Computing, cognition and the future of knowing,. by IBM

Computing, cognition
and the future of knowing
How humans and machines are forging
a new age of understanding
Dr. John E. Kelly III
Senior Vice President,
IBM Research and Solutions Portfolio

32
Those systems have been deterministic; cognitive
systems are probabilistic. They generate not just
answers to numerical problems, but hypotheses,
reasoned arguments and recommendations about
more complex — and meaningful — bodies of data.
What’s more, cognitive systems can make sense
of the 80 percent of the world’s data that computer
scientists call “unstructured.” This enables them
to keep pace with the volume, complexity and
unpredictability of information and systems in the
modern world.
None of this involves either sentience or autonomy
on the part of machines. Rather, it consists of
augmenting the human ability to understand — and
act upon — the complex systems of our society. This
augmented intelligence is the necessary next step
in our ability to harness technology in the pursuit of
knowledge, to further our expertise and to improve
the human condition. That is why it represents not
just a new technology, but the dawn of a new era of
technology, business and society: the Cognitive Era.
The success of cognitive computing will not be
measured by Turing tests or a computer’s ability
to mimic humans. It will be measured in more
practical ways, like return on investment, new market
opportunities, diseases cured and lives saved.
It’s not surprising that the
public’s imagination has
been ignited by Artificial
Intelligence since the term
was first coined in 1955.
In the ensuing 60 years,
we have been alternately
captivated by its promise,
wary of its potential for
abuse and frustrated by
its slow development.
But like so many advanced technologies that were
conceived before their time, Artificial Intelligence
has come to be widely misunderstood —co-opted
by Hollywood, mischaracterized by the media,
portrayed as everything from savior to scourge
of humanity. Those of us engaged in serious
information science and in its application in the
real world of business and society understand
the enormous potential of intelligent systems.
The future of such technology — which we believe
will be cognitive, not “artificial”— has very different
characteristics from those generally attributed
to AI, spawning different kinds of technological,
scientific and societal challenges and opportunities,
with different requirements for governance, policy
and management.
Cognitive computing refers to systems that learn
at scale, reason with purpose and interact with
humans naturally. Rather than being explicitly
programmed, they learn and reason from their
interactions with us and from their experiences with
their environment. They are made possible by
advances in a number of scientific fields over the
past half-century, and are different in important ways
from the information systems that preceded them.
Here at IBM, we have been
working on the foundations of
cognitive computing technology
for decades, combining more
than a dozen disciplines of
advanced computer science with
100 years of business expertise.
Now we are seeing first hand its
potential to transform businesses,
governments and society.
We have seen it turn big data from obstacle to
opportunity, help physicians make early diagnoses
for childhood disease and suggest creative
solutions for building smarter cities. And we believe
that this technology represents our best — perhaps
our only — chance to help tackle some of the
most enduring systemic issues facing our planet,
from cancer and climate change to an increasingly
complex global economy.
The history of computing
and the rise of cognitive
To understand the future of cognitive computing,
it’s important to place it in historical context. To date,
there have been two distinct eras of computing — the
Tabulating Era and the Programming Era — and IBM
has played a central role in the development of both.
We believe cognitive computing is the third and most
transformational phase in computing’s evolution.
(Image 1)
The Tabulating Era (1900s — 1940s)
The birth of computing consisted of single-purpose
mechanical systems that counted, using punched
cards to input and store data, and to eventually
instruct the machine what to do (albeit in a primitive
way). These tabulation machines were essentially
calculators that supported the scaling of both business
and society, helping us to organize, understand, and
manage everything from population growth to the
advancement of a global economy.
The Tabulating Era
(1900s – 1940s)
The Cognitive Era
(2011 – )
The Programming Era
(1950s – present)
Image 1

54
The Programming Era (1950s — present)
The shift from mechanical tabulators to electronic
systems began during World War II, driven by
military and scientific needs. Following the war,
digital “computers” evolved rapidly and moved into
businesses and governments. They performed
if/then logical operations and loops, with instructions
coded in software. Originally built around vacuum
tubes, they were given a huge boost by the invention
of the transistor and the microprocessor, which
came to demonstrate “Moore’s Law,” doubling in
capacity and speed every 18 months for six
decades. Everything we now know as a computing
device — from the mainframe to the personal
computer, to the smartphone and tablet — is a
programmable computer.
