What are some common misconceptions people have about AI and its capabilities? originally appeared on Quora, the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora on Twitter, Facebook, and Google Plus.

We talk a great deal about how AI and machine learning are tools. So, let’s extend that line of thinking to help make clear why certain misconceptions persist.

What is a situation where you need tools? How about when you build a house? If you’re going to build a house, will only one tool be enough? Of course not. You need everything from hammers, saws, and drills, to paint brushes, sandpaper, and levels. So, let’s say you have every tool you’ll need. Are you ready to build the house? Not unless you, or someone with you, knows how to use every one of those tools. Do you know how to use a concrete trowel? How about a framing nailer? If you don’t know how to use them, just having them doesn’t matter. Here’s another question: When do you use a hacksaw, and when do you use a jigsaw? Or should you use a circular saw or a chop saw? If you choose wrong, you’ll get the wrong results. You might even cause damage. So, not only do you have to have the tools, you have to know how to use them, and not just that, you also have to know when to use them.

This is a pervasive misconception about AI and its capabilities—that it’s a one-size-fits-all solution for every problem. Nothing could be further from the truth.

At DataVisor, we often say that you have to think beyond the algorithm. What we mean by this is that there is more to AI-powered fraud prevention than just having a great algorithm. You need an entire ecosystem of tools, techniques, and technologies. Our solution comprises rules, supervised machine learning, unsupervised machine learning, deep learning, global intelligence, a big data framework, and so much more. It is a solution built by hundreds of engineers, data scientists, researchers, and fraud experts, each of whom is contributing domain expertise. It is a solution that is tailored to address specific use cases. It is a solution that provides out-of-the-box results, that can also absorb new input and adapt accordingly, to deliver even better results. It is a living, evolving thing. So it’s a misconception to think that, to solve a problem, you just need “AI.” There’s so much more to it than that.

Of course, there are other misconceptions as well. For example, that AI will replace humans. As I’ve just been discussing, AI doesn’t replace humans. AI needs humans! But at the same time, humans need AI. AI is augmentative. It enhances our abilities. With it, we can do so much more than we can do manually.

Another misconception is that AI “learns on its own.” This is a kind of half-truth. On the one hand, unsupervised machine learning can indeed identify fraud that it’s never seen before. So it that sense, it’s acting “on its own” and “learning” as it goes. But it’s only able to do so because of the work that engineers and data scientists and fraud experts have put in.

Fang Yu is the Co-Founder and CTO of DataVisor

This question originally appeared on Quora. More questions on Quora:

* Fraud: What does “fraud” most commonly look like today?

* Artificial Intelligence: How can advanced AI solutions be used to combat bot-powered fraud?

* Computer Science: Is AI/ML overhyped in 2019?

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