Artificial Intelligence for Business

By Rajendra Akerkar

This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes.

• Language: English
• Publisher: Springer
• Release date: Aug 11, 2018
• ISBN: 9783319974361

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© The Author(s), under exclusive license to Springer International Publishing AG, part of Springer Nature 2019

Rajendra AkerkarArtificial Intelligence for BusinessSpringerBriefs in Businesshttps://doi.org/10.1007/978-3-319-97436-1_1

Introduction to Artificial Intelligence

Rajendra Akerkar¹ 

(1)

Western Norway Research Institute, Sogndal, Norway

Data

Factual, discrete, static and dynamic things and raw observations of the given area of interest are known as data. Information can be generated after systematic processing of such data. Data are often identified as numeric values within the environment. Data can also be observed as the transactional, physical records of an enterprise’s activities, which are considered as the basic building block of any information system. We require processing it before using them. Data can be defined as (Akerkar and Sajja 2010):

Data are symbols that represent properties of objects, events and their environments. They are products of observation. To observe is to sense. The technology of sensing instrumentation is, of course, to be highly developed.

Data are the things given to the analyst, investigator, or problem-solver; they may be numbers, words, sentences, records and assumptions – just anything given, no matter what form and of what origin. This used to be well known to scholars in most fields: some wanted the word data to refer to facts, especially to instrument-readings. Others who deal with hypothesis, for them data are assumptions.

Though data is evidence to something, it need not be always true; however, there is a difficulty in knowing data is true or not. This leads to further processing to generate information and knowledge from available data. For example, the temperature at a particular time on given day is a singular atom of data and treated as a particular fact. There might be several such atoms, and these can be combined in various ways using the standard operations of logic. But, there are also universal statements, such as Every day the maximum temperature is above 30 degrees. However, from logical point of view such universal statements are stronger than atoms or compounds of atoms, and thus it is more difficult to be assured about their truth. Such data are also required to be further filtered to generate necessary true information. Above all, data might be empirical data. It is very hard to assign a truth value to the fictitious non-empirical data.

Information

When data is processed, organized, structured, or presented in a given context so as to make it useful, it is called information. Though, there is information that is not data. Such distinguished information can be considered as processed data which makes decision making simpler. Processing involves an aggregation of data, calculations of data, corrections on data, etc. in such a way that it generates flow of messages. Information has normally got some meaning and purpose. That is data within a context can be considered as information.

One can add value to data in several ways:

Contextualized: tells us the purpose for which the data was gathered

Categorized: tells us the units of analysis or key components of the data

Calculated: tells us if the data was analysed mathematically or statistically

Corrected: tells us if errors have been removed from the data

Condensed: tells us if the data was summarized in a more concise form

Further, information can be processed, accessed, generated, transmitted, stored, sent, distributed, produced and consumed, searched for, used, compressed and duplicated. Information can also be of diverse types with different attributes. It can be sensitive information, qualitative or quantitative information.

Knowledge

Knowledge is considered as human understanding of a subject matter that has been acquired through proper study and experience. Information and data may be related to a group of humans and regarded as collective mass, whereas knowledge is usually based on learning, thinking and proper understanding of the problem area by an individual. Knowledge is derived from information in the equivalent way information is derived from data. It can be considered as the synthesis and integration of human perceptive processes that helps them to draw meaningful conclusions. Knowledge is justified true belief related to human actions and is created from a flow of messages. Knowledge is generally personal, subjective and inherently local – it is found within the heads of employees rather than existing objectively.

Moreover, knowledge can be possessed outside of the human mind and suggested that agents are capable of manipulating beliefs and judgements. He describes knowledge as truths and beliefs, perspectives and concepts, judgments and expectations, methodologies and know-how and is possessed by humans or other agents.

Information is the data that tells about its business and how it functions. An additional step is applied on information to convert it into knowledge, by identifying the three Is in the business as follows:

Impacts: Impact of the business on the target users group and market

Interacts: How the business system interacts with the users and other systems in the environment

Influenced: How the business is influenced by the competitors and market trends

Within the field of knowledge management, two quite distinct and widely accepted types of knowledge exist: tacit and explicit. Tacit knowledge as identified by Polanyi is knowledge that is hard to encode and communicate. It is ephemeral and transitory and cannot be resolved into information or itemized in the manner characteristic of information. Further, tacit knowledge is personal, context-specific and hard to formalize. On the other hand, explicit knowledge is exactly that kind of knowledge that can be encoded and is transmittable in language. It is explicit knowledge that most current knowledge management practices try to, and indeed can, capture, acquire, create, leverage, retain, codify, store, transfer and share.

