Can someone help me to understand the difference between Generative AI and procedural generation (which isn’t something that’s relevant for Expedition 33, but I’m talking about in general).
Like, I tend to use the term “machine learning” for the legit stuff that has existed for years in various forms, and “AI” for the hype propelled slop machines. Most of the time, the distinction between these two terms is pretty clean, but this area seems to be a bit blurry.
I might be wrong, because I’ve only worked with machine learning in a biochemistry context, but it seems likely that modern procedural generation in games is probably going to use some amount of machine learning? In which case, would a developer need to declare usage of that? That feels to me like it’s not what the spirit of the rule is calling for, but I’m not sure
From my understanding, AI is the general field of automating logical (“intelligent”) tasks.
Within it, you will find Machine Learning algorithms, the ones that are trained on exemplar data, but also other methods, for instance old text generators based on syntactic rules.
Within Machine Learning, not all methods use Neural Networks, for instance if you have seen cool brake calipers and rocket nozzle designed with AI, I believe those were made with genetic algorithms.
For procedural generation, I assume there is a whole range of methods that can be used:
Unreal Engine Megaplants seems to contain configurable tree generation algorithms, that’s mostly handcrafted algorithms with maybe some machine learning to find the parameters ranges.
Motion capture and 3D reconstruction models can be used to build the assets. I don’t believe these rely on stolen artist data.
Full on image generation models (sora, etc.) to produce assets and textures, these require training on stolen artist data AFAIK (some arrangements were made between some companies but I suspect it’s marginal).
You can use statistics to estimate a child’s final height by their current height and their parents’ height.
People “train” models by writing a program to randomly make and modify equations, then keep them depending on if new accuracy is higher.
Generative AI can predict what first result on google search or first reply on whatsapp will look like for llms.
There are problems. Training from 94% to 95% accuracy takes exponentially more resources as it doesn’t have some “code” you can fix. Hallucinations will happen.
On the other side, procedural algorithms in games just refer to handwritten algorithms.
For example a programmer may go “well a maze is just multiple, smaller mazes combined.” Then write a program to generate mazes based on that concept.
It’s much cheaper, you don’t need GPU or internet connection to use the algorithm. And if it doesn’t work people can debug it on the spot.
Also it doesn’t require stealing from 100 million people to be usable
(I kinda oversimplified generative AI, modern models may do something entirely different)
generative ai is a subset of procedural generation algorithms. specifically it’s a procedural algorithm with a massive amount of weight parameters, on the order of hundreds of billions. you get the weights by training. for image generation (which i’m assuming is what was in use here), the term to look up is “latent diffusion”. basically you take all your training images and blur them step by step, then set your weights to mimic the blur operation. then when you want an image you run the model backwards.
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Can someone help me to understand the difference between Generative AI and procedural generation (which isn’t something that’s relevant for Expedition 33, but I’m talking about in general).
Like, I tend to use the term “machine learning” for the legit stuff that has existed for years in various forms, and “AI” for the hype propelled slop machines. Most of the time, the distinction between these two terms is pretty clean, but this area seems to be a bit blurry.
I might be wrong, because I’ve only worked with machine learning in a biochemistry context, but it seems likely that modern procedural generation in games is probably going to use some amount of machine learning? In which case, would a developer need to declare usage of that? That feels to me like it’s not what the spirit of the rule is calling for, but I’m not sure
From my understanding, AI is the general field of automating logical (“intelligent”) tasks.
Within it, you will find Machine Learning algorithms, the ones that are trained on exemplar data, but also other methods, for instance old text generators based on syntactic rules.
Within Machine Learning, not all methods use Neural Networks, for instance if you have seen cool brake calipers and rocket nozzle designed with AI, I believe those were made with genetic algorithms.
For procedural generation, I assume there is a whole range of methods that can be used:
You can use statistics to estimate a child’s final height by their current height and their parents’ height.
People “train” models by writing a program to randomly make and modify equations, then keep them depending on if new accuracy is higher.
Generative AI can predict what first result on google search or first reply on whatsapp will look like for llms.
There are problems. Training from 94% to 95% accuracy takes exponentially more resources as it doesn’t have some “code” you can fix. Hallucinations will happen.
On the other side, procedural algorithms in games just refer to handwritten algorithms.
For example a programmer may go “well a maze is just multiple, smaller mazes combined.” Then write a program to generate mazes based on that concept.
It’s much cheaper, you don’t need GPU or internet connection to use the algorithm. And if it doesn’t work people can debug it on the spot.
Also it doesn’t require stealing from 100 million people to be usable
(I kinda oversimplified generative AI, modern models may do something entirely different)
generative ai is a subset of procedural generation algorithms. specifically it’s a procedural algorithm with a massive amount of weight parameters, on the order of hundreds of billions. you get the weights by training. for image generation (which i’m assuming is what was in use here), the term to look up is “latent diffusion”. basically you take all your training images and blur them step by step, then set your weights to mimic the blur operation. then when you want an image you run the model backwards.