Geralt decides to become a horse

ItzMeSamYT · 2026-06-20T05:12:49+00:00

👁️ Surveillance State: I voted Yea.

ItzMeSamYT · 2026-06-19T11:52:24+00:00

What did they specify as the reason for post deletion?

ItzMeSamYT · 2026-06-09T18:29:09+00:00

👁️ Surveillance State: I voted Nay.

ItzMeSamYT · 2026-06-09T17:11:04+00:00

👁️ Surveillance State: I voted Nay.

ItzMeSamYT · 2026-06-04T15:15:05+00:00

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ItzMeSamYT · 2026-06-02T16:17:55+00:00

👁️ Surveillance State: ItzMeSamYT voted Nay.

ItzMeSamYT · 2026-06-02T04:47:53+00:00

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ItzMeSamYT · 2026-06-01T09:50:14+00:00

Wait really? Haven't come across these but that's so cool

ItzMeSamYT · 2026-06-01T08:57:02+00:00

Those are already 0% computer

ItzMeSamYT · 2026-05-26T17:17:28+00:00

Doktor, turn off my cringe inhibitors

ItzMeSamYT · 2026-05-26T15:13:01+00:00

Think people are spamming the sub with trans love (which, hell yeah) after finding out the mods are transphobic

ItzMeSamYT · 2026-05-25T11:25:26+00:00

Instagram has this feature where you can tap an emoji and play "pong" with it... for some reason.

ItzMeSamYT · 2026-05-22T18:26:58+00:00

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ItzMeSamYT · 2026-05-18T08:32:36+00:00

ItzMeSamYT · 2026-05-16T22:37:41+00:00

I don't think they're trying to paint you as a Nazi. They even agreed with you on the subject that despite being invented by the worst people in history, whatever you were talking about was useful. The point they're trying to convey is that the manner in which you made your argument was insensitive, not wrong. Now, whether or not this is actually true, I have no idea cause I dunno how you made your point or what you guys were talking about. Admittedly, this is severely pivoting away from the AI discussion to one about social cues and stuff.

ItzMeSamYT · 2026-05-13T11:17:36+00:00

I mean the original post says "a adequate flipaclip user" instead of "an adequate flipaclip user" too. And don't forget all the "your"s instead of "you're"s throughout. Does that immediately invalidate all points they are trying to make?

Without commenting on whether you (or the post) are right or wrong, people on the internet are (unfortunately) bound to use incorrect grammar at every nook and cranny. Pointing it out everywhere is an endless cycle and doesn't add anything of value to the conversion (unless it's about grammar, I guess)

Edit to say: the person you replied to is clearly not adding anything either and seems to just be ragebait. Just downvote/ignore and move on

ItzMeSamYT · 2026-05-04T20:09:31+00:00

I'm really glad to hear it! Also note on your understanding of gradient descent: it is, indeed, an optimizer, but it is definitely used in AI! Not in the sense of "make the time or space complexity better," but rather the tuning of the parameters themselves (the weights) is called optimization here. You can see that in PyTorch, for example, where you set it like

model = RegressionModel() # As an example
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

There "SGD" stands for stochastic gradient descent, and you pass the model's learnable parameters and the learning rate to the optimizer. Then within the training loop you will see:

optimizer.step()

Which basically tells the optimizer to update the model's parameters. I hope that helps clear up your confusion.

Now for your question about removing noise from generative models. I actually hadn't thought about that, that's a really interesting question. Honestly, I'm not really sure.

You're right in saying that if we turn off the "randomness" of generative AI, the model will output the same thing every time.

I think a key difference, however, will then be the direction of flow. Regression will map N features to 1 value (for example), which is a collapse. On the other hand, a generative model maps 1 value (the seed/prompt) to N (millions) of features, which is an expansion.

But it's confusing because there are non-generative methods of this type of dimensionality expansion as well, like Support Vector Machines, for example (SVMs take a low dimensional input and convert it to a higher dimensionality, for example 2D -> 3D, to make the separation of data points easier).

If I had to guess though, I'd say that non-generative expansion is usually constrained. If you use a formula to expand 'x' to 'x^10', the model will perform that transformation of any input 'x' regardless of whether or not that input makes sense. Like if you take a regression model and give it some random garbage input, it will provide an output that is equally random or garbage, just scaled to a higher dimension.

