This post is part of our AI Experts Series, which profiles leaders whose insights can help expand your understanding of artificial intelligence and how to apply it to advance your business and career.

 

Today's spotlight features Jim Sterne, author of Artificial Intelligence for Marketing: Practical Applications (Wiley, 2017), and president of Target Marketing of Santa Barbara.

 

AI + You

In a single sentence or statement, tell us what you do.

I produce conferences, help run a professional association and offer workshops to those who are interested in digital analytics, machine learning, and data fluency.

How do you define AI? (Or, what’s your favorite definition of AI?)

Artificial intelligence is an umbrella term that covers new types of data manipulation including natural language processing, computer vision, robotics, and machine learning.

Sundar Pichai, Google’s CEO, has stated that, “AI is probably the most profound thing humanity has ever worked on.” Do you agree? Why, or why not?

I'd say the invention of the wheel, the scientific method, and long-distance communications are probably more profound...but time will tell.

How did you get started in AI?

I got into computing in 1978, into online marketing in 1993, and into digital analytics in 2000. AI is a natural extension of learning about how to use technology for marketing.

What’s your favorite example of AI in your daily life that most consumers take for granted, or don't even realize is made possible by AI?

Google, Apple, Amazon, Facebook, your phone, and eventually, your shoelaces. It's everywhere!

What excites you most about AI?

We're only just starting to scratch the surface. We have a vague idea about how AI can solve some our problems, but we haven't yet seen how it can solve new problems we have not yet addressed due to a lack of ability.

What worries you most about AI? How could it go wrong?

Bias in the data is one significant problem. Bad decisions can be made by simply accepting whatever the machine spits out, without worrying about whether the data encodes biases that we have an aversion to but have not expunged from our history. Race and gender are obvious issues, but the consideration of a wide variety of variables might subtlety downgrade individuals due to sentence structure, left-handedness, height, accent, or other insignificant and inappropriate features.

Perhaps more fearful is the use of this technology by bad actors. One need only look to Cambridge Analytica for a glimpse of a dystopia in the making.

What blows my mind the most is how complex it all is. It blows my mind that we are still trying to solve fundamental problems and tripping over seemingly insignificant problems that prove catastrophic.

What do you think is the biggest misconception about AI?

The two biggest misconceptions about AI are that it is inherently evil or that it is the answer to all problems. It seems evil that the machine is making decisions or recommendations in ways that are "unexplainable." A lack of understanding leads to mistrust. Others think AI will solve all sorts of problems that we don't understand well enough to solve in other ways. Machine learning only works when the problem and the data are very well understood.

What skill or trait do you believe has the greatest chance to remain uniquely human for the foreseeable future?

Humans will always be needed in marketing to perform three vital functions:

1. Determine which problem the machine should solve.

The machine may be able to recommend issues to address, but humans have to prioritize based on a holistic understanding of the problem set. There are so many trade-offs in choosing which problem should be tackled next, that it takes a human brain to make a 'gut feel' decision... and assume the responsibility and consequences for that decision.

2. Determine which data the machine should consider.

The machine can only crunch the numbers it is given. A human must decide which data sets might be the most informative or predictive. This requires imagination and ingenuity.

3. Determine whether the output makes sense.

The machine can make a statistically correct recommendation or take an action that accurately performs the required task, but has no ethical, moral, or even common-sense framework for identifying whether there are additional considerations. If you want to raise revenue, the machine will rightly tell you to sell $20 bills for $10. The answer is correct, but absurd.

Who has the greatest influence on how you think about AI and the future?

The people who are faced with implementing it in the trenches. People trying to create systems to sell to the rest of us. And science fiction writers.

What is a recent advance in AI that blew your mind?

What blows my mind the most is how complex it all is. It blows my mind that we are still trying to solve fundamental problems and tripping over seemingly insignificant problems that prove catastrophic.

If you were entering college, knowing what you know now, what would you study?

The human cognitive process, critical thinking, rhetoric, psychology, and business management.

If you were the dean of a business school, what is one thing you would do right now to start better preparing students for the intelligently automated future?

