Watson

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

Watson's basic working principle is to parse keywords in a clue while searching for related terms as responses. This gives Watson some advantages and disadvantages compared with human Jeopardy! players. Watson has deficiencies in understanding the context of the clues. Watson can read, analyze, and learn from natural language, which gives it the ability to make human-like decisions. As a result, human players usually generate responses faster than Watson, especially to short clues. Watson's programming prevents it from using the popular tactic of buzzing before it is sure of its response. However, Watson has consistently better reaction time on the buzzer once it has generated a response, and is immune to human players' psychological tactics, such as jumping between categories on every clue.

In a sequence of 20 mock games of Jeopardy!, human participants were able to use the six to seven seconds that Watson needed to hear the clue and decide whether to signal for responding. During that time, Watson also has to evaluate the response and determine whether it is sufficiently confident in the result to signal. Part of the system used to win the Jeopardy! contest was the electronic circuitry that receives the "ready" signal and then examines whether Watson's confidence level was great enough to activate the buzzer. Given the speed of this circuitry compared to the speed of human reaction times, Watson's reaction time was faster than the human contestants except when the human anticipated (instead of reacted to) the ready signal. After signaling, Watson speaks with an electronic voice and gives the responses in Jeopardy!'s question format. Watson's voice was synthesized from recordings that actor Jeff Woodman made for an IBM text-to-speech program in 2004.

The Jeopardy! staff used different means to notify Watson and the human players when to buzz, which was critical in many rounds. The humans were notified by a light, which took them tenths of a second to perceive. Watson was notified by an electronic signal and could activate the buzzer within about eight milliseconds. The humans tried to compensate for the perception delay by anticipating the light, but the variation in the anticipation time was generally too great to fall within Watson's response time. Watson did not attempt to anticipate the notification signal.

History

Development

Since Deep Blue's victory over Garry Kasparov in chess in 1997, IBM had been on the hunt for a new challenge. In 2004, IBM Research manager Charles Lickel, over dinner with coworkers, noticed that the restaurant they were in had fallen silent. He soon discovered the cause of this evening's hiatus: Ken Jennings, who was then in the middle of his successful 74-game run on Jeopardy!. Nearly the entire restaurant had piled toward the televisions, mid-meal, to watch Jeopardy!. Intrigued by the quiz show as a possible challenge for IBM, Lickel passed the idea on, and in 2005, IBM Research executive Paul Horn supported Lickel, pushing for someone in his department to take up the challenge of playing Jeopardy! with an IBM system. Though he initially had trouble finding any research staff willing to take on what looked to be a much more complex challenge than the wordless game of chess, eventually David Ferrucci took him up on the offer. In competitions managed by the United States government, Watson's predecessor, a system named Piquant, was usually able to respond correctly to only about 35% of clues and often required several minutes to respond. To compete successfully on Jeopardy!, Watson would need to respond in no more than a few seconds, and at that time, the problems posed by the game show were deemed to be impossible to solve.

In initial tests run during 2006 by David Ferrucci, the senior manager of IBM's Semantic Analysis and Integration department, Watson was given 500 clues from past Jeopardy! programs. While the best real-life competitors buzzed in half the time and responded correctly to as many as 95% of clues, Watson's first pass could get only about 15% correct. During 2007, the IBM team was given three to five years and a staff of 15 people to solve the problems. John E. Kelly III succeeded Paul Horn as head of IBM Research in 2007. InformationWeek described Kelly as "the father of Watson" and credited him for encouraging the system to compete against humans on Jeopardy!. By 2008, the developers had advanced Watson such that it could compete with Jeopardy! champions. By February 2010, Watson could beat human Jeopardy! contestants on a regular basis.

During the game, Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage including the full text of the 2011 edition of Wikipedia, but was not connected to the Internet. 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 signaling device, but had trouble in a few categories, notably those having short clues containing only a few words.

Jeopardy!

In 2008, IBM representatives communicated with Jeopardy! executive producer Harry Friedman about the possibility of having Watson compete against Ken Jennings and Brad Rutter, two of the most successful contestants on the show, and the program's producers agreed. Watson's differences with human players had generated conflicts between IBM and Jeopardy! staff during the planning of the competition. IBM repeatedly expressed concerns that the show's writers would exploit Watson's cognitive deficiencies when writing the clues, thereby turning the game into a Turing test. To alleviate that claim, a third party randomly picked the clues from previously written shows that were never broadcast. Jeopardy! staff also showed concerns over Watson's reaction time on the buzzer. Originally Watson signaled electronically, but show staff requested that it press a button physically, as the human contestants would. Even with a robotic "finger" pressing the buzzer, Watson remained faster than its human competitors. Ken Jennings noted, "If you're trying to win on the show, the buzzer is all", and that Watson "can knock out a microsecond-precise buzz every single time with little or no variation. Human reflexes can't compete with computer circuits in this regard." Stephen Baker, a journalist who recorded Watson's development in his book Final Jeopardy, reported that the conflict between IBM and Jeopardy! became so serious in May 2010 that the competition was almost cancelled. As part of the preparation, IBM constructed a mock set in a conference room at one of its technology sites to model the one used on Jeopardy!. Human players, including former Jeopardy! contestants, also participated in mock games against Watson with Todd Alan Crain of The Onion playing host. About 100 test matches were conducted with Watson winning 65% of the games.

