currymj

8 ms·

91.
by currymj 2y ago
i often wonder about this. because if you look at Big Law, investment banking, or private equity, there are a lot of highly-educated Americans who are willing to work as hard as anyone on earth. so it's got to be something about TSMC&#
92.
by currymj 2y ago
most of the general public doesn’t know PhD students get paid stipends. if they do know that, they don’t realize how tightly each term’s stipend is tied to a specific funding source.
93.
by currymj 2y ago
those limitations are sometimes real. However, I have read enough Fortran and C++ written by scientists, to fervently wish that their code had been written in Julia instead. In Julia, I don't think they could manage to write code so de
94.
by currymj 2y ago
The article describes Julia as "a general-purpose programming language aimed at science, engineering, and related arenas of technical computing". I think this is about right. If you are going to solve differential equations or run
95.
by currymj 2y ago
for problems where Jax works well, in theory there could be a great Julia equivalent, but in practice one has not emerged. instead every 3 or so years, a brilliant solo researcher writes an even better autograd library which is ultimately a
96.
by currymj 2y ago
android e-ink tablets are now a decent user experience and let you read ebooks through the apps. there are many form factors including e-reader like.
97.
by currymj 2y ago
if an individual pharma employee makes a donation as an individual, many of the datasets will classify it as “money from the pharmaceutical industry”. worth considering.
98.
by currymj 2y ago
Thanks for sharing this! I tend to agree that it’s easiest to understand this way. I just find it a frustrating fact about modern machine learning that in fact, the nice SDE interpretation is somehow not the “truth”, it’s just a tool for un
99.
by currymj 2y ago
it makes intuitive sense, but at the same time, you can get good performance replacing the Gaussian noise with deterministic transformations. so in the end diffusion can’t explain that much about how diffusion models work. disappointingly,
100.
by currymj 2y ago
even if you hit all the assumptions you need to make Pearl/Rubin causality work, and there is no unobserved factor to cause problems, there is still a philosophical problem. it all assumes you can divide the world cleanly into variable
101.
by currymj 2y ago
consider Switzerland. PhD salary is around the range of 80k CHF. It's not bad at all.
102.
by currymj 2y ago
their collapse didn't have anything to do with computer systems failing though. they just spent a period of about 10 years making sure they were involved in every major financial scandal due to poor judgment.
103.
by currymj 2y ago
Yes, though maybe not explicitly written down.
104.
by currymj 2y ago
if you have an MNIST classifier that just takes in images, and spits out a probability of digits 1-9, that wouldn't necessarily be generative, if it is only capable of modeling P(which digit | all pixels). But many other types of model
105.
by currymj 2y ago
Generative is a technical term, meaning that a system models a full joint probability distribution. For example, a classifier is a generative model if it models p(example, label) -- which is sufficient to also calculate p(label | example) i
106.
by currymj 2y ago
i was very careful to use the vague phrasing "features dealing with these problems" and not "Ada has a borrow checker and dependent types".
107.
by currymj 2y ago
1) memory safety at compiler level, 2) type systems that have dependent types or similar such features, are both things people have gotten interested in again recently, especially because of the excitement around Rust. (you can see this con
108.
by currymj 2y ago
agree strongly. in the long term, for society, it’s probably true that bad science will just be forgotten. in the medium term, those “weak links”, whether due to mistakes or fraud, can derail an entire area and prevent the strong link paper
109.
by currymj 2y ago
RL is unbelievably finicky, sensitive to hyperparameters, and hard to make work. If you are pursuing a research project and decide to use RL, you are making your life a lot more difficult and stressful. It's exciting to see any progres
110.
by currymj 2y ago
it was a pretty defensible term at the time the paper came out, in the context of how LLMs were being trained and used. in this paper, it's clear that the authors don't think modern LLM-based systems are just stochastic parrots.
111.
by currymj 2y ago
one issue is that having a good generative model is unavoidable as a component for lots of good, useful tasks. like translation, transcription, etc. but then of course if you have a generative model, you can use it to generate stuff.
112.
by currymj 2y ago
pre-trained model would mean training a language model to predict text, then starting from there and training it to add numbers. training from scratch would be initializing a neural network, and training it to add numbers directly.
113.
by currymj 2y ago
while the NYC subway is indeed quite safe, it's also undeniably true that there are a lot of stinking smells.
114.
by currymj 2y ago
the curated playlists used to be incredibly good. the initial release of Discover Weekly 10 years ago gave everyone such uncanny recommendations that it was a cultural event, people were talking about it, it was written up in major newspape
115.
by currymj 2y ago
it’s great that jj just looks like you use git to other people, so doesn’t inconvenience them. how does it work the other way? if i’m using jj, and my colleagues are using git, how does it cope? in particular what if they are using git in a
116.
by currymj 2y ago
there is a push to use garbage containers, and the requirements have partially rolled out, but it's a very controversial political issue.
117.
by currymj 2y ago
so here's an example: I saw the following amusing math joke on BlueSky, posted a couple days ago. https://bsky.app/profile/elliotlip.bsky.social/post/3lh5bs7g... "It's wild that I took a whole-
118.
by currymj 2y ago
completely agree. people say a classic chess engine is "thinking" when it goes deeper into the search tree, but nobody is confused about that. This colloquial language doesn't bother me so much. But there really are people wh
119.
by currymj 2y ago
it's no longer an accurate description. with recent models, while stitching together training data is still most of the story, there's obviously something more going on, especially with regards to code, mathematics, and with the a
120.
by currymj 2y ago
i am old enough to remember the opposite: people would try to sell deep learning to the mainstream ML community by pointing out that backprop is just message-passing on a Bayesian network with modified sum/product operations.

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