"Today's expert systems deal with domains of narrow specialization. For expert systems to perform competently over a broad range of tasks, they will have to be given very much more knowledge. ... The next generation of expert systems ... will require large knowledge bases. How will we get them?"
– Edward Feigenbaum, Pamela McCorduck, H. Penny Nii, from The Rise of the Expert Company. New York: Times Books, 1988.
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Association Rules find all sets of items (itemsets) that have support greater than the minimum support and then using the large itemsets to generate the desired rules that have confidence greater than the minimum confidence. The lift of a rule is the ratio of the observed support to that expected if X and Y were independent. A typical and widely used example of association rules application is market basket analysis.
Many of us have had the feeling that technology, which continues to change at an ever-dizzying pace, may be leaving us behind. That was embodied this past week during a Congressional hearing, nominally convened to investigate antitrust concerns of four big tech titans: Amazon, Apple, Facebook and Google. While the five-and-a-half-hour inquiry touched on a range topics from pesky spam filters and search results to how companies approached acquisitions, the House Judiciary subcommittee hearing laid one thing bare: A sizable disconnect appears to exist between the technology Americans are using and depending on in their daily lives and the knowledge base of people with the power and responsibility to decide its future and regulation. "Consumers and investors walk away feeling like a lot of these lawmakers don't really understand the business models to an extent that they could then navigate them and put laws in place that will dictate the future of where they go," said Daniel Ives, an analyst with Wedbush Securities. The antitrust subcommittee hearing had been convened to look into the tech giants' market dominance.
The global Artificial Intelligence company Expert System announced the release of the expert.ai NL API, the cloud-based Natural Language API that enables data scientists, computational linguists, knowledge engineers and developers to easily embed advanced Natural Language Understanding and Natural Language Processing capabilities (NLU / NLP) into their applications. This release is the first step in executing on the company's strategy to become the global platform of reference for AI-based Natural Language problem solving. The growing need for accessible and accurate AI-based NLU / NLP applications in the enterprise places increased demand on the developer ecosystem to bring speed, scale and precision to linguistic analysis. According to Gartner, "during recent years, advances in the application of machine learning (including neural networks) and knowledge graphs to natural language processing have enabled machine-based attribution that diminishes the need for human oversight. Application of the technology is broadening as well as deepening -- across industries and functional domains, and into use cases -- pushing this innovation from many years in the Tough of Disillusionment toward the Slope of Enlightenment."
Expert System's Cogito is the only Natural Language Understanding AI technology that provides a human-like understanding of the meaning of each word in a text. Cogito leverages the deepest text analysis, starting from linguistics (morphological, grammatical and syntactical analysis) to semantics, including word disambiguation and an embedded, domain-independent and pre-trained linguistic model (Knowledge Graph). This translates into the fastest, most accurate and cost effective implementation of AI in the enterprise.
Global artificial intelligence (AI) company, Expert System has announced that the French National Institute of Health and Medical Research, Inserm, will implement its Clinical Research Navigator (CRN) tool and make it available to 100 of its researchers for the next six months. This move will enable researchers to identify key clinical trials, sponsoring research facilities, lead researchers and related work, and even map networks of collaborators and key players. With CRN provided from Expert System, Inserm is able to provide researchers with unlimited access to over 100 million documents and reference information on 12 million clinical studies. One of the core functions of Expert System's CRN platform is to intelligently research and analyse content based on concepts and not just keywords. Through this centralised platform, researchers will be able to discover insights to drive their research by semantically revealing hidden connections across various information sources.
New York State Troopers PBA President Thomas Mungeer says Mayor de Blasio's new laws are putting officers at risk. The president of a union representing New York State troopers said Friday that New York City's restrictions on police officers are setting the men and women on the force up for failure. "By raising the bar and almost making it impossible for my members to safely arrest, we've had enough. I want them out," New York State Troopers PBA President Thomas Mungeer told "Fox & Friends." "What has me alarmed is that troopers that are trained in certain tactics to arrest violent people can now be arrested for using those tactics within the five counties of New York City. Those tactics are still legal in the other 57 counties that make up New York state," Mungeer said.
Today, we are bombarded by messages about the ways in which artificial intelligence (AI) is changing our world and its future promises and perils. But today's AI, called machine learning, is very different from much of AI in the past. From the 1970s until the 1990s, a very different approach, called "expert systems," appeared poised to radically change society in many of the same ways that today's machine learning seems. Expert systems seek to encode into software systems the experience and understanding of the finest human specialists in everything from diagnosing an infectious disease to identifying the sonar fingerprint of enemy submarines, and then have these systems suggest reasoned decisions and conclusions in new, real-world cases. Today, many of these expert systems are commonplace in everything from systems for maintenance and repair, to automated customer support systems of various sorts.
Expert Systems: Expert systems are the most used Artificial Intelligence tools. The expert system is software used in the activity areas in some applications to finding answers to questions presented by a user or another software. It can be used directly to support decisions in areas such as medical diagnostics, finance, or cyberspace. There are a variety of specialist systems for solutions to problems, from small technical diagnostic systems to complex, very large and sophisticated hybrid systems. Conceptually, an expert system includes a database of expert knowledge about a particular application area.
The screen shows four types of COVID-19 related entities, virus (blue), cell (pink), gene or genome (green), and disease or syndrome (red), and their relationships. All entities are Unified Medical Language System (UMLS) compatible for convenient knowledge sharing. The systems support 75 types of UMLS entities. Researchers from Florida Atlantic University's College of Engineering and Computer Science, in collaboration with FAU's Schmidt College of Medicine, have received a one-year, $90,000 National Science Foundation (NSF) RAPID project grant to conduct research using social networks and machine learning, facilitated by molecular genetics and viral infection, for COVID-19 modeling and risk evaluation. The project will create a web-based COVID-19 knowledge base, as well as a risk evaluation tool for individuals to assess their infection risk in a dynamic environment.
A major strength of frame-based knowledge representation languages is their ability to provide the knowledge base designer with a concise and intuitively appealing means expression. The claim of intuitive appeal is based on the observation that the object -centered style of description provided by these languages often closely matches a designer's understanding of the domain being modeled and therefore lessens the burden of reformulation involved in developing a formal description. To be effective as a knowledge base development tool, a language needs to be supported by an implementation that facilitates creating, browsing, debugging, and editing the descriptions in the knowledge base. We have focused on providing such support in a SmallTalk (Ingalls, 1978) implementation of the KL-ONE knowledge representation language (Brachman, 1978), called KloneTalk, that has been in use by several projects for over a year at Xerox PARC. In this note, we describe those features of KloneTalk's displaybased interface that have made it an effective knowledge base development tool, including the use of constraints to automatically determine descriptions of newly created data base items.