{"id":33190,"date":"2020-03-02T03:54:00","date_gmt":"2020-03-02T08:54:00","guid":{"rendered":"http:\/\/blog.cybercon1.com\/?p=33190"},"modified":"2020-03-02T03:54:00","modified_gmt":"2020-03-02T08:54:00","slug":"artificial-intelligence-ai-vs-natural-language-processing","status":"publish","type":"post","link":"https:\/\/blog.cyberconservices.com\/index.php\/2020\/03\/02\/artificial-intelligence-ai-vs-natural-language-processing\/","title":{"rendered":"Artificial intelligence (AI) vs. natural language processing"},"content":{"rendered":"\n

By <\/strong>Stephanie Overby<\/a> <\/strong>– How often do you now casually converse with a black (or white) box of some sort? Natural language processing (NLP)<\/a> has become an integral part of our daily lives: Whether we\u2019re asking our smartphone for directions or engaging with Alexa or Google, NLP and its sub-categories are hard at work behind the scenes, translating our voice or text input and (hopefully) providing an appropriate voice or text output.<\/p>\n\n\n\n

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But what is NLP, and how does it relate to artificial intelligence (AI)<\/a> in general? The distinctions are important, as NLP has just as much, if not more, value in the enterprise as it has in our personal lives.<\/p>\n\n\n\n

Let\u2019s start with AI, the broader category under which NLP and a number of other flavors of machine-based intelligence<\/a> reside. \u201cAI is the use of intricate logic or advanced analytical methods to perform simple tasks at greater scale in ways that mean we can do more at large scale with the workers we have, allowing them to focus on what humans are best at, like handling complex exceptions or demonstrating sympathy,\u201d says Whit Andrews, vice president and distinguished analyst with Gartner<\/a>. Read On:<\/a> <\/p>\n\n\n\n