Can ChatGPT create research questions or hypotheses? Is the role of the human mind waning in science?
I think Chat-GPT can create the research question and hypothesis. This is very dangerous for the future of research, I suggest the author of this system should restrict some fonctions to be as much as simple possible. Let's think what if Chat-GPT Is there within reach of teenagers? Will they still learn?
It’s amazing that people don’t understand that ChatGPT uses the content of existing Internet pages. Since these contain research questions and hypotheses, that’s what’s presented. Of course for the many people that don’t have the understanding, these can appear new.
Anyone can create a research question (even a simple algorithm such as ChatGPT that can take and just give you the future research section or rehash questions from an existing thesis as Stam Nicolis mentioned). The real challenge is in answering the question. ChatGPT cannot create a research methodology for an unknown problem much less try to solve it. Trying to expect that just by doing permutations on existing data to solve a novel problem is naive at best.
Prof Stam Nicolis mentioned a very fundamental base for chatgpt methods of operating. Dr. Arturo Geigel I agree that this chatgpt can’t solve many problems and questions including millennium problem. but the severe impact on youth and ppl they will be lazy to think about their research questions and so on and work their minds out however i think chatgpt can help a lot in improving our understanding of the unsolved research problems such as the above.
Hussein Muhammed ,
"chatgpt can help a lot in improving our understanding of the unsolved research problems such as the above"
This is a very dangerous statement to state unless it is backed by mathematical proof which guarantees that LLMs can actually do scientific work. Performance proofs are empirical tests that are bound by the parameters of the test and up till now I have found them not to be replacements for mathematical proofs.
My recent research(under peer review) currently analyzes claims of universal approximation of LLMs and found they are existential in nature. Other areas such as proof of semantic understanding by LLMs have methodological issues that put into question such claims. Further the proofs that they can understand logic was found to be "hints" in the data on which simple relationships could be made. When those "hints" were taken away the performance of LLMs dropped significantly[1].
I would advise that researchers first properly and carefully asses the real capabilities of LLMs and do not fall prey to the hype raised by LLMs. It will take time for people to overcome the novelty and face the reality that these LLMs are not what they purport to be.
References
[1]Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S. R., & Smith, N. A. (2018). Annotation artifacts in natural language inference data. arXiv preprint arXiv:1803.02324.
Hello Dr Hacı Hasan Yolcu
I agree with the point raised by Dr Stam Nicolis - this mechanism often just regurgitates information from the internet. So the hypotheses are not totally original. Surely, it will become more possible in the future (?) but maybe not yet now.
Dear Stam Nicolis Even though, It is amazing to be able to review millions of existing hypotheses and generate a hypothesis that is relevant to your topic without losing its way through millions of web pages.
Dear Arturo Geigel. Thank you for your great contributions. The challenging aspect of research(literature search) can be resolved by effectively utilizing the vast amount of existing data. At least, by utilizing existing research, you can avoid duplicating the previous research.
hypotheses based on patterns and knowledge within the data I've been trained on. However, the role of the human mind in science is far from waning. While AI models like ChatGPT can provide valuable insights and assist in formulating research inquiries, they are limited by the data they have been exposed to and lack the creativity, intuition, and critical thinking abilities inherent to the human mind. The human mind remains essential in designing experiments, formulating novel ideas, interpreting complex data, and advancing scientific knowledge by embracing curiosity, imagination, and the ability to make insightful connections that go beyond the realm of data-driven models. Thus, rather than perceiving AI as a replacement, it should be viewed as a powerful tool that complements and augments the intellectual prowess of scientists, enhancing their capacity to explore the unknown and unravel the mysteries of the universe.
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