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Article summary:

1. Generative AI can help users gain knowledge and refine output, but reproducing specific factual information is unreliable.

2. The generation of common sentences based on statistical inference and vectorization has limitations in correct/incorrect judgment.

3. Information pollution may occur due to false or unfocused information generated by generative AI, leading to ethical issues and the need for different concepts and functions for judgment criteria.

Article analysis:

The article titled "Generating AI: Current Thoughts" by flyingcafe provides a critical analysis of generative AI and its interaction with users. The author discusses the basics of user-AI interaction, including instructing the AI to output, instructing the AI to output instructions to the user, and instructing the AI to output reactions to the user's output. The author notes that these combinations allow users to gain new knowledge, learn experientially with the help of AI, and refine AI output for higher quality knowledge.

However, the author also highlights some limitations of generative AI. For instance, exact reproduction of specific factual information is unreliable at this time. While citation sources can be shown, it makes it harder to do tasks beyond summary search. Content summaries are reasonably effective but may miss important factors. Additionally, inappropriate prompts can result in plausible but meaningless outputs.

The article also discusses characteristics of generating common sentences based on n-gram statistical inference and information compression by vectorization. However, it is difficult to theoretically solve some problems under this current direction. Therefore, it seems necessary to introduce a different concept and function for correct/incorrect judgment.

The author notes that generative AI can be quite effective for idea support by combining heterogeneous elements because it does not require correct answers. However, there is a risk of spreading false information or unfocused information on social media platforms due to generative AI's limitations.

The article raises several ethical issues related to generative AI's data set and learning process that may lead to various ethical issues depending on their usage method. The problems of dataset depletion and information pollution are likely to become serious before long.

Overall, the article provides a balanced view of generative AI's potential benefits and limitations while highlighting its ethical concerns. However, there are some unsupported claims in the article that need further evidence or exploration. For instance, it is unclear how long-term exposure to information pollution will affect human psychological states, thinking, values, communication styles, relationships, development, learning, language, and emotions. Additionally, the article does not explore counterarguments or present both sides equally.

In conclusion, the article provides valuable insights into generative AI's potential benefits and limitations while highlighting its ethical concerns. However, it is essential to consider all perspectives and evidence before making any conclusions about generative AI's impact on society.