Sparking Innovation: Dynamic Technologies for a Dynamic Process

[The following article is a transcript of a video of Sue Feldman’s keynote session at KMWorld 2015 in Washington, DC. View the full session video at http://www.kmworld.com/Articles/Editorial/ViewPoints/Cognitive-Computing-and-Knowledge-Management-Sparking-Innovation-108929.aspx?PageNum=2]

Download the slides: Sparking Innovation

Innovation is perhaps the biggest test of a knowledge management system. We’re used to capturing information. We’re used to locking it down. We’re used to accumulating it. We’re used to creating some kinds of access to it. Innovation makes us go far beyond that. What I’ll talk to you about today is what innovation is. What the process for coming up with a new idea actually is. Then I would like to talk to you about cognitive computing because I think that it solves some of the problems that our older, traditional technologies cannot really address adequately. I’ll end by talking a little bit about where I think knowledge management has to go.

Let me start by telling you a story. Once upon a time, there was a biologist and physicist and they went for a walk. The first thing they started to do is to fall into a conversation about a fairly arcane subject: DNA. The physicist was interested in the electrical properties of DNA. The biologist knew a fair amount about that because he was also a chemist and something of an inventor. They talked and they walked and then the physicist went back home, continue to ask questions, do research, etc. The biologist kept on sending information but really went back to what he liked to do best which was tinkering with ideas and things because he was something of an inventor. After several years of research, the biologist, Esther Conwell, won the National Science medal in 2010 for her work on the conductive properties of DNA and how to enhance those because she was interested in semi-conductors. The inventor was my Dad, and that’s the kind of thing that he enjoyed, which I think is a story of what happens when you have two innovative, open-minded people.

Ingredients of Innovation

Let’s take a look at the ingredients. First of all, you need a problem or research direction. In this case it was semi-conductors. You also need opportunity. You need cross-fertilization, in this case biology and physics, which are adjacent but certainly not congruent. You need colleagues who, like you, interested in discussing. In the research that I’ve done over the years on the process of innovation, I’ve found that innovative inventions of various kinds, and discoveries tend to be sparked by good food and a bottle of wine. It’s almost a requirement. You need curiosity. You need that serendipitous encounter to create the “aha moment,” a happy accident. You also need information and you need support both in the sense of an organization willing to let you model around and support in the sense of the information that is provided to you.

What is innovation? Well, it’s a lot of things. When President Obama presented the medal to Esther Conwell, he said that innovation is fueled by a combination of caffeine and passion. Obsession actually. Certainly, it requires a new idea, but it’s rarely entirely novel. It builds on what came before and that should be of importance to knowledge managers. Game-changing innovations occur at the boundaries between subjects and organizations. It’s a group effort rather than an individual one. Developers, users, partners, and colleagues all have a part in it because they provide not just the ideas but also the need that spurs the innovator to solve a problem. It tends to occur at the lower levels of organizations. Those of us who are at the top of the organization, beware. It may disrupt industries or companies for good or ill and it is both risky and rewarding. That’s innovation.

Supporting Innovation

What’s the business case for supporting innovation? Because very often it doesn’t pay off. Those of you who have R&D departments know that that’s the case. First, revenue. If you’re successful, it drives revenue because you are first to market. That means you are able to dominate that market and in fact that’s what’s happening with cognitive computing right now. You can attract and keep customers, build customer loyalty and market buzz, shape that market the way you want. It helps you avoid disruption and stay competitive. It helps you expand into new markets.

By creating a fertile environment for R&D, you also have a pipeline of new ideas to avoid stagnation and being bypassed by competitors. You attract outstanding researchers who soon burn out and leave if you don’t provide them with that kind of organizational support and latitude because innovation is a fragile flower. It gets trampled very easily.

On my second job, a very long time ago, I got hired by someone who called me in after two weeks and said, “I heard that you are innovative, Susan. You haven’t had any ideas yet.” She was right. I never had another one for her.

The Innovation Process

What is the innovation process? It’s quite different from what goes on in knowledge management normally. First, you have to have that idea or interest. There’s no question about that. You have open discussions, wide readings, you bump into people, you talk to them in the hallways, you go out to dinner with friends who are not in the organization, and gradually you discover that there is a need, which you find intriguing.

This is a very individual process even though it requires other people. You define the problem. You eliminate some of the common ideas. You discover that other people have been there before you and you give up and do something else. Then something interesting happens: you’ve taken in all this information, you’ve stuffed it into your head, and you let it simmer.