Data and information are very important aspects of knowledge. It requires suitable processing to generate structured meaningful information to aid decision making and gain expertise for problem solving. That is, it is the level of processing which makes the content meaningful and applicable. By proper processing, we may generate reports which aid decision making, concepts for learning and models for problem solving.

Intelligence

Knowledge of concepts and models lead to higher level of knowledge called wisdom. One needs to apply morals, principles and expertise to gain and utilize wisdom. This takes time and requires a kind of maturity that comes with the age and experience.

The concept of wisdom has been traversed by the ancient Greek philosophers, such as Plato and Aristotle; although it has not been a popular topic of discussion in recent times. There seem to be several different strands to wisdom. A person may have encyclopaedic knowledge of the facts and figures relating to the countries of the world; but that knowledge, of itself, will not make that person wise. Instead, a person becomes wise by applying knowledge to complex problems of an ethical and practical type and looking for potential solutions.

Further enhancement on the wisdom is the intelligence. Intelligence is the aim of an entity to become full and complete artificially intelligent one.

Basic Concepts of Artificial Intelligence

Artificial intelligence (AI) has been existing through years; however, where it can be advanced is a matter of discussion. With the developing technologies, currently there is a huge demand of comprehensive human learning in computational aspects − capable of changing its own behavioural belief. Having the ability to decide, learn and inculcate itself based on the previous events and act upon it very diligently.

AI refers to manifold tools and technologies that can be combined in diverse ways to sense, cognize and perform with the ability to learn from experience and adapt over time, as illustrated in Fig. 1.

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Fig. 1

what is AI?

By and large, intelligence is one’s capabilities to comprehend the objective world and apply knowledge to solve problems. Intelligence of an individual consists of wide-ranging capabilities, such as: capability to perceive and understand objective things, the objective world and oneself; capability to gain experience and acquire knowledge through learning; capability to comprehend knowledge and apply knowledge and experience for problem analysis and problem solving; capabilities of association, reasoning, judgement and decision making; capability of linguistic abstraction and generalization; capabilities of discovery, invention, creativity and innovation; capability to appropriate, promptly and reasonably cope with the complex environments; and capability for predictions of and insights into the development and changes of things.

AI is not a new concept – in fact, much of its theoretical and technological underpinning was advanced over the past 62 years. For the record, AI’s official start is considered the Dartmouth conference in 1956. And to some extent, the Turing test predates even that and offered thoughts on how to recognize an intelligent machine. However, the journey of AI has been quite turbulent. Looking back, there has been substantial progress in almost all areas which were primarily considered to be part of AI. Let us look at some of the stimulating developments in terms of practical significance.

Knowledge-based systems were perhaps the most successful practical branch of AI. There have been several applications deployed at organizations all over the world. Hundreds of tools, commonly labelled expert system shells, were developed. Such systems achieved enough grandeur to become an independent discipline, to the extent of having separate academia courses. Along with the practical successes, the field also contributed to growth of AI itself. The concept of rule-based knowledge representation, emphasis on reasoning with uncertainty, issues of verification of domain knowledge, machine learning in the cover of automatic knowledge acquisition, etc. were some of the areas of academic growth.

Another area of progress has been natural language processing. Reasonable translation systems are available today for use in restricted context, mainly effective if a little human guidance can be provided to the system. Systran is a relevant example, which delivers real-time language solutions for internal collaboration, search, eDiscovery, content management, online customer support and e-Commerce. The field has also contributed to the development of the area of information retrieval. The World Wide Web is one of the major reasons for the interest in this area, with the available information far exceeding limits of human imagination. Without automated analysis and filtering, identifying and retrieving items of interest from this massive mine is challenging task. Semantic Web, content and link analysis of web pages, text mining, extraction of specified information from documents, automatic classification and personalized agents hunting for information of