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.
Los Angeles County recorded more than 1,900 new coronavirus cases Friday, another major jump, as a mandatory mask restriction for inside public places takes effect Saturday night. Over the last week, L.A. County has reported an average of more than 1,000 new coronavirus cases a day -- a tally that, though merely a fraction of the sky-high counts seen during previous surges, is still six times as high as what the county was seeing in mid-June. Daily case numbers have jumped: 1,537 new cases were reported Thursday, and 1,902 more were added Friday. COVID-19 hospitalizations also doubled over that same time period, from 223 on June 15 to 462 on Thursday. More than 8,000 coronavirus-positive patients were hospitalized countywide during the darkest days of the winter wave.
All the sessions from Transform 2021 are available on-demand now. "An underlying issue that most enterprise organizations struggle with is that their data is a disaster," noted Anthony Deighton, chief product officer at AI-powered data unification company Tamr. Deighton was moderating a panel at VentureBeat's Transform 2021 event today, which delved into practical and academic perspectives on how companies -- particularly financial institutions -- can use machine learning (ML) to improve the quality and reliability of their data. Deighton was joined by Tamr cofounder Michael Stonebraker, winner of the 2015 Turing award and a renowned computer scientist who specializes in database research; and Jonathan Holman, head of digital transformation at financial services company Santander U.K., a Tamr customer. So what is the problem that Tamr, ultimately, is setting out to solve?
AI-enhanced cybersecurity is a must in 2021 and beyond. Clearly, the industry agrees -- you'll find an endless list of AI security platforms in the marketplace. What do vendors really mean when they use the term "artificial intelligence?" AI can be a fluid term, and sometimes mean different things to different people, and although marketing teams at cyber companies are using this ambiguity to their advantage, too often when it comes to the actual implementation and use of these platforms, the technology and promise falls short of AI in it's true scientific sense. Some artificial intelligence is and will be groundbreaking for the cybersecurity industry.
With the launch of VisaNet AI, we are seeing Visa in this particular case take matters into their own hands and develop products and services that are targeted at improving the services provided by their own clients, who are not able to keep up with the pace of technological advancement. For a long time we have known that data is the new oil, and that companies who wish to stay competitive in today's landscape, need to take aggressive steps into ensuring that their strategy, infrastructure and processes are data-driven. However, within the Payments industry we know that a lot of companies are still struggling to do so. VisaNet AI, which is a set of network services that helps deliver smarter authorization, clearing, and settlement for banks, merchants and consumers, is a great example of how companies should work on improving their core services. For years, we have worked with issuers, acquirers and merchants to drive through that the performance of their authorization is at the core of what payments should be.
As with everything else in Coupa, AI has been thoughtfully applied to areas where it adds real value. One such area is financial fraud. Detecting financial fraud can be challenging, costly, and time-consuming for organizations. However, with Coupa's robust AI-powered fraud detection solution, Spend Guard, we are able to help customers catch fraud and errors in-flight before they are even paid. Within Spend Guard, one of the many checks that our customers have found valuable is in detecting duplicate invoices.
The term Artificial Intelligence could have different meanings in different context, and it could also mean different things to different people. For us at Worldpanel Expert solutions, it means moving from a traditional analytical way of analyzing data to doing more forward or future looking analytics. In this paper, we have explained what AI means for Kantar Worldpanel, why we think it's important and how we leverage it in our work to deliver value for our clients. Theoretically, Artificial Intelligence is defined as a "Field of study that gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959) Alternatively, AI in general and what everybody understands of, is a system that can mimic human intelligence, can perform intelligent tasks which usually we humans are known to perform such as interacting with the environment, problem solving, learning, reasoning etc. Much of the AI advancement in recent times has been around the ability to understand and interpret textual data in the same way we humans do and that is why we see a lot of language-based AI flooding in. This led to the common misconception that AI is magic, it can do everything and anything which doesn't talk or behave like humans is not AI.
Using machine learning algorithms for big data is a logical step for companies looking to maximize the potential of big data. Machine learning systems use data-driven algorithms and statistical models to analyze and find patterns in data. This is different from traditional rules-based approaches that follow explicit instructions. Big data provides the raw material by which machine learning systems can derive insights. Many organizations are now realizing the benefit of combining big data and machine learning.
At many firms, the marketing function is rapidly embracing artificial intelligence. But in order to fully realize the technology's enormous potential, chief marketing officers must understand the various types of applications--and how they might evolve. Classifying AI by its intelligence level (whether it is simple task automation or uses advanced machine learning) and structure (whether it is a stand-alone application or is integrated into larger platforms) can help firms plan which technologies to pursue and when. Companies should take a stepped approach, starting with rule-based, stand-alone applications that help employees make better decisions, and over time deploying more-sophisticated and integrated AI systems in customer-facing situations. Of all a company's functions, marketing has perhaps the most to gain from artificial intelligence.