We had a graphic designer who is tremendously innovative. He used to go home and take a bath. I’d go for walks. Other people do other things. They knit. They cook. They garden. Whatever it is, they have to distract the front of their brain so that the back of the brain can allow that ferment to happen and that’s great fun. But if you have too tight a deadline, you’re not going to follow that elusive idea which is half-formed because you don’t have time for it. You have to meet the deadline and the idea gets squashed. That’s a very important thing for organizations to understand. These people who are innovators need some direction, but they also need a great deal of latitude and freedom as well.

You have to explore broadly. (This is where cognitive computing and knowledge management coincide, as I’ll discuss later). You have to filter and winnow and focus and rethink and iterate and go back to the beginning and start all over again. Finally, you have something concrete enough to develop and off you go, maybe. You find the problem, do research on it, then go off and develop. You commercialize it, you throw it into the market place and you see what the consequences are–big revenue, losses, whatever it turns out to be.

You identify the problem by talking to customers, talking to colleagues, talking to sales people, and talking to other researchers. Coming back is very iterative, as most of you know. You do research and you redefine the problem.

Again, you iterate. Test it on the market like at social media. Do competitive intelligence. Then you might commercialize it and see what happens after that.

Discovering What We Don’t Know

There is set of information tasks that we try to support with knowledge management, research, and text analytics. Any sort of information access and management tool is aiming to support all of these tasks, but the tools rarely do. The problem is that we have separate tools. The creation tools may not be well integrated into the process. If they are, the fact is that in innovation we’re on the phone, we’re sending emails, we’re discussing in the hallways. We’re not capturing that.

The reasons why we make decisions and change directions are poorly known and can’t be modeled for the process to happen again. We’re losing information that’s falling off the table. We’re pretty good at finding in some ways. We’re not so good at discovering what we don’t know and uncovering patterns we don’t know enough to look at. That discovery and uncovering are key to innovation, because what we want is to find out what we don’t know so that we can invent it. We’re pretty good at analyzing information and getting better. The discussion is very often not integrated into this whole picture and the decision-making is fairly diffuse. These are information tasks that we need to be able to support.

The Role of Information and Analysis Tools

The role of information access and analysis tools in this case is to improve exploration and discovery, to introduce related information. Although we want related information, we don’t want all the information in the world.

How do we manage to promote those happy accidents without burying the searcher? We have to help with the information-finding process to eliminate queries perhaps in favor of exploration of some sort. We have to help. This is where our traditional systems also fall down, in helping the user to frame the question broadly, helping the user understand how to ask for the information they need if they don’t know they need it. We used to have knowledgeable intermediaries who did a lot of this, but that’s not what’s happening today.

The tools have to help us understand and discover unexpected relationships across all sources of information. They need to search on a concept level rather than on keywords because those are also limitations. They need to unite multiple sources of information no matter what format they’re in or where they reside. They need to collect and share and discuss. They need to enable information and people to interact in one place. Then of course they need to save us time so that we can look at enough information in order to have those ideas. That tools that have started to emerge over the last couple of years are key to supporting these expanded roles for knowledge management. Cognitive systems are the next logical step.

As an analyst, I’ve been watching the markets develop all kinds of tools: business intelligence, search, text analytics, graphics of various kinds, reporting tools, creation tools, and drawing tools. They all solve a piece of a problem.

We used to call that “Sneakernet.” The Sneakernet that goes on in the creative and innovation process is overwhelming. It’s a tremendous waste of time because it means you’re constantly rummaging back through stuff that you did 10 years ago because you know you did it already. In fact, when I was preparing this talk about innovation, I had to go back to research I did 10 years ago because I knew I’d done something about this, but I really didn’t remember where it was. It was really hard to find it; desktop search is terrible. Yet, there it was in the back of my head.

Enter Cognitive Computing

See separate article in KM World, “What is Cognitive Computing”,

http://www.kmworld.com/Articles/Editorial/ViewPoints/What-is-Cognitive-Computing-108931.aspx

Cognitive computing is going to bring us another step closer to solving some of these problems. What is cognitive computing? Last year I brought together a team of 14 or 15 people to try to define it before marketplace hype completely screwed up any idea of what it was. I don’t know if we’re succeeding or not.

