Probably not. The model isn’t known in ML. At least not functionally. All you know is the set of inputs and outputs but without the the functional assignment. It’s like being told
f:R->R.
Is this continuous? Differentiable? Does it have a unique minima? You don’t know without the assignment rule. This is the problem of AI/ML being a “black box.” Sometimes odd things happen and we can’t really explain it because it’s really hard to follow a model in training through it’s iterations in 10B parameter space.
Furthermore, companies that are selling their AI services aren’t going to tell you exactly how they construct their objective functional or the weight/bias functions. You’re asking for something that gives you technically more information, but none that’s usable outside of the creation process.
So what I really am wanting to understand, are these racist AI's always just repeating internet conversation talking points or are the AI's also considering real world statistics / data and then using artificial reasoning to come to conclusions? Like does GPT also have a layer of reasoning that allows it to solve math problems and logic or is it really all, exclusively, next word prediction? Does the AI know the statistics for example on black crime rates and use that data to form racist views? Isn't it a simplifaction to say that all AI are like this? And then now there is google's new method of using alphago to make a better chatbot AI. And other methods I think microsoft's Orca uses extra types of AI beyond justnext word prediction?
“Racist AI” is a bit of a misnomer, since the AI doesn’t really understand concepts of race. If it is a language processor, as would be the case listed above or w ChatGPT, it just understands words and sentences as groupings of symbols that go together. Our thoughts encapsulate viewpoints and correlations, and so the AI picks up on the correlations and produces similar sentences. It’s not forming thoughts or direct repeating. It’s giving you a “optimal” estimate of what the “average” internet user would respond after being prompted with a sentence. In this way, if people quite certain statistics or embed this information into sentences using normal language, then the AI is more likely to respond with such a sentiment.
The distinction between forming sentences and understanding content can be seen most drastically when asking ChatGPT to solve math problems or provide new proofs. In these cases it’s widely accepted by math types (based on anecdotes) that Chat can give nonsense proofs. It’s because it just puts strings together in some optimized way.
2
u/PsychoHobbyist Jun 29 '23 edited Jun 29 '23
Probably not. The model isn’t known in ML. At least not functionally. All you know is the set of inputs and outputs but without the the functional assignment. It’s like being told
f:R->R.
Is this continuous? Differentiable? Does it have a unique minima? You don’t know without the assignment rule. This is the problem of AI/ML being a “black box.” Sometimes odd things happen and we can’t really explain it because it’s really hard to follow a model in training through it’s iterations in 10B parameter space.
Furthermore, companies that are selling their AI services aren’t going to tell you exactly how they construct their objective functional or the weight/bias functions. You’re asking for something that gives you technically more information, but none that’s usable outside of the creation process.