{"id":444,"date":"2021-01-14T10:10:08","date_gmt":"2021-01-14T10:10:08","guid":{"rendered":"https:\/\/auisys.com\/nlu\/?p=444"},"modified":"2021-06-24T10:39:26","modified_gmt":"2021-06-24T10:39:26","slug":"this-ai-can-explain-itself-really","status":"publish","type":"post","link":"https:\/\/auisys.com\/nlu\/this-ai-can-explain-itself-really\/","title":{"rendered":"This AI Can Explain Itself. Really!"},"content":{"rendered":"\n<p>The success of Deep Learning AI systems has leveraged neural networks to solve difficult problems such as self-driving cars, spotting fraudulent banking transactions, language translation, and more.&nbsp;These AI systems are remarkably good at analyzing huge amounts of data, spotting patterns and learning from them.&nbsp;They use a system of data layers, including hidden ones, to compute results.&nbsp;Hence it get\u2019s a little tricky when such AI needs to explain the logic and reasoning behind a decision.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img loading=\"lazy\" width=\"350\" height=\"225\" src=\"https:\/\/auisys.com\/nlu\/wp-content\/uploads\/2021\/06\/ThisAIcanExolainItself-1.png\" alt=\"\" class=\"wp-image-453\" srcset=\"https:\/\/auisys.com\/nlu\/wp-content\/uploads\/2021\/06\/ThisAIcanExolainItself-1.png 350w, https:\/\/auisys.com\/nlu\/wp-content\/uploads\/2021\/06\/ThisAIcanExolainItself-1-300x193.png 300w\" sizes=\"(max-width: 350px) 85vw, 350px\" \/><\/figure><\/div>\n\n\n\n<p>Consider an industry regulator that wishes to inspect the logic underlying the denial of a financial loan.&nbsp;Similarly, an investigation into the malfunction of an autonomous vehicle will throw up questions around causes.&nbsp;These questions point to the need for Explainable AI (XAI).<\/p>\n\n\n\n<p>What powers robust XAI ?&nbsp;An answer lies in the field of Symbolic AI.&nbsp;When a system converts incoming information to symbols and then performs logic and reasoning, we obtain explainable decisions. This is how human intelligence generally operates.<\/p>\n\n\n\n<p>Consider a system that examines human-generated operational text logs in conjunction with time-series data from equipment \u2013 it then applies reasoning, using in-built business process knowledge.&nbsp;Such a system converts complex textual information into structured form via its Natural Language Understanding (NLU) engine in conjunction with a business domain knowledge base.&nbsp;The system can now understand root causes of why an equipment failed, leveraging both text based and quantitative data.&nbsp;It also allows an analyst to interact with available information using a natural language interface.&nbsp;Importantly, it can explain any output fully and transparently.<\/p>\n\n\n\n<p>XAI is likely to become very relevant as high-stakes AI applications such as complex decisions in finance, engineering, and medicine will require high accuracy and complete explainability. Knowledge-based AI &#8211; which is a form of symbolic AI &#8211; excels in XAI.&nbsp;It is rapidly scalable due to a flexible knowledge base.&nbsp; AUI Systems provides a ready Knowledge-based AI.&nbsp; We term it \u201cFully Explainable\u201d or FXAI \u2013 where each output can be explained and audited.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The success of Deep Learning AI systems has leveraged neural networks to solve difficult problems such as self-driving cars, spotting fraudulent banking transactions, language translation, and more.&nbsp;These AI systems are remarkably good at analyzing huge amounts of data, spotting patterns and learning from them.&nbsp;They use a system of data layers, including hidden ones, to compute &hellip; <a href=\"https:\/\/auisys.com\/nlu\/this-ai-can-explain-itself-really\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;This AI Can Explain Itself. Really!&#8221;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":453,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false},"categories":[7,8,9],"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\/444"}],"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=444"}],"version-history":[{"count":8,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/posts\/444\/revisions"}],"predecessor-version":[{"id":460,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/posts\/444\/revisions\/460"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/media\/453"}],"wp:attachment":[{"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/media?parent=444"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/categories?post=444"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/auisys.com\/nlu\/wp-json\/wp\/v2\/tags?post=444"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}