In the past, when you needed to do research, you would often be required to include accredited documentation for your research to be considered valid. Example: a scientific journal, a primary source, or references from curated places like jstor.org. As a result, as long as you used these tools as your source, you could be sure that your data was valid and reliable. But now, many people do their research through AI, which is NOT purely sourced from accredited data. People are now told that you can “Use AI to help with research, but you need to cross reference it to ensure the data’s validity” But if I have to cross reference AI data with another source, what was the point of using AI? It’s a bit like trying to write a research report using Wikipedia, but then having to go to jstor.org anyway. Why would I not just go to jstor to begin with? This is not an insult on AI or anything, but more that I’m trying to understand what is the value of using AI for search if the data has consistently been verified as unreliable? My concern is that since AI will train off of any data it obtains, it is “contaminated data”. There’s no real way to realistically separate the contaminated data from real data either unless you make your own AI data source with reliable data. Perhaps I’m missing something here? Just trying to understand the appeal of AI for research. submitted by /u/dogz4321
Originally posted by u/dogz4321 on r/ArtificialInteligence
Considering it can’t discern fact from fiction, or have any true insight into a problem, or a data set, it’s very limited. It could be useful to aggregate and parse large volumes of data for key metrics. But there’s already a lot of tools that can do that for less money and cycles too.