Just How Smart Are Smart Machines?
http://sloanreview.mit.edu/article/just-how-smart-are-smart-machines/[13/04/2016 1:20:59 PM]
Just How Smart Are Smart Machines?
Magazine: Spring 2016 • Research Highlight • March 15, 2016 • Reading Time: 14 min
Thomas H. Davenport and Julia Kirby
Topics
Data & Analytics, Talent Management, Big Data,
Analytics & Organizational Culture, Digital
Business, IT Strategy
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The number of sophisticated cognitive technologies that might be
capable of cutting into the need for human labor is expanding rapidly.
But linking these offerings to an organization’s business needs requires
a deep understanding of their capabilities.
If popular culture is an accurate gauge of what’s on the public’s mind, it
seems everyone has suddenly awakened to the threat of smart
machines. Several recent films have featured robots with scary abilities
to outthink and manipulate humans. In the economics literature, too,
there has been a surge of concern about the potential for soaring
unemployment as software becomes increasingly capable of decision
making. Yet managers we talk to don’t expect to see machines
displacing knowledge workers anytime soon — they expect computing
technology to augment rather than replace the work of humans. In the
face of a sprawling and fast-evolving set of opportunities, their
challenge is figuring out what forms the augmentation should take.
Given the kinds of work managers oversee, what
cognitive technologies should they be applying now,
monitoring closely, or helping to build?
To help, we have developed a simple framework that
plots cognitive technologies along two dimensions.
(See “What Today’s Cognitive Technologies Can

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Just How Smart Are Smart Machines?
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— and Can’t — Do.”) First, it recognizes that these
tools differ according to how autonomously they
can apply their intelligence. On the low end, they
simply respond to human queries and instructions;
at the (still theoretical) high end, they formulate
their own objectives. Second, it reflects the type of
tasks smart machines are being used to perform,
moving from conventional numerical analysis to
performance of digital and physical tasks in the real
world. The breadth of inputs and data types in realworld tasks makes them more complex for machines
to accomplish.
By putting those two dimensions together, we create a matrix into
which we can place all of the multitudinous technologies known as
“smart machines.” More important, this helps to clarify today’s limits
to machine intelligence and the challenges technology innovators are
working to overcome next. Depending on the type of task a manager is
targeting for redesigned performance, this framework reveals the
various extents to which it might be performed autonomously and by
what kinds of machines.
What Today’s Cognitive Technologies Can — and Can’t —
Do
Mapping cognitive technologies by how autotnomously they work and the tasks they perform shows the current
state of smart machines — and anticipates how future technologies might unfold.
Just How Smart Are Smart Machines?
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Four Levels of Intelligence
Clearly, the level of intelligence of smart machines is increasing. The
general trend is toward greater autonomy in decision making — from
machines that require a highly structured data and decision context to
those capable of deciphering a more complex context.
Support for Humans
For decades, the prevailing assumption has been that cognitive
technologies would provide insight to human decision makers — what
used to be known as “decision support.” Even with IBM Corp.’s
Watson and many of today’s other cognitive systems, most people
assume that the machine will offer a recommended decision or course
of action but that a human will make the final decision.
Repetitive Task Automation
It is a relatively small step to go from having machines support humans
to having the machines make decisions, particularly in structured
contexts. Automated decision making has been gaining ground in
recent years in several domains, such as insurance underwriting and
financial trading; it typically relies on a fixed set of rules or algorithms,
so performance doesn’t improve without human intervention.
Typically, people monitor system performance and fine-tune the
algorithms.
Context Awareness and Learning
Sophisticated cognitive technologies today have some degree of realtime contextual awareness. As data flow more continuously and
voluminously, we need technologies that can help us make sense of the
data in real time — detecting anomalies, noticing patterns, and
anticipating what will happen next. Relevant information might include
location, time, and/or a user’s identity, which might be used to make
recommendations (for example, the best route to work based on the
time of day, current traffic levels, and the driver’s preference for
highways versus back roads).
One of the hallmarks of today’s cognitive computing is its ability to
learn and improve performance. Much of the learning takes place
through continuous analysis of real-time data, user feedback, and new
Just How Smart Are Smart Machines?
