If you search YouTube, you can find the historic Jeopardy episode and watch as IBM’s Super Computer “Watson” blows away Brad Rutter, Jeopardy’s all-time biggest money winner, along with Ken Jennings, the show’s record holder for longest championship streak. The formidable players stood no chance against Watson, which ended the game with $35,734 compared to Rutter’s $10,400 and Jennings’ $4,800. Watson parsed the keywords in the Jeopardy’s clues while looking for “related” terms as responses.
The operative word is “related”. Identifying related terms and concepts is something that pure Boolean searches cannot do – after all, Boolean searching is about looking for specific keywords (i.e. those included within the string itself). The ability to identify related terms and concepts is akin to Semantic Search, which seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear to generate more relevant results.
The ability to quickly research and answer trivia questions (or provide questions for the answers, in the case of Jeopardy) is a far cry from having to boil a hiring need (skills, capabilities, and specific responsibilities in specific industries and environments) down to a series of queries to mine flawed and incomplete human capital data (i.e., resumes and social media profiles) in order to return people who have a high probability of not only being qualified for the position, but also interested in the job (i.e. “recruitable”). My short term goal is to use Watson to create the process to get and relate the data into those requests and find the right recruitable people.