1) We're continually learning so we can update our predictions when our pattern matching is wrong
2) We're autonomous - continually interacting with the environment, and learning how it respond to our interaction
3) We have built in biases such as curiosity and boredom that drive us to experiment, gain new knowledge, and succeed in cases where "pre-training to date" would have failed us
For a person to truly understand something they will have a well-refined (as defined by usefulness and correctness), malleable internal model of a system that can be tested against reality, and they must be aware of the limits of the knowledge this model can provide.
Alone, our language-oriented mental circuits are a thin, faulty conduit to our mental capacities; we make sense of words as they relate to mutable mental models, and not simply in latent concept-space. These models can exist in dedicated but still mutable circuitry such as the cerebellum, or they can exist as webs of association between sense-objects (which can be of the physical senses or of concepts, sense-objects produced by conscious thought).
So if we are pattern-matching, it is not simply of words, or of their meanings in relation to the whole text, or even of their meanings relative to all language ever produced. We translate words into problems, and match problems to models, and then we evaluate these internal models to produce perhaps competing solutions, and then we are challenged with verbalizing these solutions. If we were only reasoning in latent-space, there would be no significant difficulty in this last task.
AI can only interpolate. We may perceive it as extrapolation, but all LLMs architectures are fundamentally cleverly designed lossy compression
"The distinction between AI and humans often comes down to the concept of understanding. You’re right to point out that both humans and AI engage in pattern matching to some extent, but the depth and nature of that process differ significantly." "AI, like the model you're chatting with, is highly skilled at recognizing patterns in data, generating text, and predicting what comes next in a sequence based on the data it has seen. However, AI lacks a true understanding of the content it processes. Its "knowledge" is a result of statistical relationships between words, phrases, and concepts, not an awareness of their meaning or context"
:)
I didn't downvote, I'm just saying why I think you were downvoted.
We don't care what LLMs have to say, whether you cut back some of it or not it's a low effort wasted of space on the page.
This is a forum for humans.
You regurgitating something you had no contribution in producing, which we can prompt for ourselves, provides no value here, we can all spam LLM slop in the replies if we wanted, but that would make this site worthless.
I use llms as tools to learn about things I don't know and it works quite well in that domain.
But so far I haven't found that it helps advance my understanding of topics I'm an expert in.
I'm sure this will improve over time. But for now, I like that there are forums like HN where I may stumble upon an actual expert saying something insightful.
I think that the value of such forums will be diminished once they get flooded with AI generated texts.
(Fwiw I didn't down vote)
That was the point. If you back up to the comment I was responding to, you can see the claim was: "maybe people are doing the same thing LLMs are doing". Yet, for whatever reason, many users seemed to be able to pick out the LLM comment pretty easily. If I were to guess, I might say those users did not find the LLM output to be human-quality.
That was exactly the topic under discussion. Some folks seem to have expressed their agreement by downvoting. Ok.
Other parts of what we do looks more as a search through the space of possibilities.
And then we act and collaborate and test the ideas that stand against scrutiny.
All of that is in principle doable by machines. The things we currently have and we call LLMs seem to currently mostly address the autocomplete part although they begin to be augmented with various extensions that allow them to take baby steps in other fronts. Will they still be called large language models once they will have so many other mechanisms beyond the mere token prediction?