What is Semantic Artificial Intelligence? 

At the heart of semantic automation is a discipline known as semantic artificial intelligence (AI), the study of how human logic can be used to derive meaning from data. Representing the next step beyond “weak” AI, semantic AI, semantic AI algorithms can “learn” context and apply it to new situations. In other words, semantic AI seeks to not only interpret data, but act on those insights. 

Unlike “general AI,” semantic AI cannot solve complex problems like a human can. However, it can enable machines to store, manage and retrieve information based on meaning and logical relationships. Generally speaking, semantic AI is made up of 3 different components:

• Knowledge graphs. A collection of interlinked descriptions of concepts, entities, relationships and events. Knowledge graphs put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing.
• Artificial intelligence (AI). The simulation of human intelligence by machines and/or computer systems
• Natural language processing (NLP). A subset of AI, NLP is a branch of computer science concerned with programming machines to understand and manipulate natural language text or speech.

Though the concept of semantic AI has been floating around the technology sector for over a decade, it became front of mind once again in June 2021 when UIpath announced that semantic automation would soon become “the next big thing.” 

What is Semantic Automation?

As explained on the company’s website, “Semantic automation means adding this semantic understanding across our platform such that software robots can deal with interfaces and documents more like a human—with a higher level of understanding.

Rather than just seeing the construct and layout of a document or application screen, software robots understand the business context around everything you're doing. With an understanding like this, software robots equipped with semantic capabilities make developers much more productive by allowing them to focus on the business problem they are trying to solve. Instead of interacting with document constructs or user interface (UI) elements.”

The first generation of robotic process automation was rules-based, meaning that a developer had to tell bots what to do, step-by-step. Semantic automation, on the otherhand, uses semantic AI techniques to enable RPA bots to not only "see" awhat’s on the screen, but understand the relationships between the various documents, processes, data, and applications it's dealing with. 

Accroding to Param Kahlon, Chief Product Officer, UIpath, "Soon, software robots will be able to simply observe an activity and begin to emulate it without step-by-step instructions. They’ll recognize the process, understand what data is required, and know where to get this data and where to move it. Developers and business users will be able to initiate automation development simply by asking robots to perform a task or complete a workflow."

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