A Review of Artificial Intelligence Methods for Data Science and Data Analytics: Applications and Research Challenges

Artificial intelligence is a field which requires multidisciplinary expertise where the final goal is to automate all the human activities that presently require human intelligence. The major problem is to develop a method which works exactly the way how a human brain works. The architecture of artificial intelligence must emphasize on evaluation and redesign the nature of design process. Data science is also trending now and analytically deals to solve complex problems. Data is divided into smaller parts and its trends, behaviors are understood. The main problem in data science is to handle large quantities of data. Though there is significant increase in terms of research opportunities few challenges like lack of compute power, people power still remains a big challenge.

I. Introduction

Artificial intelligence (AI) is the intelligence exhibited by machines. Artificial intelligence is a method to simulate human intelligence using a set of algorithms and produce a new machine which can do similar work with human consciousness and also to perform parallel computing. Machine learning is subset of artificial intelligence that gives the path towards designing of computers that are intelligent. Deep learning is a subset in machine learning, used to represent data abstraction though predefined model architectures. Deep learning replicates the working of human brain in data processing and creates patterns, reduces it if possible and produces accurate results. This paper describes the methods of AI, applications, hardware and software resources used and some of the research challenges.