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.
Unlike many valuable resources, real-time data is both abundant and growing rapidly. But it also needs to be handled with great care. That was one of the key takeaways from an online workshop produced by Science Business' Data Rules group, which explored what the rapid growth in real-time data means for artificial intelligence (AI). Real-time data is increasingly feeding machine learning systems that then adjust the algorithms they use to make decisions, such as which news item to display on your screen or which product to recommend. "With AI, especially, you want to make sure that the data that you have is consistent, replicable and also valid," noted Chris Atherton, senior research engagement officer at GÉANT, who described how his organisation transmits data captured by the European Space Agency's satellites to researchers across the world.
China on Tuesday evening confirmed it will increase oversight on how local tech companies operate their platforms both locally and overseas through two new sets of rules. The first set of rules, set to be enforced on February 15, is focused on cybersecurity reviews and will require local tech companies with personal information on over 1 million users to undergo a security review before being allowed to list onto overseas stock exchanges. Announced by the Cyberspace Administration of China (CAC), the rules did not specify whether cybersecurity reviews would be required for companies that list in Hong Kong. As part of a cybersecurity review process, the Chinese government can urge tech companies to make organisational changes to fulfil their commitments to the cybersecurity review. The CAC said the new listing requirement was established to address the risk of key infrastructure, data, and personal information being used maliciously by foreign actors.
Machine learning algorithms can reveal fraud patterns much faster and more accurately than humans or traditional rule-based systems. Read this article to understand how exactly banks can benefit from ML-powered solutions in fraud detection. Each year, banking and financial institutions from all over the world lose many billions of dollars because of fraud. Machine learning seems to be the most efficient technology for detecting and preventing fraud in this rapidly evolving sphere. From this article, you'll understand how exactly banking and financial institutions can benefit from integrating ML algorithms. Plus, you'll learn about the shortcomings of traditional fraud detection techniques.
Gartner predicts that by the end of 2024, 75% of enterprises will shift from piloting to operationalizing artificial intelligence (AI), and the vast majority of workloads will end up in the cloud in the long run. For some enterprises that plan to migrate to the cloud, the complexity, magnitude, and length of migrations may be daunting. The speed of different teams and their appetites for new tooling can vary dramatically. An enterprise's data science team may be hungry for adopting the latest cloud technology, while the application development team is focused on running their web applications on premises. Even with a multi-year cloud migration plan, some of the product releases must be built on the cloud in order to meet the enterprise's business outcomes.
You would think something as numerical as income tax law would be similar to mathematical logic, but it is not, Protzenko says, because it is not written with the precision and clarity that would "make it amenable to a very mathematical reading of it." For example, that law does not mention a number may need to be rounded into whole cents. "The law won't tell you what you're supposed to do with rounding numbers and that can lead to ambiguity and a lack of specification of what's supposed to happen," he says. Healthcare law is also very complex. Faisal Khan, senior legal counsel at healthcare law firm Nixon Gwilt Law in Vienna, VA, says, "Software for HIPAA compliance must incorporate algorithms that target and hit on all the top-level statutory requirements and implementing regulations.' To make that happen, Khan says, "There must be a team of compliance-related input as many of the regulations essentially function as guidelines for companies to adhere to." That means a process or ...
Artificial intelligence (AI) techniques are reaching deeper into work environments, not only replacing and enhancing mundane jobs, but also augmenting or otherwise changing those that remain. They are permeating every aspect of business and are driving organizational strategies. In fact, Gartner predicts that by 2025, AI will be the top category driving enterprise infrastructure decisions. Yet even as interest in AI rises, several myths about this technology persist. CIOs must identify and debunk those myths, in order to devise sound strategies--or enhance existing ones--when driving implementation of AI projects.
In any debate, there are always at least two sides. That reasoning also applies to whether or not it is a good idea to use artificial intelligence technology to try stemming the advantages of cybercriminals who are already using AI to improve their success ratio. In an email exchange, I asked Ramprakash Ramamoorthy, director of research at ManageEngine, a division of Zoho Corporation, for his thoughts on the matter. Ramamoorthy is firmly on the affirmative side for using AI to fight cybercrime. He said, "The only way to combat cybercriminals using AI-enhanced attacks is to fight fire with fire and employ AI countermeasures."
Recent advances in Computer Vision and Machine Learning empowered the use of image and positional data in several high-level analyses in Sports Science, such as player action classification, recognition of complex human movements, and tactical analysis of team sports. In the context of sports action analysis, the use of positional data allows new developments and opportunities by taking into account players' positions over time. Exploiting the positional data and its sequence in a systematic way, we proposed a framework that bridges association rule mining and action recognition. The proposed Sports Action Mining (SAM) framework is grounded on the usage of positional data for recognising actions, e.g., dribbling. We hypothesise that different sports actions could be modelled using a sequence of confidence levels computed from previous players' locations.
Artificial intelligence (AI) is ubiquitous and set to be a significant driver of the world's economic activity in the next decade. It's a constellation of many technologies working in tandem to enable machines to sense, comprehend, act and learn with human-like levels of intelligence. Tools like machine learning (e.g., your credit card company sends a text about potentially fraudulent activity) and natural language processing (e.g., your phone helps you with the next likely word in a sentence) are part of the AI landscape. They'll continue to affect everything we do as we collect more data and enhance algorithms for better decision-making. As in other industries, AI will transform every layer of self-storage operation, too, including customer service, tenant access, security, finance, sales, marketing and revenue management (RM).