Semantic technology refers to the field of computer science that focuses on understanding and representing the meaning of information in a machine-readable way. It involves techniques for structuring, organizing, and linking data to enable machines to understand the context and relationships between different concepts. Times Higher Education brought this interesting topic to our attention in their article, “Useful applications of AI in higher education – for which no specialist tech knowledge is needed.”
Semantic technology and ChatGPT are related in the sense that both involve the understanding and processing of human language. However, they approach this task from different perspectives and serve different purposes.
There are potential ways to combine semantic technology with ChatGPT. For example, one could use semantic technologies to enhance the understanding of the input provided to ChatGPT or to structure the generated responses in a more semantically meaningful way. By leveraging semantic technologies, it may be possible to improve the accuracy, relevance, and coherence of the conversations generated by ChatGPT.
The biggest challenge is that most organizations have little knowledge on how artificial intelligence (AI) systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.