{"id":154,"date":"2016-11-03T17:37:11","date_gmt":"2016-11-03T23:37:11","guid":{"rendered":"http:\/\/redmonk.com\/rstephens\/?p=154"},"modified":"2016-11-22T13:12:06","modified_gmt":"2016-11-22T20:12:06","slug":"artificial-intelligence-and-the-world-of-watson","status":"publish","type":"post","link":"https:\/\/redmonk.com\/rstephens\/2016\/11\/03\/artificial-intelligence-and-the-world-of-watson\/","title":{"rendered":"Artificial Intelligence and the World of Watson"},"content":{"rendered":"

Artificial intelligence (\u201cAI\u201d) can be a loaded term. It simultaneously carries the unfulfilled promises of AI winters<\/a> past with the dreams of science fiction futures. There is undoubtedly hype, but there is also justified excitement about the underlying technological progress and promise. On top of all these seeming contradictions, there is ambiguity in the language we use to discuss AI; there are a bevy of related terms (e.g. AI, machine learning, deep learning, cognitive computing) that are frequently used inconsistently and\/or incorrectly.<\/p>\n

Frank Chen of Andreessen Horowitz<\/a> views artificial intelligence \u2013 and in particular deep learning \u2013 to be a technological shift that will be \u201cas profound and maybe even bigger<\/a>\u201d than the recent platform shifts to mobile and cloud. Though AI has arguably existed for 60 years<\/a>, it is still early days in terms of using it in production\/commercial capacities. While companies are motivated to put their existing data to better use, many are still grappling with how and when to implement an AI solution.<\/p>\n

\n

The nominees for \u2018Most likely to be suggested as inappropriate solution to a complex problem\u2019 are:<\/p>\n

\ud83c\udfc5Blockchain
\n\ud83c\udfc5Chatbots
\n\ud83c\udfc5Machine learning<\/p>\n

\u2014 Cat Lady (@NatDudley) October 31, 2016<\/a><\/p><\/blockquote>\n