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2 Copyright © 2018 by Dow Jones & Company, Inc.
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Inside
u What Exactly is Artificial Intelligence? .............................................................3
u Corporate AI Adoption Could Create Trillions in Value ................................4
u Companies Need Help Tackling Ethical Concerns Posed by AI ....................5
u Businesses Get Into the ‘Flo’ With Chatbots .....................................................6
u The Next Challenge: Grappling With AI’s Opaque
Decision-Making Process ....................................................................................7
u Meet the Authors ..................................................................................................8

Copyright © 2018 by Dow Jones & Company, Inc. 3
wsj.com/pro/ai WSJ PRO ARTIFICIAL INTELLIGENCE
What Exactly is Artificial Intelligence?
By SARA CASTELLANOS
Artificial intelligence encompasses
the techniques used to teach computers to learn, reason,
perceive, infer, communicate and make decisions similar
to or better than humans. It has come a long way since the
1956 Dartmouth College artificial intelligence workshop,
which many consider the birthplace of the discipline.
Major advancements in AI were stymied in subsequent
years, because the computational power and technology
needed to perform advanced, computer-made decisions
simply did not exist. Two periods between the 1970s
and 1990s were referred to as “AI winters” because of
waning interest in the field, which resulted in decreased
government and company investment.
Specialized types of hardware called graphics processing
units, initially built for accelerating computer
graphics for the gaming industry, were well-suited for
accelerating the training of AI systems such as neural
networks. Such innovations led to AI breakthroughs, in
which machines bested human efforts in successively
more difficult games such as Jeopardy and chess.
New AI techniques combined with cheaper computing
power and data storage have allowed companies to
crunch more data over the past decade. Since 2012,
businesses have started to figure out how to apply AI to a
growing range of problems. They developed specialized
AI models that are more accurate and faster than
humans at specific business tasks.
About half of 2,135 business leaders surveyed this year
across all sectors said their organizations have deployed
at least one AI-based system into their standard business
processes, while another 30% report piloting the use of
AI, according to a McKinsey Global Institute survey —
part of McKinsey & Co. — released in November.
“We’re in the phase of the wild West, where everybody’s
trying it out,” said Dario Gil, vice president of AI and
quantum computing at International Business Machines
Corp., and chief operating officer of IBM Research. “It’s
driven by the realization that AI is fundamental.”
When people talk about artificial intelligence, they
usually are referring to one of its subfields. Here’s a
guide to the most important concepts in AI.
MACHINE LEARNING. This is the science of getting
computers to act intelligently without being explicitly
programmed, said Andrew Ng, former chief scientist at
Chinese tech conglomerate Baidu Inc. and co-founder of
Google Brain, a research team at Alphabet Inc.’s Google.
Instead of writing all the rules, algorithms allow the
computer to determine for itself how it interprets data.
Machine learning originated in 1959 with Arthur Samuel,
who used the technique to create a computer program
that played checkers.
At credit-reporting firm Experian PLC, machine learning
allows it to fix application problems before they impact
customers. At Google’s YouTube, AI-based systems
recommend videos to users based on previous viewing
history, creating better customer experiences and
driving usage, said Eric Schmidt, former Google CEO
and technical advisor to Alphabet. “What we know
from machine learning is that there are subtle patterns
that computers can detect in the training data that we
just don’t see,” Mr. Schmidt said in November at an AI
conference hosted by the Massachusetts Institute of
Technology.
NEURAL NETWORKS AND DEEP LEARNING. These
terms are often used interchangeably, according to Dr.
Ng. They are subsets of machine learning that were
originally inspired by neurons in the human brain.
Scientists, though, have not yet discovered exactly how
the human brain works, and in recent years it’s become
apparent that deep learning might not be as similar to
the brain as once thought.
“It’s barely mimicking the human brain, if at all,” he said.
Neural networks are composed of layers of interconnected
artificial “neurons” that automatically learn about the
features of a specific object based on large amounts of
training data. For example, by looking at of images of
cats, a neural network can learn about a cat’s features by
tweaking the connections between neurons.