To provide a physical presence in the televised games, Watson was represented by an "avatar" of a globe, inspired by the IBM "smarter planet" symbol. Jennings described the computer's avatar as a "glowing blue ball crisscrossed by 'threads' of thought—42 threads, to be precise", and stated that the number of thought threads in the avatar was an in-joke referencing the significance of the number 42 in Douglas Adams' Hitchhiker's Guide to the Galaxy. Joshua Davis, the artist who designed the avatar for the project, explained to Stephen Baker that there are 36 triggerable states that Watson was able to use throughout the game to show its confidence in responding to a clue correctly; he had hoped to be able to find forty-two, to add another level to the Hitchhiker's Guide reference, but he was unable to pinpoint enough game states.

A practice match was recorded on January 13, 2011, and the official matches were recorded on January 14, 2011. All participants maintained secrecy about the outcome until the match was broadcast in February.

Applications

After the national press attention gained by the 2011 Jeopardy! appearance, IBM sought out partnerships from education to weather and cancer to retail chatbots in order convince business about Watson's alleged capabilities. This ultimately led to the failure of Watson to find a profit-making product for the company.

In 2011, the IBM general counsel wrote in The National Law Review arguing that the law profession will become more efficient and better with Watson. After the national attention Jeopardy! afforded them, began an ultimately unsuccessful and expensive project that began when the Memorial Sloan Kettering Cancer Center tried to use Watson to help doctors diagnose and treat cancer patients. Ultimately, the division cost $4 billion to develop but was sold for a quarter of that—$1 billion, in 2022. 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.

From 2012 through the late 2010s, Watson's technology was used to create applications—mostly discontinued to help people make decisions in a variety of areas, among them:

diagnosing cancer and treatment plans,

retail shopping,

medical equipment purchasing,

cooking and recipes,

water conservation,

hospitality management,

human genetic sequencing,

music development and identification,

weather forecasting

to sell ads with weather forecasts,

to tutor students,

and tax preparations,

Healthcare

IBM's Watson was used to analyze medical datasets to provide physicians with guidance on diagnoses and cancer treatment decisions. When a physician submitted a query to Watson, the system started a multi-step process by parsing the input to identify key information, examining patient data to uncover relevant medical and hereditary history, and finally compare various data sources to form and test hypotheses.

IBM claimed that Watson's could draw from a wide range of sources, including treatment guidelines, electronic medical records, and research materials. Although, company executives would later blame the lack of data on the projects ultimate failure.

Notably, Watson has not been involved in the actual diagnosis process, but rather assists doctors in identifying suitable treatment options for patients who have already been diagnosed. In fact, a study of 1,000 challenging patient cases found that Watson's recommendations matched those of human doctors in an impressive 99% of cases.

IBM established partnerships with the Cleveland Clinic, the MD Anderson Cancer Center, and Memorial Sloan-Kettering Cancer Center to further its mission in healthcare. In 2011, IBM entered into a research partnership with Nuance Communications and physicians at the University of Maryland and Harvard to develop a commercial product using Watson's clinical decision support capabilities. IBM partnered with WellPoint (now Anthem) in 2011 to utilize Watson in suggesting treatment options to physicians, and in 2013, Watson was deployed in its first commercial application for utilization management decisions in lung cancer treatment at Memorial Sloan-Kettering Cancer Center. The Cleveland Clinic collaboration aimed to enhance Watson's health expertise and support medical professionals in treating patients more effectively. However, the MD Anderson Cancer Center pilot program, initiated in 2013, ultimately failed to meet its goals and was discontinued after $65 million in investment.

In 2016, IBM launched "IBM Watson for Oncology", a product designed to provide personalized, evidence-based cancer care options to physicians and patients. This initiative marked a significant milestone in the adoption of Watson's technology in the healthcare industry. Additionally, IBM partnered with Manipal Hospitals in India to offer Watson's expertise to patients online.

IBM Watson Group

On January 9, 2014, IBM announced it was creating a business unit around Watson. IBM Watson Group will have headquarters in New York City's Silicon Alley and will employ 2,000 people. IBM has invested $1 billion to get the division going. Watson Group will develop three new cloud-delivered services: Watson Discovery Advisor, Watson Engagement Advisor, and Watson Explorer. Watson Discovery Advisor will focus on research and development projects in pharmaceutical industry, publishing, and biotechnology, Watson Engagement Advisor will focus on self-service applications using insights on the basis of natural language questions posed by business users, and Watson Explorer will focus on helping enterprise users uncover and share data-driven insights based on federated search more easily. The company is also launching a $100 million venture fund to spur application development for "cognitive" applications. According to IBM, the cloud-delivered enterprise-ready Watson has seen its speed increase 24 times over—a 2,300 percent improvement in performance and its physical size shrank by 90 percent—from the size of a master bedroom to three stacked pizza boxes. IBM CEO Virginia Rometty said she wants Watson to generate $10 billion in annual revenue within ten years. In 2017, IBM and MIT established a new joint research venture in artificial intelligence. IBM invested $240 million to create the MIT–IBM Watson AI Lab in partnership with MIT, which brings together researchers in academia and industry to advance AI research, with projects ranging from computer vision and NLP to devising new ways to ensure that AI systems are fair, reliable and secure. In March 2018, IBM's CEO Ginni Rometty proposed "Watson's Law", the "use of and application of business, smart cities, consumer applications and life in general."