3 ms·
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
by cl42 3y 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 3y 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 3y agoI'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.