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content from text-based articles. In settings where results are
measurable, learning-oriented systems will ultimately deliver benefits
in the form of better stock trading decisions, more accurate driving
time predictions, and more precise medical diagnoses.
Self-Awareness
So far, machines with self-awareness and the ability to form
independent objectives reside only in the realm of fiction. With
substantial self-awareness, computers may eventually gain the ability to
work beyond human levels of intelligence across multiple contexts, but
even the most optimistic experts say that general intelligence in
machines is three to four decades away.
Four Cognitive Task Types
A straightforward way to sort out tasks performed by machines is
according to whether they process only numbers, text, or images — the
building blocks of cognition — or whether they know enough to take
informed actions in the digital or physical world.
Analyzing Numbers
The root of all cognitive technologies is computing machines’ superior
performance at analyzing numbers in structured formats (typically,
rows and columns). Classically, this numerical analysis was applied
purely in support of human decision makers. People continued to
perform the front-end cognitive tasks of creating hypotheses and
framing problems, as well as the back-end interpretation of the
numbers’ implications for decisions. Even as analysts added more
visual analytics displays and more predictive analytics in the past
decade, people still did the interpretation.
Today, companies are increasingly embedding analytics into operational
systems and processes to make repetitive automated decisions, which
enables dramatic increases in both speed and scale. And whereas it
used to take a human analyst to develop embedded models, “machine
learning” methods can produce models in an automated or
semiautomated fashion.
Analyzing Words and Images
A key aspect of human cognition is the ability to read words and images
and to determine their meaning and significance. But today, a wide
Just How Smart Are Smart Machines?
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variety of technological tools, such as machine learning, natural
language processing, neural networks, and deep learning, can classify,
interpret, and generate words. Some of them can also analyze and
identify images.
The earliest intelligent applications involving words and images
involved text, image, and speech recognition to allow humans to
communicate with computers. Today, of course, smartphones
“understand” human speech and text and can recognize images. These
capabilities are hardly perfect, but they are widely used in many
applications.
When words and images are analyzed on a large scale, this comprises a
different category of capability. One such application involves
translating large volumes of text across languages. Another is to answer
questions as a human would. A third is to make sense of language in a
way that can either summarize it or generate new passages.
IBM Watson was the first tool capable of ingesting, analyzing, and
“understanding” text well enough to respond to detailed questions.
However, it doesn’t deal with structured numerical data, nor can it
understand relationships between variables or make predictions. It’s
also not well suited for applying rules or analyzing options on decision
trees. However, IBM is rapidly adding new capabilities included in our
matrix, including image analysis.
There are other examples of word and image systems. Most were
developed for particular applications and are slowly being modified to
handle other types of cognitive situations. Digital Reasoning Systems
Inc., for example, a company based in Franklin, Tennessee, that
developed cognitive computing software for national security purposes,
has begun to market intelligent software that analyzes employee
communications in financial institutions to determine the likelihood of
fraud. Another company, IPsoft Inc., based in New York City,
processes spoken words with an intelligent customer agent
programmed to interpret what customers want and, when possible, do it
for them.
IPsoft, Digital Reasoning, and the original Watson all use similar
components, including the ability to classify parts of speech, to identify
key entities and facts in text, to show the relationships among entities
and facts in a graphical diagram, and to relate entities and relationships
with objectives. This category of application is best suited for situations
with much more — and more rapidly changing — codified textual
information than any human could possibly absorb and retain.
Image identification and classification are hardly new. “Machine
vision” based on geometric pattern matching technology has been used
Just How Smart Are Smart Machines?
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for decades to locate parts in production lines and read bar codes.
Today, many companies want to perform more sensitive vision tasks
such as facial recognition, classification of photos on the Internet, or
assessment of auto collision damage. Such tasks are based on machine
learning and neural network analysis that can match particular patterns
of pixels to recognizable images.
The most capable machine learning systems have the ability to “learn”
— their decisions get better with more data, and they “remember”
previously ingested information. For example, as Watson is introduced
to new information, its reservoir of information expands. Other systems
in this category get better at their cognitive task by having more data
for training purposes. But as Mike Rhodin, senior vice president of
business development for IBM Watson, noted, “Watson doesn’t have
the ability to think on its own,” and neither does any other intelligent
system thus far created.
