Though there is no official definition of Semantic AI, it is generally regarded as the combination of technologies namely Resource Description Framework (RDF), Ontologies, SPARQL (pronounced "sparkle”, RDF Query language), web technologies, and AI. The aim of Semantic AI is to use semantics to enable AI code to better understand, interpret, and use reason with large collections of data.
ChatGPT is a generative AI product that uses a Large Language Model (LLM) to determine the construction of an answer in English using known patterns and probability to a question. Although these two approaches are slightly different, the potential combination offers significant benefits as it can increase the accuracy of results and make better use of context.
There are plenty of examples where Generative AI is used to generate code, it leverages its knowledge of code that it was trained on, and LLM techniques to produce code that delivers the functionality requested in the prompt. Of course, code generation is not new. There have been previous waves of hype about Third Generation Languages (3GL), Fourth Generation Languages (4GL), use of templates, code generators, etc.
The complexity of modern computing tech stacks used in the development, test and production environments to deliver load balanced, resilient, secure functionality at scale, means that the construction of the code is only one part of an IT solution.
However, the explosion in the number of “Cheat Sheets” demonstrates that the creation of effective prompts for Generative AI is not easy. In the short term, some of the Generative AI technologies will be trained on more automation code and therefore will be capable of generating more effective Automation solutions that can be rapidly deployed for testing and execution in Automation architectures.
Moreover, Machine Learning can be a mechanism to enhance the processing of RPA software robots. Machine Learning is about taking some AI functionality that has been trained on a set of data so that the result is created and “Enhancing” the trained functionality to recognize an additional set of data. For Machine Learning to take effect it needs to have enough data to amend the pattern.
In summary, Semantic AI in combination with RPA Robots offers the possibility for the technology to detect any change in application display and determine how to resolve the difference so that the task can still be completed.
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