Chat bots like GPT are fed both by great amounts of data and natural language algorithms, and they make predictions for putting words together such that they convey meaning to humans. They not only tap into a vast vocabulary and phraseology: they understand context. This helps them mimic speech patterns and engage on a wide array of subjects with an encyclopedic knowledge base. In fact, if you ask the ChatGPT artificial intelligence (AI) system a question about its own leading role in a technological revolution, it acknowledges the significant impact and the ethical concerns. This is a result of the breadth of the knowledge base touching many human opinions. This interesting news came to us from El Pais in their article, “ChatGPT is just the beginning: Artificial intelligence is ready to transform the world.”
There is a lot of hype around ChatGPT and similar tools, but it is important to remember they aren’t magic. They can’t do everything. Understanding their limitations and capabilities is key.
Most organizations have little knowledge on how AI systems make their decisions, so they struggle to keep themselves in a position to take advantage of the 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 its 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, changing search to found.