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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 Photo: 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 Photo: 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. 12

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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 Photo: 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 at Apple, Backchannel

• Preparing for the Future of Artificial Intelligence, White House Blog

• Using Artificial Intelligence to Transform Healthcare with Pinaki

Dsagupta, Hindsight, Startup Health

• Beyond Siri, The Next-Generation AI Assistants Are Smarter

Specialists, Fast Company

• Infographic: What You Need to Know About Google RankBrain,

Contently

• Facebook is Giving Away the Software it 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

Diversity in Artificial Intelligence than an AI Apocalypse, re/code

• The Tradeoffs of Imbuing Self-Driving Cars With Human Morality,

Motherboard

• If We Don’t Want AI to Be Evil, We Should Teach It to Read, Motherboard

• The Ethics of Artificial Intelligence, Nick Bostrom

• Twitter Taught Microsoft's AI Chatbot to be a Racist Asshole in Less

Than a Day, The Verge

• Algorithms Are Biased Against Women and the Poor, According to a

Former Math Professor, The Cut

• Elon Musk elaborates on his AI concerns, Sam Altman YouTube interview

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