There’s a lot of hype around AI right now. AI itself and all of the terms surrounding it can be confusing for anyone just beginning their AI journey. Where do you start? Which aspects of your business can you optimize? How do you talk about AI like a professional?

Photo by Michael Dziedzic on Unsplash

We doesn’t want to add to the confusion. The terms below provide our definition of the most commonly used terms in AI.

What is AI (Artificial Intelligence)?

When we refer to something as AI, we are talking about applying the latest in data science like machine learning, deep learning, and reinforcement learning to replace certain tasks generally performed by human beings.

These algorithms are developed by exposing them to several input and output pairs of information, known as training data. For example, let’s say that we want to build a model to take an image of a dog or a cat and output either “Cat” or “Dog,” based on what it sees in the image. To train this model, we would need to feed the algorithm hundreds of images of cats and dogs clearly marked as “Cat” or “Dog.” The model would use these images to learn the combination of pixels that likely represents a “Dog” or a “Cat,” and would slowly tune itself to accurately recognize them. Once a model is trained, it can be fed new images of cats and dogs and provide a correct output.

How does AI differ from RPA (Robotic Process Automation)?

Robotic Process Automation can be a very powerful way of automating repetitive tasks. An example would be the process of moving output from a mainframe machine with a database written in COBOL to a more traditional database, without having to build an integration between the outdated code and a new noSQL database. RPA bots can be used to take the output of the COBOL system from a computer screen and populate that output into the noSQL database. Since this task involves repeating the same exact process again and again, this can be done using RPA algorithms.

AI on the other hand is more suited for less-defined tasks, like “Tell me about the features of this image,” or, “Help me understand the emotions of the individuals in this video.” The steps involved to get to the answer are not predefined, as they must be for the use of RPA.

How does AI differ from Predictive Modeling?

Predictive models are built using machine learning techniques and also help automate tasks traditionally done by human beings. So, in that sense, predictive models are a form of AI models.

This piece originally appeared on CrowdANALYTIX.

Writer and Content Manager for CrowdANALYTIX. PhD from UT Austin.

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