Julian
@juliangrove.bsky.social
89 followers 160 following 340 posts
insert bluesky bio https://juliangrove.github.io/
Posts Media Videos Starter Packs
juliangrove.bsky.social
that geese album is not bad
juliangrove.bsky.social
guy from Geese sounds just like Thom Yorke
juliangrove.bsky.social
Apple's hold on the laptop industry is kind of interesting. 90% of the time, the computer's main job is to update itself - like sometimes you make an excel file or whatever, but otherwise, it's updates itself until it encounters an update incompatible with the hardware and you have to buy a new one.
juliangrove.bsky.social
still figuring out the logistics of blocking myself
juliangrove.bsky.social
blocking you and everyone who likes this
juliangrove.bsky.social
Started talking about characteristic functions yesterday, and one of the students went “oh, cool!”
a man with a beard is smiling with his mouth open
ALT: a man with a beard is smiling with his mouth open
media.tenor.com
juliangrove.bsky.social
What's a wetaphysics morkshop?
juliangrove.bsky.social
who knows, maybe it'll go down!
juliangrove.bsky.social
uh oh
rtommccoy.bsky.social
🤖 🧠 NEW BLOG POST 🧠 🤖

What skills do you need to be a successful researcher?

The list seems long: collaborating, writing, presenting, reviewing, etc

But I argue that many of these skills can be unified under a single overarching ability: theory of mind

rtmccoy.com/posts/theory...
Illustration of the blog post's main argument, summarized as: "Theory of Mind as a Central Skill for Researchers: Research involves many skills.If each skill is viewed separately, each one takes a long time to learn. These skills can instead be connected via theory of mind – the ability to reason about the mental states of others. This allows you to transfer your abilities across areas, making it easier to gain new skills."
juliangrove.bsky.social
it really holds water
juliangrove.bsky.social
I feel like "dog" is doing a lot of work there.
juliangrove.bsky.social
Yeah, makes sense, lol.
juliangrove.bsky.social
Just curious - how would you feel about them becoming more MOL-light?
juliangrove.bsky.social
makes you more docile
juliangrove.bsky.social
I would be honored.
juliangrove.bsky.social
Second, we want to integrate semantic representations with computational "backends", e.g., theorem provers, that can allow for more sophisticated and general reasoning about the representations of probability distributions. Right now, the reasoning is sometimes seemingly more ad hoc than necessary.
juliangrove.bsky.social
There are a couple of directions we're hoping to take things in:

First, we want to streamline the addition of certain model components that right now need to be added by hand. These include random effects, as well as certain kinds of likelihoods (like ones involving censored data).
juliangrove.bsky.social
🚨The text has several code snippets, which are taken from the Haskell project under active development here:

github.com/probabilisti...
juliangrove.bsky.social
The goal here is therefore to allow semantic theories to connect up with the probabilistic models of data which are used to test them in a completely seamless fashion, where researcher choice-points are made fully explicit.
juliangrove.bsky.social
The CCG -> Stan pipeline is implemented in Haskell using traditional techniques used to work with the lambda-calculus (e.g., beta-reduction), but the representations output by the system are genuine Stan models.
juliangrove.bsky.social
We show how the full system works, going from CCG grammar fragments for some semantic phenomenon you care about (for example, adjectival vagueness), all the way to a Stan implementation of a probabilistic model of inference judgment data you've collected in order to study the phenomenon at scale.
juliangrove.bsky.social
Brand new version of this paper (now a short book!) available at lingbuzz.net/lingbuzz/008...!