The exploration of Artificial Intelligence (AI) has posed an important question: “Can machines think like humans?”. As technology evolves and scientists push the limits of possible with AI, new advancements make the answer to that question more of a reality. Artificial Neural Networks simulate the structure of the brain. They are intended to make computers more humans and help machines master human-level intelligence.
It is undoubtedly machines have rivaled humans when it comes to reasoning and problem-solving. However, there are still capabilities of human intelligence such as imagination and inspiration that remain to be replicated by machines. That is why researchers are focusing on artificial neural networks to bridge the gap between artificial intelligence and human minds.
It starts with the way humans interpret real-world situations in multi-sensory ways that computers have yet to learn. In the 1950s, as science was pioneering AI, neural networks were explored to see if a computer can learn and make decisions like a human being.
What makes artificial neural networks interesting is that it mimics the structure of the human brain. Neuroscience has discovered that different parts of the brain are responsible for processing different parts of information and the whole brain is arranged as interconnected brain cells. The artificial neural networks (ANN) are designed to replicate the structure of the human brain as well as the mechanism.
How Do Neural Networks Work
Much like the human brain, Artificial Neural Networks works in complex ways. Neural networks are organized into layers. These layers contain several “interconnected nodes” with “activation functions”. The mechanism of Artificial Neural Networks contains an intricate interaction between “input layer”, “connections”, and “output layer”, all of which communicate. The interesting fact about Artificial Neural Networks is that they are trained much like the human brain. The more the human brain is exposed
to stimuli, the more connections it makes between neurotransmitters and builds neural networks. These neural networks are “neuroplastic”, they get stronger or weaker when the pathway is repeatedly exposed to the same stimuli. In this way, human beings learn from experience. In the same way, Artificial Neural Networks learns by example and gets better by repetition.
Neural Network Examples
Artificial Neural Networks is a fascinating technological advancement but what real-world applications do they have? Artificial Neural networks has been used in forecasting and identifying patterns of data in many industries such as:
– Customer service
– Sales forecasting
– Data sciences
– Risk management
The human brain remains a mystery to neuroscience and technology to this day. While it functions as a network of interconnected neurons, the way it processes inputs and information to output creativity, inspiration, and talent is still to be understood. The goal of Artificial Neural Network is to replicate human intelligence so AI systems can more accurately replicate human cognition. The Artificial Neural Network mechanism closely mimics its biological counterpart in the way it processes and builds on information and stimuli. Artificial Neural Network is used to solve problems and provide solutions for an array of industries from marketing to risk management. These solutions are meant to further progress for the industry and society.