As we know, search engines can’t understand what is being asked of them; they check keywords, keep an index of the words they find and where they find them, and allow users to look for words or combinations of words found in that index. Despite the ongoing refinement of search engine algorithms, they don’t follow natural language processes and, as language is inherently ambiguous, there can be a disconnect between complex search queries and detailed results.
Recently I attended a fascinating presentation at IBM on the development of Cognitive Computing. IBM have their own technology, Watson (actually several Watsons), which has been in development for a few years. Now that there is so much content available in multiple formats, dozens of languages and increasing levels of complexity, it is time to take the next step towards allowing users to analyse this ‘big data’ and put it to good use.
“Big data growth is accelerating as more of the world’s activity is expressed digitally. Not only is it increasing in volume, but also in speed, variety and uncertainty. Most data now comes in unstructured forms such as video, images, symbols and natural language – a new computing model is needed in order for businesses to process and make sense of it, and enhance and extend the expertise of humans. Rather than being programmed to anticipate every possible answer or action needed to perform a function or set of tasks, cognitive computing systems are trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, infer and, in some ways, think.” (IBM Research/Cognitive Computing)
Cognitive computing systems can have multiple applications and enable industries by helping them to discover smarter ways to explore the complexities of their big data to access knowledge and insights. Unlike search engines, cognitive systems are able to learn and interact with people in a natural language whilst learning how to apply knowledge. As they can take into account context, this offers a distinct benefit in assessing the relevance and usefulness of all content types. Working in a probabilistic and not just a deterministic manner, cognitive computing systems are well placed to take advantage of the wealth of digital content available.