How to tell if AI or machine learning is real

How to tell if AI or machine learning is realSuddenly, it seems, every application and cloud service has been fortified with machine learning or artificial intelligence. Presto! They now can do magic.

Much of the marketing around machine learning and AI is misleading, making promises that aren’t realistic—and often using the terms when they don’t apply. In other words, there’s a lot of BS being peddled. Don’t fall for those snow jobs.

Before I explain how can you tell if the software or service really uses machine learning or AI, let me define what those terms really mean:

Artificial intelligence is a wide range of cognitive technologies to enable ad hoc or situational reasoning, planning, learning, communication, perception, and the ability manipulate objects to an intended purpose. These technologies in various combinations promise to create machines or software entities that have—or at least act as if they have—the natural intelligence that humans and other animal species possess. Just as natural life’s intelligence varies dramatically across and within species, so too could the intelligence of AIs.

AI has been a popular motif in science fiction for more than a century, and it’s a particularly strong notion among techies. IBM, MIT, the U.S. Defense Department, and Carnegie-Mellon University, for example, have been doing AI work for decades, showcasing the same kinds of examples over and over again for just as long. The promises today are very much like the promise I saw at these institutions in the 1980s, but of course there’s been a lot of incremental improvement that has brought us a little closer to making the promises a reality. But we’re nowhere the scenarios of sci-fi.

Machine learning is a subset of AI. It refers specifically to software designed to detect patterns and observe outcomes, then use that analysis to adjust its own behavior or guide people to better results. Machine learning doesn’t require the kind of perception and cognition that we associate with intelligence; it simply requires really good, really fast pattern matching and the ability to apply those patterns to its behavior and recommendations. Humans and other animals learn the same way: You see what works and do that more often, while avoiding what you observe doesn’t work so well. A machine, by contrast, does only what it is told or programmed to do.

Snow job 1: confusing logic with learning

There’ve been a lot of advances in machine learning in recent years, so not all machine learning claims are snow jobs. The quick way to tell is to ask the vendor what the software or robot can learn and adjust on its own, without a software update. Plus, ask how you train it; training is how you help it learn your environment and desired outcomes.

But most of what marketers call machine learning is simply logic. Programmers have been using logic in software since Day 1 to tell programs and robots what to do. Sophisticated logic can provide multiple paths for the software or robot to take, based on parameters the logic is designed to process.

Today’s hardware can run very sophisticated logic, so applications and devices can appear to be intelligent and able to adjust on their own. But most don’t actually learn—if their developer didn’t anticipate a situation, they can’t adjust on their own to handle it through pattern-analysis-based trial and error as a true machine learning system can.