Watson is not your average desktop computer. It is comprised of 10 racks filled with IBM POWER 750 servers that are able to perform 80 trillion operations per second on 2,880 processor cores. With its 15 terabytes of memory, Watson is able to scan two million pages of content in less than three seconds. Special software created by a team of 25 developers over four years is not only able to extract facts but is also aware of inferences and context. The software can distinguish between "bat" as in baseball and "bat" as in Dracula, as it uses Natural Language Processing (NLP) to extract facts from original documents.
Anyone who watches Jeopardy has an appreciation for the cryptic answers that are often presented. A contestant must construct a valid question that produces the answer before any of his opponents. Watson's NLP is combined with IBM's DeepQA (Deep Question and Answer) technology to finally make it possible for computers to compete. The general goals for this technology are to retrieve natural language text from multiple sources such as technical reports, novels, dictionaries, encyclopedias, etc; understand, synthesize, integrate and rapidly reason over the extracted knowledge; and to deliver a meaningful response in natural language.
But the technology in Watson has far reaching application beyond challenging game show contestants. It has potential in customer eelationship management, regulatory compliance, contact centers, help desks, medicine and numerous other fields. The critical factor is its ability to work with natural language. Using NLP, it can tell you the best widget on the market by searching and compiling all the customer reviews on the internet. It can review all the weather related news reports to determine how different market areas will react to weather phenomena. How about reviewing consumer purchases and demographic data based on local customs to answer the question of who will purchase a new product? It can scan social networking sites to give a company warning when its reputation has begun to suffer or a competitor's has started to rise. Some of these things were previously possible, but they often involved tedious efforts to edit data into a standard format. Now the data does not have to be pre-edited and the answers come faster and more accurately because they take into consideration more factors.
There is also a potential dark side to this technology, however. Will there still remain a role for highly paid experts or will people become merely data gatherers feeding facts to the computer? Will machine learning make it more difficult to find people capable of making the decisions necessary to lead a business? I have already given up trying to remember phone numbers and the GPS has replaced my need to reference maps. I won't even talk about the use of calculators in schools. As we give up using our minds in everyday tasks, do we risk losing our ability to think all together?
What's the likelihood that in-store communications will shift from people to computers at some point in the future?