- Is There a Difference Between Analytics and Analysis?
- Where Does Data Mining Fit In?
- Why the Sudden Popularity of Analytics?
- The Application Areas of Analytics
- The Main Challenges of Analytics
- A Longitudinal View of Analytics
- A Simple Taxonomy for Analytics
- The Cutting Edge of Analytics: IBM Watson
The Cutting Edge of Analytics: IBM Watson
IBM Watson is perhaps the smartest computer system built to date. Since the emergence of computers and subsequently artificial intelligence in the late 1940s, scientists have compared the performance of these “smart” machines with human minds. Accordingly, in the mid- to late 1990s, IBM researchers built a smart machine and used the game of chess (generally credited as the game of smart humans) to test their ability against the best of human players. On May 11, 1997, an IBM computer called Deep Blue beat the world chess grandmaster after a six-game match series: two wins for Deep Blue, one for the champion, and three draws. The match lasted several days and received massive media coverage around the world. It was the classic plot line of human versus machine. Beyond the chess contest, the intention of developing this kind of computer intelligence was to make computers able to handle the kinds of complex calculations needed to help discover new medical drugs, do the broad financial modeling needed to identify trends and do risk analysis, handle large database searches, and perform massive calculations needed in advanced fields of science.
After a couple decades, IBM researchers came up with another idea that was perhaps more challenging: a machine that could not only play Jeopardy! but beat the best of the best. Compared to chess, Jeopardy! is much more challenging. While chess is well structured and has very simple rules, and therefore is very good match for computer processing, Jeopardy! is neither simple nor structured. Jeopardy! is a game designed for human intelligence and creativity, and therefore a computer designed to play it needed to be a cognitive computing system that can work and think like a human. Making sense of imprecision inherent in human language was the key to success.
In 2010 an IBM research team developed Watson, an extraordinary computer system—a novel combination of advanced hardware and software—designed to answer questions posed in natural human language. The team built Watson as part of the DeepQA project and named it after IBM’s first president, Thomas J. Watson. The team that built Watson was looking for a major research challenge: one that could rival the scientific and popular interest of Deep Blue and would also have clear relevance to IBM’s business interests. The goal was to advance computational science by exploring new ways for computer technology to affect science, business, and society at large. Accordingly, IBM Research undertook a challenge to build Watson as a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy! The team wanted to create a real-time automatic contestant on the show, capable of listening, understanding, and responding—not merely a laboratory exercise.
Competing Against the Best at Jeopardy!
In 2011, as a test of its abilities, Watson competed on the quiz show Jeopardy!, in the first-ever human-versus-machine matchup for the show. In a two-game, combined-point match (broadcast in three Jeopardy! episodes during February 14–16), Watson beat Brad Rutter, the biggest all-time money winner on Jeopardy!, and Ken Jennings, the record holder for the longest championship streak (75 days). In these episodes, Watson consistently outperformed its human opponents on the game’s signaling device, but it had trouble responding to a few categories, notably those having short clues containing only a few words. Watson had access to 200 million pages of structured and unstructured content, consuming 4 terabytes of disk storage. During the game, Watson was not connected to the Internet.
Meeting the Jeopardy! challenge required advancing and incorporating a variety of text mining and natural language processing technologies, including parsing, question classification, question decomposition, automatic source acquisition and evaluation, entity and relationship detection, logical form generation, and knowledge representation and reasoning. Winning at Jeopardy! requires accurately computing confidence in answers. The questions and content are ambiguous and noisy, and none of the individual algorithms are perfect. Therefore, each component must produce a confidence in its output, and individual component confidences must be combined to compute the overall confidence of the final answer. The final confidence is used to determine whether the computer system should risk choosing to answer at all. In Jeopardy! parlance, this confidence is used to determine whether the computer will “ring in” or “buzz in” for a question. The confidence must be computed during the time the question is read and before the opportunity to buzz in. This is roughly between one and six seconds, with an average around three seconds.