The Cognitive Era (2011 — )
The potential for something beyond programmable
systems was foreseen as far back as 1960, when
computing pioneer J.C.R. Licklider wrote his
seminal paper “Man-Computer Symbiosis.” Much
of modern computing is based on Licklider’s
research and insights:
“Man-computer symbiosis is an expected
development in cooperative interaction between
men and electronic computers. It will involve
very close coupling between the human and the
electronic members of the partnership. The main
aims are:
1. to let computers facilitate formulative thinking
as they now facilitate the solution of formulated
problems, and
2. to enable men and computers to cooperate
in making decisions and controlling complex
situations without inflexible dependence on
predetermined programs…
Preliminary analyses indicate that the symbiotic
partnership will perform intellectual operations much
more effectively than man alone can perform them.” 1
— J.C.R. Licklider, “Man-Computer Symbiosis,”
March 1960
Licklider knew that cognitive computing would be
a necessary and natural evolution of programmable
computing, even if he didn’t yet know how it would
be accomplished. Fifty years later, massively
parallel computing and the accumulation of oceans
of structured and unstructured data would lay the
groundwork for cognitive computing.
The world’s first
cognitive system
In February 2011, the world was introduced to
Watson, IBM’s cognitive computing system that
defeated Ken Jennings and Brad Rutter at Jeopardy!.
It was the first widely seen demonstration of
cognitive computing, and it marked the end of the
so-called AI winter. The programmable systems
that had revolutionized life over the previous six
decades could not have made sense of the messy,
unstructured data required to play Jeopardy!.
Watson’s ability to answer subtle, complex,
pun-laden questions made clear that a new era
of computing was at hand.
Since Jeopardy!, Watson has tackled increasingly
complex data sets and developed understanding,
reasoning and learning that go far beyond deciphering.
Indeed, the goal of cognitive computing is to illuminate
aspects of our world that were previously invisible —
patterns and insight in unstructured data, in
particular — allowing us to make more informed
decisions about more consequential matters.
The true potential of the Cognitive Era will be
realized by combining the data analytics and
statistical reasoning of machines with uniquely
human qualities, such as self-directed goals,
common sense and ethical values.
This is what Watson was built to do, and is in fact
already doing. Banks are analyzing customer
requests and financial data to surface insights to
help them make investment recommendations.
Companies in heavily regulated industries are
querying the system to keep up with ever-changing
legislation and standards of compliance. And
oncologists are testing ways in which cognitive
systems can help interpret cancer patients’ clinical
information and identify individualized, evidence-
based treatment options that leverage specialists’
experience and research.
What is the experience of this like for the
professionals involved? World-renowned oncologist
Dr. Larry Norton of the Memorial Sloan Kettering
Cancer Center, which is working with Watson
to help physicians personalize cancer treatments,
says, “Computer science is going to evolve rapidly,
and medicine will evolve with it. This is coevolution.
We’ll help each other. I envision situations where
myself [sic], the patient, the computer, my nurse
and my graduate fellow are all in the examination
room interacting with one another.” 2
We first saw hints of this symbiosis after Watson’s
chess-playing predecessor, Deep Blue, defeated
World Chess Champion Garry Kasparov in 1997.
Following that demonstration, Kasparov would go
on to participate in new “freestyle” chess leagues,
in which players are free to use any computer
programs they like. In these leagues, some players
compete unassisted. Some rely entirely on computer
programs. But those players who combine computer
input with their intuition and competitive instincts
are the most successful. 3
“Teams of human plus machine dominated even the
strongest computers. Human strategic guidance
combined with the tactical acuity of a computer was
overwhelming. We [people] could concentrate on
strategic planning instead of spending so much time
on calculations. Human creativity was even more
paramount under these conditions.” 4
— Garry Kasparov
The technical path
forward and the science
of what’s possible
While Licklider helped inform a philosophical
approach to cognitive computing, there was little he
could do to articulate the technical path forward.
That path is still being defined, constantly adjusting
as the world outside our computer labs evolves.
In particular, we are keenly aware of how data is
shaping the future. Gartner estimates that the
world’s information will grow by 800 percent in the
next five years, and that 80 percent of that data
will be unstructured. It includes everything humans
have recorded in language (from textbooks to
poems), captured in images (CAT scans to family
photos) and recorded in sounds. It is the data
hidden in aromas, tastes, textures and vibrations.
It comes from our own activities, and from a planet
being pervasively instrumented.

76
In a global economy and society where value
increasingly comes from information, knowledge
and services, this data represents the most
abundant, valuable and complex raw material in
the world. And until now, we have not had the
means to mine it effectively.