What are the problems that cognitive computing attacks? They’re the ones that we have left on the table because we can’t put them into neat rows and columns. They’re ambiguous. They’re unpredictable. They’re very human. There’s a lot of conflicting data. There’s no right and wrong, just best, better, and not such a good idea but maybe. This data requires exploration not searching. You just have to keep poking at it and shifting things around.

When I’m at the beginning of a project, I find myself jotting down ideas and then arranging them on a large table because sometimes they fit together one way and sometimes they fit together another. You need to uncover patterns and surprises, and computers are very good at this because they don’t get embarrassed by wrong ideas. Although they all have the biases that they get from their programmers, their biases are different from yours.

The situation is shifting as well. As we learn more, we change our focus and our goals. We go back and ask the same question that we often do, but we do so hoping for different results because we’ve already learned that stuff is not so easy in today’s systems. If you go to Google and ask the same question, you’re not always going to get the same answers, but you’ll get similar answers. But if you were looking for pictures of Java because you’re planning your vacation today and two months from now you want flights, the system won’t know your progress and your decisions.

The Value of Context

How do we make a cognitive system into a partner so that it keeps track of who we are and what we want to know at this time? It gives best answers based on who you are, where you are, what you know, what you want to know, and when you want to know it. It is very individually focused. Its aim is problem solving beyond information gathering. It gives recommendations based on who you are. I want to give you a couple of examples because context, we have found, is one of the key differentiators of a cognitive system.

In 2011, IBM designed a computer named “Watson” that won Jeopardy against two human champions. That was the beginning of cognitive computing. (You can see it on YouTube: https://www.youtube.com/watch?v=Puhs2LuO3Zc).

For me, as a person who has been in the chaotic world of search and text analytics all my life, it was a validation that the kinds of things that we do–the search index as opposed to the database–were actually really useful for very complex problem-solving. That was the beginning.

For another example, think about patient care. The emphasis on who, what, where, and when you are is one of the differentiators for cognitive computing. We all need slightly different slants on the same question. Let’s say we have a patient who has a disease. We know his genetic makeup, his age, his history of smoking, that he has certain allergies, etc. We also know where he is, what kind of access he has to medical care. We also have access to enormous amounts of information especially in health care and possible treatments and confidence scores. How does this change health care, because this is life and death?

Today, in standard health care, if you have a disease, or a particular kind of tumor, there are treatment guidelines. It doesn’t matter if you’re black, white, female, male, young, or old. That’s how you treat them. That’s not the way it needs to work. Instead, imagine you have all that information–more information than any doctor can amass in his head–and you’ve ingested it and you can start to match that person as a query, against that information and all of the applicable drugs side effects and what’s known from clinical trials. You come up with 2 or 3 treatments. Maybe the system says, “Have you considered that if you did this test we would have more confidence in recommending?” It’s a dialogue now. It’s a dialogue that supports the doctor and the patient in their decision on a treatment and that’s the kind of medical care I want. That’s another kind of context.

Suppose you’re an investor. In that case the context is for the portfolio, the personality. Are you conservative? Are you a risk-taker? How old are you? Do you want a lot of data or do you just want to be told what to invest in? Are you an influencer? How old are you? What’s your previous investment history? What are the market trends? What is your investment strategy?

All of those things need to be taken into account. That’s what human investment advisors do, but they’re not all-knowing. Starting with the evidence, the information, and then the ability to make a better judgment instead of a gut, intuitive decision is a very good idea–especially if it’s your money.

Consider the company, CustomerMatrix. They have a sales application. It sits on top of sales force. They do a lot of this. They look at who the sales person is. They look at who the manager is, who the strategist is for the company. They give different answers, but the thing that I’m fascinated by is that they also have ingested your business goals and your business strategy. They will make recommendations according to the usefulness of approaching one prospect or another for acquisition, or sales, or another department based on how a positive outcome will influence the business of the entire company as opposed to just that sales person’s commissions. It’s not a bad idea.

Another example is ExpertSystem. I’m just showing you that this can be very familiar. These systems do the usual text analytics things. They extract sentiment. One example is about a cat that was a popular resident one of the train stations. But they extract things like sadness and give people who are writing news, for instance, a very good idea by comparing the number of hits, number of readers, and number of Tweets, etc., against the kinds of extractions they’ve done already in order to understand better what readers are looking for. This is kind of a building block and the context is this particular event.

Elements of Innovation

What are the elements of innovation? People, collaboration tools, access tools, information of different types, and a work environment that is designed for cross-fertilization.

