The document discusses the current state and future potential of artificial intelligence (AI) in business, separating hype from reality. It emphasizes that AI will change job roles rather than eliminate them, and suggests five practical steps businesses can take to integrate AI in a meaningful way. Key points include starting with specific business problems, fostering empathy in the workforce, and ensuring a clear understanding of AI capabilities.

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Hype vs. Reality
The AI Explainer
January 2017
Produced by Luminary Labs in partnership with Fast Forward Labs

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Artificial intelligence (AI) is everywhere, promising self-driving cars,
medical breakthroughs, and new ways of working.
But how do you separate hype from reality? How can your company
apply AI to solve real business problems in 2017?
In September 2016, Luminary Labs convened 30 executives in
healthcare, machine learning, and analytics for a grounded discussion
on these questions with machine learning expert Hilary Mason,
founder and CEO of Fast Forward Labs, and Sandy Allerheiligen, VP
of data science and predictive and economic modeling at Merck.
Here’s a synopsis of what we discussed, and what AI learnings your
business should keep in mind for 2017.
AI and the Near Term

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We’ve all seen the sensational headlines: The robots are
coming, and they’ll take our jobs! AI can do your job faster
and more accurately than you can!
The Hype
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The
Reality
Human jobs won’t go away, but they will change. Roles will
be more creative and specialized as AI is integrated into the
workday. Better data leads to better math leads to better
predictions, so people using AI can automate the tedious
work and take action on the insights.

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In the short term
AI does the math faster, saving money by automating
normally complex processes.
It makes your life easier even now, behind the scenes.
This is what it looks like today.

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The Nest thermostat remembers what temperatures you like
and adjusts automatically, like turning the temperature down
when you’re away and turning it up when you’re on your way
home. This saves users time, energy, and money.
Photo: Nest 6

7. 7Photo: Netflix 7
Netflix’s predictive analytics recommend what you might
want to watch next—and what studios should create next—
based on viewer data. Amazon, iTunes, Pandora, and other
companies use predictive analytics to make better
recommendations.

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Salesforce Einstein applies natural language processing to
analyze text from e-mails exchanged with customers to
estimate the likelihood that a user will buy, detect deals a
team is at risk of losing, and recommend actions to improve
sales. 8

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In the longer term
AI will transform industries.

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For example, algorithms help healthcare professionals
recognize anomalies or patterns in medical images with
more accuracy than the human eye. Over time, this can
result in a library of knowledge that can lead to potential
disease cures. 10

11. 11Photo: NVIDIA Coporation
One of AI’s promises is to make self-driving cars safer.
Everyday driving decisions, such as whether to stop abruptly
or swerve to avoid hitting an obstacle, will be powered by AI.
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AI will help redesign the entire shopping experience, optimizing
everything with more and better data. Retailers will seamlessly
stock the precise number of goods needed on shelves at any
given time, and know which product at which price should be
highlighted to a specific customer as they navigate a store.
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Where do you start?
Five ways to look past the shiny-object phase and into
practical AI planning in 2017.

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1. Don’t fear the robots. The idea is to augment, not replace,
work. AI can absorb cognitive drudgery, like turning data points
into visual charts, calculating complex math formulas, or
summarizing the financial news of the day into a single report.
This frees up people to focus on acting on the insights.
Photo: Flickr user joao_trindade 14

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2. Start with the problem, not the solution. Before launching an
AI program, identify concrete business problems, then consider if
AI can help. For example, rather than ask, “What can we use AI
for?”, think, “Where could we make our operations more efficient?”
or “What decisions are we making without data?”
Photo: Flickr user Robert Couse-Baker 15

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3. Emphasize empathy. The more machines we employ, the
more people skills we need. Leaders must build empathy across
the organization to help employees see impact. Focus on how AI
can help workers add more human value, rather than replace
them. For example, McDonald’s added robots to their franchises,
but doesn’t plan to cut human jobs. Photo: Flickr user EasySentrisentri 16

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4. Engage the skeptics. Understand what they fear and start
there. Fast Forward Labs’ Hilary Mason shared an example of
winning buy-in by demonstrating how machine learning could
solve a problem for an overburdened regulatory team.
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18. 18Photo: Flickr user JDHancock
5. Remember: It’s not magic. If a vendor can’t explain their AI
product or service in terms you understand, don’t buy it. Much of
what’s called AI today (“AI personal assistants,” anyone?) is
actually humans wrangling a trove of data behind the scenes. If it
doesn’t make sense, it might not be real.
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Glossary
Some AI terms are used primarily for marketing
purposes, while others are more technical.
Here are our translations for common terms you may
hear, whether you’re being sold an AI product or
partnering with a team of AI experts. It’s a great
starting point for becoming an AI leader in your
organization.