Programmable systems are based on rules that
shepherd data through a series of predetermined
processes to arrive at outcomes. While they are
powerful and complex, they are deterministic —
thriving on structured data, but incapable of
processing qualitative or unpredictable input.
This rigidity limits their usefulness in addressing
many aspects of a complex, emergent world,
where ambiguity and uncertainty abound.
Cognitive systems are probabilistic, meaning
they are designed to adapt and make sense of the
complexity and unpredictability of unstructured
information. They can “read” text, “see” images
and “hear” natural speech. And they interpret that
information, organize it and offer explanations of
what it means, along with the rationale for their
conclusions. They do not offer definitive answers.
In fact, they do not “know” the answer. Rather,
they are designed to weigh information and ideas
from multiple sources, to reason, and then offer
hypotheses for consideration. A cognitive system
assigns a confidence level to each potential insight
or answer.
A mistake Watson made during Jeopardy! illustrates
this. At the end of the first day of competition,
the category for “Final Jeopardy” was “US Cities,”
and the clue was, “Its largest airport was named
for a World War II hero; its second largest, for a
World War II battle.” The answer is Chicago (O’Hare
and Midway). Watson guessed, “What is Toronto?????”
There are many reasons why Watson was confused
by this question, including its grammatical structure,
the presence of a city in Illinois named Toronto
and the Toronto Blue Jays playing baseball in the
American League.
As a result, Watson’s confidence level was incredibly
low: 14 percent. If this had been a regular Jeopardy!
clue, where contestants must buzz in, rather than
one in “Final Jeopardy,” that confidence level would
have caused Watson not to press its buzzer. Watson
knew what it didn’t know as indicated by the five
question marks shown in Image 2.
However, cognitive systems
can learn from their mistakes.
Large-scale machine learning
is the process by which
cognitive systems improve
with training and use.
After ingesting a corpus of knowledge, curated by
experts on any given subject, cognitive systems are
trained by being fed a series of question-and-answer
pairs. This machine “knowledge” is then enhanced
as humans interact with the system, providing
feedback on the accuracy of the system’s responses.
When Watson played Jeopardy!, it did one thing —
natural language QA, based on five technologies.
Today, QA is only one of many Watson capabilities
available as an application programming interface.
Since then, we have developed more than two
dozen new APIs, powered by 50 different cognitive
technologies. This is a critical distinction between the
technical approach to cognitive computing and other
current approaches to Artificial Intelligence. Cognitive
computing is not a single discipline of computer
science. It is the combination of multiple academic
fields, from hardware architecture to algorithmic
strategy to process design to industry expertise.
Many products and services
that we use every day — from
search-engine advertising
applications to facial recognition
on social media sites to
“smart” cars, phones and
electric grids — are beginning
to demonstrate aspects of
Artificial Intelligence.
Most consist of purpose-built, narrowly focused
applications, specific to a particular service. They
use a few of the core capabilities of cognitive
computing. Some use text mining. Others use image
recognition with machine learning. Most are limited
to the application for which they were conceived.
Cognitive systems, in contrast, combine five
core capabilities:
1. They create deeper human engagement:
Cognitive systems create more fully human
interactions with people — based on the mode,
form and quality each person prefers. They take
advantage of the data available today to create
a fine-grained picture of individuals — such as
geolocation data, web interactions, transaction
Image 2
history, loyalty program patterns, electronic
medical records and data from wearables —
and add to that picture details that have been
difficult or impossible to detect: tone, sentiment,
emotional state, environmental conditions and the
strength and nature of a person’s relationships.
They reason through the sum total of all this
structured and unstructured data to find what really
matters in engaging a person. By continuously
learning, these engagements deliver greater
and greater value, and become more natural,
anticipatory and emotionally appropriate.
2. They scale and elevate expertise:
Every industry’s and profession’s knowledge is
expanding at a rate faster than any professional
can keep up with — journals, new protocols, new
legislation, new practices and entirely new fields.
A clear example is found in healthcare, where it
is estimated that in 1950, it took 50 years to
double the world’s medical knowledge; by 1980,
seven years; and in 2015, less than three years.
Meanwhile, each person will generate one million
gigabytes of health-related data in his or her
lifetime, the equivalent of about 300 million books.
Cognitive systems are designed to help
organizations keep pace, serving as a companion
for professionals to enhance their performance.
Because these systems master the language of
professions — the language of medicine, or sales,
or cuisine — they can both understand and
teach complex expertise. This reduces the time
required for a professional to become an expert.