What kills innovation? Lack of organizational support, party-line thinking, no time to think, too-rigid innovation systems, lack of encouragement of innovation, poor or limited information and information access, and of course, information overload. We want lots of information but we don’t want too much. That’s a tall order for knowledge management.

Re-imagining KM

Can we re-imagine knowledge management? What can we do to give us a sort of informed serendipity? How do we do this? Cognitive computing can help with this, but there are some changes we need to make–not just in our systems and our tools, but also in our thinking. To bring us back to that DNA metaphor, we need to get away from structures in some cases. They’re useful, but not only do we need to capture information and conserve it, we also need to cut it loose. We need to loosen our grip on the information bits that are attached to those taxonomies, that are contained on those cells of the databases, and that are in the document and the text analytics systems categorized to within an inch of their lives.

Let them loose. Let them float around, bump into each other, and give innovators the opportunity to create their own information soup, if you will, to explore without forcing them into the structures that we have created. Because what they want to do is to find the unexpected by creating schemas and taxonomies we are giving them what we expect in terms of how information works. This is a tall order for knowledge management, and I leave it with you as a challenge and a question.

Asking the right question

This week, a research group asked me, “For cognitive computing to take off, what advances do you think we need to make the in next five years?” I answered the question, first listing the major components of a cognitive system, and then discussing which ones were still fairly primitive. But the question continues to haunt me. The fact is that we’ve had most of the components for cognitive computing for a very long time. Language understanding, machine learning, categorization, voting algorithms, search, databases, reporting and visualization tools, genetic algorithms, inferencing, analytics, modeling, statistics, speech recognition, voice recognition, haptic interfaces, etc., etc. I was writing about all of these in the 1990’s. As hardware capacity and architectures have advanced, and our understanding of how to use these tools has evolved, we have finally been able to put all these pieces together. But the fact is, that we have had them for decades.

Here’s what we don’t have: an understanding of how people and systems can interact with each other comfortably. We need to understand and predict the process by which people interact to question, remove ambiguity, discuss and decide. Then we need to translate that process into human-computer terms. Even more, we need a change in attitude among developers and users. Today, we tend to think about the applications we develop in a vacuum. The human initiates a process and then stands back. The machine takes the query, the problem statement, and processes it, spitting out the answer at the end. Users, because of their expectations that machines will not be information partners, helping the information problem to evolve and then finally be resolved.

That’s not the way a human information interaction happens. If two people exchange information, they first negotiate what it is they are going to discuss. They remove ambiguity and define scope. They refine, expand or digress. This process certainly answers questions, but it does more: it builds trust and relationships, and it explores an information space rather than confining itself to the original question. That’s what we need to improve human-computer interactions: first, help in understanding the question. Then, we need better design to enable that question to evolve over time as we add more information, resolve some pieces and confront more puzzles.

What Does Watson Know About Me

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.

Here’s what Watson deduced:

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.

*Compared to most people who participated in our surveys
One more question, Watson, do you ever give someone a negative analysis?
[FYI:  here’s what I pasted into the box that resulted in this analysis:

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.]

What makes a good data scientist?

Ken Rudin, Director of Analytics at Facebook, spoke at HP’s Big Data Conference in Boston today. He attacked the four myths of big data:

  • That you need Hadoop to attack big data
  • That big data gives you better answers
  • That data science is a science
  • That the reason for big data is to get “actionable insights” from the data.

Of course, there is a kernel of truth in all of these, but there are many tools that are useful in big data, and the answers you get from it are only as good as the questions you ask. Perhaps the most important point he made is that data science is both a science and an art. Those of us who have been in some part of the information industry for longer than we care to admit agree with him. You certainly need the tools, and being a whiz in the “how” of finding and analyzing information is important. That’s the science.

But it’s only half the battle. Knowing how to ask a good question is an art. Good askers of questions must be good listeners. They are steeped in the background of the organization. They absorb the underlying reasons for why information is needed, and how it will be used. Information analysis is a way station toward an action. It’s part of the process of gathering evidence to support a decision. If you just gather information for the sake of having it, it may be interesting, but it’s not useful.

What Rudin said is that our approach to why we gather information is evolving. It has moved from “Tell me our status” to “Tell me why it’s happening” to today’s, “What should I do about it?” But, he says, that’s not enough. Because you also have to decide to act on that recommendation in order to change a process, change a metric, change a policy or change behavior. People who can ask the right questions, balance the science and the art, and act on the conclusions will redefine the role of the data scientist or the analyst in the organization. And change the organization in the process.

