IBM Watson

IBM Watson is a computer system capable of answering questions posed in natural language. It was developed as a part of IBM's DeepQA project by a research team, led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson. The computer system was initially developed to answer questions on the popular quiz show Jeopardy! and in 2011, the Watson computer system competed on Jeopardy! against champions Brad Rutter and Ken Jennings, winning the first-place prize of US$1 million. In February 2013, IBM announced that Watson's first commercial application would be for utilization management decisions in lung cancer treatment, at Memorial Sloan Kettering Cancer Center, New York City, in conjunction with WellPoint (now Elevance Health). In 2022, IBM divested and spun-off their Watson Health division into Merative, which was sold to Francisco Partners, an American private equity firm. The division cost $4 billion to develop but was sold for $1 billion. By 2023, Watson resulted in IBM losing 10% of its stock value, costing four times more than what it brought to the company and resulting in mass layoffs.

Description

Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering. The system is named DeepQA (though it did not involve the use of deep neural networks). IBM stated that Watson uses "more than 100 different techniques to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses." In recent years, Watson's capabilities have been extended and the way in which Watson works has been changed to take advantage of new deployment models (Watson on IBM Cloud), evolved machine learning capabilities, and optimized hardware available to developers and researchers.

Software Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework implementation. The system was written in various languages, including Java, C++, and Prolog, and runs on the SUSE Linux Enterprise Server 11 operating system using the Apache Hadoop framework to provide distributed computing. Other than the DeepQA system, Watson contained several strategy modules. For example, one module calculated the amount to bet for Final Jeopardy, according to the confidence score on getting the answer right, and the current scores of all contestants. One module used the Bayes rule to calculate the probability that each unrevealed question might be the Daily Double, using historical data from the J! Archive as the prior. If a Daily Double is found, the amount to wager is computed by a 2-layered neural network of the same kind as those used by TD-Gammon, a neural network that played backgammon, developed by Gerald Tesauro in the 1990s. The parameters in the strategy modules were tuned by benchmarking against a statistical model of human contestants fitted on data from the J! Archive, and selecting the best one.

Hardware The system is workload-optimized, integrating massively parallel POWER7 processors and built on IBM's DeepQA technology, which it uses to generate hypotheses, gather massive evidence, and analyze data. Watson employs a cluster of ninety IBM Power 750 servers, each of which uses a 3.5 GHz POWER7 eight-core processor, with four threads per core. In total, the system uses 2,880 POWER7 processor threads and 16 terabytes of RAM. According to John Rennie, Watson can process 500 gigabytes (the equivalent of a million books) per second. IBM master inventor and senior consultant Tony Pearson estimated Watson's hardware cost at about three million dollars. Its Linpack performance stands at 80 TeraFLOPs, which is about half as fast as the cut-off line for the Top 500 Supercomputers list. According to Rennie, all content was stored in Watson's RAM for the Jeopardy game because data stored on hard drives would be too slow to compete with human Jeopardy champions.

Data The sources of information for Watson include encyclopedias, dictionaries, thesauri, newswire articles and literary works. Watson also used databases, taxonomies and ontologies including DBpedia, WordNet and YAGO. The IBM team provided Watson with millions of documents, including dictionaries, encyclopedias and other reference material, that it could use to build its knowledge.

Operation Watson parses questions into different keywords and sentence fragments in order to find statistically related phrases. Watson's main innovation was not in the creation of a new algorithm for this operation, but rather its ability to quickly execute hundreds of proven language analysis algorithms simultaneously. The more algorithms that find the same answer independently, the more likely Watson is to be correct. Once Watson has a small number of potential solutions, it is able to check against its database to ascertain whether the solution makes sense or not.

Comparison with human players

… excerpt ends here. Continue reading the full article.