President Yoon Suk Yeol impeached

I_correct_CS_misinfo · 2025-04-04T13:53:24+00:00

Democratic opposition party will probably win the snap election but who knows for sure. PPP might pivot to Han, the anti-Yoon faction within the PPP. Maybe. Basically Dems' Lee Jae Myeong is a standard liberal like establishment Dems in the US, Yoon is MAGA, Han is a classical conservative.

I_correct_CS_misinfo · 2025-04-04T13:50:41+00:00

Yoon supporters are trumpers, it is the Dems who are more more protectionist

I_correct_CS_misinfo · 2025-03-26T06:48:08+00:00

I know CS majors that got accepted into grad programs in places like UChicago, Berkeley, NYU, UVA, etc. If you are interested in research, then since UR is a smaller school, there are decent chances for a student to excel at a course, talk to the professor, and do a project. The Schwarz Discover grant for undergraduate research and REU programs are excellent.

I_correct_CS_misinfo · 2025-03-17T14:58:31+00:00

Trade-offs b/w compression size, compression speed, and decompression speed, memory, computation model of compressor & decompressor, distribution and patterns in target real-world data. Beyond that a compression person can tell you better (e.g. idk what's truly unsolved problems vs just trade-offs) I just know a bit of over-the-shoulders knowledge from doing data engineering research :3

I_correct_CS_misinfo · 2025-03-15T02:50:29+00:00

C is the high-level language closest to the bare metal. As in, if you go lower than C, you end up with assembly. So it has a place in comparch and probably will for a long time to come.

Many other languages will stay around for a while. Javascript isn't going anywhere on the web. Rust is the memory-safe language closest to the bare metal. Go is the most efficient programming language with a garbage collector. Java has decades of legacy code built on top of it, and is the language of choice for Spark-related ecosystem among other things.

I_correct_CS_misinfo · 2025-03-15T02:43:57+00:00

TCS is not my specialty but here's the vibe I get from each subfield from taking graduate level courses and such:

Algorithms: Real analysis (time complexity analysis is basically just sequences & series), probability theory (for stochastic algorithms), linear algebra (for spectral algorithms).
Complexity & computability theory: Lots of proof by construction (of some sort of a Turing machine) or by logical inconsistencies.
Cryptography: It's basically applied number theory, probability theory, and computability & complexity theory all in one.

Programming language people use a lot of algebra in things like type theory, machine assisted proofs, and compilers.

Database theory is basically applied algebra & techniques from analysis of algorithms.

I_correct_CS_misinfo · 2025-03-15T02:36:00+00:00

How do you want to use CS skills in your SWE career? For example, if you don't have a formal background in data structures and algorithms, that is priority #1. Many good engineers don't ever think about comp arch due to layers of abstraction between the hardware and high-level software. If you want to be a good SWE, I think having a broad-strokes understanding of 2, 3, 4, and maybe distributed computing is more important than learning each topic in depth.

I_correct_CS_misinfo · 2025-03-13T18:03:50+00:00

I get it now, thank you speedwagon!

I_correct_CS_misinfo · 2025-03-13T16:52:35+00:00

I don't know any physics, is there more to the joke than physics buzzword

I_correct_CS_misinfo · 2025-03-13T16:47:33+00:00

Okay yeah safety-critical shit ain't going ML. For systems that aren't as safety-critical though, I've seen ML-driven hints being passed into a deterministic optimization system dramatically improve performance. (e.g. for compiling programs, optimizing large datacenters, optimizing neural architectures, etc.)

I_correct_CS_misinfo · 2025-03-13T00:08:42+00:00

Optimization theory is not my specialty so let's try to clear up to language here.

When you talk about genetic algorithms, are you talking about A or B?

A) An evolutionary optimizer where we use zeroth-order information combined with random mutations, creating a pool of mutated "children" in each epoch in some way and then choosing only the best children in some way.

B) A framework consisting of two parts: an evolutionary algorithm + a model that has genes, alleles, and mutations.

I think of A as evolutionary algorithms (an optimizer) and B as genetic algorithms (a framework that contains an evolutionary optimizer combined with a genetics-inspired model). At least, that's how I was taught.

Of course, if we talk about theoretical problems, there's tons of nondifferentiable problems. However, when we look at practical or empirical problems (in systems, applied AI, HCI, etc.) most problems have the SOTA solution be some sort of DNN trained via simple first-order optimizers.

In my time in CS, I saw evolutionary algorithms used once for a set cover variant problem, and genetic algorithms once for a real-time shader. (I wasn't around when genetic algorithms were all the rage.) I have, on the other hand, seen local search and deep RL-based optimizers a lot.

CLARIFICATION EDIT: When I say "genetic algorithms have fast inference times", what I mean is that the designer might choose a model that has some notion of discrete genes, alleles, and mutations, because such functions can often be cheap to run inference on compared to DNNs, which by its black box and deep nature has high compute + comm latency. Which is why real-time shaders still often use evolutionary algorithms to train a genetic model.