We agree.

Prezi: Seeking Cloud-Based Search that “Just Works”

During the course of our research on search in the cloud, we’ve been collecting some case studies on uses of SaaS-delivered search. Two of the major reasons that companies give us for moving their search to a cloud-based model are that first, they need a scalable, flexible model that can vary with the demands of the business, and second, that search is not their core business so they prefer to rely on outside experts who can deliver a solid reliable foundation on which they can build specialized applications.

Businesses that are dynamic information exchanges require this kind of scalable reliability. They need to make their information available quickly, and cater to the dynamics of their users. Search is critical to their business. Prezi (prezi.com) is a good example of this kind of use. This cloud-based software company enables its customers to brainstorm and collaborate, create unusual presentations, and share the results, no matter their location or device. Their search needs at this stage are basic—good matching of queries to documents and quick updating of their index. They started with about 200 million documents, but the volume to grow to 1 terabyte, doubling annually. Prezi did not want to hire or develop the expertise to build search from scratch, and they needed flexible, scalable search to match their growing business. Their customers need to find materials both they and others have developed, and they want to find images by topic without the time consuming delays of creating and standardizing tags.

To make its materials searchable quickly and easily, Prezi developed a database of images that are associated with the text in the same slide. The contents change constantly, however, and they need to upload those images and make them searchable automatically using the related text. Furthermore, they anticipate adding and indexing new sources. For this purpose, they envisioned using search as “a materialized view over multiple sources.” In other words, a single gateway to all their information.

To accomplish this, they needed stable, reliable and expandable search. The materials had to be accessible to its users no matter their device or location. Peter Neumark, a Prezi software engineer told us that they were looking for search that they could “pay for, use and forget about.”

Selecting a Search Infrastructure

Prezi’s previous search solution was slow, and didn’t function well enough as a key-value store. They also required a solution that allowed them to relate an image to its neighboring text easily. They decided to look at Amazon’s CloudSearch to solve these problems and deliver relevant material to searchers quickly and reliably. In other words, they were looking for search that “just worked”. They didn’t want to maintain it themselves, and, because they were familiar with them, they wanted to continue to use the AWS API’s, which they like.

When they did head-to-head testing, they found that CloudSearch was cheaper, faster, more reliable and expandable, and easier to synch with their Amazon DynamoDB database. They liked its auto-scaling features that would grow with their data and their business.

Rolling out CloudSearch and Future Plans

Prezi are “happy campers”. They deployed CloudSearch in 3 weeks, and are seeing lower cost, lower latencies, and virtually no need to pay attention to their basic search foundation. Their next step will be to roll out additional domains and sources. They like the idea of adding domains rather than changing the initial schema. They will also make the search function more visible on their site, now that they no longer need to worry about its reliability and speed.

IBM’s Watson Expands its Toolbox: Acquires AlchemyAPI

With its acquisition last week of AlchemyAPI, IBM’s Watson Group added new, tools and expertise to its already-rich and growing array. Alchemy API’s technology complements and expands the core IBM Watson features. It collects and organizes information with little preparation, making it a quick on-ramp for building a collection of information that is sorted and searchable. It works across subject domains, and doesn’t require the domain expertise that the original Watson required. Its unsupervised deep learning architecture is designed to extract order from large collections of information, including text and images, across domains.

In contrast, the original Watson tools used to understand, organize and analyze information demands some subject expertise. For best results, experts are required to build ontologies and rules for extracting facts, relationships and entities from text. The result is a mind-boggling capability to hypothesize, answer questions, and find relationships, but it takes time to build and is specific to a particular domain. That is both good and bad, because they provide a depth of understanding, but at a significant cost in terms of time to get up and running. The Watson tools are also text-centered, although significant strides have been made to add structured information as well as images and other forms of rich media.

AlchemyAPI was designed to solve precisely these problems. It creates a graph of entities – and the relationships among them, with no prior expectations for how this graph will be structured. It is entirely dependent on what information is in the collection. Again, this is both good and bad. Without subject expertise, topics that are not strongly represented in the collection may be missing or get short shrift. Both approaches have their limits, as well as their advantages. Experts add a level of topic understanding—of expectations—of what might be required to round out a topic. Machines don’t. But machines often uncover relationships, causes and effects, or correlations that humans might not expect. Finding surprises is one of the strongest arguments for investing in big data and cognitive computing.

