Marketing with Artificial Intelligence and Predicting Consumer Choice

Any company's ability to predict consumer behavior is critical to its success. To attain this goal in artificial intelligence marketing, a variety of predictive analytic tools are available, each with its own set of pros and limitations. This study project aims to bring these very varied methodologies together and demonstrate their strengths, shortcomings, and ideal uses. It serves as a link between the person who must use or acquire these problem-solving techniques and the community of professionals who perform the analysis. It's also a useful and easy-to-understand reference to the numerous astounding improvements that have recently been made in this intriguing sector

Analysis and synthesis of abstract data types through generalization from examples

The discovery of general patterns of behavior from a set of input/output examples can be a useful technique in the automated analysis and synthesis of software systems. These generalized descriptions of the behavior form a set of assertions which can be used for validation, program synthesis, program testing and run-time monitoring. Describing the behavior is characterized as a learning process in which general patterns can be easily characterized. The learning algorithm must choose a transform function and define a subset of the transform space which is related to equivalence classes of behavior in the original domain. An algorithm for analyzing the behavior of abstract data types is presented and several examples are given. The use of the analysis for purposes of program synthesis is also discussed

Artificial intelligence implementations in company management, e-commerce, marketing, and finance

AI has been used in the e-commerce and financial firms to improve customer experience, supply chain management, operational efficiency, and mate size, with the primary goal of developing standard, consistent product quality control strategies and the search for new ways to reach and serve customers at a low cost. Two of the most widely utilized AI techniques are machine learning and deep learning. These models are used by individuals, organizations, and government agencies to predict and learn from data. Machine learning algorithms for the food industry's complexity and variety of data are currently being developed. Machine learning and artificial intelligence uses in e-commerce, company management, and finance are discussed in this article. Some of the most common applications are sales growth, profit maximization, sales forecasting, inventory control, security, fraud prevention, and portfolio management

Execution or OPS5 Production Systems on a Massively Parallel Machine

In recent years, the development of expert systems implemented by rule-based production systems has emerged as one of the dominant paradigms in the field of artificial intelligence. While production systems offer important advantages in large-scale AI applications, their use in such applications is typically very costly in execution time. In this paper, we describe an algorithm for executing production systems expressed in the OPS5 language on a massively parallel multiple-SIMD machine called NON-VON, portions of which are currently under construction at Columbia University. The algorithm, a parallel adaptation of Forgy's Rete Match, has been implemented and tested on an instruction-level simulator. We present a detailed performance analysis, based on the implemented code, for the averaged characteristics of six production systems having an average of 910 inference rules each. The analysis predicts an execution rate of more than 850 production firings per second using hardware comparable in cost to a VAX 11/780. By way of comparison, a LISP-based OPS5 interpreter running on a VAX 11/780 typically fires 1 to 5 rules per second, while a Bliss-based interpreter executes 5 to 12 rules per second

A Knowledge-Based Expert Systems Primer and Catalog

For more than 20 years, artificial intelligence techniques have been applied to the development of computer programs that solve difficult problems. Although several expert systems are well known, it is all too easy to circumscribe the field based on these few examples. The purpose of this paper is to present the fundamentals of the field (the Primer), and to give a broad overview via concise descriptions of many rule-based expert systems and knowledge engineering frameworks (the Catalog)

The role of experience in common sense and expert problem solving

Issued as Progress reports [nos. 1-5], Reports [nos. 1-6], and Final report, Project no. G-36-617 (includes Projects nos. GIT-ICS-87/26, GIT-ICS-85/19, and GIT-ICS-85/18