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naraburns  nihil supernum  1yr ago (text post)   16082 thread views

Culture War Roundup for the week of December 19, 2022

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I've been asking it to generate some new monsters and other content for D&D. I've been adding new enemies to Roll20 and wanted to generate new variants of existing monsters, so I'd say something like "generate a kobold, but it has these attributes and does X, Y, and Z." I'm also working on a growing system as a player downtime option, so I asked it to "generate a list of fantasy plants. Give each one a growing time in weeks, flavor text, and if it responds to X, Y, or Z type of cultivation." I was fairly impressed at what it could consistently give me, though it obviously has a ways to go. It was a great way to fish for inspiration and mechanics before refining them into something usable.

Right now, it's best used as a way to rapidly generate ideas/content before someone who knows what he's doing polishes and fixes it. I haven't seen it output anything that was passable out of the box. This is how I feel about AI image generators too; in the hands of an artist, they have insane potential.

The real strength of the software is its working memory of context. You can issue corrections, prompt it with more information, tell it to adjust something, and it'll do it. That's what impressed me more than the generation itself, I think. The main limitation right now is it doesn't remember anything outside of a session, and it has trouble going past half a dozen revisions or so. This is to be expected since it's a free service at the moment, so I only see this improving.

That's a really cool idea, could you give us an example of a monster it created? And what prompt you used to get it?

Late response, but I didn't save the prompts for any of the monsters, since the prompts were usually just something like: "Generate me a statblock for variant of a kobold for dungeons and dragons 5th edition. It has an ability to place curse debuffs on enemies that debilitate them throughout combat. It should be extremely threatening and a high-priority target."

I wanted that particular enemy to feel occult and threatening. I wanted it to cast nasty debuffs that weren't any existing spell. They didn't need to be fancy, but I didn't want to just fill another monster statblock with existing spells. I had a general idea of what I wanted, but didn't have any ideas that stood out to me for what the debuffs should be.

It gave me a basic kobold that had a few daily use abilities, basically ray attacks that forced a save or the enemy would suffer some serious nastiness for the next few hours. One made them vulnerable to all damage, another gave them disadvantage on anything strength related. The AI even gave it the ability to cast one of them as a reaction to being targeted for an attack, which was very funny. I wound up reducing the effect from several hours to just the end of combat, since the effects were so powerful for such a low-level enemy.

Try "Create a new SCP monster with....". There is lots of SCP Internet content on which, I suspect, the chatbot has been trained.

You can issue corrections, prompt it with more information, tell it to adjust something, and it'll do it.

How is this implemented? Neural networks are universally stateless.

For GPT (and transformers in general), the "state" is the transcript of the conversation so far. A single response from GPT involves many queries to the (stateless, as you said) model asking for the next token until a special end-of-document token is reached or a maximum number of tokens limit is hit. Only so many tokens fit in the window, causing the described issues with it losing track of the conversation if the transcript gets too long: it's no longer seeing the entire history and the recent history isn't always enough to know what's going on.

Thanks, so if I understand correctly, the trick to make the neural network take into account past state, is to feed him all at once, not just the current sentence, but the whole dialogue history.

If so, well that is a basic primitive solution and its nice that it kind of work however because of the architecture of said networks, this solution doesn't scale to any long text, book or long dialogue.

Because the length of the input is capped to a max, and because the bigger the input is the less accurate the prediction will be.

Neural networks are fundamentally unfit for late binding and long exchanges.

While there exists hacks to improve regarding this goal:

https://arxiv.org/abs/2004.05150

https://arxiv.org/abs/2001.09694

I still believe transformers are unfit and will be the dawn of the current but yet invisible AI winter.

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