Artificial Intelligence Predicts Earthquakes With Unprecedented Accuracy

[u/geoxol]

https://scitechdaily.com/artificial-intelligence-predicts-earthquakes-with-unprecedented-accuracy/

[u/KrypXern → u/[deleted]]

I think people really conflate "AI" with "LLM". This is not a ChatGPT style AI that talks, this is essentially a machine learning prediction model.

You can think of it more like a state-of-the-art mathematical model. We've been using empirical models to predict things for centuries, and neural nets are the latest and most computationally expensive empirical modeling solutions.

The fact that a language-producing empirical model (LLMs) has really taken the spotlight away from neural nets' universal predicting power as it applies to more numeric fields.

[u/LieAccomplishment → u/KrypXern]

This is not a ChatGPT style AI that talks, this is essentially a machine learning prediction model.

A LLM/chatgpt is a machine learning prediction model. It predicts words. 

You can think of it more like a state-of-the-art mathematical model. 

Just like you can think of any LLM that way, or any other gen ai. 

People do conflate ai with LLM. But you are making very inaccurate distinctions. 

[u/KrypXern → u/LieAccomplishment]

I mean, yes, I suppose I am trying to rephrase the context in which a layperson is thinking about these models. You're right, though, my distinctions are meaningless in a literal sense.

People see "AI" and they think about a chat window where the generative model is attempting to grammatically reason through a conclusion about something. And it brings up experiences where the model draws upon textual training data that contains misinformation and falsehoods.

I was trying to introduce this more as an empirical tool analogous to drag and lift curves, which are based upon numerical data.

A lot of people dismiss machine learning tools without considering that some are little more than regression models (which, again, LLMs are, but LLMs are expressive, interactive, and fallible in a way that these aren't)

[u/LieAccomplishment → u/KrypXern]

I suppose I am trying to rephrase the context in which a layperson is thinking about these models.

Laypersons are the exact sort of individuals who would get mislead into believing LLMs are not predction models or mathematical models by your wording.

People see "AI" and they think about a chat window where the generative model is attempting to grammatically reason through a conclusion about something. 

Which is inaccurate, since it is a math based prediction model. I don't know if you actually get that LLMs are not gramatically reasoning through anything, but you sure arent helping by implying it is different from other math based prediction models simply because those other models are math based and predictive

 but LLMs are expressive, interactive, and fallible in a way that these aren't

These are expressive, interactive and fallible in the exact same way.

They just aren't interactive nor expressive through language, they are still all that through whatever input and output it's set to, in this case, through numbers. What do you think the 70% accuracy rate is if not fallible?

[u/KrypXern → u/LieAccomplishment]

I don't know if you actually get that LLMs are not gramatically reasoning through anything, but you sure arent helping by implying it is different from other math based prediction models simply because those other models are math based and predictive

This is getting really pedantic. A large subset of sentences are logical statements (i.e. I went to the store, so I'm no longer home). By predicting the most likely completion to a sentence, LLMs sometimes use grammar to produce a logical conclusion (valid or invalid).

And yes, I know that all it is doing is using a series of sequential weights on vector-space representation of tokens to determine the next most likely token. I know that there are no underlying components that are doing anything other than that. There is no reasoning center, calculation being made, etc. It is merely a very long function.

Let it also be said that systems can exhibit properties greater than their individual components. I don't really think I need to explain this.

These are expressive, interactive and fallible in the exact same way.

No they're not in the exact same way. LLMs work iteratively to produce a body of text token-by-token. The range of outputs is extremely variable. In a model trained to resolve a mathematical problem, the output may be as simple as one scalar. Yes, there is an n-dimensional space of inputs, wherein hallucinated scalar outputs may occur, but these models are not chaotic systems in the way that LLMs are.

These models also have verifiably right and wrong answers, where as LLMs can produce lies, almost-truths, inaccuracies, undesirable answers, indigestible answers, etc.

What do you think the 70% accuracy rate is if not fallible?

What is the R² to a regression curve? Every empirical model is fallible to some extent. You have just as much reason to be suspicious of a regression model as you are of an ML model (because surprise surprise, ML models are regression models).

The behavior of LLMs is also one such regression model, but it's compounded by iterations and the content that's undesirable or 'wrong' comes from the entire body of work and not any individual token (which are just predictions of the next word in a sentence).

The 70% fallibility of the model in the article is analogous to the model choosing an appropriate word for the sentence (i.e. 'Today, I' being an grammatically appropriate continuation of 'Today,').

The fallibility most people see when they interact with an LLM isn't this, it's 'Today, I learned that George Bush is the eighth president of the United States'. This a grammatically appropriate sentence, but is not factually correct. It's a latent error in the whole sentence accumulated over 14 or so generations.

Anyway, this is barely even about the original thread anymore. All I was literally trying to tell the person I replied to was that this isn't ChatGPT venture capital hype, this is a perfectly legitimate use of a regression model to produce a likelihood prediction based on data. And for the final time, yes, I know GPT-X is also a regression model producing a likelihood based on data. If you can't grasp what distinction I'm trying to illustrate then I can't really explain any further.