In this acquisition, Watson continues the path that helped it win Jeopardy!—by combining every possible tool and approach that might increase understanding. IBM can now incorporate multiple categorizers, multiple schemas, multiple sources, and multiple views and then compare the results by the strength of their evidence. This gives us more varied and rich results since each technology contributes something new and crucial. Like the best human analysts, the system collects evidence, sorts through it, weighs it, and comes to more nuanced conclusions.

The Watson platform adds a major piece to information systems that is often unsung. It orchestrates the contributions of the technologies so that they support, balance and inform each other. It feeds back answers, errors, and user interactions to the system so that Watson learns and evolves, as a human would. In this, it removes some of the maddening stodginess of traditional search systems that give us the same answers no matter what we have learned. In seeking answers to complex, human problems, we need to find right answers, perhaps some wrong answers to sharpen our understanding, and certainly the surprises that lurk within large collections. We want a system that evolves and learns, not one that rests on the laurels of a static, often outdated ontology.

Mirroring this technology architecture, the IBM’s Watson Group similarly requires a group of closely knit, strong minded people who are experts in their separate areas of language understanding, system architecture, voting algorithms, user interaction, probability, logic, game theory, etc. Alchemy contributes its staff of deep learning experts, who are expected to join the Watson Group. It also brings its 40,000 developers worldwide, who will broaden the reach and speed the adoption of cognitive computing.

LifeLearn Sofie: A Cognitive Veterinarian’s Assistant

Like human medicine, veterinary medicine has leaped into the digital age, embracing big data, telemedicine, online access for customers, online education for practitioners, digital marketing, and social media. Both sets of practitioners are also under increasing pressure to handle more patients in less time, and to keep up with a growing body of research that becomes outdated quickly.

However, there are some key differences between human and animal medical practitioners. Complex as human medicine is, it still targets only one species. Veterinarians, however, must be prepared to deal with everything from anacondas to zebras, and conditions that range from general wellness and internal medicine to cardiology, oncology and beyond. And, their patients can’t talk.

LifeLearn is a spin-off from the University of Guelph’s Ontario Veterinary College in Ontario, Canada. When they were founded 21 years ago, it was with the goal of providing educational and support services, resources, technology and tools to veterinary practices. As the field has evolved, though, so have they. LifeLearn’s Innovations Group is betting on new technologies like digital monitoring devices for animals to provide solid data on patients.

When the chance to partner with IBM’s Watson came along, it seemed to Jamie Carroll, LifeLearn’s CEO and Dr. Adam Little that creating a better digital assistant could solve some of the problems that veterinarians face today. LifeLearn is one of the first partners selected by IBM Watson and is using the technology to develop a cognitive veterinary assistant, called LifeLearn Sofie™, that can ingest massive amounts of data, and forage in real time for clues and connections that will allow a veterinarian to diagnose an animal’s condition quickly and accurately. Like other Watson-based assistants for physicans being developed, LifeLearn’s Sofie is training a veterinary version of Watson that uses the information it has amassed and analyzed to generate evidence-based hypotheses and suggest the best treatment options.

Preparing the content for Watson has been a massive undertaking. Working with leading hospitals, LifeLearn has reduced that process from weeks to hours. The LifeLearn staff have also had to train Watson to answer nuanced complex questions for which there is no single right answer. For each topic, their Watson trainers must create a set of questions that would be germane to a vet working through a case. They are now able to produce 25,000 question/answer pairs per month.

LifeLearn has built not just the underlying knowledge base, but analyzed how veterinarians gather and use information. Based on their decades of experience, they have developed an interactive application that enables veterinarians to ask questions and receive the top answers that are scored for confidence. The system learns from each interaction, and from feedback from users, who are asked to score the responses for relevance, quality of information, appropriate length and depth of answers.

LifeLearn’s goal is to make Sofie a specialist in every corner of veterinary science. To succeed, they must uncover how veterinarians make decisions. But there is an additional challenge: to educate veterinarians to understand the promise and limitations of cognitive computing—that there is no right answer, only some that are more appropriate than others, given the patient, its owner, and the circumstances of the medical condition. Living with uncertainty and complexity, and providing guidance in how to do this as well as possible is the aim of applications like LifeLearn’s Sofie.