Questions And Answers On Cognitive Computing: IBM Watson

COGNITIVE COMPUTING- IBM WATSON
The term cognitive computing has been used to refer to new hardware and/or software that mimic the functioning of the human brain. Like a human, a cognitive computing application learns by experience and/or instruction. The CC application learns and remembers how to adapt its content displays, by situation, to influence behaviour. This means a CC application must have intent, memory, foreknowledge and cognitive reasoning for a domain of variable situations.
Cognitive-based systems are able to build knowledge and learn, understand natural language, and reason and interact more naturally with human beings than traditional systems.
Cognitive systems can quickly identify new patterns and insights. Over time, they
For each clue, Watson's three most probable responses were displayed on the television screen. Watson consistently outperformed its human opponents on the game's signalling device, but had trouble in a few categories, notably those having short clues containing only a few words.
Meeting the Jeopardy Challenge requires advancing and incorporating a variety of QA technologies including parsing, question classification, question decomposition, automatic source acquisition and evaluation, entity and relation detection, logical form generation, and knowledge representation and reasoning.
Winning at Jeopardy requires accurately computing confidence in your answers. The questions and content are ambiguous and noisy and none of the individual algorithms are perfect. Therefore, each component must produce a confidence in its output, and individual component confidences must be combined to compute the overall confidence of the final answer. The final confidence is used to determine whether the computer system should risk choosing to answer at all. In Jeopardy parlance, this confidence is used to determine whether the computer will “ring in” or “buzz in” for a question. The confidence must be computed during the time the question is read and before the opportunity to buzz in. This
In evidence collection and scoring (analogous to backward chaining), DeepQA also uses NLP and search over unstructured information to find evidence for ranking and scoring answers based on natural language content. DeepQA’s direct use of readily available knowledge in natural language content makes it more flexible, maintainable, and scalable as well as cost efficient in considering vast amounts of information and staying current with the latest content. In a clinical setting, for example, it can be used to develop a diagnostic support tool that uses the context of an input case — a rich set of observations about a patient’s medical condition — and generates a ranked list of diagnoses (differential diagnosis) with associated confidences based on searching and analyzing evidence from large volumes of content. Physicians and other care providers may evaluate these diagnoses along many different dimensions of evidence that DeepQA has extracted from a patient’s electronic medical record (EMR) and other related content sources. For medicine, the dimensions of evidence may include symptoms, findings, patient history, family history, demographics, current medications, and many others. Each diagnosis in the differential diagnosis includes links back to the original