IBM Watson - Introduction to IBM Watson

Editor's Notes

• #3: Main point: Bringing about a transformation in what was as a society expect of technology does not happen overnight. Watson has been an iterative growth process that continues this day and into the future. Further speaking points: Watson was a research project in IBM starting in 2006. The effort was led by a team of 15 IBM researchers working in collaboration with a pool of top universities as a “Deep QA” project. Jeopardy! was selected as the ultimate test of the machine’s capabilities because it relied on many human cognitive abilities traditionally seen as out of scope for machines such as ability to discern double meanings of words, puns, rhymes, and inferred hints. It also demanded extremely rapid responses and the ability to process vast amounts of information to make connections typically requiring a lifetime of immersion in pop culture and participation in the general human experience. With Jeopardy! in the past, IBM and Wellpoint, one of the US’s largest health insurers, announced a partnership to pilot Watson for use among member hospitals and the insurance organization itself with a goal of improving patient outcomes and health treatments. As a byproduct, it is also expected to improve the productivity of healthcare professionals. From Healthcare, Watson is expected to branch into other industries that rely on analytic solutions to managing unstructured data. Additional information: Financial services organizations and call center operations are seen as high potential areas.
• #5: Main point: Data is growing at an astounding rate. It is growing so fast that we often lack the ability to use it to its full potential. The highly unstructured nature of this data makes the challenge that much more difficult. This is a real problem for business. It makes informed decisions more difficult to make. Business leaders need a way to find hidden patterns and isolate the valuable nuggets that they need to make business decisions. Further speaking points: Yet, the rewards for finding a way to harness the data into useful information are great; 54% of companies in this year’s study with MIT/Sloan are using analytics for competitive advantage… and that number has surged 57% in just the past 12 months. “Dying of thirst in an ocean of data”… It’s an apt analogy. Data is everywhere. 90% of it didn't exist just two years ago. The vast majority of it is totally useless for any given goal and therefore amounts to noise and a hindrance to finding the key useful information needed in a specific time and place. Additional information: See information and stats
• #6: Main Point: At the core of what makes Watson different are three powerful technologies - natural language, hypothesis generation, and evidence based learning. But Watson is more than the sum of its individual parts. Watson is about bringing these capabilities together in a way that’s never been done before resulting in a fundamental change in the way businesses look at quickly solving problems Solutions that learn with each iteration Capable of navigating human communication Dynamically evaluating hypothesis to questions asked Responses optimized based on relevant data Ingesting and analyzing Big Data Discovering new patterns and insights in seconds Further speaking points:. Looking at these one by one, understanding natural language and the way we speak breaks down the communication barrier that has stood in the way between people and their machines for so long. Hypothesis generation bypasses the historic deterministic way that computers function and recognizes that there are various probabilities of various outcomes rather than a single definitive ‘right’ response. And adaptation and learning helps Watson continuously improve in the same way that humans learn….it keeps track of which of its selections were selected by users and which responses got positive feedback thus improving future response generation Additional information: The result is a machine that functions along side of us as an assistant rather than something we wrestle with to get an adequate outcome
• #11: Main Point: Watson represents a whole new class of industry specific solutions called cognitive systems. It builds on the current paradigm of Programmatic Systems and is not meant to be a replacement; programmatic systems will be with us for the foreseeable future. But in many cases, keeping pace with the demands of an increasingly complex business environment and challenges requires a paradigm shift in what we should expect from IT. We need an approach that recognizes today’s realities and treats them as opportunities rather than challenges. Further speaking points: For example, most digitized information of the past was structured. It was organized into tables, stored in easily identified cells in databases, and easily searched and accessed. Unstructured information was largely ignored as too difficult to utilize…and therefore it lay fallow. Similarly, traditional IT has largely limited itself to deterministic applications. 2+2=4. 100cm in a meter. Situations where there is only one answer to a question But this rules out a whole world of real world situations that have a more probabilistic outcome. It is very likely that the car will not start because of a dead battery but there is a chance there is a clog in the fuel line. It is very likely to be sunny tomorrow but it may rain. Traditional IT relies on search to find the location of a key phrase. Emerging IT gathers information and combines it for true discovery. Traditional IT can handle only small sets of focused data while IT today must live with big data. And traditional IT interacts with machine language while what we as users really need is interaction the way we ourselves communicate – in natural language.