AI systems, including language models like ChatGPT, are trained on large datasets to learn patterns and generate responses. However, they can sometimes produce incorrect, nonsensical, or misleading answers.
They rely on patterns and statistical associations in the data they were trained on, which can lead to incorrect or nonsensical responses.
Biases present in the training data can propagate and result in biased or inaccurate responses.
They lack the common-sense reasoning and intuitive understanding that humans possess.
They do not possess the ability to reflect upon their own knowledge, question their assumptions, or recognize their limitations. They operate solely based on the patterns they've learned from the training data.
They are designed to assist and augment human capabilities, but they can still exhibit limitations, errors, and what might be perceived as "stupidity" in certain situations. Ongoing research and development in the field of AI aim to address these limitations and improve the performance and reliability of AI systems.