{"id":206,"date":"2020-07-20T18:27:00","date_gmt":"2020-07-20T18:27:00","guid":{"rendered":"https:\/\/auisys.com\/nlu\/?p=206"},"modified":"2021-06-16T16:14:53","modified_gmt":"2021-06-16T16:14:53","slug":"machines-that-understand-have-arrived-but-theres-a-catch-2","status":"publish","type":"post","link":"https:\/\/auisys.com\/nlu\/machines-that-understand-have-arrived-but-theres-a-catch-2\/","title":{"rendered":"Machines that Understand have arrived. But there\u2019s a catch."},"content":{"rendered":"\n

Artificial Intelligence and Machine Learning (ML) are terms that abound in this age of digital transformation. ML is based on statistical pattern matching \u2013 it\u2019s great for finding you a song amongst thousands or identifying an anomaly within a document – after being trained on a large data set of similar documents. Does this system understand the meaning and context of the language and information in the documents? No, and we agree with Melanie Mitchell<\/a> on this topic, but it\u2019s effective enough to solve some practical problems. <\/p>\n\n\n\n

At the other end of the AI spectrum is AGI (Artificial General Intelligence), of the kind embodied by the character \u201cData\u201d<\/a> of Star Trek fame. This android robot functioned at mental (and physical) capacities close to human \u2013 with forgivable challenges in understanding puns, jokes and complex human emotions. Achieving this level of AI has some time to go, as some experts opine.<\/a><\/p>\n\n\n\n

In the meantime, is it possible for AI to deliver results beyond the pattern matching ML or of the Deep Learning kind? An AI that truly understands context and meaning in language? And can apply logic, reasoning, and math to information? And thereby can deliver value as a digital partner to humans \u2013 by making contextual sense of complex notes and transcripts to extract embedded insights? Or by truly understanding contact center conversations to identify risk and issues that may be escalated and lead to financial damages?<\/p>\n\n\n\n

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Yes, but there\u2019s a catch. Machines can truly understand and extract meaningful information from complex language \u2013 when they mimic human cognition. We know that humans derive context and meaning from what they read or hear by referring to their very own database \u2013 the knowledge existent in their brains!<\/p>\n\n\n\n

We\u2019ve created such an Understanding machine at AUI Systems. With its own brain \u2013 a semantic knowledge base. One that\u2019s flexible, transparent and scalable. A teachable brain. Not data-driven, but knowledge-based.<\/p>\n\n\n\n

The result is true understanding of complex earnings call transcripts, contact center conversations, and myriad other knowledge that can be taught to this \u201cbrain\u201d and is then accessible via natural language interaction. So, while the Star Trek android will become a reality one day, practical industry problems beyond the reach of machine learning are being solved by our Understanding system. We\u2019ll be sharing more in subsequent articles here and on social media. <\/p>\n","protected":false},"excerpt":{"rendered":"

Current AI approaches such as Machine Learning and its sub field Deep Learning are delivering rapid progress in computer vision, language translation and more. But there\u2019s a stumbling block in the nature of understanding meaning and context within natural language-based information. AUI\u2019s Understanding system overcomes this hurdle using a knowledge-based approach.<\/p>\n","protected":false},"author":2,"featured_media":303,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false},"categories":[7,8],"tags":[],"authors":[{"term_id":16,"user_id":2,"is_guest":0,"slug":"harpal","display_name":"Harpal Parmar"}],"_links":{"self":[{"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/posts\/206"}],"collection":[{"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/comments?post=206"}],"version-history":[{"count":9,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/posts\/206\/revisions"}],"predecessor-version":[{"id":308,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/posts\/206\/revisions\/308"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/media\/303"}],"wp:attachment":[{"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/media?parent=206"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/categories?post=206"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/tags?post=206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}