Machine Learning: Separating the Hype from Reality

August 21, 2017 Korey Thurber

From a hype perspective, machine learning is just about at its peak. Overnight, it seems, everyone went from knowing next to nothing about it, to speaking about it every day. “Marketing attribution” and “omni-channel” went through similar buzzword phases, though Artificial Intelligence (AI) has gone beyond buzzword status: a recent Gallup poll found that 26 percent of Americans fear the technology will eliminate their jobs.

As I mentioned in my last post on machine learning, computers cannot yet replace humans. They can, however, do a terrific job sifting through raw data, images, and articles that, to the average human, look like nothing more than clutter. When we move past the hype, it is clear that artificial intelligence and machine learning are already forcing massive changes in the way companies do business, enabling much more personalized and relevant interactions with consumers.

The reality is, more companies than giants like Google and IBM are using machine learning and other forms of AI today.

For example:

  • Tesla leverages advanced AI and machine learning to allow its relatively new autopilot service to continually learn and improve.
  • Opera Solutions' flagship technology platform utilizes machine learning for evaluating massive amounts of all types of data (social, transactional, cookies and so on). It enables “in-the-moment” experiences by extracting time sensitive insights 75% faster than the legacy tool sets.
  • Prisma, one of my favorite apps, became a huge hit in 2016 thanks to AI that can take any photo from your smartphone to simulate works of art by the likes of Picasso, Munch, Van Gogh and others.
  • Iris AI is using machine learning to sift through massive pools of scientific data and millions of published articles to find “needles in a haystack” to support scientific research.
  • Zebra Medical is applying machine learning techniques to the field of radiology that will allow computers to predict multiple diseases with far more accuracy.
  • Uber uses AI and machine learning to minimize wait time, determine the trip with the fewest detours, and almost instantly determine the price of the trip.
  • Pinterest is increasingly leveraging machine learning to pour through images to surface content that resemble objects consumers are most interested in.

How does it all happen?

Very simply put, via the advances in computing software and the processing power of computers. Machine learning programs are created to take advantage of these advances. For example, programs are written that take full advantage of parallel processing to perform a wide variety of statistical analytics at scale.

Computers are extremely literal beasts and do precisely what you tell them or program them to do. If there is an error in your code—and I have made many in my years of coding in SAS and SQL—that program will keep running and generating the same mistake over and over until you tell it, via a change in the code, to stop.

Machine learning, which enables analytics at scale, is essentially giving computers the power to learn from experience, very much like humans do (and other animals, for that matter). There are many different types of machine learning, but the hottest form currently is called “deep learning.” It leverages artificial neural networks, which is a simplified computing system modeled on the information processing and communication function of neurons in the human body, to identify patterns from massive pools of data.

For example, if you show a neural network enough pictures of a horse, it will eventually be able to tell you if a picture it has never seen before is a horse. Taking it a step further, if you show the neural network enough pictures of a certain breed of horse, like an Arabian, it will eventually be able to distinguish Arabians from other breeds of horses.

Neural networks are not new, but the exponentially increasing power of computers has enabled deep learning machines to simulate many billions of neurons. Simultaneously, the exponentially growing pool of data generated by the internet each day has provided the fuel for these machine learning programs to consume.

Conclusion

A human mind can process at most about five data elements and analyze them for patterns. An algorithm, however, can process all data points—and process them in a totally unbiased way that humans just aren't capable of. Machine learning is a form of AI that solves problems by using the massive processing power of computers to find patterns.

And it's not terrifying science fiction; it is already working diligently behind the scenes for numerous brands we are familiar with and interact with regularly.

For marketers, it will be critical to integrate machine learning to create meaningful and relevant communications. My next post will cover what that could look like: Leveraging Machine Learning and Applying It to Marketing

About the Author

Korey Thurber

As Chief Data and Analytics Officer for Harte Hanks, Korey Thurber manages ideation, development & delivery for customer analytics and data solutions. With more than 20 years of experience managing large international teams of data scientists, analysts & data solutions providers, Korey has helped many brands to successfully adapt & integrate analytics to develop, execute and optimize marketing strategies across diverse online and offline media. His extensive knowledge & experience has made him a valuable asset for many clients across multiple industry verticals including Financial Services, Wealth Management, Retail/CPG and Non-Profit.

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