If you’re printing the cards you can use a different aruco marker for each size. These are easily detected and you can infer the dimensions and pose of the entire card.

http://mecaruco2.readthedocs.io/en/latest/notebooks_rst/Aruco/aruco_basics.html

It shouldn't be too difficult to produce a watermark provided the output is something on the order of a paragraph. However, I don't think its always possible. For instance if I ask ChatGPT to replicate the previous paragraph by replacing all nouns and verbs and to keep the same meaning.

Further tweaking by a human should completely destroy any residual.

This may be the size of the datasets, but i it's hard to say how many parameters will be needed for a good llm that's just really good at explaining code.

> digital watermark

Wouldn't it be easier to store the model outputs or a perceptual hash, and then provide a way to determine if some text is similar to prior ChatGPT output? I assumed they were already doing something like this to collect usage data as they scrape new content.

ChatGPT already has a unique writing style, I'm not sure how you could add anything to the text which couldn't be trivially removed and do better

./popquiz_hotshot.sh

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This tool actually doesn't look at memory right now, just actual computation. Usually loading your model into memory eats up basically the max memory until the training is done, even if compute usage is very low.

If your training is hanging and still burning GPU cycles that'd be harder to detect I think.

I sent you at chat

I mean, seems like a basic convolution neural network would work well for this.

Just use detectron2

I don't think there are any laws that protect their data in this way, except perhaps contract law because they have a hidden ToS that you have to accept to use their service. As long as you use it for free though, I'm not sure there is consideration, and well... I don't know how they would go about proving misuse or damages.

Certainly it would not be copyright law, given that GPT3 itself was trained on copyrighted data...

I mean it's not like there are no libraries for human segmentation which already run on these devices....

less than 500MB is used for code learning, 690GB is used for culture, geography, history, fiction and non-fiction... 2GB for cats, 2GB bread, horses, dogs, Cheese, Wine, Italy, France, Politics, Television, Music, Japan, Africa. less than 1% of the training is on science and technology, i.e. 300MB is biology, 200MB chemistry, 100MB physics, 400MB maths...

The question is how the fuck does one get a 4090? I am really at my wits end, Amazon has a few at double MSRP.

1. Choose your niche (speech recognition/image classification/LLMs/whatever)
2. Start your own blog with good* technical content (i.e. not the shovel crap you see on Medium), and see if you can write some guest posts for an existing blog with decent traffic. Open-source your code on GH. Spread on social media.
3. Give presentations at a few local events and make it clear you're also available for freelancing.

It might take a month or two but people will start contacting you.

* this is important, your blog content/presentation actually has to be worth reading. It doesn't have to be cutting-edge, but it has to be novel enough to convince someone that you have something special to offer. Implementing a lesser-known paper and showing your results is usually a good start (also it teaches you just how hard it is to recreate something based on a paper alone).

Nice, I think an extension where this could be beneficial is when your process hangs - it's using full GPU memory but not training, this happened to me recently training models with fairseq. (I am not sure how you can catch these conditions)

Understanding what stable diffusion models are is easy. Understanding why they work and VAEs don’t is hard, especially when you consider they are just defective VAEs.

This and the back blaze report are among my favorite articles.

This is actually a reasonable estimate.

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I have a little script called gpu_Speed that blows up my laptop if it drops below 50 mph % GPU utilization

Sure. I’ll need an iOS engineer as well and god know what models are supported on device currently, so it’ll be 250k and I’ll need 6 months.

I always wonder how people get customers.

If the goal is for a commercial endeavor I suggest bringing on an ML-specialized shop. Good ones can knock this out in a few sprints. PM me if you need more info.

> This means, with an average of 60 watt idle and 350 watt max for a RTX 4090

RTX 4090 "idles" (stream at background) at 10-15 watt. 4k144hz monitor might change it, but 60 watt is way too much for GPU only.