Machine learning is a subfield of computer science (more particularly soft computing and granular computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".

Tremendous data is an extensive term for data sets so significant or complex that customary data planning applications are deficient. Employments of gigantic data consolidate Big Data Analytics in Enterprises, Big Data Trends in Retail and Travel Industry, Current and future circumstance of Big Data Market, Financial parts of Big Data Industry, Big data in clinical and social protection, Big data in Regulated Industries, Big data in Biomedicine, Multimedia and Personal Data Mining

Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers. AI is becoming more and more a part of everyone’s life.

Artificial Intelligence Driving Ambient Intelligence.  Artificial Intelligence  can  be  used  for  a  multitude  of  purposes  ranging  from  how  resources  available  to  the  user   are   deployed,   through   intelligent   control,   to   monitoring resource use.

The promise of the smart grid is round the corner. However research and society cannot wait for the approval of many standards and grid codes, especially when these codes can restrict more the independence of the electricity users from the suppliers. In this sense, the demand side management can be satisfied by using local energy storage and generation systems, thus performing small grids or microgrids. Microgrids should able to locally solve energy problems, hence increase flexibility and flexibility.

 A multi-agent world and give simple affective life to agents in the form of rudimentary emotions and emotion-induced actions. In addition the agents are able to reason about emotion episodes that take place in one another's lives. The implementation includes representations for twenty-four emotion types (based on the work Ortony et al., 1988) and 1400 emotion-induced actions. Agents have rudimentary personalities, including an interpretive component which causes them to construe the world in idiosyncratic ways leading to emotional states, and an expressive component which give agents a unique profile for manifesting their emotions.

In philosophy, ontology is the study of the kinds of things that exist. It is often said that ontologies “carve the world at its joints.” In AI, the term ontology has largely come to mean one of two related things. First of all, ontology is a representation vocabulary, often specialized to some domain or subject matter.

Mathematical Preliminaries & Notation gives some definitions of fundamental mathematical concepts that are used in AI, but are traditionally taught in other courses.

AI applications are widespread and diverse and include medical diagnosis, scheduling factory processes, robots for hazardous environments, game playing, autonomous vehicles in space, natural language translation systems, and tutoring systems.

The field of ethics (or moral philosophy) involves systematizing, defending, and recommending concepts of right and wrong behaviour. Philosophers today usually divide ethical theories into three general subject areas: metaethics, normative ethics, and applied ethics.

Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction.

Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects.

Computational Creativity is a sub-area of Artificial Intelligence research, where we study how to build software which can take on some of the creative responsibility in arts and science projects. This has practical aspects, and we build creative software to generate artefacts such as poems, mathematical theories, board games, video games, abstract and representational art.