Alejandro Acelas
Posts 4
Comments16
For anyone considering working on the ORCID-TAXID mapping tool, I suspect it might be an unusually approachable project for those with some familiarity with biological publications and programming. Even without what ORCID or TAXID stood for before reading this post, I managed to construct a barebones demo in 30 minutes using ChatGPT and the Europe PMC API (which has an option to search by ORCID ID, though some quick manual searches suggest it isn't comprehensive). I think in under 30 hours you could build a decently useful product by adding features like:
- Querying PubMed too alongside Europe PMC to get better publication coverage
- Searching publications by author name (since PubMed doesn't offer filtering by ORCID ID) and filtering out clear false positives using simple heuristics like author affiliation and publication area
- Creating more fine-grained criteria for identifying organisms in publication abstracts and texts
I will message the post authors and offer to do this myself, but if you already have background in biological sciences and are looking for a cool upskilling project, you would probably be a better fit than me for this.
For those wanting a quick encapsulation of Nietzsche's morality, I recommend Arjun's other post on the topic. It's both unusually succinct and well-written.
A reason why the political orientation gap might be less worrying than it appears at first sight is that it probably stems partly from the overwhelmingly young bent of EA. Young people from many countries (and perhaps especially in the countries where EA has greater presence) tend to be more left leaning than the general population.
This might be another reason to try onboarding older people to EA more relative to the pool of new members, but if you thought that would involve significant costs (e.g. having less young talented EAs because less community building resources were directed towards that demographic), then perhaps in equilibrium we should have a somewhat skewed distribution of political orientations.
I'm not sure if I understand where you're coming from, but I'd be curious to know: do you think similarly of EAs who are Superforecasters or have a robust forecasting record?
In my mind, updating may as well be a ritual, but if it's a ritual that allows us to better track reality then there's little to dislike about it. As an example of how precise numerical reasoning could help, the book Superforcasting describes how rounding Superforecasters predictions (interpreting .67 probability of X happens as .7 probability of X happening) increases the error of the prediction. The book also includes many other examples where I think numerical reasoning confers a sizable advantage to its user.
Oh, I totally agree that giving people the epistemics is mostly preferable to hanging them the bottom line. My doubts come more from my impression that forming good epistemics in a relatively unexplored environment (e.g. cause prioritization within Colombia) is probably harder than in other contexts.
I know that at least our explicit aim with the group was to exhibit the kind of patience and rigour you describe and that I ended up somewhat underwhelmed with the results. I initially wanted to try to parse out where our differing positions came from, but this comment eventually got a little long and rambling.
For now I'll limit myself to thanking you for making what I think it's a good point.
Oh, amazing! I didn't knew Alex had a Substack. And indeed, it's full of advice for using LLMs for work.
Here's a relevant link for those interested: https://lawsen.substack.com/p/lean-into-laziness
On the same genre there's also these posts from Shakeel Hashim and Peter Hartree on how they use LLMs: