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This seems like a fairly useful tool, but I'd be a bit cautious - the tradition of poring over a carefully collected and curated data set using tools you unders

This seems like a fairly useful tool, but I'd be a bit cautious - the tradition of poring over a carefully collected and curated data set using tools you understand the strengths and weaknesses of shouldn't be lightly tossed aside. That process can help researchers spot unusual anomolies that lead to novel discoveries, while an automated tool might just discard all outliers. Incidentally, the far more concerning issue is the use of approaches like this to generate data which opens the doors to a plague of hard-to-detect scientific fraud. In that past, many such high-visibility fraudulent efforts have been detected because the fraudsters duplicated data (or reversibly processed old data in some manner) that was spotted by others in the field, e.g. https://en.wikipedia.org/wiki/Sch%C3%B6n_scandal https://en.wikipedia.org/wiki/Sch%C3%B6n_scandal Often these fraudulent productions are inspired by the desire to be first to publish, a situation in which everyone thinks they know how a system works but they're all rushing to get credit (and hence Nobel Prizes and patents etc.) by generating the data from a 'successful experiment' before anyone else can.

cl42 4y ago
100% agree with you. There are two things driving my work with this demo: (1) A lot of researchers are bad at writing code, but they can audit it. This is true for sociologists, psychologists, etc. so I'm hoping something like this can help. (2) Philosophically, I disagree with the debates that LLMs can't produce new knowledge. I think there's merit to this if we're talking about whether the LLM neural network itself synthesizes new knowledge via its weights... However, why can't we have an LLM try and merge multiple data sets, analyze them, and report back to a human? To your point + concerns, I think a human still needs to be very careful and actually revisit the analysis for any promising findings, but at least some of the grunt work can be taken care of!
- jkh1 4y ago
  > A lot of researchers are bad at writing code, but they can audit it. Can they really? It seems to me that users of this would be those that can't write the code they need. How would they be in a position to audit what they get?
  - cl42 4y ago
    I'll use myself as an example. I love Pandas + Scikit Learn, but am by no means an expert. Every time I want to build a logistic regression, I have to go back to the docs to review the API. When I was doing my Master's degree in "Social Science of the Internet" at the Oxford Internet Institute (a sociology + data science program, with many students coming from non-STEM backgrounds), everyone was comfortable debating P values, standard errors in regressions, etc. but many students were extremely intimidated by reading Python docs and/or using the REPL.

cl42 4y ago

100% agree with you. There are two things driving my work with this demo: (1) A lot of researchers are bad at writing code, but they can audit it. This is true for sociologists, psychologists, etc. so I'm hoping something like this can help. (2) Philosophically, I disagree with the debates that LLMs can't produce new knowledge. I think there's merit to this if we're talking about whether the LLM neural network itself synthesizes new knowledge via its weights... However, why can't we have an LLM try and merge multiple data sets, analyze them, and report back to a human? To your point + concerns, I think a human still needs to be very careful and actually revisit the analysis for any promising findings, but at least some of the grunt work can be taken care of!

jkh1 4y ago
> A lot of researchers are bad at writing code, but they can audit it. Can they really? It seems to me that users of this would be those that can't write the code they need. How would they be in a position to audit what they get?
- cl42 4y ago
  I'll use myself as an example. I love Pandas + Scikit Learn, but am by no means an expert. Every time I want to build a logistic regression, I have to go back to the docs to review the API. When I was doing my Master's degree in "Social Science of the Internet" at the Oxford Internet Institute (a sociology + data science program, with many students coming from non-STEM backgrounds), everyone was comfortable debating P values, standard errors in regressions, etc. but many students were extremely intimidated by reading Python docs and/or using the REPL.