Future of Artificial Intelligence -IBM Watson

IBM Watson is a “reasoning” computer system capable of answering questions posed in natural language developed in IBM’s DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM’s first CEO and industrialist Thomas J. Watson.

In February 2013, IBM announced that Watson software system’s first commercial application would be for utilization management decisions in lung cancer treatment at Memorial Sloan Kettering Cancer Centre in conjunction with health insurance company WellPoint.

So what exactly is WATSON?

IBM Watson is at the forefront of a new era of computing, cognitive computing. It is radically a new type of computing, very different from those old programmable systems. Conventional computing solutions based on mathematical principles that emanate from the 1940’s are programmed based on rules and logics intended to derive mathematical precise answers, often following every decision tree approach. But with today’s wealth of big data and need for more complex evidence-based decisions, such a rigid approach often breaks or fails to keep up with available information. Cognitive computing enables people to create a profoundly new kind of value and finding answers and insights locked away in volumes of data. Whether we consider a doctor diagnosing a patient, a wealth manager advising a client on their retirement portfolio or even a chef creating a new recipe, they need a new approach to put into context the volume of information they deal with on a daily basis in order to derive value from it. This process serves to enhance human expertise

 Watson is a system that solves the problems just like a human does.Just as humans become experts by going through the process of observation, evaluation and decision making. Cognitive systems like Watson uses similar process to reason about the information they read .Watson can do this at massive speed and scale.

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How does Watson do it?

Unlike conventional approaches to computing which can only handle neatly organised structured data such as what we store in a database. Watson can understand unstructured data which is 80% of the data today. Watson relies on natural language which is govern by rules of grammar, context and culture. When It comes to text Watson doesn’t just look for keyword matches, or synonyms like search engine, but it actually reads and interprets text like a person. It does this by breaking down the sentence grammatically, relationally and structurally discerning meaning from the semantics of the written material.

Watson understand context. Watson tries to understand the real intent to the user’s language and uses that understanding to possibly extract logical responses and draw inferences to potential answers through a broader way of linguistic models and algorithms.

When Watson goes to work in a particular field, it learns language, the jargon and the motive thought of that domain. Take the term cancer for instance. There are many different types of cancer and each type has different symptoms and treatments. However those symptoms can also be associated with diseases other than cancer. Treatments can have side effect and affect people differently depending on many factors. Watson evaluates standard of care practices and thousands of pages of literature that capture the best science in the field and from all of that, Watson identifies the therapies that are best choices for the doctors in the treatment of the patient.

A must watch video that tells how artificial intelligence will change our life: IBM WATSON

With the guidance of human experts Watson collects the knowledge required to have literacy in a particular domain what’s called a corpus of knowledge. Collecting a knowledge starts with loading the relevant body of the literature into Watson, building the corpus also require some human intervention to call through the information and discard anything that is out-of-date, poorly recorded or immaterial to the problem domain. Then the data is pre-processed by Watson building indices and other metadata that makes working with that content more efficient. This is known as ingestion.

At this time Watson may also create a knowledge graph to assist in answering more precise questions. Now that Watson has ingested the corpus, it needs to be trained by human expert to learn how to interpret the information. To learn the best possible responses and acquire the ability to find patterns, Watson partners with experts who train in using an approach called machine learning. An expert will upload training data into Watson in the form of question answer pairs that service ground truth. Watson is now ready to respond to highly complex situations and quickly provide a range of potential responses and recommendations that are backed by evidence

Today Watson is revolutionizing the way we make decisions, becoming expert and sharing expertise in fields as diverse as law, medicine and even cooking. Further Watson is discovering and offering answers in patterns we hadn’t known existed faster than any person or group of people ever could in ways that make a material difference every day. Most important of all Watson learns, adapts and keeps getting smarter just as we do.

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