Artificial Neural Network and Evolutionary Computation – AI vs Humans

It is the third post of the series in AI vs. Humans. This post will take a Look into the algorithm of Artificial Neural Network or ANN and Evolutionary Computation. If you have missed the first two posts, I would suggest you read it.

Artificial Neural Network and Evolutionary Computation – AI vs. Humans

Lets straight away start with the algorithms, be patient as it might be a little geeky. However, I won’t be including any mathematical explanation. It will simple, plain English with examples.

Artificial Neural Network

Artificial Neural Network (ANN) is a set of Artificial Neurons, which are connected and interact as a group. The interaction is based on several mathematical and sophisticated algorithms. It is an extension to Neural Network, which is biological, meaning it relates to humans animals or any carbon-based organic creature.

Neural Network models in Artificial Intelligence are ANN. ANN implies more on maths, which in turn is used for information processing, which depends on how the Neurons are connected and what environmental factors make them respond.

The above Figure explains how ANN works. The artificial neurons are connected like human neurons. The inspiration for making ANN was from biology. Every neuron could call specific functions or forward the request to underlying neurons with the information sent from the previous neuron. It makes the work of ANN simple. Looking into the figure, we can see how each neuron(a circle in the chart imitates one neuron) contacts the other, which in turn to others.

Input is given to neurons, which in turn call others for underlying hidden functions and outputs the result.

Due to the availability of millions of neurons and high connectivity, it is also possible for neurons to connect or call the required function through some other way. It is called routing. Like in case you are traveling on the road, and there is considerable traffic ahead, you detour to another method. Similarly, neurons choose to change the behavior and give the same result even if there is high information processing. It might become a little slow as its not an optimal path.

Evolutionary Computation

Evolutionary Computation Algorithms involve things related to the evolution of AI, which is similar to humans algorithms, which are being written to make AI more human. It includes reproduction, recombination, mutation, the survival of the fittest, etc.

It might be not possible for a robot or an AI program to give birth biologically, but they can always make another, but the real question is how will they evolve as humans evolve with time. For example, a  program can build another program, depending on his ability to program, his efficiency to produce another code, “Program Creating Another Program.”

They will be surviving based on their efficiency and computational speed. We can imagine a program changing its code to work better or even produce another program as a child of it.

Learning can be another basis of their evolution. In Ai based Games, they change the strategy as the user changes his way of playing. They take into consideration previous data and the algorithm to predict what’s the next move. Thus, making the program smarter as it meets more new conditions.

It is predicted that after AI and robots reach a stable state, they will have an equivalent place as a human does. We will have small programs or even a humanoid having similar rights as we do.

The next post will be the last post of this series; I will cover the Current and Future State of AI, including some good live examples and links.

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