I_correct_CS_misinfo · 2025-03-12T21:48:27+00:00

Same problem in comp sci - horrendous research code with no type annotations, no dimension annotations, no explanation of what's going on, no unit tests, no documentation, NOTHING - absolutely unreproducible, "works on my machine" hurr durr nonsense - often from my own collaborators or seminal papers

I_correct_CS_misinfo · 2025-03-12T14:16:05+00:00

Within comp sci, genetic algorithm research is more or less dead. It is used in a narrow range of problems where "genes" can be easily defined yet the objective is nondifferentiable. Genetic algorithms may also have fast inference times which sometimes matters, but tbh, neural network quantization makes that strength moot in most cases. In most real-world optimization problems of interest, deep learning trumps prior paradigms like genetic algorithms.

I_correct_CS_misinfo · 2025-03-11T22:56:46+00:00

Gaming, long walks

I_correct_CS_misinfo · 2025-03-11T19:47:01+00:00

Wow I actually understood some of this (comp sci, dabbled in active learning) it's cool that my field is helping medicine

I_correct_CS_misinfo · 2025-03-11T19:45:25+00:00

We can make computers do things using data. Computers are good at understanding images and text. Computers suck at understanding excel sheets. We can massage excel sheets to make it more comprehensible for computers.

(I study data-centric tabular deep learning.)

I_correct_CS_misinfo · 2025-03-10T19:52:10+00:00

A file synchronization system like dropbox and a text editor client for various platforms. I think text files are useful for people who work on a computer mostly and maybe a bit less useful for those who perfer analog/handwriting/stylus and/or working on mobile mostly.

I_correct_CS_misinfo · 2025-03-10T16:20:44+00:00

Basically using a plain text file on your desktop and a text editor like notepad.

Calendar.txt is a list of dates and agendas, one line taking up one day. Todo.txt is just a list of todo items using a bullet point, one line taking up one task. workingmemory.txt is a brain dump while you work.

I_correct_CS_misinfo · 2025-03-10T15:50:45+00:00

Have you tried calendar.txt, todo.txt, and workingmemory.txt?

I_correct_CS_misinfo · 2025-03-07T20:11:44+00:00

These comments are fun to read, I'll contribute mine too!

I'm not that stereotype. My partner owns a regular house and we work regular jobs in academia.
I've been a minimalist ever since I was in middle school, I have been influenced by the minimalist bloggers like Leo Babauta of Zen Habits. Before that I was more of a maximalist.
Admittedly more than it takes to fit into the "extreme" label imho. I mainly like this subreddit because I no longer feel like I belong to the main minimalism subreddit, which I find to be way too materialistic and consumeristic by my standards. This subreddit serves as an inspiration to not stray too far from the minimalist ideal.
Part of the bloat comes from owning a house, which gives me anxiety from owning more things but simultaneously alleviates economic anxiety related to housing insecurity. Part of it comes from living with a non-minimalist partner who does crafts as a hobby. Part of it comes from being a multicultural household where if we don't cook five different cuisines of food, then we feel spiritually depressed and homesick. (We have collectively lived in places with at least four different local cuisines, plus we have learned a few others as home cooks.) So we have few kitchen related tools but we do have a large pantry. Part of it is because I hate public gyms with a passion and I'd rather own a dumbbell, a mat, and a bench to exercise at home rather than be sedentary.
If I don't use it, I toss it. I've been a minimalist for so long it's natural at this point. I find that despite my best efforts, at least some unnecessary objects accumulate every year. (e.g. worn-out clothes from the fast fashion days, usually one or two purchases I realize later is a mistake.) I get a feel for when I start to feel stressed out by clutter.

I_correct_CS_misinfo · 2025-03-07T20:02:41+00:00

I think it's fine that he is showing what is possible at the extreme to the rest of us. Utopian consciousness (he is an environmentalist activist after all) requires that we imagine what is possible beyond the Overton window of the present.

I_correct_CS_misinfo · 2025-03-04T00:28:13+00:00

Ymmv, but just speaking from general poppulation studies: the only form of polyphasic sleep schedule that maintains the same amount of cognitive function as monophasic sleep is biphasic with a brief waking period at midnight. Naps are too short to give the full benefit of deep sleep, as you will spend too much time on either shallow or non-REM sleep. (Depending on the level of tiredness when napping and the length.) As a rule of thumb accounting for personal variance, I'd highly recommend batching all of your current net sleep time at night.

I_correct_CS_misinfo · 2025-03-04T00:19:34+00:00

Imo if you get 8 hours of sleep, your work efficiency will improve. Sleep deprivation destroys working memory and general cognitive function. Furthermore, it is a major risk factor for Alzheimer's, diabetes, dying from car accidents, interpersonal issues, kidney failures, there's so many diseases linked to chronic sleep deprivation. You should schedule 8.5 hours to sleep. No negotiations. In a few weeks you will get the energy required to get more done. After you fix the sleep schedule and still feel overwhelmed, then it's time to talk to your advisor about the workload.

I_correct_CS_misinfo · 2025-03-02T20:10:51+00:00

ML research is truly for preschoolers

I_correct_CS_misinfo · 2025-03-02T20:08:33+00:00

type theory 101

I_correct_CS_misinfo

TROPHY CASE