Though both stand to benefit many industries — especially marketing — as they become increasingly mainstream, It’s important to properly differentiate between “artificial intelligence” and “machine learning.”

The artificial intelligence revolution may yet be in its infancy, but the ever-increasing value of AI technologies is already apparent across industries as diverse as automotive manufacturing, real estate asset management, and countless others. In the advertising and marketing spheres, specifically, AI tech stands to greatly improve production, predictive targeting, and consumer experience.

While the potential for such technologies to fuel growth and innovation is certainly exciting, discussions of these new tools aren’t always conducted with the proper amount of precision. Specifically, there remains a good deal of confusion about the difference between “AI” and “machine learning,” and though the two terms are closely related, they shouldn’t be used interchangeably. Indeed, in the strictest sense, machine learning is actually a subset of AI. Let’s take a look at what differentiates the two technologies:

Understanding the Difference between AI and Machine Learning

The idea of “artificial intelligence” as we conceive of it now stretches back to the Dartmouth Conferences of 1956, where a group of computer scientists discussed the possibility of creating a “smart” machine capable of carrying out tasks that typically require some sort of human intelligence.

Over the years, AI-based technologies have been dichotomized into “weak” and “strong” instances of AI. A weak AI will be programmed to complete a specific task — manufacturing a particular part on a factory floor, for instance — and though it may be a task requiring some simulacrum of human intelligence, the output will always be determined ahead of time. In other words, a machine endowed with weak AI is able to execute its designated task to perfection, but it isn’t designed to accomplish anything else.

Strong AI, on the other hand, is capable of adjusting its outputs based on shifting goals and inputs. A strong AI system will take whatever masses of data it is fed and uncover hidden trends and patterns, enabling it to find the most efficient way to accomplish the tasks it was assigned. In recent years, machine learning algorithms have been one of the most talked about — and one of the most frequently deployed — versions of strong AI.

Shortly after the Dartmouth Conferences, AI pioneer Arthur Samuel proposed that instead of programming AI machines with everything they need to know in order to mimic human thinking — ultimately a Sisyphean task — computer scientists should work toward a system capable of learning things on its own and gradually expanding its own knowledge base in the same way that a human child might.

Programmers have experimented with a variety of algorithmic approaches since Samuel’s proposal in 1959 — decision tree learning, inductive logic programming, clustering, reinforcement learning, Bayesian networks — but with the recent development of incredibly powerful processing chips, machine learning has the opportunity to make an unprecedented leap forward.

In short, the “learning” accomplished by a machine learning system depends upon the system’s ability to gather, organize, and analyze remarkable volumes of raw data and derive insights from these data processes. The more data the system has access to, the more it will learn — and the more nuanced its insights will become — which is why processing power has, to this point, been the limiting factor in machine learning systems’ “education.” With more processing power come more complex neural networks, which at the moment are undergirding the development of a particularly powerful type of machine learning called “deep learning.”

The Value of Machine Learning to Marketing

The potential value of AI — and machine learning in particular — to the marketing sector cannot be overstated. The cornerstone of successful marketing is placing the right ad in front of the right consumer at the right time, and machine learning platforms offer marketers a way of doing so with previously unparalleled precision and consistency.

According to Blazent’s 2016 State of Enterprise Data Quality report, marketing organizations are starting to take notice of everything that machine learning has to offer. Of the roughly 200 C-level and senior IT leaders surveyed, around two-thirds confessed having “a strong appetite for machine learning and predictive analytics.” What’s more, 22 percent of marketing organizations already have machine learning programs in place and 42 percent plan to implement them in the near future.

At Saatchi & Saatchi Wellness, machine learning algorithms have already enabled us to help pharmaceutical brands refine their HCP behavioral targeting models and identify and predict which patient types are least likely to adhere to a particular therapeutic regimen. As the technology underlying machine learning systems — and AI in general — continues to mature, there’s no telling what previously impossible marketing tasks will become integrated into our daily operations. So long as we learn to coexist with and strategically leverage artificial intelligence systems, we have the opportunity to become better, more tactical marketers — marketers with the power to revolutionize the brands we serve.

Written by Vladislav Ryvkin

I have been working in or studying healthcare for approximately 9 years. I have a B.S. in biotechnology, and an M.S. in biomedical sciences. After completing my studies, I was an analyst for EmblemHealth, New York’s largest non-profit health insurer. There, I worked on many of their health-related quality measures, including osteoporosis, rheumatoid arthritis, and medication adherence. I joined the Saatchi team in April of 2016, where I have been working in the fields of women’s health, surgical products, and rare disease. I have worked with all kinds of medical and marketing data and am proficient in SQL, SAS, and R programming. My expertise lies in data science and business intelligence.

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