Super Intelligence

Eric Fingerman

By a "superintelligence" we mean an intellect that is much smarter than the

best human brains in practically every field, including scientific creativity,

general wisdom and social skills. This definition leaves open how the

superintelligence is implemented: it could be a digital computer, an

ensemble of networked computers, cultured cortical tissue or what have

you. It also leaves open whether the superintelligence is conscious and has

subjective experiences.

Entities such as companies or the scientific community are not

superintelligences according to this definition. Although they can perform a

number of tasks of which no individual human is capable, they are not

intellects and there are many fields in which they perform much worse than

a human brain - for example, you can't have real-time conversation with

"the scientific community".

Superintelligence requires software as well as hardware. There are several

approaches to the software problem, varying in the amount of top-down

direction they require. At the one extreme we have systems like CYC which

is a very large encyclopedia-like knowledge-base and inference-engine. It

has been spoon-fed facts, rules of thumb and heuristics for over a decade by

a team of human knowledge enterers. While systems like CYC might be

good for certain practical tasks, this hardly seems like an approach that will

convince AI-skeptics that superintelligence might well happen in the

foreseeable future. We have to look at paradigms that require less human

input, ones that make more use of bottom-up methods.

Given sufficient hardware and the right sort of programming, we could

make the machines learn in the same way a child does, i.e. by interacting

with human adults and other objects in the environment. The learning

mechanisms used by the brain are currently not completely understood.

Artificial neural networks in real-world applications today are usually

trained through some variant of the Backpropagation algorithm (which is

known to be biologically unrealistic). The Backpropagation algorithm

works fine for smallish networks (of up to a few thousand neurons) but it

doesn't scale well. The time it takes to train a network tends to increase

dramatically with the number of neurons it contains. Another limitation of

backpropagation is that it is a form of supervised learning, requiring that

signed error terms for each output neuron are specified during learning. It's

not clear how such detailed performance feedback on the level of

individual neurons could be provided in real-world situations except for

certain well-defined specialized tasks.

A biologically more realistic learning mode is the Hebbian algorithm.

Hebbian learning is unsupervised and it might also have better scaling

properties than Backpropagation. However, it has yet to be explained how

Hebbian learning by itself could produce all the forms of learning and

adaptation of which the human brain is capable (such the storage of

structured representation in long-term memory - Bostrom 1996).

Presumably, Hebb's rule would at least need to be supplemented with

reward-induced learning (Morillo 1992) and maybe with other learning

modes that are yet to be discovered. It does seems plausible, though, to

assume that only a very limited set of different learning rules (maybe as few

as two or three) are operating in the human brain. And we are not very far

from knowing what these rules are.

Creating superintelligence through imitating the functioning of the human

brain requires two more things in addition to appropriate learning rules

(and sufficiently powerful hardware): it requires having an adequate initial

architecture and providing a rich flux of sensory input.

The latter prerequisite is easily provided even with present technology.

Using video cameras, microphones and tactile sensors, it is