The difference between Artificial Intelligence and Machine Learning Explained in 5 Minutes

 
 

Chances are, you’ve probably heard the terms “artificial intelligence” and “machine learning” at your last conference. They are often used broadly and sometimes (falsely) used interchangeably.

As a result, the difference between AI and machine learning in marketing can still be a little unclear.

And you wouldn’t be unique - only 19 percent of marketers currently have a strong understanding of how AI, machine learning and predictive modeling differ, while 37 percent admit they don’t clearly know the difference, according to current research by Everstring and Heinz Marketing.

But if you have five minutes, we can clear up the confusion right now. Listen to our podcast, or read on below.

Listen to Our Podcast Interview with AI Lead Alexei Kounine

The term artificial intelligence (AI) refers to machines and machinations that can perform tasks that are characteristic of human intelligence. While that definition may seem broad, AI for businesses typically refers to mechanisms which perceive environmental factors and autonomously take actions that maximize the chances of successfully reaching pre-defined goals – without human intervention.

Put more concretely, think of AI as an umbrella expression for everything related to making machines perform tasks as human brains normally would.

There is still a lot that falls under that idea though, right?

Marketers can think about AI as computer-based tools that enhance engagement marketing platforms and marketing automation software with human-like skills such as:

  • Reasoning
  • Planning
  • Learning
  • Decision-making
  • Optimizing

For brevity, here at Selligent Marketing Cloud, we define AI as a self-learning system that can adapt its behaviors based on insights gained as it processes information.

Now, machine learning, on the other hand, is more about the mechanics – the mathematical models and algorithms – behind how a computer system learns. It’s related to how massive quantities of data from different sources can be utilized, and then applying that data in a machine to learn from experience.

Compared to the pioneer days of artificial intelligence, this approach is really a paradigm shift from the early days. Before machine learning, programmers “taught” computers to utilize data by writing complex command strings. But nowadays, in order to perform the same flexible and complex tasks unlocked by machine learning, the old teaching method would require writing literally millions of lines of code, with complicated rules and decision trees to provide specific instructions for specific tasks. Any new and unknown problems would require a programmer to write new code to solve them.

So with that said, machine learning is precisely what makes AI more fluid and adjust course based on available data. As a marketer, you may hear people use other terms, which are basically subsets of machine learning:

  • Deep learning
  • Deep neural networks
  • Innovation insights learning
  • Adversarial learning

On a brand level, the large majority of what brands and marketers do right now is associated with machine learning and deep learning. We here at Selligent Marketing Cloud have built and trained our own AI-engine, Selligent Cortex, specifically for the needs of engagement marketers.

Marketers can use AI technology to learn from the data they’ve collected about their customers. This intelligence, stored in Universal Consumer Profiles, can then be applied with AI to learn a consumer’s favorite product, color, flavor etc. based on behavioral data – and used to serve personalized offers to each of thousands of customers. It can build customer journeys “on the go” and adjust AI marketing automation based on real-time behaviors to create a sense of timeliness and situational awareness that customers appreciate.

It may sound like a paradox, but enlisting machine learning and artificial intelligence will ultimately make your marketing come across as more human and empathetic. And help you deliver relevance at scale for large customer segments by watching tastes and preferences evolve, while independently adjusting AI marketing initiatives for maximum impact.

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