Artificial Intelligence and Neural Networks

The first scientific article on Artificial Intelligence (AI) was published by Alan Turing in 1950. The first full-time research was started in 1954 at Carnegie Mellon University. It all started at the Dartmouth conference, where ten young researchers had the same dream of using a computer to model the ways humans think. Their hypothesis was that mechanisms of human thought could be precisely modeled and simulated on a digital computer, which is what the whole foundation of Artificial intelligence is based on.

AI is a field of research, application, and instruction which attempts to have computers performing tasks of human beings with approximately the same intelligence. Today, AI is finding vast applications in the industry, education, and military fields with extensive research worldwide. Areas of Artificial intelligence research are currently divided into 2 major branches – science and engineering.

Neural networks and deep learning

Artificial Intelligence can be implemented with the development of neural networks. The motivation for developing neural network technology stemmed from the desire to develop an artificial intelligence system that could perform “intelligent” tasks similar to those performed by the human brain. A neural network is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing. For example, they can model complex relationships between inputs and outputs or find patterns in data.

Neural networks and deep learning

Neural networks have been successfully applied to a broad spectrum of data-intensive applications, such as:-

  • Creating a neural network model for a physical plant then using that model to determine the best control settings for the plant. 
  • Detect when a machine has failed so that the system can automatically shut down the machine when this occurs. 
  • Allocate the assets in a portfolio in a way that maximizes return and minimizes risk.
  • Military application, including automatic target recognition and tracking, mine detection, and radar image enhancement.
  • Assisting doctors with their diagnosis by analyzing the reported symptoms and/or image data such as MRIs or X-rays. 
  • Finding the set of demographics that have the highest response rate for a particular marketing campaign. 

Developments in Neural networks have stimulated a lot of enthusiasm and criticism. Some comparative studies are optimistic, some offer pessimism. For many tasks such as pattern recognition, no one approach dominates the others. The choice of the best technology should be driven by the given application’s nature. We should try to understand the capabilities’ assumptions and applicability of various approaches and maximize their complementary advantages to develop a better intelligence system. Such an effort may lead to a synergistic approach that combines the strength of neural networks with other technology.

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