Artificial intelligence (AI) and artificial neural networks (ANN) are two exciting and intertwined fields in computer science. There are, however, several differences between the two that are worth knowing about.
The key difference is that neural networks are a stepping stone in the search for artificial intelligence.
Artificial intelligence is a vast field that has the goal of creating intelligent machines, something that has been achieved many times depending on how you define intelligence. Despite the fact that we have computers that can win at “Jeopardy” and beat chess champions, the goal of AI is generally seen as a quest for general intelligence, or intelligence that can be applied to diverse and unrelated situational problems.
Many of the AIs built up to this point have been built with a purpose, such as running a ping pong playing robot or dominating at “Jeopardy.” This is the inevitable result when computer scientists sit down and create something to do a specific task – they end up with something that can do that task and not much else.
To get around this problem of task-orientated AIs, computer scientists started playing around with artificial neural networks. Our generally intelligent brains are made up of biological neural networks that make connections based on our perceptions and outside stimulus.
A grossly simplified example is the pain from getting burned. When this happens for the first time, a connection is made in your brain that identifies the sensory information known as fire (flames, smell of smoke, heat) and relates it with pain. This is how you learn, at a very young age, how to avoid getting burned. Through this same neural network, we can do a lot of general learning like “ice cream tastes good” and even make deductive leaps like “there are always clouds before rain” or “stocks always rally in December.” These leaps are not always correct (there is bad ice cream and there are stocks that drop in December), but they can be corrected through experience, thus allowing adaptive learning.
Artificial neural networks try to recreate this learning system on computers by constructing a simple framework program to respond to a problem and receive feedback on how it does. A computer can optimize its response by doing the same problem thousands of times and adjusting its response according to the feedback it receives. The computer can then be given a different problem, which it can approach in the same way as it learned from the previous one. By varying the problems and the number of approaches to solving them that the computer has learned, computer scientists can teach a computer to be a generalist.
Although this conjures up images of computers taking over the world and harvesting humans as seen in Hollywood movies like “The Martrix,” we are still a long way from neural networking our way to artificial intelligence. The problems being tested on neural networks are all expressed mathematically. You can’t hold a flower up to a computer and tell it to guess the color by the smell, because the smell would have to be expressed in numbers and then the computer would have to catalog those numbers in memory, along with images of flowers emitting that smell.
That said, artificial neural networks that can be given more inputs of things like smell – and the capacity to learn from all those inputs – may be on track to produce the first artificial intelligence that meets the standards of even the most hardcore AI enthusiast.
In essence, artificial neural networks are models of human neural networks that are designed to help computers learn. Artificial intelligence is the Holy Grail some computer scientists are trying to achieve using techniques like mimicking neural networks.