with knowledge though.
You should have asked chatgpt to explain the comment to you cause that’s not what they say
Yeah it always strikes me how religious extremism is framed. You rarely hear about christian extremists, who operate in the open on all social networks.
Yet, you could argue that Christian extremists have done more harm to western societies in the last 20 years than any Islamic group.
That’s a nice hypothetical but the facts of this case are much simpler. Would you agree that a country is sovereign, and entitled to write its own laws? Would you agree that a company has to abide by a country’s laws if it wants to operate there? Even an American company? Even if it is owned by a billionaire celebrity?
Then you have to agree that piracy is theft and people pirating content should be sued.
Even if you were extremely generous and didn’t factor in the scams in your analysis, the reality is that a Blockchain solves problems 99.9% of people will never face. This breaks the whole imagined model, when your product is ultra niche but relies on mass adoption for its security.
Even if you were extremely generous and didn’t factor in the scams in your analysis, the reality is that a Blockchain solves problems 99.9% of people will never face. This breaks the whole imagined model, when your product is ultra niche but relies on mass adoption for its security.
If you like to write, I find that story boarding with stable diffusion is definitely an improvement. The quality of the images is what it is, but they can help you map out scenes and locations, and spot visual details and cues to include in your writing.
Just yesterday I got an ad for actual shrooms! The website even had a “how is this legal” section, but the legal theory in there was… Not very convincing…
Then these models are stupid
Yup that is kind of the point. They are math functions designed to approximate human tasks.
These models should start out with basics of language, so they don’t have to learn it from the ground up. That’s the next step. Right now they’re just well read idiots.
I’m not sure what you’re pointing at here. How they do it right now, simplified, is you have a small model designed to cut text into tokens (“knowledge of syllables”), which are fed into a larger model which turns tokens into semantic information (“knowledge of language”), which is fed to a ridiculously fat model which “accomplishes the task” (“knowledge of things”).
The first two models are small enough that they can be trained on the kind of data you describe, classic books, movie scripts etc… A couple hundred billion words maybe. But the last one requires orders of magnitude more data, in the trillions.
That’s what smaller models do, but it doesn’t yield great performance because there’s only so much stuff available. To get to gpt4 levels you need a lot more data, and to break the next glass ceiling you’ll need even more.
Very useful in some contexts, but it doesn’t “learn” the way a neural network can. When you’re feeding corrections into, say, ChatGPT, you’re making small, temporary, cached adjustments to its data model, but you’re not actually teaching it anything, because by its nature, it can’t learn.
But that’s true of all (most ?) neural networks ? Are you saying Neural Networks are not AI and that they can’t learn ?
NNs don’t retrain while they are being used, they are trained once then they cannot learn new behaviour or correct existing behaviour. If you want to make them better you need to run them a bunch of times, collect and annotate good/bad runs, then re-train them from scratch (or fine-tune them) with this new data. Just like LLMs because LLMs are neural networks.
I was banned from awful.systems yesterday for posting a personal experience about GenAI that wasn’t negative 💀
Oh boy. You’re about to discover the wide world of male sex toys and it’s gonna rock you.
Also their role system is badass. It’s incredibly fine grained and makes it possible to manage large communities with plenty of different user levels.
I don’t know about the US but in European GDPR parlance, of it can be reversed then it is NOT anonymized and it is illegal to claim otherwise. The correct term is pseudonymized.
Artists are perfectly able to use the fediverse, that is not what is stopping them.
They don’t come because they need to be where their fans are. That is why Cara will only be a splash: their niche is artists who place more value on the anti-AI slant than on meeting their audience where it lives. By definition that is not conducive to a lot of organic growth.
No the article is badly worded. Earlier models already have reasoning skills with some rudimentary CoT, but they leaned more heavily into it for this model.
My guess is they didn’t train it on the 10 trillion words corpus (which is expensive and has diminishing returns) but rather a heavily curated RLHF dataset.