AI-Driven Enterprise Search
Leveraging Best of Breed AI, Machine Learning, and Cognitive Services to Make Search Work
There have been many applications of AI to improve internet applications such as personalization, recommendations, discovering information, understanding what users are asking for, and proactively delivering information to internet users.
AI has been applied to solutions like IBM Watson to assist in Drug Discovery, Oncology treatment, and care management. Another example of AI application is how Netflix provides accurate recommendations to its users based upon the sheer volume of viewer history available at its fingertips.
According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increasing from a “sporadic usage” in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011. He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Other cited examples include Microsoft’s development of a Skype system that can automatically translate from one language to another and Facebook’s system that can describe images to blind people. In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes.
Bot capabilities offer a completely new experience for end users looking to interact with or discover organization information. Azure Bot Services, Google DialogFlow and Amazon Lex are promising services providing capabilities in this area.
The concept of natural language communication between end users and software has been dreamt of for decades. The delivery of it is becoming real through capabilities like Microsoft’s Linguistic Analysis API, Amazon Comprehend and Google Natural Language API.
In terms of machine learning concepts, these are best applied within the context of recommendation and relevancy improvements. Analytic datasets captured throughout the search experience can be leveraged to provide accurate content recommendations and automatic modifications to relevancy of results, providing better experiences to users with less administration overhead.
The days of full-text search being the primary method for aligning a user’s query with relevant data are quickly coming to an end. Utilizing AI capabilities to extract concept and meaning from organization data no longer relies on keyword matching, instead aligning user intent with content meaning, regardless of the actual words used on either side.
The medium of video continues to grow at a rapid pace across all industries. In many instances, these videos are simply ignored in terms of search and discovery, given that they are not made up of easy to access text. Through capabilities like Microsoft Video Indexer, Google Cloud Video Intelligence, and Amazon Rekognition Video, the concepts locked away in corporate video content become available and discoverable by all users.
The most impactful data found within many documents is often locked away in image-based charts, pictures, and diagrams. Like video, with the lack of easily identifiable text, this high value content is left aside by traditional search deployments. Amazon Rekogntion Image, Google Cloud Vision, and Microsoft Computer Vision unlock this content for users.