Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by subject-matter expert and retired United States Air Force Colonel Gene Lee - who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise - in a high-fidelity air combat simulator. The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA - a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.

Artificial Intelligence (AI) may be regarded as an attempt to understand the processes of perception and reasoning that underlie successful problem solving and to incorporate the result of this research in effective computer programs. At present, AI is largely a collection of sophisticated programming technique that seek to develop systems that attempt to mimic human intelligence without claiming an understanding of the underlying processes involved. Artificial Intelligence (AI) can offer may advantages over traditional methods, such as statistical analysis, particularly where the data exhibits some form of non-linearity. Some existing application of spatial analysis and modeling techniques includes artificial neural networks and rule-based system fuzzy logic . Neural Network are biologically inspired and it is based on a loose analogy of the presumed working of a brain.

An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development.

Overview: Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the uncertainty in data. It was introduced by Dr. Lotfi Zadeh of UC/Berkeley in the 1960's as a means to model the uncertainty of natural language. Fuzzy logic is useful to processes like manufacturing because of its ability to handle situations that the traditional true/false logic can't adequately deal with. It lets a process specialist describe, in everyday language, how to control actions or make decisions without having to describe the complex behavior. See "Fuzzy Logic and Neural Networks - Practical Tools for Process Management" (PC AI May/June 1994, p. 17) for a clear and concise explanation of Fuzzy Logic.

Classification software: building models to separate 2 or more discrete classes using Multiple methods Decision Tree Rules Neural Bayesian SVM Genetic, Rough Sets, Fuzzy Logic and other approaches Analysis of results, ROC Social Network Analysis, Link Analysis, and Visualization software Text Analysis, Text Mining, and Information Retrieval (IR) Web Analytics and Social Media Analytics software. BI (Business Intelligence), Database and OLAP software Data Transformation, Data Cleaning, Data Cleansing Libraries, Components and Developer Kits for creating embedded data mining applications Web Content Mining, web scraping, screen scraping.

The digital computing world is built on a structure of Boolean logic applied to binary values -- one or zero, yes or no, in or out. But this powerful structure is a gross oversimplification of the real world, where many shades of gray exist between black and white. In everyday life, we use quasimetric notions that are clearly related to numerical concepts or values but lack precision or demarcation. If I'm a server time-stamping thousands of files, digital certificates or transactions, I need very fine distinctions. But if I'm asking a co-worker what time it is, do I really care that it's 11:49:54 a.m.

Presented at Land-Information Systems: Developments for planning the sustainable use of land resources, Hanover 20-23 Nov. 1996 Proceedings to be published by European Commission. A paper presented at the Management Science/Operations Research Working Group Session at the SAF National Convention, Washington, D.C. Bare, B. and Mendoza, G. 1992. "Ecosystem analysis using fuzzy set theory." "Modelling management of agricultural ecosystems using fuzzy set theory: methodological issues." Paper presented at the joint meetings of the Western Agricultural Economics Association and the Canadian Agricultural Economics and Farm Management Society, 1993, Edmonton, Alberta. A rational method for assessing irrigation performance at farm level with the aid of fuzzy set theory.

Well, the lecture looks good, however, I have not understood the concepts of these neural networks and fuzzy logic on data analysis area. If we use the same data to model itself, then the question is what we are actually modeling - these simply turn out to be nothing but simply some form of advanced curve fitting tools. The most important feature would be if these tools can give us satisfactory modeling of some variables such as heat transfer coeffecient that cannot be measured easily from some other easily measurable variables, then only it makes sense. If it is the case of estimation and cross validation where the estimation depends on measured quantities, then modeling can be easily achieved such as using some dimensionless physical numbers and why we need the fuzzy logic or neural network in such cases, it is not clear yet.