Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature

Abstract

Key Terms in this Chapter

Machine Learning: Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that allow computers to learn from and make predictions or decisions based on data, without being explicitly programmed. In machine learning, an algorithm is trained on a large dataset and uses that data to make predictions or decisions based on new, unseen data. The goal of machine learning is to build models that can automatically improve their accuracy over time, by continuously learning from the data they process.

Bayesian Networks: Also known as Belief Networks or Bayesian Belief Networks are a type of probabilistic graphical model that represents relationships between variables and the uncertainty surrounding those relationships. The basic building block of a Bayesian network is a node, which represents a variable, and the edges between nodes represent the relationships or dependencies between variables. The probability distribution of each variable is modeled using a set of conditional probabilities, which describe how the variable depends on its parent variables in the network. Bayesian networks can be used to perform tasks such as probability inference, which involves computing the probability of a particular event given certain observations, and decision-making, which involves choosing the best course of action based on uncertain information. They are particularly useful in applications where the relationships between variables are complex and there is a high degree of uncertainty.

Neural Networks: A neural network is a type of artificial intelligence model that is inspired by the structure and function of the human brain. It is a collection of interconnected nodes or “neurons” that process and transmits information, allowing the network to learn from examples and make predictions or decisions based on input data. Neural networks can be trained to perform a variety of tasks, such as image recognition, speech recognition, and natural language processing.

Deep Learning: Deep learning is a subfield of machine learning that focuses on using artificial neural networks with multiple layers (hence “deep”) to learn from and make predictions or decisions based on large amounts of complex and diverse data. Unlike traditional neural networks, deep learning algorithms use multiple layers of interconnected nodes to process and analyze data, allowing them to learn and make decisions at a higher level of abstraction.

Fuzzy Logic: This is a mathematical framework for representing and processing uncertainty and imprecision in information. It was developed as an alternative to classical (or “crisp”) logic, which only deals with binary values of true and false. In fuzzy logic, information is represented by degrees of truth, rather than binary values, allowing for more nuanced and flexible representations of uncertainty. In fuzzy logic, variables can take on values between 0 and 1, representing the degree to which they are true or false. These degrees of truth can be combined using fuzzy rules, which are statements that relate the values of multiple variables to each other. The output of a fuzzy system is a fuzzy set, which represents the degree of membership of the system's output in various possible categories. This output can then be defuzzified, or translated into a crisp value, by using various methods, such as the center of gravity or the mean of maximum.

Genetic Algorithm: This is a type of optimization algorithm that is inspired by the process of natural selection and evolution. It is a search-based algorithm that uses principles of genetics and natural selection to find solutions to optimization problems. In a genetic algorithm, a population of potential solutions is generated, and each solution is evaluated based on a fitness function. The solutions with the highest fitness are used to generate new solutions through the process of selection, crossover, and mutation, in which parts of the high-fitness solutions are combined to create new, potentially better solutions. This process is repeated over several generations until a satisfactory solution is found or a stopping criterion is reached. Genetic algorithms are commonly used in a variety of fields, including engineering, finance, and gaming, to solve complex optimization problems that are difficult to solve with traditional optimization methods.

Natural Language Processing: Is a subfield of artificial intelligence and computer science that focuses on the interaction between computers and humans using natural language. The goal of NLP is to develop algorithms and models that enable computers to understand, interpret, and generate human language. NLP has a wide range of applications, including text classification, sentiment analysis, machine translation, and question-answering.

Support Vector Machine: SVM is a supervised learning algorithm that uses a boundary, called a hyperplane, to separate the data into different classes. The hyperplane is chosen in such a way that it maximizes the margin, or the distance, between the closest data points from each class, called support vectors. SVM can handle non-linearly separable data by transforming it into a higher dimensional space where a linear boundary can be applied. SVM is commonly used for text classification, image recognition, and bioinformatics, among other applications. It is known for its high accuracy, ability to handle large datasets and robustness to outliers.