Performing Digital Tasks
One of the more pragmatic roles for cognitive technology in recent
years has been to automate administrative tasks and decisions. In order
to make automation possible, two technical capabilities are necessary.
First, you need to be able to express the decision logic in terms of
“business rules.” Second, you need technologies that can move a case
or task through the series of steps required to complete it. Over the past
couple of decades, automated decision-making tools have been used to
support a wide variety of administrative tasks, from insurance policy
approvals to information technology operations to high-speed trading.
Lately, companies have begun using “robotic process automation,”
which uses work flow and business rules technology to interface with
multiple information systems as if it were a human user. Robotic
process technology has become popular in banking (for back-office
customer service tasks, such as replacing a lost ATM card), insurance
(for processing claims and payments), information technology (IT) (for
monitoring system error messages and fixing simple problems), and
supply chain management (for processing invoices and responding to
routine requests from customers and suppliers).
The benefits of process automation can add up quickly. An April 2015
case study at Telefónica O2, the second-largest mobile carrier in the
United Kingdom, found that the company had automated over 160
process areas using software “robots.” The overall three-year return on
investment was between 650% and 800%.
Performing Physical Tasks
Just How Smart Are Smart Machines?
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Physical task automation is, of course, the realm of robots. Though
people love to call every form of automation technology a robot, one of
Merriam-Webster’s definitions of robot is “a machine that can do the
work of a person and that works automatically or is controlled by a
computer.”
In 2014, companies installed about 225,000 industrial robots globally,
more than one-third of them in the automotive industry. However,
robots often fall well short of expectations. In 2011, the founder of
Foxconn Technology Co., Ltd., a Taiwan-based multinational
electronics contract manufacturing company, said he would install one
million robots within three years, replacing one million workers.
However, the company found that employing only robots to build
smartphones was easier said than done. To assemble new iPhone
models in 2015, Foxconn planned to hire more than 100,000 new
workers and install about 10,000 new robots.
Historically, robots that replaced humans required a high level of
programming to do repetitive tasks. For safety reasons, they had to be
segregated from human workers. However, a new type of robots —
often called “collaborative robots” — can work safely alongside
humans. They can be programmed simply by having a human move
their arms.
Robots have varying degrees of autonomy. Some, such as remotely
piloted drone aircraft and robotic surgical instruments and mining
equipment, are designed to be manipulated by humans. Others become
at least semiautonomous once programmed but have limited ability to
respond to unexpected conditions. As robots get more intelligence,
better machine vision, and increased ability to make decisions, they
will integrate other types of cognitive technologies while also having
the ability to transform the physical environment. IBM Watson
software, for example, has been installed in several different types of
robots.
The Great Convergence
Slowly but surely, the worlds of artificially intelligent software and
robots seem to be converging, and the boundaries between different
cognitive technologies are blurring. In the future, robots will be able to
learn and sense context, robotic process automation and other digital
task tools will improve, and smart software will be able to analyze
more intricate combinations of numbers, text, and images.
We anticipate that companies will develop cognitive solutions using the
building blocks of application program interfaces (APIs). One API
Just How Smart Are Smart Machines?
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might handle language processing, another numerical machine
learning, and a third question-and-answer dialogue. While these
elements would interact with each other, determining which APIs are
required will demand a sophisticated understanding of cognitive
solution architectures.
This modular approach is the direction in which key vendors are
moving. IBM, for example, has disaggregated Watson into a set of
services — a “cognitive platform,” if you will — available by
subscription in the cloud. Watson’s original question-and- answer
services have been expanded to include more than 30 other types,
including “personality insights” to gauge human behavior, “visual
recognition” for image identification, and so forth. Other vendors of
cognitive technologies, such as Cognitive Scale Inc., based in Austin,
Texas, are also integrating multiple cognitive capabilities into a
“cognitive cloud.”
