Cognitive Computing and

Knowledge Management:

Sparking Innovation By Sue Feldman

See more at cognitivecomputingconsortium.com.

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Editor's note: The following article is a transcript of a video of Sue Feldman's keynote session at

KMWorld 2015 in Washington, DC.

nnovation 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.

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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.

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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.

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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

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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

What is Cognitive Computing?

What goes into cognitive computing? There's a certain amount of natural language involved.

That's absolutely true. It's probabilistic, it's non-deterministic and it drives a lot of IT people crazy

because they expect the answer, not some possible answers. It's very iterative. It's

conversational. It's contextual. It learns in-depth about not only you but about everybody else

using the system, and it keeps getting smarter.

Machine learning is a requirement. It usually has a big data knowledge base. That means that if

you have lots of data, even though somebody is living in the long tail in terms of his needs or

profiles, you still have enough data, enough evidence to be able to understand that individual.

You're not just aiming your business at the center of the bell-shaped curve. Analytics are built in

various kinds and these technologies are highly integrated. It's not services-oriented

architecture, though that's also important. They influence each other.

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What Do Cognitive Systems Do?

What do these systems do? They analyze big data. They understand human language on

multiple levels. They uncover relationships across sources. They understand and filter by

context. They find patterns in the data that you didn't know existed. They find the black swans

among the white swans, the surprises.

This is extremely valuable to competitive organizations, governments, and individuals. They

learn from new information and new interactions. As you use the system, it gets better--we

hope.

What is the next leap? For example, we can find drugs in the database of drugs and even side

effects of drugs that are useful for controlling diabetes, but what's the best drug in this

circumstance for this patient? We're looking for best, not just a list.

Or: Who's funding this terrorist organization, and how are the funds delivered? This is also a

really useful question and one we've been asking for a long time. How big a threat is this

organization? Can you make a recommendation about the level at which you need to react to

what's going on?

Another example: Can you identify the most risky product or customer problems? It's very

difficult to know what to pay attention to in the world of information overload and big data.

That's the kind of thing that you would want a bunch of humans to sit down and discuss, but

there's an awful lot of information to weigh in through. Can we make the system a partner? Can

the system make some recommendations, and then sit down and discuss it? We'll probably find

stuff that we didn't know.

What Cognitive Computing Isn’t

Now we know what cognitive computing is. But what is it not?

Cognitive computing is more than big data or artificial intelligence. There are books coming out

that say that machine learning is cognitive. I disagree, and so did the group of experts we

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convened to define this topic. It's not robotics. It's not drones. It's not humanoid. It's not entirely

autonomous.

At least, that's what we feel right now. It is not a singularity and it's not a replacement for

humans. It's an aid. It's another tool for us so that we can begin to understand our world and our

problems and solve them more effectively.

What Does a Cognitive System Look Like?

Your senses take in clues from all over and then they discuss what they have found with other

people in innovation. Both of those are really important but, those synapses, those connections

are something that is very hard to mimic.

A traditional information system is very linear and sequential. You ask the question, it goes into

the index, you've indexed all the documents, the documents get matched to the question usually

with the terms in the question, the system outputs the information, and then you make a

decision. The problem is, there really isn't enough interaction. It's almost as if the designers felt

that people couldn't be trusted and therefore we had to have a wall between what we had in our

heads and what they have in the system.

In a cognitive system, instead of a direct question you have a problem statement or exploration,

and there's a great deal of iteration. One of the things that was a huge breakthrough for Watson

on Jeopardy was analyzing the question. Before they ever threw the question into the system,

they defined what kind of question it was and what kind of answer might be required. They had

hundreds of different question types for Jeopardy. This is what people do too, right? If I ask

someone in Washington, D.C., "How do I get to Reagan?" You're not going to tell me stuff about

President Reagan because you're a person and you know what my context is.

By analyzing who, what, where, when types of questions, by extracting the elements of those

questions, we are already ahead. More than that, when we ask a query we usually focus it down

and make it as specific as possible. We have to because we're going to get garbage otherwise.

That's not what we really want to do. At this point, especially in innovation, we want to explore. If

we're exploring, we want to expand and create hypotheses. We'll use each of those hypotheses

to collect evidence to support or to deny that particular hypothesis.

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That's what these systems do--particularly the Watson-like ones. We send in an expanded

query or problem statement to a system that has done the orchestration of how to answer that

particular kind of question. Different kinds of questions are more effectively answered by

different combinations of tools. You may have four categorizers in one of these systems. How

that question is answered depends on the type of question, which array of categorizers is most

useful, and which one needs to have priority.

After we’ve done the question analysis and expansion, it goes into a cognitive processor that is

somewhat analogous to the index that hits the information store and does the similarity

matching. Out comes, perhaps, a huge pile of information. But confidence scores have been

assigned, evidence has either proven or disproven some of the hypotheses, or perhaps they've

been combined. There is lots of stuff going on in there. There are sometimes voting algorithms

to help with the confidence scoring. For Jeopardy, there was game theory, which is why you got

such crazy wagers like, I'll wager $727 on this question, which is not a very human kind of thing

to do. But apparently, Watson knew that it could afford to lose $727 but not 728. Anyway, the

output is a data set. The filters are the “who,” “what,” “where,” “when” of who the person is,

where they are in the task, what they've asked before.

The hypotheses are filtered down and sent to the exploration loop. The exploration loop is a set

of tools that help visualize and analyze the information. At each step, what the system has done

in terms of interaction, question answering, filtering, and analyzed data sets gets thrown back

into their cognitive processor, making it smarter, more dynamic. That's important too because it

follows you along in the process.

Because computers don't have your senses, in order to accomplish this, we have an array of

technologies that work together. These include facial recognition, rich media understanding,

content-intelligent services, machine learning, real-time voice translation, speech recognition,

and taxonomies. Any tool that's useful to solve a particular kind of problem needs to be bundled

into these systems.

Right now, at the dawn of the cognitive age, we're still trying to figure out what configurations

work best for which problems. That's the kind of research that I'm working on right now because

people keep asking me, what kind of problem is this good for and what do I look at? What are

the tools?

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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.

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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

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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 an 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

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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.

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Author biography

Sue Feldman, Co-Founder of the Cognitive Computing Consortium, CEO and Founder of

Synthexis

Sue Feldman is founder and CEO of Synthexis, a consulting firm that provides business

advisory services to vendors and buyers of cognitive computing, search and text analytics

technologies. Since 1990, she has been instrumental in shaping market research and

understanding in search and text analytics. She speaks frequently at industry events on topics

such as trends in computing, conversational systems, big data technologies, and the hidden

costs of information work.

In her book, The Answer Machine (Morgan & Claypool, 2012), Sue discusses the technologies

behind information seeking and analysis, and their central role in the future of computing. Before

founding Synthexis, Sue was Vice President for Search and Discovery Technologies at IDC

(International Data Corporation), where she directed research on the technologies and markets

for search, text analytics, categorization, translation, mobile and rich media search. Ms.

Feldman holds degrees from Cornell University in linguistics and from the University of Michigan

in information science.

Reach her sue@synthexis.com.