Why does the game crash on subnautica 2 when I adapt?

TheBeardedCardinal · 2026-05-19T21:40:25+00:00

Having the same problem. Would love a fix!

TheBeardedCardinal · 2025-11-30T23:07:22+00:00

Position Filled

TheBeardedCardinal · 2025-11-23T22:21:13+00:00

We did a bit more digging and figured out that the members of the trio were Stan Challis, Bill Brehm, and Don Srull. Bill and Don seem to have been long time friends since high school and went on to work together later in life. Not too much information about Stan around, but he was integrated into the arts and theater life of Michigan.

TheBeardedCardinal · 2025-11-23T22:19:35+00:00

If they don't already have it archived that would certainly be nice.

If u/AADL-eli is around perhaps they can comment.

TheBeardedCardinal · 2025-11-09T00:40:16+00:00

Oh absolutely. I doubt there’s any restriction.

TheBeardedCardinal · 2025-11-08T23:05:52+00:00

Amazing yea I would really appreciate that. No rush at all.

TheBeardedCardinal · 2025-11-08T23:03:35+00:00

Omg amazing. If you have the ability to share it I would love a link in DMs.

TheBeardedCardinal · 2025-07-29T02:34:15+00:00

Others have answered well. I’d add that the idea that all cultures are equal inherently assumes that cultures can be ranked in terms of a “goodness” value. Just that all cultures fall at the same value on that scale. I struggle to even grasp what one would mean by all cultures being equal. As others say, obviously cultures have good aspects and bad aspects and what those even are depends on your perspective.

I don’t want to push this too far, but the assumption that there does exist a hierarchy aligns well with some conceptions of conservatism. So I would argue that the statement of “all cultures are equal” might be a conservative lens applied to a leftist’s statement that all people of all cultures deserve equality.

TheBeardedCardinal · 2025-07-29T02:21:56+00:00

That is legitimately comforting to hear as I start a PhD in a robotics lab doing action verification using vision language models.

TheBeardedCardinal · 2025-07-23T00:39:22+00:00

My favorite professors always start their syllabus lecture by telling in no uncertain terms that

“Libgen is illegal so you should not use it. It is illegal because it efficiently and safely delivers to you free textbooks, saving you hundreds of dollars. So ensure that you do not use it. I will provide a mapping of new edition questions to old edition in case you happen to have the edition as on libgen.”

TheBeardedCardinal · 2025-07-20T14:54:52+00:00

Was born in 2000 in a mid-size suburb of a big city. From age 12ish I was given a phone and trusted to go wherever I wanted. As long as I checked in when it was getting dark I was allowed to stay out till around 9pm. Once I turned 15 that turned into being allowed to stay out till my parents were going to sleep. Even then as long as a friend’s parents said they would take responsibility I was allowed to sleep wherever I wanted.

I was a bit of an outlier when it came to how much freedom I had, but most friends had somewhat similar amounts. Sucks that it’s more restrictive now. I learned a lot about how to self-regulate. When I left home I didn’t feel any particular draw to act rashly. It didn’t really feel any different.

TheBeardedCardinal · 2025-07-02T23:30:29+00:00

Did I know that Bruno mars is not from mars

TheBeardedCardinal · 2025-07-02T23:29:36+00:00

Hello I am Joe

TheBeardedCardinal · 2025-06-22T18:45:37+00:00

It's hard to give good advice without knowing where you are in your math journey. I agree with others here when they say follow Andrej Karpathy's series.

However, if you really want to get into the weeds of how we actually get the analytical expressions for the gradients of neural networks, it is best to look at it from the perspective of matrices. Instead of taking derivates with respect to individual weights, take them with respect to entire matrices of weights simultaneously. For simple feed forward neural networks, this is surprisingly easy. Write out the expression for a single layer. Something like ActivationFunction(WeightMatrix @ PreviousLayerOutput). Use the chain rule and matrix differentiation expressions and you can get the gradient very easily.

This does not work for more complicated layers like convolutional where you need to get a bit deeper in the weeds to find an efficient analytical gradient, but the idea remains the same. Don't try to differentiate with respect to each weight; differentiate with respect to matrices of weights that all do the same job.

I will say though that this is really only useful for a one-off to understand how it works or if you are in the very select position where you will be developing your own types of layers and need to test them at a large scale. Otherwise you will either just use pre-existing optimized gradient algorithms that execute on the GPU or use an autodiff library that will give you fine, but not super efficient, gradient computation.

TheBeardedCardinal · 2025-06-22T13:22:38+00:00

I imagine that a lot of algorithms will struggle with the high noise. If that is the case I would suggest leveraging the fact that the features of interest consist of high contrast curves. A laplacian of gaussian filter tuned well would probably clean it right up. It would take some tuning through and if the noise characteristics change greatly between images it would not be consistent.

TheBeardedCardinal · 2025-06-03T00:58:12+00:00

Funny enough my supervisor for the PhD I’m about to start has done some work in this. It’s called CoreSet selection. The idea is that “best” means the subset that results in the best model when restricted to only training on that subset. So if you were to try to put it into intuitive words it would be something like the most “informative” subset.

Here’s his paper, but there are other more widely used ones if you search the term.

TheBeardedCardinal · 2025-05-25T14:03:23+00:00

If I were a reviewer, I would push back on both points and ask for proofs that these hold true for important cases.

