Good enough to know if I have to read it or not. Still ends up disappointed half of the time because an abstract is meant is often a bit clickbaity.

That must be a big document! I may need to adjust the code for more caching

... isn't that the point of the summary at the start of a paper?

... isn't that the point of the summary at the start of a paper?

I have been spending time with Tortoise TTS since yesterday

couldn't produce my voice yet but i am understanding :/

it is also super slow - damn slow on rtx 3060 - cuda running

I also get a RateLimitError recently when I tried

I couldn't find map page in wrotescan. Can you highlight to me how can I get it to work?

Hey, great idea, looks very interesting. Do you use the abstract as an input or do you actually parse the paper? I built something quite similar: http://www.arxiv-summary.com which summarizes trending AI papers as bullet points. However, I think a chrome extension allows for a much more flexible paper choice, which is really great.

I really like chatgpt but i typically find the abstract good enough to summarize the paper

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Even if it will become illegal, the democracy of Machine Learning depends on it being legal. If Getty wins this, it would mean that a few pretty large companies would be the only ones that can build large models because they “own” most of the data. Facebook for example does a lot of stuff to prevent people scrape public data from their apps.

Genuinely interested in learning how to build such things, can you explain how did you build this extension and linked it to chat gpt from system design perspective.

would like to see a sample of the source movie file for res and for artifacts of compression process. it looks impressive anyhow.

I'm not sure if you have a good overview of ML research if this is your claim. Sounds like you've read too many blog posts on transformers. I'd suggest going through some conference proceedings to get a good overview, there's some pretty rigorous (not just stats) stuff out there. I agree though that there is a substantial subset of research in ML that works towards tweaking and pushing the boundaries of what can be achieved with existing methods, which is for me personally exciting to see! A lot of cool stuff came out of scaling up and tweaking the architectures.

https://chrome.google.com/webstore/detail/arxivgpt/fbbfpcjhnnklhmncjickdipdlhoddjoh

To use this extension, simply install it and visit a link to an arXived paper. It will generate a summary of the paper, including a one sentence summary, 3-5 questions for the authors, and 3-5 suggestions for related topics. The query prompt can be customized to fit your specific needs and preferences

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.

Google's API is pretty cheap. Might be free depending on how much you need.

Poker depends on looking far enough ahead to be able to play game theory optimal (GTO) moves that maximize the expected value over a long run of hands. You can train a transformer on a ton of data, and get it to predict context-specific plays, but if the number of possible decision-branches is growing exponentially, is this enough?

But honestly, I don't know much about these types of RL-type problems. How is AlphaGo structured?

https://arxiv.org/pdf/2212.14034.pdf

I'm working in game dev, and on side project I'm currently building a little (newbie) ml framework in c# to discover FFNN, CNN and probly RNN. I'm currently struggling on the backpropagation in convolutionnal layer but that's a matter of time before it works (I hope) 😂

I'm very curious to see the possible applications in game AI. I already did some testing projects before, simple ML agents with small fully connected networks.. But I want to go further and probly try to mix the utility based ai pattern with reinforcement learning methods or genetic algorithm.

I also think Convolutionnal network could maybe be used to input some 'spatialized' data to an ai agent and allow him to take decision about movement or so..

I am curious about this too!

It would have been MUCH harder to prove if they spent a day preprocessing the images first!

So you're saying Stability wouldn't have issues if they hired an intern to git clone a watermark remover and put the images through it first?

You search the training image database for pictures of koalas with wine glasses... And there won't be many examples in there, and you check each one.

The question is can you use copyrighted info to train a model. The answer is we don't know yet.

The current lawsuit that will define precedent on this is for image generation using copyrighted Getty images in a training model. It's proven that Getty images are used because the watermark shows up in the output of the model many times which is the answer to "how can they prove it".

Once that is defined, then we will know if it is legal or not in those jurisdictions. And then we will get to the "do we do it anyways even though it's illegal?"