I would develop courses in tool applicability. When should you use linear regression? When should you use a random forest? When should you use a neural net? When is an Excel spreadsheet enough?

Marketing + AI

What advice would you give to marketers looking to pilot AI in their organizations?

Find a very narrow problem to solve that has an easy-to-measure outcome. Incoming email routing, multivariate testing on a landing page, email open rate. If you have enough transactions, then these simple problems are the best for learning about AI.

What is the biggest challenge marketers should plan for as they scale AI?

Data trustworthiness. Like the ingredients in a can of soup, marketers are going to need to understand their data flow supply chain well enough to know where the incoming data didn't live up to the promise of cleanliness, timeliness, consistency, etc. That, and suffering from over-blown expectations.

What question(s) would you advise marketers ask vendors who claim to have AI-powered technology?

First, ask enough questions to determine if the vendor actually understands what "AI" means. If they're just using it as brochureware, you have to wonder what else they are misguided about. But mostly, test it! Run a trial! Do a proof of concept! If it's better than what you're using now and better than the others you are considering it doesn't matter if it's artificial intelligence or an artificial hip...it's better.

What percentage of marketing tasks will be intelligently automated to some degree in the next five years?

• 0%
• 1 – 10%
• 11 – 25%
• 26 – 50%
• 51 – 75%
• 76 – 99%
• 100%

What’s one marketing job you see AI fully automating and eliminating in the next five years?

Marketing Data Scientist.

What’s one marketing job you see AI creating that doesn't exist today?

What can marketers do to ensure the ethical use of AI in their marketing?

Have an opinion and state it clearly and often. If your ethics are aligned with the company, they will appreciate that somebody is representing the customers and keeping a watch on the brand. If your ethics are not aligned with the company, you shouldn't be working there.

Find a very narrow problem to solve that has an easy-to-measure outcome.

How can brands achieve personalization without invading privacy?

OPT IN.

OPT IN.

OPT IN.

Fully informed and on a drip-irrigation basis.

How can brands become more human as they intelligently automate tasks and roles?

You can easily automate tasks as the tools improve. When you start automating roles you, by definition, stop being human.

What resource(s) would you recommend to marketers who want to understand and apply AI?

Artificial Intelligence for Marketing: Practical Applications  ;-)

Plus an infinite variety of resources depending on what role is of interest to the marketer. First, the individual must decide if they want to be:

• Business Decision Maker — Assumes risk
• Analyst — Lives in two worlds
• Predictive Analyst — Builds models to extrapolate numbers
• Data Engineer — Builds data platforms for others
• Data Scientist — Fits algorithms to problems
• Machine Learning Engineer — Marries data with math theory

Rapid Fire

Voice assistant you use the most?

• Alexa
• Google Assistant
• Siri
• Don’t use voice assistants
• Other

Which will be more valuable in 10 years?

• Liberal arts degree
• Computer science degree

First publicly traded technology company to reach $2 trillion market cap?

• Alibaba
• Alphabet
• Amazon
• Apple
• Facebook
• Microsoft
• Tesla
• Other

Preferred cloud for building AI solutions?

• Amazon Web Services (AWS)
• Google Cloud
• Microsoft Azure
• Don’t use or prefer any of them
• Other

Best guess, how long until we achieve artificial general intelligence (AGI)?

• 1 – 5 years
• 6 – 10 years
• 11 – 20 years
• 21 – 50 years
• 51+ years
• Never

Net effect over the next decade?

• More jobs eliminated by AI
• More jobs created by AI
• AI won’t have a meaningful impact on jobs

What does an AI agent win first (or at least share with a human)?

• Nobel Peace Prize
• Oscar
• Pulitzer
• Won’t win any of the above

Favorite AI movie?

Bicentennial Man

Favorite AI book?

Ancillary Justice

Favorite AI-powered marketing technology your company uses that regularly reduces costs and/or increases revenue?

X.AI

Favorite piece of AI content you've created that you'd like to share with our readers? Include any relevant links.

• Have Your AI Call My AI

Final Thoughts

Any final thoughts on AI for our readers?

Learn which tools are best for which problems.