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Wednesday, May 18, 2022

The Role Of Artificial Intelligence In Future Technology

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The Role Of Artificial Intelligence In Future Technology: Artificial Intelligence (AI) is the science and engineering concerned with the computational understanding of intelligent behavior and therefore the creation of intelligent machines.

AI embodies a heterogeneous set of tools, techniques, and algorithms, including neural networks, genetic algorithms, symbolic AI, and deep learning.

These major areas are showing exponential growth and making significant impacts in diverse areas like health care, space, robotics, and military.

With the increasing amount of data, ubiquitous connectivity, high-performance computing, and various algorithms present at our disposal, AI is going to add a new level of efficiency and sophistication to future technologies.

One of the primary goals of AI field is to produce fully autonomous intelligent agents that interact with their environments, find out optimal behaviors, improve over time through trial and error almost like humans.

It has been a long-standing challenge, ranging from robots, which can sense and react to the world around them, to purely software-based agents, which can interact with natural language and multimedia.

Current AI technologies are utilized in driving, aviation, medicine, online advertising, image recognition, and personal assistance.

The recent success of AI has captured the imagination of both the scientific community and the public. An example of this is autonomous cars providing the ability to make intelligent decisions on maneuvers in variable, real-traffic road conditions.

Another example is the AlphaGo and AlphaZero, developed by Google DeepMind, to play the board game Go, and becoming the first machine to beat a professional player.

This has led to both the excitement and fear in many that AI will surpass humans in most of the fields.

The birth and evolution of AI

The start of AI is believed to be made by Alan Turing with his question “CAN MACHINE THINK?”. The Turing test, developed by Turing in 1950, is a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

The test set some requirements to build a truly intelligent machine that requires knowledge representation, natural language, machine learning, automated reasoning, vision, and robotics for the full test.

Since then, the term AI was first introduced by John McCarthy and it was closely associated with the field of “symbolic AI”, which was popular until the end of the 1980s.

In the 1990s, the new concept of “intelligent agent” emerged. An agent is a system that perceives its environment and undertakes actions that maximize its chances of being successful.

To overcome some of the limitations of symbolic AI, subsymbolic methodologies such as neural networks, fuzzy systems, evolutionary computation, and other computational models started gaining popularity, leading to the term “computational intelligence” emerging as a subfield of AI.

Different approaches and methods are being used in AI. Two major methodologies or
beliefs are the top-down and bottom-up methods.

The top-down theorists believe in mimicking the human brain’s behavior with computer programs, whereas the bottom-up theorists believe that the best way to achieve AI is by building electronic replicas similar to the human brain’s complex network of neurons.

Recently, the term AI encompasses the whole conceptualization of a machine that is intelligent in terms of both operational and social consequences. read full pdf here

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