Most of the potential benefits that brain implants could provide in healthy subjects could be obtained at far less risk, expense, and inconvenience by using our regular motor and sensory organs to interact with computers located outside of our bodies. Even if there were an easy way of pumping more information into our brains, the extra data inflow would do little to increase the rate at which we think and learn unless all the neural machinery necessary for making sense of the data were similarly upgraded. Since this includes almost all of the brain, what would really be needed is a “whole brain prosthesis–—which is just another way of saying artificial general intelligence. Yet if one had a human-level AI, one could dispense with neurosurgery: a computer might as well have a metal casing as one of bone.
The idea of using learning as a means of bootstrapping a simpler system to human-level intelligence can be traced back at least to Alan Turing’s notion of a “child machine,” which he wrote about in 1950:
"Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain."
Reklam
Studies have shown that the parts of the modern human brain that “light up” during this sort of toolmaking activity are those areas required for the fine motor control of lips and tongue—the parts of the brain involved in vocalization. Perhaps we aren’t better toolmakers than other animals because we are more intelligent; perhaps we are more intelligent than other animals because we were better toolmakers. If alien beings remain stuck at the level of “stone knives and bearskins” then perhaps they never develop high intelligence.
Generally speaking, artificial intelligence differs from artificial neural nets in the level of human intervention it requires. With an AI algorithm, all the information needed for a solution must be preprogrammed into a database, whereas artificial neural nets learn on their own. AI is based on the principles of deductive reasoning, whereas neural nets are inductive. This means that with Al, each new situation the system encounters may require another programmed rule. For example, when AI is used to program the behavior of a robot, all the desired behavior patterns must be worked out and programmed a priori-the robot can't adapt its behavior to changes in the environment. Consequently, AI programs can become quite large and unweildy in their attempt to address a wide range of different situations.
Artificial neural nets, on the other hand, automatically associations or relationships between parts of the network according to the results of known situations, adjusting to each new situation and eventually generalizing their behavior by correctly guessing the output for inputs never seen before. The disadvantage of artificial neural nets, however, is that they cannot be programmed to do a specific task, like adding numbers. The sets of examples or "training sets" of data the network must be fed in order to bring it closer to the desired solution must be chosen very carefully; otherwise, valuable time is wasted-or worse, the network doesn't do what it is supposed to do.
Keşke bunu görebilecek kadar yaşayabilseydik
It will not be surprising if this type of learned intelligence reaches the level of human intelligence some time before this century is over.
Human-Level Intelligence
Once one breaks free of the constraint that everyone’s activities must generate income, the sky’s the limit.
