Bulldozer Intelligence: Here's Why LLMs Won’t Be AGI
A bulldozer has just two functions, for moving or breaking things. All the versatility of a bulldozer revolves around this. No matter how good a bulldozer is at carrying out its task, it won’t excel and acquire new capabilities like building or molding. If a bulldozer faces a harder challenge it cannot solve within its forte, it throws more weight at it or a bigger bulldozer will do it. So, if it can’t break or move it, throw a bigger weight at it. They are functional tools; the same as LLM (Large Language Models).
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The quality of the data trained determines the whole capability of LLMs; the higher the parameters, the better the LLM gets. When an LLM is faced with a task, it scans through billions of tokens guessing and cross-checking each word against millions of others in a spatial relation looking for a means to an end.
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LLMs are functional intelligence; they work to achieve an end goal. An LLM aims to generate as many words as efficiently as possible. It does this by sieving through words at a faster pace. While LLMs create new things through synthesis, which mimics a sign of human intelligence, LLMs lack creativity—a major component of intelligence.
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Imagining AGI (Artificial General Intelligence) is an encompassing form of intelligence at an average human pace. The average human brain constitutes an autotelic behavior, not as a functional tool. The brain works continuously, not as a means to an end. Human intelligence constitutes both functional intelligence and an autotelic one.
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When thinking of AGI, it is more about the creativity of its process and less about the output. The human brain, when faced with a harder problem, doesn’t increase its capacity but is more in the process of thinking about a solution.
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LLM solves problems with brute force at a faster pace; more problems equal more computing. When building AGI, the end goal should not target the utility but the process of action. When big enough, a bulldozer will carry out its function (move or break) effectively but that doesn’t make it a smarter tool but a functional one. Fed with enough data and computing, an LLM will generate better responses and words.
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It doesn’t make it smarter but more functional and one-dimensional. LLMs are more of a human tool than a form of intelligence. The hallucination of LLMs is more of a feature than a bug; although it is a regurgitation of information, it also showcases the capability of LLMs to act in its context.
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The human brain composes all forms of knowledge with adaptiveness. The brain switches to the best-performing mode at a flick knowing when to apply the best possible knowledge gotten over a large array of data (experience) gathered over the years. It doesn’t apply functional fixation thinking.
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When developing AGI, compute won’t be the determining factor, algorithm will. Building an autotelic way of thinking is one step toward building general intelligence.
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