Watson also used databases, taxonomies and ontologies including DBPedia, WordNet and Yago. The sources of information for Watson include encyclopedias, dictionaries, thesauri, newswire articles and literary works. 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. Its Linpack performance stands at 80 TeraFLOPs, which is about half as fast as the cut-off line for the Top 500 Supercomputers list. IBM master inventor and senior consultant Tony Pearson estimated Watson's hardware cost at about three million dollars. Īccording to John Rennie, Watson can process 500 gigabytes (the equivalent of a million books) per second. In total, the system has 2,880 POWER7 processor threads and 16 terabytes of RAM. 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. 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. 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.
#El whatson software#
Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework implementation. It is no longer purely a question answering (QA) computing system designed from Q&A pairs but can now 'see', 'hear', 'read', 'talk', 'taste', 'interpret', 'learn' and 'recommend'. 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. 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." 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 high-level architecture of IBM's DeepQA used in Watson
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