If it has learned those patterns well, it should be able
to look at a new, or “test,” image and correctly identify
it. If it stumbles, its engineers can give it more data or
modify the structure of the neural network. While neural
networks can achieve extraordinary feats, they’re also
fragile, easy to fool and can be subject to adversarial
attacks, said IBM’s Mr. Gil. IBM researchers, for example,
have demonstrated that neural networks can be fooled
into misidentifying an image by doctoring a few pixels.
Neural networks power deep learning systems. The ability
to create such systems with multiple layers has led to
advances in speech recognition and computer vision. Deep
learning forms the foundation behind natural language
processing, which powers web searches and chatbots.
ADVERSARIAL NETWORKS. In this technique, an AI
system can be embedded with two neural networks.
One network’s goal is to make accurate classifications,
of images, for example. The other network generates
samples that are meant to fool the first network. This
method ensures that the first network isn’t fooled by
variations of images. Adversarial networks could be a
path toward unsupervised learning, in which a machine
could make logical inferences without requiring as
much human-labeled training data. These cutting-edge
systems are not yet widely deployed. n
— Steven Norton contributed to this report.

4 Copyright © 2018 by Dow Jones & Company, Inc.
Corporate AI Adoption Could Create Trillions in Value
By the Numbers
• Tech giants such as Google and Baidu spent an
estimated $20 billion to $30 billion on AI in 2016,
according to McKinsey Global Institute.
• In 2017, according to CBInsights, $15.2 billion was
invested in AI startups around the world, and nearly
half (48 percent) of that total went to China; 38
percent was invested in the United States.
By ANGUS LOTEN
Artificial intelligence will help
unleash a wave of new enterprise capabilities and
innovative business models in the years ahead. But in the
meantime most firms are using it to tackle more mundane
tasks, according to chief information officers and industry
analysts.
Rather than smart robot assistants or autonomous
supply chains, AI-backed tools are far more likely
to monitor information-technology infrastructure
or handle basic customer queries. These kinds of
deployments --in addition to new ones -- are expected
to spread fast, as AI is driving a significant amount of
business value.
Artificial intelligence is expected to generate a total of
$1.2 trillion in global business value this year, up 70%
from 2017, according to a Gartner Inc. report. That figure
is expected to more than triple by 2022, to $3.9 trillion,
as AI contributes to improved customer interactions,
cost reductions and new revenue sources.
Gartner analyst Chirag Dekate said for most firms the
bulk of gains are coming from taking a tactical approach
to AI deployments, with businesses applying smart tools
to specific and relatively straightforward tasks.
A recent McKinsey Global Institute survey found 47%
of companies worldwide have embedded at least one
AI-backed capability in their business processes, up from
20% a year ago. An additional 30% said they are testing
AI with pilot projects in place. MGI is the business and
economics research arm of McKinsey & Co.
By sector, telecom, high-tech and financial-services firms
were ahead of others in adopting smart applications,
the survey found. And though enterprise spending on
AI is poised to increase, just 58% of survey respondents
said less than one-tenth of their total budget for digital
technology is currently going into AI.
Some of the barriers to AI deployments they cite include
a lack of clear strategy, a skills gap and functional silos
constraining end-to-end applications. The survey,
which was conducted online earlier this year, included
responses from 2,135 business officials across multiple
regions, industries and company sizes.
Autodesk Inc. CIO Prakash Kota said the design-software
maker currently is using AI-powered software tools to
perform repetitive tasks, such as answering common
questions about access authorization and other queries
from its employees and customers alike. By deploying
virtual assistants, workers are able to focus on tackling
more complex issues or learning new skills -- ultimately
boosting productivity, he said.
Jabil Inc., a St. Petersburg, Fla.-based supply chain
management software maker, has started replacing
its manual circuit board inspections with AI-backed
scanners that can catch defects with an 80% accuracy
rate, far better than human inspectors, said Gary
Cantrell, Jabil’s CIO.
“Boards that would pass the inspection process, all the way
through before a problem was identified, we were catching
on first pass, much earlier in the process,” making it more
efficient and cost effective, Mr. Cantrell said. n
“R2-D2 is still a long way off.”
