Suddenly, 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.

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.

. But we all quickly see how they fall apart in areas outside their programming, resorting to a simple web search for what they weren’t programmed to learn. No doubt Apple, Microsoft, and Google are using machine learning on the back end to make them appear smarter.

If someone claims an application, a service, or a machine is smart, you’re almost certainly getting snowed. Of course, people will use the word “smart” as a shortcut to mean “more capable logic,” a phrase that won’t sell anything. But if they don’t explain what “smart” means specific to their offering, you know they think you’re dumb.

The fact is that most technologies labeled “smart” are not smart, merely savvy. The difference is that smart requires intelligence and cognition, whereas savvy requires only information and the ability to take advantage of it (it’s no accident that “savvy” come from the French word for “to know”). A savvy app or robot is a good thing, but it’s still not smart. We’re simply not there yet.

Even IBM’s vaunted Watson is not smart. It is savvy, it is very fast, and it can learn. But it’s been around in various forms at IBM since the 1980s, so if Watson were truly that smart, IBM would be ruling the business world by now. Watson won’t cure diseases, make peace in the Mideast, create new tax breaks, or solve world hunger. But it can help people better handle all sorts of actions, if the price is right.

If you keep that goal in mind and are truly getting machine learning and AI precursors in your business, you’ll be satisfied. But don’t expect a sci-fi fantasy version like Data from Star Trek, HAL from 2001: A Space Odyssey (inspired by IBM’s 1960s AI research!), or Philip K. Dick’s androids in Do Androids Dream of Electric Sheep? And don’t trust vendors that sell their technology under such guises.