MAKING SEARCH INTELLIGENT WITH AI

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Flora Patterson
5 years ago
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2 A 5-Minute Guide MAKING SEARCH INTELLIGENT WITH AI AI: These Days, It s Just About Everywhere AI artificial intelligence that lets machines act and react like humans - has been around for a good long while now, but for most of us, until recent times it s been behind the scenes. As AI has become more intelligent, however, it s become more pervasive. It s evolving quickly and is already helping humans perform many tasks better and much faster than previously possible. And today some form of AI is finding its way into just about everything. Everyday life examples abound: Consumer recommendation engines like those used by Netflix and Amazon Personal assistants like Siri and Alexa Self-driving cars Smart-home devices Improved diagnostics in medicine Some of these examples fall into the not very sophisticated category. It doesn t take a lot of intelligence to make a film recommendation based on the historic choices you ve made. (It doesn t take all that much brainpower to not recommend Deadpool to someone who has only ever watched Rom-Coms.) Other applications are more complex. Voice-based personal assistants need to understand accents and interpret free-form requests. What these applications have in common is that they rely on AI to interpret questions and find the best answers. They re all about the search. So it s no surprise that AI is inextricably wound in with enterprise search, which makes content across an enterprise databases, intranets, document repositories - intelligently searchable. 2

3 How Does Enterprise Search Use AI? Just as AI has been around for a long time, so has enterprise search, dating back at least to the early 1950 s when an IBM research engineer first proposed the creation of an electronic information searching system. Fast forward to the waning days of the 20th century, when Google s founders decided they wanted to organize and search all the information on the worldwide web. As Google grew, it invested heavily in AI. And as Google grew (alongside other web search engines like Bing and Yahoo) and AI became more sophisticated, expectations of the caliber of results that search could produce rose. And these expectations extended to searching across an enterprise s internal systems, where AI-powered search is increasingly deployed. The technologies that AI relies on AI-powered search relies heavily on three AI-related technologies: natural language processing, text analytics, and machine learning. Before we define each of these technologies, let s look at an example that illustrates why these technologies matter. First, we need to keep in mind that not all queries are simple and straightforward. If a user types in capital of the United States, there s no ambiguity. And only one answer. But delivering the right answer is more difficult when there is ambiguity. Consider a frequently cited example. The answer to the question where can I see the bulls? depends on whether the person asking is someone looking for livestock, and a basketball fan who wants to know what local sports bar will be showing the Chicago Bulls game. The right answer depends on context and prior conversations/searches. A user who in the past has searched for cattle feed and State Fair more than likely wants to actually see some animals. A user who had looked for Chicago restaurants and who had asked whether Michael Jordan was better than Lebron James is most likely interested in basketball games. Now this may not be a 100% accurate inference. It s conceivable that a cattle rancher visiting Chicago wants to watch a game. Even with sophisticated AI technology, search remains a guessing game. But as AI advances, the guesses get better and better. 3

4 AI advances: natural language processing, text analytics, and machine learning The bulls vs. Bulls example, while simple, demonstrates how valuable natural language processing (NLP) and text analytics are in today s machine learning (ML) -based enterprise search. When a query isn t absolutely clear, search uses context and history to decide what a user means, and NLP and text analytics help search analytics puts queries into a relevant context. ML is what enables the computer to learn to get smarter automatically, without having to be explicitly programmed. Natural Language Processing (NLP) That means having connectors to scores of structured and unstructured data types in traditional relational databases and modern data frameworks such as Hadoop, NoSQL databases such as MongoDB and MarkLogic, columnar databases such as Apache Parquet, and data serialization engines like Apache AVRO. The platform should also include an SDK that allows developers to build connectors to new or proprietary data types. Many customer service organizations allow their customers to enter free-form support inquiries. NLP can be used to translate these queries so that they can be directed to the correct content source or, if needed, to the right support personnel. Text Analytics If NLP handles the front end of search, taking in the human conversation and translating it so that the computer understands it, text analytics classifies, groups, clusters, and mines that information for concepts and patterns. Using a variety of techniques, text analytics categorizes the underlying content and is able to come up with the relevant answers. Text analytics can be used by enterprise search to comb through corporate communications and documents to identify compliance violations, such as exposing customers personally identifiable information or ing company-proprietary information. Machine Learning (ML) ML is what makes the intelligence in AI more intelligent. ML programs keep track of what people search for, what they click on, and whether they get the information they need. As data mounts up, a ML program is able to reverse engineer an algorithm based on the accumulating data. 4

5 Maintenance of today s increasingly complex machinery and equipment uses both sensorbased structured data and the unstructured observations of technicians. ML learns over time those issues that tend to be related, and can be used to offer preventive maintenance, fending off a costly repair problem before it occurs. AI-powered enterprise search is making customer services organizations more productive and customers more satisfied. It s helping organizations avoid risk and comply with government and industry regulations. In a world increasing reliant on quick access to relevant content, it s giving employees across your organization get the information they need to improve their job performance. AI is able accomplish all of this through powerful technologies like natural language processing, text analytics, and machine learning. Want to learn more about these key technologies and how they re used in today s enterprise, check out our 5-Minute Guides Attivio is the leading cognitive search & insight platform company. Our mission is to put search at the core of every enterprise. By easily integrating every data source into one always-learning platform, Attivio enables Fortune 500 enterprises to answer the most complex questions, by providing immediate insight across all of their information. Our AI-powered solutions drive innovation, improve operational efficiencies, and transform business outcomes at scale, and feature industry-leading capabilities including natural language processing, machine learning, analytics, and knowledge graphing. For more information, please visit 5

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