A rule-based system may be viewed as consisting of three basic components: a set of rules [rule base], a data base [fact base], and an interpreter for the rules. In the simplest design, a rule … can be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.
– from The Origin of Rule-Based Systems in AI. Randall Davis and Jonathan J. King, reprinted as Ch. 2 of Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Bruce G. Buchanan and Edward H. Shortliffe (Eds.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1984.
It is standard practice in managing payments to block potentially fraudulent transactions via a set of rules. These rules can be very effective in mitigating fraud risk, and practitioners in the industry are comfortable with this approach. Quite often these rules are able to mitigate the losses from fraudulent transactions without producing a correspondingly high alarm rate. For example, a fraud team might create rules based on a location and block transactions from risky zip codes. They might also create rules to block transactions from cards used too frequently by blocking any transactions for cards with more than 4 previous transactions in the past 30 minutes.
Commerce has evolved from its many different forms – from in-store purchases and mail order businesses to online market places and omni-channel commerce. Digital downloads and instant fulfillment services like Uber are transforming the way people consume goods and services. Online shopping has slowly eroded the traditional brick and mortar space of shopping malls. Consumers' retail trips in November and December, the two biggest shopping months out of the year, dropped from 35 billion visits in 2010 to 17.3 billion visits in 2013, according to a report from Cushman & Wakefield. In spite of rapid rise and adoption of modern commerce, fraud prevention is still based on archaic methods that are rigid, resource constrained and time consuming.
President Donald Trump cancelled his visit to the United Kingdom -- his first since taking office -- which was scheduled to happen in February this year, multiple reports revealed Thursday. The reason he gave for cancelling his visit was heavily criticized by British politicians. During his visit, he was supposed to inaugurate the new U.S. embassy in London as well as hold talks with U.K. Prime Minister Theresa May on Feb. 26 -27. Trump put the blame of having to cancel his U.K. visit on former President Barack Obama. After Trump's tweet, a number of British politicians celebrated the fact he will not be visiting their country, adding that the reason he gave for cancelling was untrue.
The core of most modern Machine Learning (ML) systems is automated neural networks (ANNs). The training of ANN's requires large data sets. One misconception of those data sets is the idea that "if we get enough data, we can make the system 100% accurate." Yes, that can happen, but it's not what we really want. Many methods can be used to group data into relevant categories.
"Data Models can be the critical link between business definitions and rules, and the technical data systems that support them," said Donna Burbank, Managing Director of Global Data Strategy, while speaking during her presentation Building Actionable Data Governance through Data Models & Metadata at the DATAVERSITY Enterprise Data Governance Online 2017 Conference. She discussed how Data Models can be used to create and maintain a Metadata-driven Data Governance program in an organization. Burbank has over 20 years of experience in Data Management, Metadata Management, and Enterprise Architecture, with experience on the "process and consulting side of things" as well as the "nitty-gritty technical – how you actually get it done and make it actionable" she said. This session focused specifically on "how things like Data Models and Metadata Management can help you do that." "Data Governance is always referred to as the people, process, and policies around data and information, but Burbank recommended starting by clarifying your business goals and objectives, and ensuring they are relevant.
There is so much going on that has potential to improve the way that insurers operate, it's hard to know which opportunities to address, and also, which risks are too important to ignore. At SAS, we are fortunate to have a team of industry experts who understand how to contextualise the coming AI opportunities for insurers. This is my summary of various ideas they have published, to help you cut through the hype…. Our recent SASchat discussed the readiness of the insurance ecosystem for artificial intelligence (AI). There was a strong feeling that the insurance industry could really benefit from AI and machine learning, with use cases including claims fraud prevention, and the idea that AI would improve efficiency across the whole process, from underwriting through to claims.
The core of most modern Machine Learning (ML) systems is automated neural networks (ANNs). The training of ANN's require large data sets. One misconception of those data sets is the idea that "if we get enough data, we can make the system 100% accurate." Yes, that can happen, but it's not what we really want. Many methods can be used to group data into relevant categories.
Technology leaders like Google, Amazon, Microsoft, and Apple have recently built personal assistants that use Artificial Intelligence to deliver customized search results. This trend is now gaining momentum in healthcare where the doctors expect the computer to assist them in treating their patients when in clinic. In the current times, besides prescribing medicines, doctors have to keep themselves updated on the evolving guidelines and drug discoveries. Doctors today have insufficient time to devote for the diagnosis and treatment of their patients. What they need is a system that can improve patient outcomes in a limited time.
An artificial intelligence company has launched this week what it says is the first autonomous AI tool for determining the "trustworthiness" of a story. Called Unpartial and developed by San Jose, California-based Recognant, the tool is an extension for the Chrome browser. CEO Brandon Wirtz told me that other browsers may be supported at some point. Recognant has described the tool as being a detector of "fake news," but it's actually a "trustworthiness" detector. It doesn't check facts or validate the source, but instead uses generated rules to evaluate the internal validity of a story.
Robotic soccer is an ideal task to demonstrate new techniques and explore new problems. Moreover, problems and solutions can easily be communicated because soccer is a well-known game. Our intention in building a robotic soccer team and participating in RoboCup-98 was, first, to demonstrate the usefulness of the self-localization methods we have developed. Second, we wanted to show that playing soccer based on an explicit world model is much more effective than other methods. Third, we intended to explore the problem of building and maintaining a global team world model.