try your best to explain and convince WR. also the two WAs might help u convince in post rebuttal, so just spend a lot on it!

Здравствуйте уважаемые господа я из Кыргызстана и изобрёл Вечный двигатель и от воздуха работаюший и отопления можно получать без огня опсолютно без огня Господа надо делать первую чтоб люди видели и поверили на Вечнвй двигатель господа ишо в мире нету такой система господа я зделаю Вечный Двигатель ишо не кому не удалось мой телефон Кыргызстане +996 707 52 42 17 или+996 770 77 77 44 Кеңешбек звоните или СМС пишите пожалуста господа я сейчас болею инсультом реч не понятно господа

Just speculation here: maybe they store generated text in a buffer and when they run out of memory buffer can be flushed to get allocation back for other tasks.

OP, do not do this. This person has no idea how HIPAA* compliance works.

Hey! I had a very similar doubt and was hoping you could provide some insight. I came across this CLIP Guided Diffusion Colab Notebook by Katherine Crowson. It's really cool and I've played a little with it.
I want to know if I can generate the same image over and over again. I've tried setting the seed but I'm new to this so can someone give me some intuition or links to some related work in this area. Any help would be appreciated.

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I do not know any benchmark that would measure that. It would also be quite challenging to compare to SaaS like Pinecone (it should be running on the same infrastructure to have comparable results). When it comes to Milvus, as far as I know, they use prefiltering for filtered search (https://github.com/milvus-io/milvus/discussions/12927). So they need to store the ids of matching entries somewhere during the vector search phase, possibly even all the ids if your filtering criteria do not exclude anything.

Yes

You are correct -- they don't usually occur simultaneously. Usually, you would train and then test afterwards, but I've shown the test performance as a function of the number of training iterations, just so you can see what happens with generalization.

(Sometimes people do examine curves like this using validation data, so they can see when the best time to stop training is though)

The test loss goes back up because it classifies some of the test answers wrong. With more training iterations, it becomes more certain about it's answers (e.g., it pushes the likelihood of its chosen class from 0.9 to 0.99 to 0.999 etc.). For the training data, where the everything is classified correctly, that makes it more likely and decreases the loss. For the cases in the test data where its classified wrong, it makes it less likely, and so the loss starts to go back up.

Hope this helps. I will try to clarify in the book. It's always helpful to learn where people are getting confused.

https://docs.sciml.ai/DiffEqFlux/stable/

This is something that I often read, that other LLMs are undertrained, but how come the OpenAI one is the only one not to be ? Datasets ? Computing power ?

I see. Interesting. I thought it was generating one by one like that. I wonder why it sometimes encounters error after generating a long text and just stops half way through the task - which happened to me frequently.

There's also the rumor mill that Whisper was used to gather a bigger text corpus from videos to train GPT 4.

You're right, it could be that 3.5 is already using that approach. I guess the emergent cognition tests haven't yet been published for GPT-3.5 (or have they?) so it's hard for us to measure performance as individuals. I guess someone could test text-davinci-003 on a bunch of cognitive tasks on the PlayGround but I'm far too lazy to do that :)

People often quote Chinchilla about performance, claiming that there's still a lot of performance to be unlocked when we do not know how GPT 3.5 was trained. GPT 3.5 could very well be Chinchilla-optimal, even though the 1st version of davinci was not Chinchilla-optimal. We know that OpenAI has retrained GPT 3 due to the increased context length going from 2048 to 4096 to the apparent 8000ish tokens for ChatGPT.

>what is very special about the model than the large data and parameter set it has

OpenAI have a good marketing department and the web interface is user friendly. But yeah there's really no secret sauce to it.

The model generates the text snippet in a batch, it just prints it a character at a time for dramatic effect(and to keep you occupied for a while so you don't overload the horribly computationally expensive cloud service it runs on with multiple queries in quick succession), so yeah definitely scaling questions before it could be ran as a google replacement general casual search engine.

Google (and DeepMind) actually have better LLM tech and models than OpenAI (if you believe their published research anyway). They had a significant breathrough last year in terms of scalability: https://arxiv.org/abs/2203.15556

Existing LLMs are found out to be undertrained and with some tweaks you can create a smaller model that outperforms larger ones. Chinchilla is arguably the most performant model we've heard of to date ( https://www.jasonwei.net/blog/emergence ) but it hasn't been pushed to any consumer-facing application AFAIK.

This should be powering their ChatGPT competitor Sparrow which might be reeleased this year. I am pretty sure that OpenAI will also implement those ideas for GPT-4.

1 borderline and 2 weak rejects. Is there any chance?

I am making a solver in pytorch :p

Is default unit variance considered low enough?

There are : check the ConvNext V2 paper

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Thanks for the suggestion, but I don't think legal will approve. It's medical data.

X-rays

Wrong, I am not in any rut.

New accounts, being in a rut, saying wrong just for the sake of saying something eventhough nothing was wrong....

If i look at your behaviour it clearly shows that you are fighting your own inner demons instead of really replying to what somebody has said (otherwise u wouldnt put so much of self-fantasized allegations in your posting).

I hope this kind of self-therapy works out for you, but i doubt it helps with anything.