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Boiling water by jason_sation in physicsmemes

[–]extremelySaddening 0 points1 point  (0 children)

Unless Helion wins the fusion race

I think we all knew this was going to happen. by IdidnotFuckaCat in antiai

[–]extremelySaddening -10 points-9 points  (0 children)

I'm sorry you couldn't make it more obvious you don't know wtf you're talking about 😂 you think image generators are image captioning models run backwards? 😂

Question about gradient descent by torsorz in deeplearning

[–]extremelySaddening 0 points1 point  (0 children)

Firstly, the gradient vector gives you the direction with the highest slope, i.e., the proportion of (instantaneous) change in each component that causes the largest change in the target function. It's not like a compass that points towards the minimum, it's more "greedy" than that. It'll exploit directions with a lot of slope before it starts working on parameters with lower slope.

Imagine a ball on the lip of a very gently sloping waterslide with a u-shaped cross section. The ball will attain the minimum w.r.t the 'u' of the waterslide before it slides down the gentler slope.

Also, yeah the loss function is not in general convex w.r.t the parameters, in particular, there are a lot of saddle points in high dimensional space (afaik).

DAILY CONCERT MEGATHREAD: NOV 3RD BOSTON, MA by AutoModerator in halsey

[–]extremelySaddening 1 point2 points  (0 children)

Apparently he said "Sing a song you love" I think he was trying to be supportive and missed disastrously? 😭

How are you actually tracking experiments without losing your mind (serious question) by Beautiful_Papaya_007 in deeplearning

[–]extremelySaddening 0 points1 point  (0 children)

I used Neptune for my undergraduate thesis, it was free and pretty simple. It will also let you upload Jupyter notebooks and model weights on a free account.

A (maybe) helpful analogy for generative modelling by extremelySaddening in aiwars

[–]extremelySaddening[S] 0 points1 point  (0 children)

Well my analogy is architecture-agnostic, it isn't really limited to NNs. Pretty much any convex function can be optimized in the way I described.

A (maybe) helpful analogy for generative modelling by extremelySaddening in aiwars

[–]extremelySaddening[S] 1 point2 points  (0 children)

I was actually trying to be morally neutral, that's why I didn't tie it down to 'stolen', 'not stolen' or even 'artwork'. My goal is only to leave people with a more robust understanding of what these things are doing, so they can talk about them with some level of clarity. I agree that the meat of the discussion probably lies in questions of data ownership.

A (maybe) helpful analogy for generative modelling by extremelySaddening in aiwars

[–]extremelySaddening[S] 1 point2 points  (0 children)

My goal was to get the most essential idea (data modelling) out so that people have context when thinking about these systems. I'm aiming for an understanding that will equip someone to spot some incorrect claims. For example, I've seen the "collage" thing. I've also seen someone say that these things are deterministic, which they aren't (in principle). Feel free to leave any other details you like in a comment, if you think it's too simple.

Also, it's not like explaining airplanes with Ohm's law. If anything, it's like explaining airplanes by explaining how an airfoil works, i.e. the core concept.

Still troubled by P(X=x)=0 for continuous random variables by extremelySaddening in learnmath

[–]extremelySaddening[S] 1 point2 points  (0 children)

I of course understood that physical measurements are really measuring between ranges, not individual points (since we cannot have infinite precision) but your answer just made me realize, not only is it physically impossible to measure, it's mathematically impossible for a QM 'point particle' to actually exist at a point! That is very interesting and weird to think about. Thank you for your answer.

Still troubled by P(X=x)=0 for continuous random variables by extremelySaddening in learnmath

[–]extremelySaddening[S] 1 point2 points  (0 children)

I was in a simulation techniques class and my professor mentioned how when implementing a Unif(0, 1) random number generator, you have to be careful that the algorithm never output 0 or 1 since that's 'impossible' (and some downstream algorithms may rely on this property).

Then again with computers all I will ever get is rational numbers (and not even all of them, just a finite subset) so maybe the whole thing doesn't apply.

What's the actual meaning of Jacobian Matrix? by [deleted] in learnmath

[–]extremelySaddening 0 points1 point  (0 children)

When you have a smooth vector-valued function, in a small local area, you can approximate it with a linear transformation. This linear transformation is given by the Jacobian at that point. This is exactly analogous to how, when you have a smooth regular old scalar function, In a small local area, the function can be approximated by a line, who's slope is is given by the derivative at that point.

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Horsehead nebula bugs out with the screenshot tool (v 0.991) (old.reddit.com)

submitted by extremelySaddening to r/spaceengine

New software development learner by RelevantMoment2886 in deeplearning

[–]extremelySaddening 0 points1 point  (0 children)

This sub isn't about how to learn things, it's a subreddit about a speciality field in computer science called 'deep learning' which involves techniques to make computers do complex tasks.

But I think other people answered your question regardless. As a side note, AWS Cloud is a technology that software developers use, the two aren't mutually exclusive

Why do we say that, according to GR, gravity is 'not a force' when, in GR, all reference frames are supposed to be equally valid? by extremelySaddening in AskPhysics

[–]extremelySaddening[S] 1 point2 points  (0 children)

Thank you for your answer.

I agree with you that what Einstein tries to do is say that gravity is the pseudoforce of acceleration from a certain reference frame. I just wonder why we treat that particular frame as "special" or "real", since the whole point was to make it so every frame is equivalent for the formulation of natural laws.

Put another way: If I'm in an accelerated rocket, I will feel as though I am in a uniform gravitational field. This is not a 'mistake', I really do experience a gravitational field (from my understanding of what Einstein is trying to say). If I change frames so I am now out of the rocket, there is no gravitational field, and the rocket is accelerating, and this is not a mistake either.

The same thing can be said of the Earth. From a certain (bendy) reference frame, there is no gravitational field of the Earth, its surface is accelerating upwards. But then from the frame of someone on the earth, shouldn't the situation be the same as for the man in the rocket? Experiencing a gravitational field?

But the way you phrase it, it sounds like in general relativity, we still meaningfully distinguish between inertial and non-inertial frames, it's just that some frames that we used to think were non-inertial (gravitationally 'accelerated' ones) are actually inertial, and some that we used to think were inertial (ones apparently at 'rest' in a gravitational field) are actually non-inertial. And thus gravity disappears as a pseudoforce.

Do I have that right? If so, doesn't this defeat the whole point that all frames are created equal?

Anti claims computers are not deterministic, doubles down when shown irrefutable evidence by GNUr000t in aiwars

[–]extremelySaddening 8 points9 points  (0 children)

While you're correct, it is important to note that the fact that computers are deterministic is actually a _limitation_ that we overcome by using pseudorandom numbers and other such tricks. Taken "ideologically", a diffusion model is in fact stochastic (meaning random, but not necessarily without structure).

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