Editor's note: Jerry Thomas is president and chief executive of Arlington, Texas-based research firm Decision Analyst Inc. 

The business news outlets are cluttered with stories about artificial intelligence (AI) and machine learning (ML) startups. Major corporations are rushing to set up internal teams and divisions to exploit AI and machine learning. Graduate schools are turning out data scientists and business analysts with training in the two areas.

The big technology companies are creating AI software and systems. Marketing executives are anxious to apply AI and ML to optimize marketing and advertising processes and programs. It’s as though the gods have descended from the heavens to share ultimate truth with we humans. 

But what is artificial intelligence and how does it relate to machine learning? What do these terms mean?

Intelligence, whatever it is, is presumed to reside inside of biological creatures (cells, bacteria, plants, animals, insects, humans). We don’t think of intelligence as something possessed by a rock or a mineral or other non-living substances. All biological creatures can make decisions or choices that increase their chances of survival. Let’s define intelligence, then, as an ability to make a decision, to choose among alternative paths or possibilities in order to achieve some objective.

Artificial, in this context, means non-living or non-biological. So AI is an ability of some non-biological entity (machine, computer, software, system, algorithm) to make choices or trigger actions that help solve a problem or achieve an objective. We’ll come to “machine learning” later.

The beginning of modern artificial intelligence, as it is now commonly thought of, traces its origins to the development of computers during and following World War II and the possibilities spawned by those machines. The arrival of these powerful machines gave rise to much thinking about...