Despite the growing capabilities of cognitive technologies, most
organizations that are exploring them are starting with small projects to
explore the technology in a specific domain. But others have much
bigger ambitions. For example, Memorial Sloan Kettering Cancer
Center, in New York City, and the University of Texas MD Anderson
Cancer Center, in Houston, Texas, are taking a “moon shot” approach,
marshaling cognitive tools like Watson to develop better diagnostic and
treatment approaches for cancer.
Designing a Cognitive Architecture
Hardware and software will continue to get better, but rather than
waiting for next- generation options, managers should be introducing
cognitive technologies to workplaces now and discovering their
human-augmenting value. The most sophisticated managers will create
IT architectures that support more than one application. Indeed, we
expect to see organizations building “cognitive architectures” that
interface with, but are distinct from, their regular IT architectures.
What would that mean? We think a well-designed cognitive
architecture would emphasize several attributes:
The Ability to Handle a Variety of Data Types
Cognitive insights don’t just come from a single data type (text, for
example). In the future, they will come from combining text, numbers,
images, speech, genomic data, and so forth to develop broad situational
awareness.
Just How Smart Are Smart Machines?
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The Ability to Learn
Although this should be the essence of cognitive technologies, most
systems today (such as rules
engines and robotic process automation) don’t improve themselves. If
you have a choice between a system that learns and one that doesn’t, go
with the former.
Transparency
Humans and cognitive technologies will be working together for the
foreseeable future. Humans will always want to know how the
cognitive technologies came up with their decision or recommendation.
If people can’t open the “black box,” they won’t trust it. This is a key
aspect of augmentation, and one that will facilitate rapid adoption of
these technologies.
A Variety of Human Roles
Once programmed, some cognitive technologies, like most industrial
robots, run their assigned process. By contrast, with surgical robots it’s
assumed that a human is in charge. In the future, we will probably need
multiple control modes. As with self-driving vehicles, there needs to be
a way for the human to take control. Having multiple means of control
is another way to facilitate augmentation rather than automation.
Flexible Updating and Modification
One of the reasons why rule-based systems have become successful in
insurance and banking is that users can modify the rules. But
modifying and updating most cognitive systems is currently a task only
for experts. Future systems will need to be more flexible.
Robust Reporting Capabilities
Cognitive technologies will need to be accountable to the rest of the
organization, as well as to other stakeholders. We’ve spoken, for
example, with representatives of several companies using automated
systems to buy and place digital ads, and they say that customers insist
on detailed reporting so that the data can be “sliced and diced” in many
different ways.
State-of-the-Art IT Hygiene
Just How Smart Are Smart Machines?
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ABOUT THE AUTHORS
Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College in Wellesley, Massachusetts, as well
as a fellow of the MIT Initiative on the Digital Economy. Julia Kirby is a Boston-based editor and writer. They are the authors of the book Only Humans Need Apply:
Winners and Losers in the Age of Smart Machines (forthcoming, HarperCollins).
TAGS: Artificial Intelligence, Automation, Robotics, Smart Technology
REPRINT #: 57306
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Cognitive technologies will need all the attributes of modern
information systems, including an easy user interface, state-of-the-art
data security, and the ability to handle multiple users at once.
Companies won’t want to compromise on any of these objectives in the
cognitive space, and eventually they won’t have to.
What’s more, if the managerial goal is augmentation rather than
automation, it’s essential to understand how human capabilities fit into
the picture. People will continue to have advantages over even the
smartest machines. They are better able to interpret unstructured data
— for example, the meaning of a poem or whether an image is of a
good neighborhood or a bad one. They have the cognitive breadth to
simultaneously do a lot of different things well. The judgment and
flexibility that come with these basic advantages will continue to be the
basis of any enterprise’s ability to innovate, delight customers, and
prevail in competitive markets — where, soon enough, cognitive
technologies will be ubiquitous.
Clearly, smart machines are advancing at the things they do well at a
much faster rate than we humans are. And granted, many workers will
need to call on and cultivate different capabilities than the ones they
have relied on in the past. But for the foreseeable future, there are still
unlimited ways for humans to contribute tremendous value. To the
extent that wise managers leverage their talents with advanced
technology, we can all stop dreading the rise of smart machines.
Next Article »
Debating Disruptive Innovation
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