ISSN: 2229-371X

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Modern Evolution of artificial intelligence and machine learning

Pearl Gross*

Department of Computer Science, Florida State University, Tallahassee, USA

*Corresponding Author:
Pearl Gross
Department of Computer Science,
Florida State University,

Received: 05-Sep-2022, Manuscript No. GRCS-22-76647; Editor assigned: 07-Sep-2022, Pre QC No. GRCS-22-76647 (PQ); Reviewed: 21-Sep-2022, QC No. GRCS-22-76647; Revised: 26-Sep-2022, Manuscript No. GRCS-22-76647 (A); Published: 05-Oct-2022, DOI: 10.4172/2229-371X.13.4.003.

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About the Study

The study of computing, automation, and information is known as computer science. Algorithms, computation theory, information theory, and automation are examples of theoretical fields in computer science (including the design and implementation of hardware and software). Computer science is typically thought of as a field of academic study apart from computer programming.

Data structures and algorithms are essential to computer science. The theory of computation focuses on generic classes of issues that can be solved using abstract models of computation. The creation of images is addressed by computational geometry and computer graphics. Database theory is concerned with the administration of data repositories, while programming language theory addresses various ways to express computer operations. Software engineering focuses on the principles and design that go into creating software, while human-computer interaction studies the interfaces through which humans and computers communicate.

Operating systems, networks, and embedded systems are a few examples of fields that focus into the principles and design of complex systems. The design of computer hardware and other devices that use computers is referred to as computer architecture. The goal of artificial intelligence and machine learning is to replicate human and animal problem-solving, decision-making, environmental adaption, planning, and learning processes. Computer vision, a branch of artificial intelligence, tries to comprehend and process image and video data, whereas natural language processing focuses on comprehending and processing textual and linguistic data.

Artificial Intelligence techniques support perception, reasoning, learning, and problem-solving, which helps cognitive computing. Reinforcement learning, one of the Artificial Intelligence techniques, aids in context analysis by learning from prior experience and applying/responding with the suggestion/prediction and logic of the situation. AI can have a variety of intelligences, including linguistic, musical, logical, and spatial. Different Artificial Intelligence techniques and algorithms are suggested for resolving issues in each domain and circumstance. Driving a car, playing video games, using chatbots, and many other current Artificial Intelligence applications assist in simulating cognition.

A subset of artificial intelligence is machine learning. It is described as a collection of methods for instructing computers to recognise patterns in data that may be used for forecasting and future prediction, as well as a quality control check for performance optimization. Computers now have the ability to learn without being explicitly programmed due to artificial intelligence. Finding those hidden patterns might significantly increase an organization's shareholder value because some patterns might be concealed. Please take note that although machine learning focuses on carrying out certain tasks, data mining works with searching specific information.

Also take notice that multi-layer neural networks are employed in deep learning, a subset of machine learning, for a variety of applications such as but not limited to image and facial recognition, time series forecasting, autonomous vehicles, language translation, etc. Convolution neural network (CNN) and recurrent neural network are two examples of deep learning techniques (RNN).