also imo this is a habit that is cultivated by constant practice (say, from local collaboration/mentorship or OSS). Instead of a whopping 12-week course, a workshop talk or informal tricks-sharing is perhaps more suitable

The Internet has almost too many resources on general SE best practices (super useful for code release). What's lacking are good programming practices in the context of day-to-day research, e.g., versioning datasets, tracking experiments, reporting prelim findings, reacting to constant pivots

Why bother coming up with an "artificial" project when there are natural ones and the goal (I assume) is to train better researchers anyway?

I actually relate to much of the presentation on state management.

Jupyter shines in plotting and interactive demoing. E.g., a use case not fulfilled by console or scripts: prompt engineering. Jupyter (1) does not reload model weights and (2) can fold/clear historical long outputs like logits

A PhD *student* paranoid with code. I guess that’s what makes me a student 🥲

You were blessed with a codebase that's easy to work with, or the ability to build one. IMO factoring is tricky for different, ever-shifting research goals. See a discussion on "single-file implementation" and "Does modularity help RL libraries?" at iclr-blog-track.github.io/2022/03/25/p...

What’s wrong with Jupyter notebooks 😂

That’s quite a lot of investment in a course for phds lol. How about allowing collaborated projects in your graduate seminar?

Also collaborating with others in the same repo motivated both of us to write better code than we would otherwise.

Speaking as a phd paranoid with code:

goodresearch.dev is good.

A guilty pleasure of mine is reading not only good research repo, but also their full git history if released. Factored code is not always easy to change and a big refactor commit says something.

Some misread it as geopolitics instead of racism.

And caring for others, that’s not exactly part of a researcher’s job description or perf review.

I made up the second one to save myself from greater disappointment.

All I am saying is I don't assume a prior definition, nor do I observe your latent thought process

I’m not sure what conclusion I can draw from this poll.

And disclaimer - this is absolutely not affiliated with neurips.

Credit goes to everyone who participated in this mini poll. Thank you - you made my day!

The most common follow up was “it depends on your definition of intelligence”, to which I replied “by your definition of intelligence.”

A selection of comments:

“..very stupid”
“Language models? Definitely!”
“It’s not a yes/no question”
“Yes… if they saw that in training data”
“Not true intelligence”
“AIs have no heart”
“Some are intelligent and some aren’t. Just like humans”
“I don’t have money to test it out”

Extra: search for our wall of shame and fame @cornelltech.bsky.social (trigger alert) (whoa CT has a bsky account?!)

7/7