In addition, because these systems are taught
by leading practitioners — whether in customer
service, oncology diagnosis, case law or any other
field — they make available to broad populations
the know-how of the best.

98
3. They infuse products and services
with cognition:
Cognition enables new classes of products and
services to sense, reason and learn about their
users and the world around them. This allows for
continuous improvement and adaptation, and for
augmentation of their capabilities to deliver uses
not previously imagined. We see this happening
already with cars, medical devices, appliances and
even toys. The Internet of Things is dramatically
expanding the universe of digital products and
services — and where code and data go,
cognition can now follow.
4. They enable cognitive processes
and operations:
Cognition also transforms how a company
operates. Business processes infused with
cognitive capabilities capitalize on the
phenomenon of data, from internal and external
sources. This gives them heightened awareness
of workflows, context and environment, leading
to continuous learning, better forecasting and
increased operational effectiveness — along with
decision-making at the speed of today’s data.
This is good news in a world where, for example,
an average billion-dollar company spends
almost 1,000 person hours per week managing
its suppliers.
5. They enhance exploration and discovery:
Ultimately, the most powerful tool that cognitive
businesses will possess is far better “headlights”
into an increasingly volatile and complex future.
Such headlights are becoming more important
as leaders in all industries are compelled to place
big bets — on drug development, on complex
financial modeling, on materials science innovation,
on launching a startup. By applying cognitive
technologies to vast amounts of data, leaders
can uncover patterns, opportunities and
actionable hypotheses that would be virtually
impossible to discover using traditional research
or programmable systems alone.
If cognitive computing is to fulfill its true promise,
the underlying platform must be broad and flexible
enough to be applied by any company in any
industry. And it must be able to be applied across
industries. To do that requires a holistic approach to
research and development, with the goal of creating
a robust platform with a range of capabilities to
support diverse applications from an ecosystem
of developers.
This platform must encompass machine learning,
reasoning, natural language processing, speech
and vision, human-computer interaction, dialog and
narrative generation and more. Many of these
capabilities require specialized infrastructure that
leverages high-performance computing, specialized
hardware architectures and even new computing
paradigms. Each grows from its own scientific or
academic field. But these technologies must be
developed in concert, with hardware, software, cloud
platforms and applications that are built expressly
to work together in support of cognitive solutions.
With Watson’s rapid evolution, we are getting a
glimpse of what is possible. One example is a
cognitive medical imaging application that analyzes
X-rays, MRIs and ultrasound images. It processes
the natural language of medical journals, textbooks
and articles. It uses machine learning to correct and
improve its understanding. And it develops deep
knowledge representations and reasoning that can
help surface possible diagnoses. To do this requires
specialized graphic processing units to support
the large scale of data and human domain expertise
to guide its learning and interpret its results.
The power of this new model can be applied to any
domain. Oil and gas companies can combine
seismic imaging data with analyses of hundreds of
thousands of papers and reports, current events,
economic data and weather forecasts to create risk
and reward scenarios for exploratory drilling. Or, by
analyzing test scores, attendance and information
about student behavior on digital learning platforms,
schools can create longitudinal student records
and personalized education plans.
In one of the most promising near-term applications
of cognitive computing, IBM is working with more
than a dozen leading cancer institutes to accelerate
the ability of clinicians to identify and personalize
treatment options for patients. The program seeks
to reduce from weeks to minutes the time it takes to
translate DNA insights, understand an individual’s
genetic profile and gather relevant information from
medical literature. The resulting analysis allows
doctors to target the specific cancer-causing
genetic mutations in each patient. Watson completes
the genetic material and medical literature review
process in only a few minutes, producing a report
and data visualization of the patient’s case and
evidence-based insights on potential drugs that may
be relevant to an individual patient’s unique DNA
profile. The clinician can then evaluate the evidence
to determine whether a targeted therapy may be
more effective than standard care for the patient.
Implications and
obligations for
the advance of
cognitive science
The Cognitive Era is the next step in the application
of science to understand nature and improve the
human condition. In that sense, it is a new chapter of
a familiar story, and the controversy surrounding
Artificial Intelligence is merely the latest example of
the age-old debate between those who believe in
progress and those who fear it. Within the scientific
community — as opposed to the media and popular
entertainment — the verdict is in. There is broad
agreement on the importance of pursuing a cognitive
future, along with recognition of the need to develop
the technology responsibly.
“Technology creates possibilities and potential,
but ultimately, the future we get will depend on the
choices we make. Technology is not destiny.