—Gartner Inc. Research Vice President Brian Burke
0.0 0.2 0.4 0.6 0.8 1.0 1.2
by
2030
2000s
1990s
1800s
0.3%
0.4%
0.6% 1.2%
AI & GDP
AI could potentially deliver additional global economic activity
of around $13 trillion by 2030, or about 16 percent higher
cumulative GDP compared with today. This amounts to about
1.2 percent additional GDP growth per year.
If delivered, this impact would compare well with that of
other general-purpose technologies through history, such
as the introduction of steam engines during the 1800s, the
impact from robots during the 1990s, and the spread of IT
during the 2000s.

Source: McKinsey Global Institute

Copyright © 2018 by Dow Jones Company, Inc. 5
Companies Need Help Tackling
Ethical Concerns Posed by AI
By SARA CASTELLANOS
Running parallel with artificial
intelligence's
expanding role in business, and virtually everywhere
else, is a growing awareness of the ethical guardrails
needed to guide the technology's implementation, a
concern shared among business leaders, consumers and
even AI researchers.
Microsoft Corp. recently created a new position to help
companies deploying AI learn how to prioritize ethical
principles, including fairness, accountability and
transparency, in algorithm development.
“A lot of customers we engage with are grappling with
the ethics discussion potentially for the first time,”
says Tim O’Brien, Microsoft’s general manager of
AI programs. “A lot of people with formal education
in engineering and science didn’t have any formal
education of ethics.”
Mr. O’Brien educates Microsoft customers about AI
ethics and responsible AI design. He also conducts
research to identify cultural and geographic differences
in how people think about fairness and transparency in
AI. Microsoft has been advocating for the development
of transparent, inclusive and ethical AI tools internally
for several years, but Mr. O’Brien said it was time they
brought that awareness to Microsoft customers.
Concerns about transparency and ethics are barriers
in implementing AI. For example, about 60% of 5,000
executives polled in a recent study by International
Business Machines Corp.’s Institute of Business Value
said they were concerned about being able to explain
how AI is using data and making decisions in order to
meet regulatory and compliance standards. That’s up
from 29% in 2016.
Here’s some advice from Mr. O’Brien on tackling the
ethical issues raised by AI:
Elevate the role of ethics
Senior technology and business leaders should speak
to employees about the importance of the responsible
use of AI within the company. Support has to come
from the top down, he said, adding that advocacy for AI
ethics should eventually become part of the company’s
culture.
Be aware of the dangers of AI bias
Make sure the datasets used to train AI algorithms are
as complete as possible, Mr. O’Brien said. For example,
if a facial recognition model is trained with images
of white males, error rates for people of color will be
high. Companies also need to consider the potential
pitfalls of AI bias, which can be more severe for certain
industries than others. If a financial services company
is considering using AI for loan approvals, the company
needs to ensure it’s not discriminating against certain
groups of people, he said.
Consider creating specialized jobs
If a company is considering an AI deployment, it
might behoove the business to hire specific so-called
ethicist roles. Ethics experts would be in charge of
focusing full-time on educating employees on ethical
considerations surrounding AI. They could also be in
charge of developing cross-functional teams to act
as sounding boards to sort through the implications
of AI deployments. It’s also crucial to think about the
diversity of the teams that are building AI-enabled
products, Mr. O’Brien said.
Bias detection tools can help
Cloud-based AI bias detection tools could be useful
in the development stage, in order to help prevent
different kinds of bias in training data or machine
learning models. For example, such tools could
help detect whether an algorithm used to evaluate
housing applications is discriminating against certain
populations. n

6 Copyright © 2018 by Dow Jones Company, Inc.
Before committing a software bot to code, Progressive developers had
to understand Flo, the character that has been synonymous with the
brand for the past decade, Progressive Chief Marketing Officer Jeff
Charney said.
Progressive's Flo bot can recognize some categories of words outside
of insurance jargon but is oblivious to others, helpful for avoiding a
reputation-damaging encounter.
