As long as there have been working computers, starting with those giant vacuum tube machines as big as a house, there have been programmers trying to make them intelligent, like a human being. Those first versions of “AI” (artificial intelligence) were really primitive. (When going out, ask: Is it raining outside? When the answer is “Yes,” get an umbrella. Otherwise, do not get an umbrella. This was in an AI program in the UK, of course). It consisted mostly of long lists of IF … THEN … ELSE … statements.
When the art of programming advanced, we got rule-based programs with large tables of rules, composed of the answers of topic experts who were questioned for days about what they knew and how they got to conclusions. These were called “knowledge programs,” and some were even usable.
While programmers tried to make a program that behaved like a human, neurologists were researching how the human brain worked. They found that the brain consists of cells (neurons) connected by threads (axons and dendrites) to other neurons. Using those threads, the neurons send signals in an electrical or chemical way to those other cells. These brain tissues became known as biological neural nets.
These biological neural nets became the model used by the most ambitious developers of computer-based artificial intelligence. They tried to copy the working of the human brain in software. It was the start of a decades-long journey of stumbles, roadblocks, failures, and slow but steady progress. The “Artificial Neural Net” (just NN for short in IT and computer sciences) became the most versatile of the artificial intelligence programs.
There is one very big difference between these NN and the more traditionally programmed knowledge programs. Traditional programming uses IF-THEN-ELSE structures and rule tables. The programmer decides what the reaction (output) will be to a given event (input).
The behavior of a NN is not programmed. Just like a biological NN, it is trained by experience. A NN program without the training is good for nothing. It extracts the characteristics of “right” and “wrong” examples from the thousands or millions of samples it is fed during training. All those characteristics are assigned a weight for their importance.
When a trained NN is fed a new event, it breaks it down into recognizable characteristics, and based on the weights of those characteristics, it decides how to react to the event. It is often nearly impossible to trace why an event resulted in a specific reaction. Predicting what the reaction will be to an event is even harder.
An empty NN, a blank slate, is not AI. A trained NN can become AI. Where a knowledge program reacts in a predictable way to a programmed event, a well-trained NN reacts in an original way to an unknown event. That reaction should be within the parameters of what we consider a “good” reaction. This creates a complete new set of challenges in testing a trained NN. Has it become AI, and is it smart enough to delegate some tasks to it?
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