How Does Watson Do It?
The system behind Watson, which is called DeepQA, is a massively parallel, text mining–focused, probabilistic evidence-based computational architecture. For the Jeopardy! challenge, Watson used more than 100 different techniques for analyzing natural language, identifying sources, finding and generating hypotheses, finding and scoring evidence, and merging and ranking hypotheses. What is far more important than any particular technique the IBM team used was how it combined them in DeepQA such that overlapping approaches could bring their strengths to bear and contribute to improvements in accuracy, confidence, and speed.
DeepQA is an architecture with an accompanying methodology that is not specific to the Jeopardy! challenge. These are the overarching principles in DeepQA:
- Massive parallelism. Watson needed to exploit massive parallelism in the consideration of multiple interpretations and hypotheses.
- Many experts. Watson needed to be able to integrate, apply, and contextually evaluate a wide range of loosely coupled probabilistic question and content analytics.
- Pervasive confidence estimation. No component of Watson commits to an answer; all components produce features and associated confidences, scoring different question and content interpretations. An underlying confidence-processing substrate learns how to stack and combine the scores.
- Integration of shallow and deep knowledge. Watson needed to balance the use of strict semantics and shallow semantics, leveraging many loosely formed ontologies.
Figure 1.5 illustrates the DeepQA architecture at a very high level. More technical details about the various architectural components and their specific roles and capabilities can be found in Ferrucci et al. (2010).
Figure 1.5 A High-Level Depiction of DeepQA Architecture
What Is the Future for Watson?
The Jeopardy! challenge helped IBM address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After three years of intense research and development by a core team of about 20 researchers, as well as a significant R&D budget, Watson managed to perform at human expert levels in terms of precision, confidence, and speed at the Jeopardy! quiz show.
After the show, the big question was “So what now?” Was developing Watson all for a quiz show? Absolutely not! Showing the rest of the world what Watson (and the cognitive system behind it) could do became an inspiration for the next generation of intelligent information systems. For IBM, it was a demonstration of what is possible with cutting-edge analytics and computational sciences. The message is clear: If a smart machine can beat the best of the best in humans at what they are the best at, think about what it can do for your organizational problems. The first industry that utilized Watson was health care, followed by security, finance, retail, education, public services, and research. The following sections provide short descriptions of what Watson can do (and, in many cases, is doing) for these industries.
The challenges that health care is facing today are rather big and multifaceted. With the aging U.S. population, which may be partially attributed to better living conditions and advanced medical discoveries fueled by a variety of technological innovations, demand for health care services is increasing faster than the supply of resources. As we all know, when there is an imbalance between demand and supply, the prices go up and the quality suffers. Therefore, we need cognitive systems like Watson to help decision makers optimize the use of their resources, both in clinical and managerial settings.
According to health care experts, only 20% of the knowledge physicians use to diagnose and treat patients is evidence based. Considering that the amount of medical information available is doubling every five years and that much of this data is unstructured, physicians simply don’t have time to read every journal that can help them keep up-to-date with the latest advances. Given the growing demand for services and the complexity of medical decision making, how can health care providers address these problems? The answer could be to use Watson, or some other cognitive systems like Watson that has the ability to help physicians in diagnosing and treating patients by analyzing large amounts of data—both structured data coming from electronic medical record databases and unstructured text coming from physician notes and published literature—to provide evidence for faster and better decision making. First, the physician and the patient can describe symptoms and other related factors to the system in natural language. Watson can then identify the key pieces of information and mine the patient’s data to find relevant facts about family history, current medications, and other existing conditions. It can then combine that information with current findings from tests, and then it can form and test hypotheses for potential diagnoses by examining a variety of data sources—treatment guidelines, electronic medical record data and doctors’ and nurses’ notes, and peer-reviewed research and clinical studies. Next, Watson can suggest potential diagnostics and treatment options, with a confidence rating for each suggestion.
