We recently posted a blog titled, What is Natural Language Understanding (NLU) that explained why NLU, a subset of Natural Language Processing (NLP), is getting more attention as businesses look to conversational interfaces (e.g. Alexa) and AI-powered chatbots to handle everything from customer support to sales to filing insurance claims.
Artificial intelligence provide businesses with novel solutions for a wide variety of problems. AI-powered answers and insights platforms like Attivio help companies draw more relevant and more intelligent insights from their mountains of data by enhancing experiences like enterprise search, customer support, and IT service management (ITSM). Attivio employs natural language processing (NLP) to help users and companies get the most out of these experiences and to make them as user-friendly as possible.
As we’ve explained in many blogs and 5-minute guides, a cognitive search platform should combine AI technologies such as natural language processing, machine learning, and knowledge graphing to deliver a contextualized search and discovery experience without compromising security. Those technologies can turn ordinary search into something much more powerful and transformative for any organization. But in the hands of life sciences companies, it can help deliver drugs and other therapies that lessen suffering and save lives.
Today’s business users don’t search for documents that may have the information they seek buried within, instead they ask their systems for answers. This shift in attitude is a key driver behind the move to cognitive search.
Any large enterprise is packed with disparate sources of data coming from any of a variety of different systems. Cognitive search is about creating connections between this data so that employees can get answers quickly, so they spend more time on core activities, and they make better informed decisions.
From a business perspective, this means creating experiences that match how a user interacts with information.
These days, there’s pretty much a trade show for everything — and predictive analytics is no exception. The most recent stateside edition of Predictive Analytics World™ took place in Chicago in June of this year. And predictive maintenance occupied a prominent place on the agenda. One presentation concerned failure and fault detection for Rolls Royce.
There was a time when predictive maintenance encompassed only the analysis of structured data. In 2013, a blogger on the data science website Simafore suggested four ways predictive analytics could improve equipment maintenance: trend analysis, pattern recognition, critical range and limits, and statistical process analysis. Conspicuously absent in the discussion of the tools, techniques, and data types that supported predictive analytics was unstructured information and text analytics.
Remember the frustrations of key word search? If you need a refresher, just check out If Google Was a Guy. It’s funny—in a way that makes you kind of squirm in your seat. Of course, in the early days of search engines, power searchers used Boolean query operators. We have George Boole, 18th-century English mathematician, to thank for that.
In fact, there are still Boolean purists out there who take Google, Bing and other modern search engines to task for no longer fully supporting Boolean queries. Nevertheless, natural language processing (NLP) evolved pretty much to relieve search users from the burden of having to memorize Boolean query operators and how to use them.