Businesses Get Into the ‘Flo’ With Chatbots
Insurer explores new terrain as it turns its
popular spokeswoman into a sales bot
By KIM S. NASH
Before software developers at
Progressive Corp. wrote a line of code last year to build a
chatbot version of Flo — the insurer’s quirky character in
the white apron — they spent a few days at a conference
center discussing how to render in algorithms a mascot
that has represented Progressive for a decade.
A bot can entertain but ultimately it must propel
business, explains Dan Witalec, Progressive's customer
acquisition leader. “We’re trying to sell insurance.”
Artificial intelligence tools, now easier to use and more
affordable, allow companies to enable text-based chatbots
like Flo to help customers get quick answers and conduct
transactions. Yet doing business via text, where tone
and context is everything, pushes corporate software
programmers into new territory, as they must teach
systems to understand and “chat” in natural language.
Giving personality to a bot helps a company connect
emotionally to customers and strengthen its brand,
particularly in the financial services industry where
products and services are largely similar. That’s critical
at Progressive: Ten times as many people follow Flo on
Facebook than follow the company itself — 4.7 million to
475,000.
Therefore creating a software bot to talk to customers
about insurance means programmers must understand
the character that has become synonymous with the
brand, said Jeff Charney, Progressive’s chief marketing
officer. Employees at the company’s campus outside
Cleveland are immersed in Flo — Flo-themed art hangs
on walls and Flo bobble-heads nod on desks.
“She personifies us. We personify her,” he said. The
company declined to say how many people have used Flo
the bot since it launched on Facebook Messenger last
October.
About 37% of U.S. consumers would be willing to make a
purchase through a chatbot, according to a 2016 survey
of about 4,000 people by market research company
Harris Insights and Analytics and advertising agency
DigitasLBi. About 36% of adults say they prefer digital
customer service, including bots and email, to working
with a human, according to a Forrester Research Inc.
survey in 2017 of 4,500 people in the U.S.
While good implementation of a bot can offer a
competitive advantage, there are risks. The AI that
powers many of these virtual assistants can be
unpredictable. Bots can be programmed to learn
continuously through conversation or to stick to a
limited set of questions and answers.
Matt White, manager, PL acquisition experience, said the
company decided to limit Flo’s AI smarts. The bot can
recognize some categories of words outside of insurance
jargon but is oblivious to others.
So far, the only full business transaction that Flo the bot
can conduct is to provide a car insurance quote. When
asked to file a claim or sell a policy, the bot acknowledges
that it understands the request and then offers buttons
linking to an agent or Progressive’s full website. “There
are limits to this technology,” Mr. Witalec said. “We have
to design off-ramps.” n

Copyright © 2018 by Dow Jones Company, Inc. 7
The Next Challenge: Grappling With AI’s Opaque
Decision-Making Process
By SARA CASTELLANOS
As companies look to drive innovation
and competitive advantage, some executives say they are
coming up against a big challenge: advanced AI systems
are not able to explain how they make decisions.
Executives at companies such as Uber Inc. and the
research and development arm of Xerox Corp. said
that they’re investing in an area of AI research called
“interpretability,” in an effort to understand exactly
how complex AI systems solve problems.
“It’s a really fruitful area of research and it’s been
massively neglected for the whole history of machine
learning,” said Zoubin Ghahramani, chief scientist at
Uber, at an AI conference hosted by O’Reilly Media Inc.
and Intel Corp.’s AI division in New York in May.
Machine learning enables computers to learn from data
with minimal programming, and is a large part of artificial
intelligence, a term that encompasses the techniques used
to teach computers how to learn, reason, perceive, infer,
communicate and make decisions like humans do.
Researching interpretability is important for ethical,
reputational and legal reasons in which it’s necessary to
figure out how an automated system made a particular
decision, Mr. Ghahramani said. But the importance of
interpretability depends on the specific AI application,
he said. Interpretability matters in the medical field,
less so for captioning images, he said.
Within Uber, there is an active group looking at so-
called AI neuroscience, which involves understanding
the behavior and architecture of AI systems, he said.
