Artificial Intelligence and Machine Learning Algorithms

Artificial Intelligence and Machine Learning Algorithms

Amit Kumar Tyagi (School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India) and Poonam Chahal (MRIIRS, Faridabad, India)
DOI: 10.4018/978-1-7998-0182-5.ch008

Abstract

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.
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Introduction About Artificial Intelligence& Machine Learning

Computer Vision is a subdivision of computer science which is integrated with the usual mining, analysis and consideration of constructive information. In simple words, computer vision means “How machines can/ a machine sees/ solves problems without a human-being”. In the past decade, this area is too popular and has still attracted several research communities to develop machines better than human being (in terms of work-efficiency, thinking-level or solving problems). For example, Sophia is a recent and enhanced robot which is being developed by the Hong Kong based company Hanson Robotics. It is the first robot to come to get the Saudi Arabia citizenship in 2016. So, it can be said that the computer vision domain is the becoming the upcoming field of research that can solve various problems related to virtualization. The computer vision has been expanding and emerging with the new and advanced technologies or concepts (like Blockchain, Internet of Everything, etc.) and applications that utilize different computer vision techniques. Among all existing technologies (in recent years), over a hundred applications/ many organizations have moved to the practice and execution of Artificial Intelligence techniques.

Machine Learning techniques required in their business/ to give boost to the aim of computer vision. Hence, to fulfil the vision of smart worlds/ requirements, artificial intelligence, and machine learning allows tools/ applications to become more accurate (in terms of values) in predicting results (without being explicitly programmed). For artificial intelligence algorithms, several inferences, rules and logic that were used in the systems which were created using traditional techniques of Artificial Intelligence are not meeting the today’s requirement of the changing world. In divergence, systems that focus on the analysis and detection the patterns that are existing in dataset for classification, clustering, regression, are becoming the overriding system of AI. In addition to the existing mechanisms, the domain of AI can be further taken into the form of three main groups like Artificial Slight intellect, Artificial Overall Intelligence, and Artificial Super Intelligence. On the other way round there are numerous categories of existing techniques of Machine Learning (ML) algorithms used in fulfilling the objective of computer vision like supervised (regression, decision tree, random forest, classification) and unsupervised (Clustering, Association Analysis, Hidden Markov Model (HMM), etc.) and semi-supervised. In simple words, computer vision is the science and technology of machines that a machine sees (without a human-being). Computer vision is an exploration extent that comprises numerous methods to approach several graphic problems. In recent years, over a hundred applications/ many organizations have been replaced by Artificial Intelligence, Deep Learning and other Machine Learning techniques to give boost to the aim of computer vision. Hence, to fulfil the vision of smart worlds/ requirements, artificial intelligence, and machine learning allows tools/ applications to become more accurate (in terms of values) in predicting results (without being explicitly programmed).

Key Terms in this Chapter

Machine Learning: Machine learning (ML) is the branch of computer science that comes under the umbrella of Artificial Intelligence. ML deals with the learning of machines to perform various tasks that can be done better than human beings.

Artificial Intelligence: Artificial intelligence (AI) deals with the creating of machines with the mind. The creation of machines that can work better than human.

Data Mining: In general terms, data mining is a process of finding several new patterns in a huge collection of data sets using numerous techniques like classification, clustering, regression, etc. to predict future trends.

Reinforcement Learning: This area of deep learning includes methods which iterates over various steps in a process to get the desired results. Steps that yield desirable outcomes are content and steps that yield undesired outcomes are reprimanded until the algorithm is able to learn the given optimal process. In unassuming terms, learning is finished on its own or effort on feedback or content-based learning.

Computer Vision: AI is used for dissimilar applications as vehicle plate identification on which number is written and also in facial recognition.

Information Retrieval: ML and DL learning techniques are used in applications like searching by search engines, text matching and search by finding similarity, and image filtering.

Neural Networks: It includes machine learning with deep learning methods that utilizes huge amounts of training data to detect association among various variables.

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