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Know the differences between AI and machine learning

Unless you have been living under a rock the last several years, you’ve heard two terms being used a lot: “artificial intelligence” (AI) and “machine learning”. On the surface, these two terms sound like they could possibly be the same thing. While related, AI and machine learning are not the same. Today we are busting some common myths about AI and machine learning to help you better understand them, especially when it comes to marketing technology!

Myth 1: AI = ML

AI is a computer science branch attempting to build machines capable of intelligent behaviors. But what is AI without machine learning? Machine learning pushes computers to learn the way humans do in order to think like humans (for example, search engine algorithms). Machine learning is one of the approaches to AI and focuses on learning, specifically how to perform a task without explicit rules or steps provided.

To sum it up — machine learning is a subset in the broad field of artificial intelligence.

Myth 2: AI is machines replacing humans.

Nope. AI is not the dystopian future that the media paints it to be. AI is more of a complement to humans, not a replacement. There are many practical uses for AI that streamline the workflow for humans in a business setting, without taking jobs.

A great example is software that prioritizes support tickets. What if machine learning could help your customer support department resolve issues more efficiently with the help of software to prioritize support tickets? While that’s less time needed for humans to sort through support tickets and manually assign priority for their resolution, that leaves the team with more time to actually help customers.

Myth 3: The algorithm is all that matters.

Wrong! It’s the data. AI’s strength is its ability to analyze data and connect all those dots that humans miss or don’t have time to examine. Even the best algorithm in the world is nothing with sparse data that’s inaccurate and poorly organized.

To use this concept in marketing, for optimization, in particular, the marketing measurement and media buying systems need to be tightly integrated. The full system will then start buying ads with different attributes (various publishers, placements, creatives, strategy, etc.) Based on the instant feedback, like clicks and conversions, and its internal attribution system, it will then update itself to buy more efficient ads to maximize/minimize a desired KPI(s).  There is still a human component involved that must plot the data points and define the cost function that will define the reward or penalty for the system.

Myth 4: Machine learning is just providing data summaries.

Machine learning is used in predictive analysis. Knowing what you did in the past is only a means to figuring out what you would like to do in the future. While these learning algorithms are not yet as smart and accurate as scientists, they are a lot faster – and in marketing, time is money!

AI and machine learning can be powerful, but the required human element is what ultimately makes machine learning successful. With a powerful machine learning component in our DriveBy module we have seen first-hand the success that integrating intelligent algorithms can be when applying it to conversion technology.

By | 2017-08-30T15:56:58+00:00 August 1st, 2017|AI, eCommerce Marketing, Machine Learning|

About the Author:

Chief Technology Officer of CPT and co-founder of Push, Haig brings skills and knowledge in a variety of areas, including design, finance, web development, accounting, technical development, and branding. With more than 15 years in the internet marketing space and five years of software development and eCommerce, he has a proven track record of start-ups and enterprise-level custom architecture, working with teams large and small. He has developed and launched 18 proprietary platforms, and developed technology that increased gross margin by 27% and generated more than 500 million ad impressions per year. Holds B.S. in Business and Advertising, with minor in Computer Science from University of Kansas.