Got a question? Just ask Siri, Alexa or Cortana. Your answer will come immediately \u2014 and chances are it will be the right answer.\nWhile these sorts of interactions may have once seemed like something out of the Star Tek franchise, where people and computers routinely carry on back-and-forth conversations, there\u2019s nothing sci-fi or futuristic about any of this. Today\u2019s digital assistants are entirely real and are enriching our lives in countless ways. For that, we can thank artificial intelligence (AI) systems and natural language processing (NLP) algorithms, along with the high performance computing (HPC) systems that make it all go.\nSo what is NLP? In a few words, natural language processing is a form of AI that allows a computer application to understand human language, either spoken or written. As Dell EMC data scientist Lucas Wilson explains, NLP applications \u201cuse computers to translate languages, convert voice to text and back again, and create human-like conversational agents to help customers deal with issues, questions and concerns.\u201d1\nPutting NLP to workThe use cases for natural language processing are all over the map, from automating customer service and help desk functions to analyzing and translating spoken or written language. Let\u2019s look at a few examples of the ways in which organizations are putting NLP to work streamline processes, improve customer service and gain other business benefits.\nRetail\nIn the retail world, AI-driven chatbots that leverage NLP are now just about everywhere \u2014 and they are multiplying rapidly. A recent study by\u00a0Juniper Research\u00a0found that the global number of successful retail chatbot interactions will reach 22 billion by 2023, up from an estimated 2.6 billion in 2019.2\nFor retailers, chatbots are now one of the keys to automating and streamlining customer interactions. They help shoppers get the information and answers they need quickly and efficiently. As Juniper Research notes, chatbots can help retailers deliver high-quality user experiences in a low-resource way, boosting customer retention and satisfaction, and reducing operating costs.\nHealthcare\nFor healthcare providers, NLP systems can be one of the keys to automating burdensome processes, including the transcription of spoken or written notes from clinical staff members. NLP can also be used for \u201ctext mining,\u201d or searching through documents to quickly find information related to patients and their care, the content of clinical studies and more.\nAs Gartner notes, NLP technology can turn text or audio speech into encoded, structured information that \u201cmay be used simply to classify a document, as in \u2018this report describes a laparoscopic cholecystectomy,\u2019 or it may be used to identify findings, procedures, medications, allergies and participants.\u201d3\nBanking\nChatbots are making widespread inroads into the banking and financial services industry. A study from\u00a0Juniper Research\u00a0found that the operational cost savings from using chatbots in banking will reach $7.3 billion globally by 2023, up from an estimated $209 million in 2019. This represents time saved for banks in 2023 of 862 million hours, equivalent to nearly half a million working years, the firm says.4\n\u201cChatbots in banking allow heavily automated customer service, in a highly scalable way,\u201d notes a Juniper Research author. \u201cThis type of deployment can be crucial in digital transformation, allowing established banks to better compete with challenger banks.\u201d\nThe same NLP-driven technologies can be used to streamline and accelerate internal banking processes. For example, a Dell Technologies article notes that Lloyds Bank in the UK created a chatbot to help staff easily navigate the organization\u2019s vast knowledge base.\nBuilding and running NLP applications\nWhile some NLP applications can require massive amounts of processing power from HPC systems, it doesn\u2019t take a supercomputer to develop or run them. Many off-the-shelf HPC solutions are now available for training and running NLP applications. For example, the new Dell EMC Ready Solution for AI \u2013 Deep Learning with Intel delivers a ready-to-go solution for the development of AI-driven applications, including NLP systems. It provides an optimized solution stack that simplifies the entire workflow, including all the hardware, software and services needed to help organizations get AI solutions up and running quickly.\nThe backend development technologies for NLP applications are also becoming more accessible. That\u2019s the case with the resources made available via the Intel AI Lab. In 2018, the lab introduced an open-source library for NLP developers, called\u00a0NLP Architect. This resource, available through a\u00a0GitHub repository,\u00a0allows users to explore state-of-the-art deep learning topologies and techniques for NLP and natural language understanding (NLU), a closely related application. The NLP Architect provides an ideal platform for research and collaboration.5\nKey takeaways\nNatural language processing is now just about everywhere, and it is helping organizations automate and streamline processes, improve customer service and reduce operational costs. And NLP systems are getting easier to build and deploy, thanks to new ready-to-deploy HPC systems that are optimized for AI applications and to new development resources like the NLP Architect from the Intel AI Lab.\nTo learn more\n\nFor a broader look at NLP systems, see the article \u201cNatural Language Processing Could Be Key to Your Company\u2019s Digital Transformation\u201d by Dell EMC data scientist Lucas Wilson, Ph.D.\nTo explore leading-edge HPC solutions for powering AI-driven applications, visit Dell EMC Ready Solutions for AI.\n\n____________________________\nAdvancing the Frontiers of AI\nDramatic advances in data analytics and high performance computing capabilities have created a foundation for the adoption of AI-driven applications in the enterprise. But these enabling technologies are only part of the AI story. The other part is the rise of smarter algorithms that can glean insights from massive amounts of data. In this series, we explore these building blocks for AI solutions in enterprise environments.____________________________\n\nLucas Wilson, Dell EMC, via CIO.com, \u201cNatural Language Processing Could Be Key to Your Company\u2019s Digital Transformation,\u201d June 5, 2019.\nJuniper Research, \u201cChatbot Interactions in Retail To Reach 22 Billion By 2023, As Ai Offers Compelling New Engagement Solutions,\u201d May 8, 2019.\nGartner, \u201cIT Glossary: Natural Language Processing,\u201d accessed August 24, 2019.\nJuniper Research, \u201cBank Cost Savings Via Chatbots To Reach $7.3 Billion By 2023, As Automated Customer Experience Evolves,\u201d February 20, 2019.\nIntel, \u201cIntroducing NLP Architect by Intel AI Lab,\u201d May 23, 2018.