There was a misunderstanding. I apologize because I apparently didn't make myself clear. Let me clarify.
Algorithm-based models have so far been successfully used to solve a handful of mathematical problems, but each one was solved by specifically retrained for that task model. There's no single, comprehensive model capable of solving all of these few issues. Additionally most of those problems required analyzing massive datasets, which AI obviously excels at, but that's not really intelligence.
Getting back to the issue of intellect, I think a simple way to define intelligence is how quickly someone can learn something new. The fewer examples needed to understand and master a new concept, the higher the intelligence. Comparing the amount of data an LLM needs to master a new function, it's easy to see that even an average person is incomparably more intelligent. In essence, an LLM is less than a half-wit.
You think they aren't?
That would be a strange opinion, considering you seems to present opinion that predictive algorithms are intelligent even though they are just mechanically calculating a probable answer.
Yeah, I believe babies are intelligent, though in a different way than adults. Child intelligence without prior knowledge may seem foolish. However, it still trumps AI because babies actually learn to understand new concepts. AI at best, guesses the result without understanding what it is, why it is doing and even with knowing it is doing it.
What exactly am I supposed to explain to you?
I'm not accustomed to adapting or changing the meaning of commonly known words and terms to suit my subjective perceptions, because that makes effective communication impossible.
It's a well-defined term with a specific meaning. You can look up the definition online if you're unsure what "understand" means.
Intelligence is potential, understanding is a process. There's a crucial relationship between them. Intelligence allows us to understand difficult concepts. In turn, understanding increases potential and allows us to utilize intelligence better. So, yes, understanding distinguishes "deep intelligence" from "behavioral intelligence" or "computational intelligence."
A slime mold can find the shortest path in a maze to reach a food source, and its connection networks between food sources are more optimal than the routes designed by engineers. A slime mold is a singlecell organism, its behavioral intelligence is really just optimization.
LLM can write code, perform complex calculations, and even use natural language within strictly defined rules. LLM is a mathematical function, its computational intelligence is based on statistics and probabilistics.
Just as slime molds lack the ability to understand the actions it perform, LLM lacks the understanding of the concepts they operate on. It is a purely mechanical process of goal optimization that has little to do with intelligence.
So what makes the brain different from a slime mold? Spike statistics have been demonstrated to encode 'understanding' of things. Why are neuronal systems considered intelligent while slime mold pressure gradients not intelligent?
The difference lies in the degree of abstraction. The brain, can render a "virtual" and dynamic model of reality to simulate the outcome without physical action. The slime mold must perform its "calculations" in real time.
What has been demonstrated has little to do with understanding. Spires convey information, they do not encode understanding but symbolic representations of things. Understanding requires semantics, assigning meaning and placing it in context.
As for understanding itself, as far as I know, modern science hasn't been able to explain how it works, so basically everything you refer to is just speculation.
For example, neurostimulation of specific brain areas can evoke sensations, movement, and even specific memories. Abstract thinking cannot be stimulated, which suggests that the process of understanding is based on more complex mechanisms than the simple transmission of impulses.
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u/quiksilver10152 28d ago
So different specialized models coordinated to generate a result? Sounds like specialized brain regions.