Thanks for sharing! very interesting paper!

Seo in the next level i think

How could we recognize that you want whole processing to be done purely in-browser? Anyway, just google for "webassembly barcode reader". There are implementations available.

Likely

The possible implementation path is just using it, lol, it is already capable of doing that.

At this moment, your two bets are:

• OpenAI watermarks their generated text and you have models which cam detect this watermark
• a bigger, better model comes out which can detect synthetic text (although then THAT model becomes the problem)

You could also counter misinformation with a fact checking model, but there are two big problems:

• we are nowhere near developing useful AI that can reason
• the truth is subjective and full of dogmas, ex. look at how most countries implement dogmas regarding the holocaust - your model would, without a severe transformation of society itself, be biased and capable of spreading propaganda in a general sense, and misinformation as a subset of propaganda

Therefore I believe your question should be: when can we expect to have models that only share the "truth of the victor". And that's already happening with ChatGPT now, as it seems to be spreading western liberal views.

Researchers and people working in the field discussed this topic in a workshop and you can read their findings in this paper, it's very interesting

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Yes. Yes. No.

Too much inaccurate information will fill up the internet with language models.

why was the neural network stopped at like 1000 steps? why are we comparing a physics informed neural network to a neural network at a different number of steps lol

Also correct me if I'm wrong but don't we care about how the model generalizes? I think we can show that some NN will fit to any training set perfectly given enough steps, but this is already common knowledge no?

It will happen. Balance that with the good that it is and will be doing.

Makes no sense.

Well how is it relevant then? Im happy to be corrected but I dont see how its relevant to this post

It just tells you that there is a well approximating NN for any given function. It doesn't tell you how to find such a NN and it doesnt tell you about extrapolation capabilities of a NN which is well approximating on just a subdomain (which is what this post here is mainly about) either.

The universal approximation theorem in practice just gives a justification for why using NNs as function approximators could be a reasonable thing to do. That's already pretty much the extent of their relevancy to practical issues though

I like it. On a meta level, giving the machine a bit of a priori knowledge about the shape of things to come, that makes a lot of sense.

When the self-driving car hits an obstacle, they will both obey mostly Newtonian sums.

Effectively embedding that knowledge (the differential equations) might make the system less useful for other applications, but should very cheaply improve it's chances on a lot of real-world problems.

Robotics is largely done with PID feedback things. Some more understanding of the behaviour of springs etc etc should help a lot. Quite possibly in other domains, hard to know where such things apply.

I don't quite see how approximation theorems aren't relevant to approximation problems. I'm not criticising the post, I just thought your response was a bit wide of the mark, not much fun.

Yes, I pad them to make them square and resize to 224 using bilinear algorithm, and /255, then the inverse for predicting.

I need to run it in the browser, I thought it was clear from the post. I found a JS port but it seems flacky. They use CNN for 2d detection though, at least in some cases.

Think they mean do you apply the same normalization you applied to images during training to the new images you are trying to predict

I would say, if it is not a very wild environment, nn may be excessive.

Quick discovery: https://note.nkmk.me/en/python-opencv-barcode/

But scanners do not read digital data, or even find out the position of the object, and they use light reflection/absortion, there is no digital information reading.

Try yolo tiny. Must detect well if it is trained properly. But do you think you need a nn model? Scanners read barcodes without neural networks.

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Oh, that helps. I am not sure how true this is though. For example, TF.Keras and TF.SavedModel cant be converted into one another and have different features..Both can be used to predict but only one can be re trained and "tweaked" or extended from JS itself. And I am not sure you can convert Pytorch weights to Keras, but I will investigate. Apparently there is ONX that can be used to do it. I just dont want to train something that can not be converted and loaded into a browser.

What I learnt so far is that Sliding Window, Region of Interest, and Yolo are more like ways to prepare your data, and mostly any CNN could do the job, with more or less precision, I may be wrong. I am following this series https://www.youtube.com/watch?v=XXYG5ZWtjj0&list=PLhhyoLH6Ijfw0TpCTVTNk42NN08H6UvNq&index=2&ab_channel=AladdinPersson