“It’s definitely important to us and something
we’re paying attention to,” he said, adding that he’s
personally interested in understanding how AI systems
can explain their decisions by writing reports.
Uber is among several companies, private institutions
and researchers interested in building a greater level
of trust between humans and machines through
transparency in artificial intelligence.
The research arm of the U.S. Department of Defense is
coordinating an effort to build “explainable AI” systems
that can translate complex algorithmic-made decisions
into language humans can understand, for example.
Capital One Financial Corp., too, is researching ways
that machine-learning algorithms could explain the
rationale behind their answers, which could have far-
reaching impacts in guarding against potential ethical
and regulatory breaches as the firm uses more artificial
intelligence in banking.
PARC, a research and development lab wholly owned
by Xerox Corp., also has researchers devoted to
transparency in AI, said Tolga Kurtoglu, CEO of PARC,
at the AI conference.
Not all problems require very detailed level of
explainability, Mr. Kurtoglu said. But there are
enough problems “where we absolutely need to build
transparency into the AI systems, where we’d benefit
as a society from the ability of those algorithms to
articulate themselves.”
An AI system that can explain its decisions is helpful for
two main reasons: when you have to decide whether to
override the decision it’s making, and when you want to
try to improve decisions it will make in the future, said
Peter Norvig, director of research at Google, who also
spoke at the conference.
Google is developing tools to understand artificial
neural networks and other types of machine learning
algorithms, Mr. Norvig said in an email. Researchers at
Google have visualized what a machine learning system
is learning and the ability to debug a deep learning
system, he said. n

8 wsj.com/pro/ai
STEVEN ROSENBUSH is enterprise technology editor at WSJ
Pro, guiding coverage of the interplay of business and technology
for a professional audience. He oversees the group’s CIO Journal,
Cybersecurity and Artificial Intelligence teams. Previously, he
worked as assistant managing editor for capital markets at
Institutional Investor magazine. He led telecommunications
coverage at BusinessWeek and USA Today, and is author of a book,
Telecom Opportunities for Entrepreneurs. He has a bachelor’s
degree in English from Wesleyan University.
Reach him at steven.rosenbush@wsj.com
STEVEN NORTON is a former reporter for CIO Journal.
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IMAGE CREDITS: p1: ktsimage/iStock; p2 p5: Liu Zishan/Shutterstock; p4: Kelli R. Parker/The
Wall Street Journal; p6: Kim S. Nash/The Wall Street Journal; p7: iMrSquid/iStock
KIMBERLY S. JOHNSON is the WSJ’s Professional Products
Editor, responsible for the organization’s specialized, premium
content. She was previously editor of CFO Journal, the Wall Street
Journal’s corporate finance group. Ms. Johnson’s journalism
career spans two decades and includes stints at The Boston Globe,
Associated Press, The Denver Post and Global Post. She holds a
bachelor’s degree in broadcast journalism and a master’s degree
in business and economics journalism from Boston University
Reach her at kimberly.johnson@wsj.com.
SARA CASTELLANOS covers emerging technologies for The
Wall Street Journal’s CIO Journal in New York. She previously
covered tech, startups and venture capital at the Boston Business
Journal. Prior to that, she spent four years covering politics in the
Denver area. She graduated from the University of Denver with a
bachelor’s degree in journalism.
Write to Sara at sara.castellanos@wsj.com
ANGUS LOTEN is a reporter for CIO Journal, focusing on the
business economics of information technology. Previously, he
has worked at the Bangkok Post, the Montreal Gazette and the
Toronto Star. He has a master’s degree from Columbia University’s
Graduate School of Journalism.
Write to Angus at angus.loten@wsj.com
KIM S. NASH is deputy editor of WSJ Pro Cybersecurity, which
covers the tactical and strategic dimensions of cybersecurity
for a business audience. She recently was recognized by the
National Association of Corporate Directors for her reporting on
tech governance. Previously, she served as managing editor of
CIO Magazine and investigative reporter at Baseline. She holds a
bachelor’s degree in journalism from Boston University.
Write to Kim at kim.nash@wsj.com
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