We have always talked about how future computers will think like brains. Well it is a scientific possibility that it can happen. The present technology based on circuits has its limit and we are closing in on that limit. That’s why scientists are turning to completely new forms of technology for future computers. Well got a clue? It is right inside your head. Artificial Neural networks is the future of computers.
Difference between Artificial Neural Networks and Algorithmic Computers
The biggest difference between the two technologies is the approach they use to solve a problem. While Conventional Computers can solve problems based on an ordered set of instructions. The problem is, you have to know what the instructions are first, then only you can tell the computer what to do. The main benefit is that the results are predictable. However the drawbacks are that you can do things one step at a time and you can’t ask it a question you don’t know how to solve.
The neural network on the other hand process information like a brain i.e. large number of interconnected neurons all work at the same time to solve a problem. Instead of following a set of instructions they follow examples. It simply means that a neural network learns how to solve a problems based on limited information. And of course when you don’t know how to solve a problem, you also don’t know what the solution will be. So like our brain neural network might arrive at wrong solutions. One of the major drawbacks of a neural is system that it is unpredictable.
Both the systems have it’s applications. It is tough to say which one is better. For example if you want some equations solved then algorithmic computers will do it efficiently. Need to quickly detect lung cancer then neural networks can do that. They can even work together like how algorithmic computers are often used to “supervise” neural networks.