Robotic Process Automation (RPA), AI and Machine Learning

RPA automates business workflows, or clerical processes, by emulating human interaction within a graphical user interface (GUI).

For example, RPA can record a series of tasks taken in a GUI, such as cursor moves and buttons clicks, and translate that series of actions to code, so that those tasks can be performed without human intervention in the future.

RPA can be optimized for some GUI actions by applying machine- and deep-learning algorithms to perception problems, like recognizing a button or an edit field. Given the graphical nature of RPA, deep learning’s image recognition capabilities are perfectly suited to the task. You might say that the “software robot” of RPA is the arms and legs, and the machine learning component is the “brain”.

Machine learning models can be inserted into RPA workflows to perform machine perception tasks, like image recognition: tasks that the human brain can perform in a second, whose output can be plugged into a larger flow of business logic.

For one example of applying AI to RPA, please see Skymind’s RPA page.

Differences Between RPA, AI and Machine Learning

RPA and AI are two horizontal technologies that are distinct in their goals and interfaces.

RPA is intended to save business and white-collar workers time. RPA is built by RPA engineers via a GUI, or a graphical interface, which they use to arrange the sequence of tasks RPA automates. For the most part, RPA is based on rules, or if-then statements that tell a program what to do under certain conditions.

AI is an umbrella term that includes rules engines like the kind mentioned above. But that’s not the exciting side of AI, and it’s usually not what people mean when they refer to AI these days. Usually, they are referring to machine learning or deep learning; i.e. programs that are capable of rewriting themselves in response to their environment or the data they’re exposed to.

AI is a horizontal technology that makes decisions about data. Sometimes it makes decisions, or predictions, based on rules that humans manually wrote (rules engines); sometimes it makes decisions based on a bunch of numeric parameters that it arrived at after much trial and error (machine learning).

Advances in AI allow us to make more accurate decisions about the data we’re looking at. In some cases, that accuracy can surpass human accuracy.

RPA and AI overlap in that you can infuse RPA with AI. Most RPA vendors are not currently using advanced AI in the products they ship, but that is changing. Useful applications of advanced AI in RPA could include image recognition (in order to recognize images on a screen more reliably) or text analysis.

Many AI tools, such as SKIL and Eclipse Deeplearning4j, are accessed with an API, some with a GUI.

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