IBM Watson’s Personality Insights claims it can deduce who you are based on 100 words of your writing. Unable to resist, I entered the preface from my book, The Answer Machine, into IBM Watson Personality Insights service.
You are shrewd.
You are philosophical: you are open to and intrigued by new ideas and love to explore them. You are self-controlled: you have control over your desires, which are not particularly intense. And you are compromising: you are comfortable using every trick in the book to get what you want.
Your choices are driven by a desire for prestige.
You consider helping others to guide a large part of what you do: you think it is important to take care of the people around you. You are relatively unconcerned with tradition: you care more about making your own path than following what others have done.
In 2011, a very clever machine from IBM named Watson defeated two human champions in the quiz game, Jeopardy. Watson is an answer machine, and its Jeopardy win was proof that it could be done. The press was immediately abuzz: would machines replace humans? Would we need teachers, programmers, or writers in the future? Could we automate doctors?
The short answer to these questions is no, we still need people. But the better question to ask is how to join man and machine so that we can address more complex problems than either can manage alone. Machines excel at performing repetitive tasks. They don’t get tired and they don’t get bored. They are good at crunching vast amounts of information to find patterns, whether they make sense or not. They have no emotional investment in theories, and are consistent to a fault. They don’t get embarrassed if they return the wrong answer. Machines, however, are very bad at making the intuitive leaps of understanding that are necessary for breakthrough thinking and innovation. Humans excel at this kind of thinking. People can balance the imponderables that are almost impossible to program: diplomacy, subtlety, irony, humor, politics, or priorities. People are good at making sense of data and synthesizing ideas. Above all, people can understand and make exceptions to rules. Machines can’t. We need people to make decisions, but we need machines to help us filter through more information in order to make better-informed decisions. People need this assistance because they are swimming in an overwhelming sea of information, and need time to think if they are to innovate and act wisely.
For this kind of help, new types of more “intelligent,” language-capable machines, like IBM’s Watson, are a necessity. Marrying intelligent machines with humans holds great promise: machines to do the repetitive work and forage through massive amounts of information looking for patterns and evidence to support or reject hypotheses, while humans supply the necessary judgment, intuition, and system override to determine which patterns make sense. This collaboration divides up the work into what each party—machine or human—does well. Anything that follows a predictable pattern is a good candidate for automation. Health care tasks are a good example of this duality: let the machine enter diagnostic codes based on existing rules. Let advanced information systems find the latest research on treating illnesses. Gathering information, organizing it, weighing the probability of its pertinence to a particular patient—this what a Watson does well. This frees clinicians to work with patients, assess the evidence and use it to improve patient care.
Watson is an answer machine. Part search engine, part artificial intelligence, part natural language technology, and stuffed with the specialized information to answer questions on a specific subject, be it medicine, finance, or Jeopardy. Answer machines sift through mountains of information to find patterns. Although they don’t make complex decisions that try to balance costs, emotional aspects, or ethics, they free up humans to do what machines can’t do: consider the factual and thenon-factual and then make well-informed choices. They provide better answers, faster, than current search engines do. Watson is one visible, well-publicized example of an answer machine, but there are many others that are arriving on the scene, albeit with less fanfare. Search engine technologies– the focus of this book – have undergone a metamorphosis of their own. The simple search engine of the 90’s, which matched keywords and phrases, has been transformed into a multifaceted access point to all kinds of information—in multiple formats and from a multitude of sources. Indeed, today the term “search engine” is a misnomer. Like Watson, search today comprises more technologies than keyword search: categorization, clustering, natural language processing, database technologies, analytical tools, machine learning, and more.
This book examines the metamorphosis of search from its awkward command line youth to the emergence of something much more complex, something I call an answer machine. The following chapters look at the role that information plays in the work and personal lives of people today, the tools we have developed to interact with digital information, and the future for these technologies as they move from engineering marvels to everyday tools.]