Modelling Human Intelligence
Essay title: Modelling Human Intelligence
Modeling Human Intelligence
Levels of Organization in General Intelligence
Notes
1. purpose of human intelligence modelling
traditional AI – model a single thougt, model logic, not intelligence
expert systems- model a large amount of knowledge, automate capabilities of eliminating solution states available as solution to a problem, not intelligence
neural networks- reduce the concept of intelligence to the most basic of actions in the brain, neurons firing, that too statiscally defined , not model for intelligence, ultimately deterministic determined by mathematical equtions, cannt hope to model ture thought
need for human modelling, is plausible to consider that we will be able to achieve human brain like speed 10^17 flops, but until computers capable of modeling human thought, their contribution to total amount of intelligence on earth remains vanishingly small, if however hey do become capable of thought, the advantages that accrue to them will lead to tremendous leaps, the most significant being the fact that they can modify their own architecture leading to growth in intellignece that cannot be experienced by human population. also if they are capable of human thought ,the hardware that they will be capabling of designing will accrue direct advantages to them, leading to exponential growth in capabilty as each achievement speeds the advent of the next.
The academic purpose of modern prehuman AI is to write programs that demonstrate some aspect of human thought – to hold a mirror up to the brain. The commercial purpose of prehuman AI is to automate tasks too boring, too fast, or too expensive for humans. Its possible to dispute whether an academic implementation actually captures an aspect of human intelligence, or whether a commercial application performs a task that deserves to be called “intelligent”.
Right now the amount of networked silicon computing power on the planet is slightly above the power of a human brain. The power of a human brain is 10^17 ops/sec, or one hundred million billion operations per second (2), versus a billion or so computers on the Internet with somewhere between 100 millions ops/sec and 1 billion ops/sec apiece. The total amount of computing power on the planet is the amount of power in a human brain, 10^17 ops/sec, multiplied by the number of humans, presently six billion or 6×10^9. The amount of artificial computing power is so small as to be irrelevant, not because there are so many humans, but because of the sheer raw power of a single human brain
Smartness is the measure of what you see as obvious, what you can see as obvious in retrospect, what you can invent, and what you can comprehend. To be more precise about it, smartness is the measure of your semantic primitives (what is simple in retrospect), the way in which you manipulate the semantic primitives (what is obvious), the structures your semantic primitives can form (what you can comprehend), and the way you can manipulate those structures (what you can invent).
All humans who have not suffered neural injuries have the same semantic primitives. What is obvious in retrospect to one is obvious in retrospect to all. (Four notes: First, by “neural injuries” I do not mean anything derogatory – its just that a person missing the visual cortex will not have visual semantic primitives. If certain neural pathways are severed, people not only lose their ability to see colors; they lose their ability to remember or imagine colors. Second, theorems in math may be obvious in retrospect only to mathematicians – but anyone else who acquired the skill would have the ability to see it. Third, to some extent what we speak of as obvious involves not just the symbolic primitives but very short links between them. I am counting the primitive link types as being included under “semantic primitives”. When we look at a thought-sequence and see it as being obvious in retrospect, it is not necessarily a single semantic primitive, but is composed of a very short chain of semantic primitives and link types.
Different humans may have different degrees of the ability to manipulate and structure concepts; different humans may see and invent different things. The great breakthroughs of physics and engineering did not occur because a group of people plodded and plodded and plodded for generations until they found an explanation so complex, a string of ideas so long, that only time could invent it. Relativity and quantum physics and buckyballs and object-oriented programming all happened because someone put together a short, simple, elegant semantic structure in a way that