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by visarga
793 days ago
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> The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, concerning priors, experience, and generalization difficulty. (Chollet, 2019, https://arxiv.org/pdf/1911.01547.pdf) Priors here means how targeted is the model design to the task. Experience means how large is the necessary training set. Generalization difficulty is how hard is the task. So intelligence is defined as ability to learn a large number of tasks with as little experience and model selection as possible. If it's a skill only possible because your model already follows the structure of the problem, then it won't generalize. If it requires too much training data, it's not very intelligent. If it's just a set number of skills and can't learn new ones quickly, it's not intelligent. |
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Yes, learning is an important aspect of human cognition. However, the key factor that humans possess that LLMs will never possess, is the ability to reason logically. That facility is necessary in order to make new discoveries based on prior logical frameworks like math, physics, and computer science.
I believe LLMs are more akin to our subconscious processes like image recognition, or forming a sentence. What’s missing is an executive layer that has one or more streams of consciousness, and which can reason logically with full access to its corpus of knowledge. That would also add the ability for the AI to explain how it reached a particular conclusion.
There are likely other nuances required (motivation etc.) for (super) human AI, but some form of conscious executive is a hard requirement.