unlike humans, AI does not inherently have a nationality or personal interests, as it lacks consciousness and self-awareness. Therefore, its knowledge, in theory, could be more universal in scope than that of individual human beings, but it is not automatically so.
If the training data is diverse and represents a broad range of perspectives and cultures, the AI's knowledge may be more universal in scope. However, if the training data is biased or limited to certain regions or cultures, the AI's knowledge could be skewed and not representative of the whole world.
Some AI models are designed to generalize well across different domains, while others may be more specialized in certain tasks. The extent to which an AI model can apply its knowledge to diverse contexts can vary based on its design.
This can help improve the universality of their knowledge over time, as they can adapt to changes and incorporate information from different sources.
If AI developers and researchers are not mindful of bias and ethical concerns, the AI's knowledge could perpetuate existing prejudices and limitations found in the training data.
Efforts to ensure unbiased and diverse training data, as well as responsible AI development practices, can contribute to making AI knowledge more universal in scope.