Commentary,  J Comput Eng Inf Technol Vol: 10 Issue: 1

Short Note Artificial intelligence

Tejo Chandra Vanteddu^*

Department of Microbiology, Andhra University, Vishakhapatnam, India

*Corresponding Author:
Tejo Chandra Vanteddu
Department of Electronics System
Dublin City University, Dublin, Ireland
E-mail: tejochandra@gmail.com

Received: February 2nd, 2021 Accepted: February 16th, 2021 Published: February 23th, 2021

Citation: Vanteddu T, (2021) Short Note Artificial intelligence J Comput Eng Inf Technol 10:2 DOI: 10.37532/jceit.2021.10 (2).253

Abstract

Keywords: Artificial intelligence; human intelligence; Deep learning techniques; Machine learning.

Keywords

Artificial intelligence; human intelligence; Deep learning techniques; Machine learning.

Introduction

Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video. When most people hear the term artificial intelligence, the first thing they usually think of is robots. That's because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.

Machine Learning

Machine learning is the concept that a computer program can learn and adapt to new data without human intervention.

Machine learning is a field of artificial intelligence (AI) that keeps a computer’s built-in algorithms current regardless of changes in the worldwide economy.

Deep Learning

Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.

Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism followed by disappointment and the loss of funding (known as an "AI winter") followed by new approaches, success and renewed funding. After AlphaGo successfully defeated a professional Go player in 2015, artificial intelligence once again attracted widespread global attention. For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals e.g. robotics" or "machine learning the use of particular tools logic or artificial neural networks), or deep philosophical differences. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.