Palworld studio Pocketpair says its new publishing division won't handle games that use generative AI: 'We don't believe in it'
Palworld studio Pocketpair says its new publishing division won't handle games that use generative AI: 'We don't believe in it'
I think it could work to give dynamic and varied answers to secondary characters given good prompts and other guardrails to preserve the immersion. As long as the core elements of the games are not AI generated slope, and developers are honest about where it was used.
As an amateur game dev, I believe AI will crash out for the public before it becomes truly useful for programming. I’ve heard colleagues try to use AI , but it often just creates more work. When the AI doesn’t know the answer, which is often. it makes something up, leading to errors, crashes, or hidden issues like memory leaks. I’d rather write the code correctly from the start and understand how it works, than spend hours hunting down problems in AI-generated code, only to never find the issue. Full disclosure I use Chatgpt to edit my dialogue as my English is not great.
Your anecdote checks out with a study I heard about. Office teams that were using LLMs for a few months reported that results are faster, but editing took longer than doing it conventionally in the first place. Generating boiler plate code and documentation could be another very useful use case in software dev, and I don't really care if that's used. Like in your use case, spell/grammar checking, using LLMs is a natural development of the tools that we already had. Your text processors marks errors, who cares if it's powered by an LLM or by a huge heuristic rule set?
I don't think AI code generation is going to be a revolution anytime soon, but AI voice and AI image generation is likely going to stay.
I am using them as a side tool for development. I think LLMs are already very performent for web knowledge search (e.g. replacing a search on stackoverflow), suggestions, explanations and error detection. Although is it worth the resources consumption? Not sure, but I can't afford not staying on top of the tooling available for my job. However, I agree, in my experience, the edit/agent modes are not efficient for coding, for now.
Generating secondary dialogues for a video game is quite a lower quality requirement than software engineering. So I think it could work there. It requires sounding natural, not being exact, LLMs are good at this.
My anecdote for AI and coding is that it’s a good replacement for google searching, especially when you are learning a new language.
You need to understand the fundamentals first, but asking the AI how to do a task in C when you’ve only coded in JS is very helpful. It’s still wrong, but it’s not like Stack Overflow is more accurate.
You'd think that that's the one thing LLMs should be good at – have characters respond to arbitrary input in-character according to the game state. Unfortunately, restricting output to match the game state is mathematically impossible with LLMs; hallucinations are inevitable and can cause characters to randomly start lying or talking about things thy can't know about. Plus, LLMs are very heavy on resources.
There are non-generative AI techniques that could be interesting for games, of course; especially ones that can afford to run at a slower pace like seconds or tens of seconds. For example, something that makes characters dynamically adapt their medium-term action plan to the situation every once in a while could work well. But I don't think we're going to see useful AI-driven dialogue anytime soon.
You seem to imply we can only use the raw output of the LLm but that's not true. We can add some deterministic safeguards afterwards to reduce hallucinations and increase relevancy. For example if you use an LLM to generate SQL, you can verify that the answer respects the data schemas and the relationship graph. That's a pretty hot subject right now, I don't see why it couldn't be done for video game dialogues.
Indeed, I also agree that the consumption of resources it requires may not be worth the output.
I am really praying for the day corporate drops this foolish nonsense of foisting it on their company and employees - maybe even gasp enabling their teams to access and use the tools that help them do better and more creative jobs.
Because AI can fit into a lot of people’s toolsets really nicely, especially in creative fields like game design. Just need to drop the idea that AI is an authoritative final answer to our design problems and instead realize that it’s just another tool to help us get to those solutions.
Pocketpair Publishing boss John Buckley
Any relation to loss guy?
We're all cousins, so probably?
Isn't that Garfield's owner
That's Jon Arbuckle
We don't believe in AI, says the developer of AI Art Impostor
With how badly that game was received, maybe they understood the point. Maybe
Obligatory @Angry_Autist@lemmy.autism.place
The difference between "generative AI" and "procedural generation" cannot be meaningfully nailed down.
I think it can - procedural generation consist of procedures, that is elements designed by humans, which are just connected into a bigger structure. Every single template, rule and atomic object (e.g. a single room in a generated house) is hand-designed, and as such no matter what comes out the elements and connections were considered by a real human. On the other hand, generative AI is almost always some sort of machine learning, that is an approximation of what a good structure of something should be, but it is only a very poor, randomised approximation. You have absolutely no guarantees nor constraints on what might pop out of the model - that is my main concern with genAI, though the whole outputted thing looks reasonable, upon closer inspection it has a lot of inconsistenties.
I think you are reading in the "designed by humans" part. Even when that is nominally true, the whole point of procedural generation is to create a level of complexity and emergence that the outputs are surprising and novel. Things no one expected are desirable. I think the distinction being drawn is not meaningful; in both cases, it is entirely possible and likely that no human being understands how a given output was arrived at.
Nonsense. Procedural generation is a rule-based deterministic system while generative AI is probabilistic and data driven. It's fundamentally different.
Okay, but (ignoring that procedural generation can also be probabilistic) what is the functional difference? The point I'm getting at is that you cannot banish the one without necessarily limiting the other.
Markov chains are both probabilistic and data-driven. For example. LLMs are not that far removed from markov chains. Should game developers be allowed to use latent spaces or is that too sloppy AI?
You don't need any preexisting training data for procedural generation
Where are all these prompt based image generators that identify themselves as procedural generation?
🤣🤣🤣👌👍
Yeah the company that ripped off Nintendo (I couldn't give two shits, don't screech) totally hates AI 🤣👌👍.
Hello AI!
"We might deal in derivative IP, but it's our derivative IP!"
Derivative over generative any day if you ask me.
To be fair Nintendo was heavily inspired by other artists work when designing Pokemon.
Nintendo wasn't "inspired" by shit. They made an ice cream cone a Pokémon. Keys on a ring? Pokémon. 8 varieties of elemental flavored dog? Check. Oh hey cool look a 2d image on a computer oh wait it's actually a Pokémon. Dog? Cat? Snake? Bird? Horse? All Pokémon. IMO nothing in Pokémon is actually "inspired", only ripped off.
Derivative IP is so fucking different from gen AI. It's stupid IP laws that force what could have been a commercial fangame to instead be legally distinct.