We shape our destiny.” 5
— Erik Brynjolfsson, MIT
Specifically, we must continue to shape the effect of
cognitive computing on work and employment. Like
all technology, cognitive computing will change the
nature of work done by people. It will help us perform
some tasks faster and more accurately. It will make
many processes cheaper and more efficient. It will
also do some things better than humans, which has
been the case since the dawn of civilization. What
has always happened is that higher value is found in
new skills, and humans and our institutions adapt
and evolve. There is no reason to believe it will be
different this time. Indeed, given the exponential
growth in knowledge, discovery and opportunity
opened up by a Cognitive Era, there is every reason

1110
to believe that the work of humans will become
ever-more interesting, challenging and valuable.
Equally important is the need for societal controls
and safeguards. Again, such concerns are not
unique to intelligent systems. Questions about
security — both individual and institutional — attach
themselves to every transformational technology,
from automobiles to pharmaceuticals to mobile
phones. These issues are already urgent, and
will remain so as cognitive technologies develop.
They are fueled especially by today’s radical
democratization of technology — driven by the
rapid spread of networks and the cloud, and
the accompanying reduction in costs.
We believe that the answer lies not in attempting
to limit that democratization, but rather in embracing
it while designing cognitive systems with privacy,
security and human control integrated into their fabric.
Paving the way for
the next generation
of human cognition
In the end, all technology
revolutions are propelled not
just by discovery, but also
by business and societal
need. We pursue these new
possibilities not because we
can, but because we must.
As with every revolutionary technology, our initial
understanding will be limited — both by the world’s
complexity and by our own deeply ingrained biases
and heuristics. However, for all these limitations,
progress is imperative. Indeed, we pay a significant
price for not knowing: not knowing what’s wrong
with a patient; not knowing which part of the
population wants to buy a product; not knowing
where to find critical natural resources; not knowing
where the risks lie in every investor’s portfolio.
“The greatest bar to wise action and the greatest
source of fear is ignorance. A tiny candle gives
misleading light and throws huge and ominous
shadows. The sun at noon gives great light
and throws no shadows. It is time to get this whole
problem of men and machines under a blazing
noonday beam. Computers will never rob man
of his initiative or replace the need for his creative
thinking. By freeing man from the more menial
or repetitive forms of thinking, computers will
actually increase the opportunities for the full use
of human reason.” 6
— Thomas Watson Jr., former Chairman and CEO
At IBM, we believe that many of the perennial
problems of the world can be solved — and we
believe that cognitive computing is the tool that will
help us accomplish this ambitious goal.
The much-hyped drama of “man vs. machine”
is a distraction from that, and it rests on an exciting
but misguided fiction. Cognitive systems are not
our competitor, nor will they become so. Neither the
science nor the economics support such fears.
Cognitive systems, as they actually exist, are a tool
to deepen the relationship that really matters — the
relationship between humans and the world.
In so doing, we will pave the way for the next
generation of human cognition, in which we think
and reason in new and powerful ways. It’s true that
cognitive systems are machines that are inspired
by the human brain. But it’s also true that these
machines will inspire the human brain, increase our
capacity for reason and rewire the ways in which
we learn. In the 21st century, knowing all the answers
won’t distinguish someone’s intelligence — rather,
the ability to ask better questions will be the mark
of true genius.

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Produced in the United States of America
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All Rights Reserved
IBM, the IBM logo and ibm.com are trademarks or registered
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in which IBM operates.
1. Licklider, J.C.R. Man-Computer Symbiosis. IRE Transactions on
Human Factors in Electronics. 1960. http://groups.csail.mit.edu/
medg/people/psz/Licklider.html
2. Kelly, John E. and Steve Hamm. Smart Machines: IBM’s Watson
and the Era of Cognitive Computing. New York. Columbia
University Press, 2014.
3. Kelly, Kevin. The Three Breakthroughs That Have Finally
Unleashed AI on the World. Wired Magazine. October, 2014.
4. McAfee, Andrew. Did Garry Kasparov Stumble into a New
Business Process Model? Harvard Business Review. February,
2010. https://hbr.org/2010/02/like-a-lot-of-people/
5. Brynjolfsson, Erik and Andrew McAfee. The Second Machine
Age: Work, progress, and prosperity in a time of brilliant
machines. New York. W.W. Norton Company, 2014.
6. Watson, Thomas Jr. IBM Archives. https://www-03.ibm.com/
ibm/history/exhibits/watsonjr/watsonjr_quoted.html

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