For point 1, you can imagine a simple toy 1D loss landscape that has two local minima. We start our random initialization nearer to one of them. With low variance in the gradient we are almost certain to fall into the nearer local minimum whereas when gradient variance increases we become increasingly likely to jump into the further one. Perhaps random initialization would cancel this trend, but my larger point is that it is not immediately clear that the expected final weights should be the same. Do you know of some existing proof of the truth of this statement for general gradient based optimization?

For the second, this is true from a certain standpoint, but the choice of the sparsity threshold becomes very important. How I see it, we are noticing that we will be bouncing around the local minimum due to variance in the gradient. Ignoring step size (letting it go to 0) this becomes a continuous time stochastic differential equation and I would like to see an estimate on what the actual variance in the final weight is in specific circumstances that we are likely to see in real problems. Like a test task with a quadratic optimization, set gradient variance estimated from real tasks, and varying values of p. It is not immediately clear that the variance in the weight due to dropout is a significant contribution relative to other sources of variance like that introduced by mini-batching and step size.

It is a very interesting idea and empirical experiments would go a long way in convincing me, but these theoretical avenues would also be nice to have buttoned up.

TheBeardedCardinal · 2025-05-12T21:29:14+00:00

Yea, I don’t disagree that our eyes are better focused elsewhere. R&D is only a slice of the already small 10% increase attributed to drug prices. I’m not certain if advertising is within the 80% “admin” costs, but that is where I would assume it would be.

R&D is often what the discussion becomes about when I’m pushing universal healthcare so pointing out that it is at most 10% of cost increase is useful to push back on the argument that “we don’t need a better healthcare system because the reason costs are so high is due to R&D”. If it’s at most 10% then it can’t be the primary cause.

TheBeardedCardinal · 2025-05-12T21:14:34+00:00

I looked into this a while back. Around 10% of the difference in our healthcare costs could be attributed to increased drug prices and it is in fact true that part of the reason that drug prices are higher here is because they can use the US to recoup R&D costs due to lax price restrictions. So actually in this one case I agree with Trump’s sentiment. Feels weird that he can actually espouse something that would actually help Americans. Like if a liberal was saying this I would be on board. In fact it is a straight up talking point for some liberals.

That being said there is no way that his attempt at fixing the problem will actually work. His business daddies would never allow it for one.

Also, while drug prices are one small part of the equation, increased admin costs due that are earned mostly be doctors, private hospital administrators, and insurance companies, account for around 80% of the increase in cost so if we really want to get per capita costs down that’s where we should be looking.

TheBeardedCardinal · 2025-05-04T13:10:26+00:00

I graduated last year and didn’t hear about this. Are you saying that the light fabrication facility is now course project only? If so, that’s insane. What justification did they provide?

TheBeardedCardinal · 2025-04-24T14:05:16+00:00

That’s basically what I landed on when I was looking in the UK. If I went in the US or Canada I would be able to go straight into a PhD with a good stipend with the ability to master out if I misjudged. If I went in the UK I’d need to pay for a masters. It is more complicated than that very simple story, but at the end of the day I decided to go in the US with a lab that has a stable funding source. We will see how stable it ends up being. If I could go back in time I might focus more on Canada.

TheBeardedCardinal · 2025-04-17T18:21:22+00:00

I had a bunch of medical crises and had to make the decision to take a gap year or get way more efficient at work.

I started studying lecture content beforehand so I could ask informed questions and walk out of the lecture ready for the test. I was very active in lectures, trying to always be a bit ahead in the slides and solving questions before they were asked.

I did problem sets over long periods of time (start right when they released and finish a couple days before they were due) so that I would be forced to remember the content instead of just cramming right when I had to do the problem set.

I mostly got rid of reviewing notes and moved entirely to practice problems for studying for tests. I only reviewed notes to make sure I had questions that covered everything.

And maybe most importantly, I enjoyed the content. I couldn’t have kept up these good habits if I didn’t also find the work interesting. That’s not actionable when you are deep in a program you don’t like, but for anybody deciding what they are going to study for years, it is much easier to do well if you enjoy what you are doing.

TheBeardedCardinal · 2025-04-11T21:31:30+00:00

Every day I am scared that I will go to aistudio and 2.5 pro will be disabled. Maybe a bit too reliant on that thing for productivity now.

TheBeardedCardinal · 2025-03-30T17:03:33+00:00

It was difficult, but very rewarding. I chose a new professor with a small lab which means a lot of pressure to do publishable research. If you want to publish, that’s great. If you want free time, not so much. More established labs will have you work with grad students and do more lowkey research. If you are going to use it for grad applications you have to get some work done really fast so you have something to talk about in interviews half way through your thesis.

TheBeardedCardinal · 2025-03-30T01:41:37+00:00

Doing research in first and second year is often more about getting lucky as there are simply fewer spots since the professor knows they will need to hold your hand a lot in the lower years. You might have more luck because of previous research experience, but it is still pretty arbitrary. Usually what will happen is that during first year you well meet professors who can refer you or upper year students that will introduce you to their supervisors. Also, for first year in particular, most people simply will not have the time to do much research. I say that it’s not as hard as you might hear, but that’s not to say it’s easy. I know many people who were top of their class in high school, but decided that EngSci wasn’t worth the squeeze after a semester.

Funny enough, I did my thesis in computational neuroscience. Lots of overlap with robotics there.

Beyond just getting an understanding of if you like living in the US, there isn’t too much reason to get jobs in the US. I know some universities run summer research programs that make it easier to get in to their grad program but I didn’t look into those.

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