yes t is the timestep index

The variance of your sample x_t, simple as that.

Also cause often it's s damn rabbit hole and just as you finish, nvidia comes up with the same thing just 10 times faster.

Maybe also BERT for 1 word answers to counter the neverding answers from ChatGPT

Will is it then the "dangers of scaling LLM" or "even with top notch technology people are just people".

Well very many humans can persuade gullible humans to perform actions on their behalf. Problem are people. Furthermore I actually would trust a LLM more than the average human.

If a LLM model tells you it would rob a bank it's not that the model would do that could it walk around. It's what a statement that has a high likelihood in the considered language for the specific data looks like. And if it's chat gpt the response is also tailored to suit human preference.

Well the thing is you aren't the first one to think about that. They do this for very long already and know that what they do is legal here. They would not waste millions in training it just to throw it away afterwards.

Edit: Chatgpt uses GPT3. Search the dataset it used.

Google it they have full transparency. If you find a text by yourself there maybe ask if they can remove it. First of all, the data is only used for stachastic gradient descent and the model has no idea about the content it read, it only can model probabilities of words, e.g. it learned to speak but it only speaks such that it mostly outputs what makes sence in a bayesian way.

So the model is already trained and it didn't even read all of the data, those huge models often only read each instance of sample once at maximum, since they learn that "well".

Also in the law text you wrote I understand it that if you opt out in the future, it doesn't make past data processing wrong. The model is already trained, so they don't have to remove anything.

They also mostly have a whole ethics chapter in their papers, maybe you go check it out. Ethics etc is not smth unknows and especially such big companies also have some people working on that in their teams.

Do you know the dataset is was trained on even?

No it doesn't raise ethical concerns. You literally have to agree about usage about your data and at least in Europe should be able to opt out of everything if you want. You should 100% know this, those are the rules of the game. Just cause you don't read the terms of agreements doesn't make it unethical for companies to read your data. Sure if you then use it for insurances that won't help you cause you will become sick w.h.p. that's another thing. But don't act surprised.

You think the whole internet is free to run? Anyways, they don't use any of your data to train it.

Well like reinforcement learning uses a lot of markov chains, forward/backwards filtering/smoothing etc. Kalman filters are also a sort of Gaussian Process Regression. There is a huge overlap in the classical ML part with signal processing. No specific paper but it's just that ML and especially deep learning often takes already existing ideas from physics or ee and try to apply it on some data, see what happens.

Kaggle Titanic, we have all been there.

Paper is linked at the top of the page, they won't release code as per last lines of the paper.

I don't know if something good exists. If you have a strong gpu you could try denoising autoencoders but afaik it's only "real-time" when you get like a 10k gou and for this price you can also do it realtime with cpus.

njobs or so, it's in the documentation

It is, you can specify the number of threads it uses tho. For realtime it doesn't work I guess but it's the best I have ever seen actually.

Based on spectral gating, you can provide audio of the noise so it works better, works for stationary and non stationary noise.

Check out the noisereduce library!!

Cause it is worse metric wise.

Which will lead to ultra high taxes in europe and is even legal in some(if you work both 100% without the other noticing). I don't see a problem, nearly noone would do that where I live, free time > money (at least knowing that the income from one ML job is probably quite enough).

mining rigs are for mining not dl

yeah you take the min of the sample and the msx of the sample. If that makes sense is another question.

Well uint 8 goes to 255, so there you take those values. Images come in that format often but the ReLUs and other activations hate it so better take it to a 0-1 range. Btw min max just subtracts the min of the sample and then divide by max. I don't see the problem.

Edit: Also think about why we do BatchNormalization