Can We Kill the Term “Artificial Intelligence” Yet?

We’re deepening the credibility crisis in data science

1. Background
2. History of AI
3. Limits of ML
4. Next Steps
5. Killing AI
6. Closing Thoughts

TL;DR: Data scientists have a responsibility to shepherd the term artificial intelligence out of the world. We need to show maturity in walking back the near unattainable promise embedded in this misused phrase.

Background

“That’s a really strange pronunciation of machine learning, friend.”

This is my response to a fellow data scientist’s use of the words artificial intelligence during a mid-week phone call about how we might grow our business.

He pauses, and, cognizant that snarky interruptions aren’t an effective teaching tool, I take a deep breath and explain: “Let’s say machine learning because it more accurately describes the process of teaching mathematical models to generate insights by providing them with training examples.”

As I’ve deepened my understanding of data science, I’ve increasingly found that the phrase artificial intelligence sparkles with false promise:

• The term misleads decision-makers into investing in advanced analytics before their organization has reached sufficient data maturity. After all, intelligence should be able to cope with a little data complexity, right?
• It confuses the heck out of young students, who might’ve applied a little extra attention in their statistics class if they understood that was the path to a promising career in data science.
• It distracts from tried and true methodologies within data science.

I’m ready to kill artificial intelligence.

History of Artificial Intelligence

The academic discipline got its start in 1955 with the goal of creating machines capable of mimicking human cognitive function. Learning, problem solving, applying common sense, operating under conditions of ambiguity — taken together, these traits form the basis for general intelligence, the long-standing goal of artificial intelligence.