Watson also has the potential to transform health care by intelligently synthesizing fragmented research findings published in a variety of outlets. It can dramatically change the way medical students learn. It can help healthcare managers to be proactive about the upcoming demand patterns, optimally allocate resources, and improve processing of payments. Early examples of leading health care providers that use Watson-like cognitive systems include MD Anderson, Cleveland Clinic, and Memorial Sloan Kettering.
As the Internet expands into every facet of our lives—ecommerce, ebusiness, smart grids for energy, smart homes for remote control of residential gadgets and appliances—to make things easier to manage, it also opens up the potential for ill-intended people to intrude in our lives. We need smart systems like Watson that are capable of constantly monitoring for abnormal behavior and, when it is identified, preventing people from accessing our lives and harming us. This could be at the corporate or even national security system level; it could also be at the personal level. Such a smart system could learn who we are and become a digital guardian that could make inferences about activities related to our life and alert us whenever abnormal things happen.
The financial services industry faces complex challenges. Regulatory measures, as well as social and governmental pressures for financial institutions to be more inclusive, have increased. And the customers the industry serves are more empowered, demanding, and sophisticated than ever before. With so much financial information generated each day, it is difficult to properly harness the right information to act on. Perhaps the solution is to create smarter client engagement by better understanding risk profiles and the operating environment. Major financial institutions are already working with Watson to infuse intelligence into their business processes. Watson is tackling data-intensive challenges across the financial services sector, including banking, financial planning, and investing.
Retail industry is rapidly changing with the changing needs and wants of customers. Customers, empowered by mobile devices and social networks that give them easier access to more information faster than ever before, have high expectations for products and services. While retailers are using analytics to keep up with those expectations, their bigger challenge is efficiently and effectively analyzing the growing mountain of real-time insights that could give them the competitive advantage. Watson’s cognitive computing capabilities related to analyzing massive amounts of unstructured data can help retailers reinvent their decision-making processes around pricing, purchasing, distribution, and staffing. Because of Watson’s ability to understand and answer questions in natural language, it is an effective and scalable solution for analyzing and responding to social sentiment based on data obtained from social interactions, blogs, and customer reviews.
With the rapidly changing characteristics of students—more visually oriented/stimulated, constantly connected to social media and social networks, increasingly shorter attention spans—what should the future of education and the classroom look like? The next generation of educational system should be tailored to fit the needs of the new generation, with customized learning plans, personalized textbooks (digital ones with integrated multimedia—audio, video, animated graphs/charts, etc.), dynamically adjusted curriculum, and perhaps smart digital tutors and 24/7 personal advisors. Watson seems to have what it takes to make all this happen. With its natural language processing capability, students can converse with it just as they do with their teachers, advisors, and friends. This smart assistant can answer students’ questions, satisfy their curiosity, and help them keep up with the endeavors of the educational journey.
For local, regional, and national governments, the exponential rise of Big Data presents an enormous dilemma. Today’s citizens are more informed and empowered than ever before, and that means they have high expectations for the value of the public sector serving them. And government organizations can now gather enormous volumes of unstructured, unverified data that could serve their citizens—but only if that data can be analyzed efficiently and effectively. IBM Watson’s cognitive computing may help make sense of this data deluge, speeding governments’ decision-making processes and helping public employees focus on innovation and discovery.
Every year, hundreds of billions of dollars are spent on research and development, most of it documented in patents and publications, creating enormous amount of unstructured data. To contribute to the extant knowledgebase, one needs to sift through these data sources to find the outer boundaries of research in a particular field. This is very difficult, if not impossible work, if it is done with traditional means, but Watson can act as a research assistant to help collect and synthesize information to keep people updated on recent findings and insights. For instance, New York Genome Center is using the IBM Watson cognitive computing system to analyze the genomic data of patients diagnosed with a highly aggressive and malignant brain cancer and to more rapidly deliver personalized, life-saving treatment to patients with this disease (Royyuru, 2014).