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Artificial intelligence (AI): Marketing term that describes a
continuum of non-living analytical power, fueled by fast
processing and data storage’s declining costs. Applications
today are termed weak AI (like IBM Watson), which are
algorithms built to accomplish a specific task. Strong AI (like
Skynet) is a term for hypothetical future applications that will
replicate human intelligence.
Big data: Buzzword alluding to a machine’s ability to
generate insights and learn from massive data sets,
because sensors, software, and recordkeeping generate a
lot of data. For example, The Weather Company and IBM
researched weather’s impact on business by analyzing
millions of data points from weather sensors, aircraft,
smartphones, buildings, and vehicles.
The big picture

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Machine learning: Method of automated analytical model
building. Machine learning lets computers find hidden
insights without being explicitly programmed where to look.
For instance, Facebook’s machine learning software uses
algorithms and data points to show a user suggested
friends, display relevant ads, and detect spam.
Algorithm: Formula that represents a relationship between
things. It’s a self-contained, step-by-step set of operations
that automates a function, like a process, recommendation,
or analysis. For example, Netflix’s recommendation
algorithms can predict what movies a consumer might want
to watch based on their viewing history.
Most important to remember

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Deep learning: Branch of machine learning that uses
multiple layers of distributed representations (neural
networks) to recognize patterns in digital sounds, images,
or other data. For example, Google’s DeepDream photo-
editing software allows neural networks to “hallucinate”
patterns and images in a photo.
Neural networks: Computational approach that loosely
models how the brain solves problems with layers of inputs
and outputs. Rather than being programmed, the networks
are trained with several thousand cycles of interaction.
Businesses can use these to do a lot with a little; for
example, neural networks can generate image captions,
classify objects, or predict stock market fluctuations.
Nuts and bolts

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Natural language processing: Field of study in which
machines are trained to understand human language using
machine-learning techniques. It’s useful for automatic
translations, chatbots, or AI personal assistants. Think of
the robot voice that picks up your helpline call and asks,
“What can I help you with?” or an automated chatbot that
responds to your texts.
Parsing: The process of evaluating text according to a set
of grammar or syntax rules. You can build algorithms that
parse text according to English grammar rules, for example,
to aid natural language processing.
Nuts and bolts

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Recommended reading

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AI: The big picture
• The Hype and Hope of Artificial Intelligence, The New Yorker
• What Counts as Artificially Intelligent? AI and Deep Learning,
Explained, The Verge
• The Extraordinary Link Between Deep Neural Networks and the
Nature of the Universe, MIT Technology Review
• The Competitive Landscape for Machine Intelligence, Harvard
Business Review
• What Do People—Not Techies, Not Companies—Think About
Artificial Intelligence?, Harvard Business Review

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How companies use AI today
• An Exclusive Look at Machine Learning atApple, Backchannel
• Preparing for the Future ofArtificial Intelligence, White House Blog
• Using Artificial Intelligence to TransformHealthcare with Pinaki
Dsagupta, Hindsight, Startup Health
• Beyond Siri, The Next-GenerationAI AssistantsAre Smarter
Specialists, Fast Company
• Infographic:What You Need to Know About Google RankBrain,
Contently
• Facebook is GivingAwaythe Softwareit Uses to Understand Objects
in Photos, The Verge
• How AI is Changing Human Resources, Fast Company
• Beyond Automation, Harvard Business Review

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Ethical considerations
• The Head of Google’s Brain Team is More Worried about the Lack of
Diversityin Artificial Intelligence than anAI Apocalypse, re/code
• The Tradeoffs of Imbuing Self-Driving Cars With Human Morality,
Motherboard
• If We Don’t WantAI to Be Evil, We Should Teach It to Read, Motherboard
• The Ethics of Artificial Intelligence, Nick Bostrom
• Twitter Taught Microsoft'sAI Chatbotto be a RacistAsshole in Less
Than a Day, The Verge
• AlgorithmsAre BiasedAgainst Women and the Poor, According to a
Former Math Professor, The Cut
• Elon Musk elaborateson hisAI concerns, Sam Altman YouTube interview

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