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Intelligent Computers, Artificial Intelligence, and Neural Networks


The computer system today, which has grown significantly, has been part of the technological evolution in society; living without computers today is like stopping progress, as they are the essential tool in any daily activity, helping us to maintain our accounts, develop new technologies, and connect us to the entire world. Read the Best info about Deep learning technology.

A conventional computer can perform millions of operations per second but cannot make decisions independently. In other words, a computer cannot perform tasks independently; it must be programmed, but a single program can only do one job, and multiple tasks require multiple programs. Computer manufacturers provide us with the hardware needed to operate the computer, and software developers offer programs that allow us to use the hardware as a tool, but what if a computer could make decisions without using large amounts of specialized software?

New technology developers have looked for alternatives to accomplish this task. An astonishing discovery was made, for example, while studying the human brain 50 years ago: it is possible to implement an artificial system based on the same architecture of biological neural networks and their operation, so they develop artificial intelligence and neural networks.

Artificial intelligence is nothing more than a set of techniques based on human brain behavior, primarily in learning and decision-making. For the most part, living beings are biological systems that learn and, based on that learning, are capable of making decisions, most of which are based on survival.

Artificial intelligence systems, like biological systems, require learning and decision-making. However, unlike biological systems, artificial systems operate based on mathematical algorithms, and knowledge is induced for specific purposes. There are many categories of artificial intelligence, but in the case of intelligent computers, the most commonly used are artificial neural networks and genetic algorithms.

The neural networks are nothing more than a mathematical emulation of the brain’s neural system, with each element of the biological system replaced by a mathematical equivalent. An artificial neural network can perform tasks that a regular computer cannot, such as image recognition, speech recognition, and decision-making. The disadvantage of this system compared to a programmed system is that it must be trained; in other words, a neural network without training is like a newborn child entering the world. As a result, scientists developed various models of neural networks, each with different abilities and differences.

Artificial intelligence and neural networks are now used in software to emulate the parallel nature of a neural network to a linear system. Voice recognition, character recognition (OCR), image reconstruction, and other applications are the most common. Still, they are also being implemented in hardware, where the linear structure of processors is changed to a neural system, which takes advantage of the parallel nature of neural networks. Neural Processors are the name given to this new technology.

Currently, neural processors are being used in specific applications, such as robotics, where implementation is simple. Still, hybrid systems using microprocessors and neural processors, typically used in servers, have recently been developed.

One might believe that artificial intelligence technology will help society achieve a higher standard of living. Still, we must remember that passing on our natural abilities to machines makes us more dependent on them and that dependency only inhibits our intellectual capacities. To avoid this, we must view these machines as tools for improvement.

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