You are correct that the quality of AI-generated content is largely dependent on the quality of the input data that the AI is trained on. This is known as the "garbage in, garbage out" principle. If the input data is inaccurate, incomplete, or biased in some way, then the AI's output will reflect those same limitations.The main problem with AI-generated stuff: garbage in = garbage out.
However, it's worth noting that many efforts are underway to improve the quality and diversity of AI training data, and to address potential sources of bias or inaccuracy. This includes techniques such as data cleaning and preprocessing, as well as efforts to collect more diverse and representative data sets. Additionally, researchers are developing new algorithms and techniques to improve the accuracy and reliability of AI-generated content, even when the input data is imperfect or incomplete.
Despite these challenges, AI-generated content has many potential applications in a wide range of fields, from language translation and image recognition to scientific research and business analytics. As AI continues to advance and improve, it has the potential to revolutionize many areas of human activity and to generate new insights and innovations.