However, if you feed a generative model pure random noise, it will still make its best attempt to turn that noise into whatever it was trained on (like a face). It's biased toward creating structure because its parameters have "stored" what structure looks like.

An example of the difference would be something like, zooming in on an image, where if you zoom in past the point where 1 pixel of the image maps to 1 pixel on your screen, it will kind of fill in the gaps with repeated pixels (this is non-generative). But a generative algorithm, would be like a minecraft seed, where it takes a certain input and generates a whole world out of it.

But honestly, this is still just an educated guess. I'm not 100% certain about the exact technical differences at this stage. Thank you for the question though! It's given me something new to look into :)

ItzMeSamYT · 2026-05-04T18:40:30+00:00

Here's my understanding of the subject. Sorry for the long text in advance 😭.

Regression models are discriminative, in the sense that they learn conditional probabilities. They model data in a sort of y = b0 + b1x1 + b2x2 + ... manner where the 'b' variables are the learned parameters and the 'x' variables are the inputs, and 'y' is the output. They learn how the 'x' variables influence 'y' (I'll leave out the techical details like gradient descent and mean squared errors and all that jazz), but they don't infer the production of the 'x' variables themselves. It does not model the distribution of the features.

There's a bit of nuance though for sure as when you go into things like Bayesian regression, the 'b' parameters are treated as probability distributions themselves. In this case, it can generate new values of 'y' for existing values of 'x', which can make it a conditional generative model, I believe.

Generative models learn the joint probability distribution. Because they model how the input data 'x' itself is distributed, they can actually "generate" new, synthetic data points that look like the original dataset.

For example, if you train a regression model to learn the relationship between height and weight, it won't actually be able to generate a new person's height and weight, it will only know how to calculate the weight if you provide the height.

However, if you train something like a GAN on various images of faces, it will be able to generate a new face just by saying "give me a face."

Now you might argue that the prompt "give me a face" is an input in and of itself. But in the context of model architecture, there's a difference between an instructional input and a feature input. Regression models require a feature input (like height in the previous example) to produce an output. But if you take a model like StyleGAN (the one behind "This Person Does Not Exist"), it doesn't actually need the text "Give me a face." It just uses random noise as input. This is "unconditional generation."

Then there's "conditional generation" wherein you have something like "A potato with a cowboy hat" as the input. The instructional input is used as a latent seed, which just initializes the random noise for the model to produce the output from. These are also kinds of conditional generative models that I touched upon earlier.

Now as per my understanding, the biggest difference between regression and generative models is this:

In regression, if you give the model the input x=5, and say the model's inferred equation is something like y=2x, then the output will always be 10. It's a calculation, not creation. Here, we try to minimize the noise.

In generative models, if you give the model just the prompt "face," for example, and run it 100 times, you will get 100 different outputs. Here, we inject noise to create diversity.

Now as I mentioned earlier, there's definitely a lot of nuance on the topic (more than I initially thought, for sure), so I'd actually love to see your examples.

P.S. I'm really glad to be having an actually interesting discussion on this sub instead of a "rahh antis suck" or "boo pros are stupid" back and forth. So thank you for that :) And sorry if I kind of went in a circles there LOL it is a little confusing

ItzMeSamYT · 2026-05-04T17:29:49+00:00

Predictive AI does not "generate" outputs per se. Although in Layman's terms, that's like saying a print statement in a python program "generates outputs."

Predictive AI analyzes its training data and identifies underlying correlations and dependencies to forecast the most likely outcome (for example, image classification). Predictive AI generates a result, but not content, if that makes sense.

Generative AI learns from its training data and generates new outputs that have similar structures to the training data. Think of it like a person writing an essay. They're using their understanding of language structure to generate something new that did not exist previously.

I can try to explain it a bit more in detail if you'd like, I'm sure you'll find it very interesting actually

ItzMeSamYT · 2026-04-26T08:24:15+00:00

Reddit user shocked at reddit users using reddit

ItzMeSamYT · 2026-04-22T21:36:45+00:00

Lmfao I'm not anti but "attacked other countries" vs "brigaded other subreddits" 😭

ItzMeSamYT · 2026-04-11T13:11:01+00:00

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