going_ham

At this point, I won't hesitate a single bit of doubt that this company literally stole stable diffusion and built on top of it. Like how will we ever know? If they were so good they could have released before SDXL. But they waited.

Yes! That would be so cool. Also they are supporting Nvidia 2000 series GPUs which is completely bonkers!

This really put NVIDIA in clutch for the gaming.

NeRFs are cool technology that has its place and use. It is going to help with scene reconstruction and so on. And that is the reason why these CV researchers are flocking into using it. Despite knowing the limitations, they are giving their best to improve this technology. What I believe is NeRF is insufficient to be meaningful on its own. If you look into the architecture from this problem (the paper in the post), it clearly shows that they have some refinement phase going later on. A single RGB 2D to 3D model is such an ill-posed, we have to consider a lot of priors before diving into it.

There needs to be more foundational work in this field that can outperform or even improve the NeRF-based techniques. And the current herd mentality of researchers should be changed into exploring the alternatives. There is a reason why expensive automobile companies still rely on the physical modelling of their design. It's hard to simulate the physical conditions only through CAD modelling. Sure NeRFs are cool and they can make impressive results. That doesn't necessarily mean it is the means to an end. Look where rasterization brought us! NeRF is like rasterization. It is going to be used. But highly quality graphics was possible through GI and ray tracing! NeRF needs something equivalent that is physically grounded.

Now that is the same idea I also believe in. People are just capitalizing on it like it's a child's toy. This thing can have profound societal implications but no one writes about it. Is there a reason?

Also some of the people hyping it around are confident. How can they be so confident? Is it just blatantly lying into another's face and hoping you won't get caught?

Haha. That was a funny response from the GPT. I attacked your bot unintentionally!

On serious note, do you think as society progresses with use of AI, our brain will be geared toward filtering everything or we will lose the trust system that is prevalent in the society?

Think about it, the more people are being shut for bot, they more likely they will stop interacting online and this might eventually lead to a lot of people discarding the interaction. For most part, life is pretty average. And if average people are out, what might be the implications?

This was immediately my first thought when I saw the product. When is the expiry day? Why aren’t they making it clear?

Also I think this will inspire other companies and help those companies (cough microsoft, apple) create their own version which they will integrate to their own lineup. It will be interesting seeing it in future.

Disclaimer: I am not AI and I typed this response in my tablet. People for some reason have tendency to assume I answer like a bot.

Just some ramblings and experiences. There are tech related content too. I am thinking of updating and revamping it.

https://entangledlogs.com

My circle is 97.4% perfect, can you beat that? https://neal.fun/perfect-circle/

The dot is the center! Try to adjust to that!

My circle is 97.1% perfect, can you beat that? https://neal.fun/perfect-circle/

Sorry doc, I wrote that comment in a smartphone without putting any thought. What I wanted to say was: > there are 7B parameters. A parameter is a weight assigned to single neuron.

I hope this clarifies the answer now.

Now that is done I am quite curious on how you came up with the idea it was written by ChatGPT? I just wanted to simplify as best as I could. It’s funny you thought it that way.

What could I have done so that it didn’t sound like response from ChatGPT? I am asking it ti prevent future misunderstandings. I thought my grammatical errors would be enough to show it wasn’t a ChatGPT response.

Looking forward to your reply!

1. The trained model has 7B parameters or weights for each neuron.

2. It can handle upto 8k tokens. Tokens are usually some representation for a word. If your tokens are characters then, "h", "e", "y" represent 3 tokens for hey. Most of the algos use byte pair encoding. For example "hand-le" has two tokens "hand" and "le". This is a very crud example which is enough to give the gist but is not accurate. You can look into byte pair encoding for more details.

3. The token size 1.5T token means they have huge variations for input and output. Simply put, it was trained on large data corpus.

I hope this simplifies it. You can research further if you are interested! Hope it helps!

This is a great example to show the underlying mechanism!

> You've also seen a bunch of my content on Hacker News because I'm one of the only people writing about it - if very much like not to be!

With all due respect, I would also like to market someone else who has also been posting similar content, but for some reason those posts never make it to the top. If you don't believe me, you can check the following submissions:

[0]: https://news.ycombinator.com/item?id=35745457

[1]: https://news.ycombinator.com/item?id=35915140

They have been consistently putting the risks of LLMs. Thanks for spreading the information though. Cheers.

What does this imply?

Not to sound rude, but how are you guys going to determine differences between user input and say, an input from an external sources like pdf, email, webpage, webapps? Do you have thoughts on it? If I make an application, I will want to link to external systems.

If there isn’t any way to distinguish it, I bet the attack surface is too large. If it is restricted to QA without external interface, then usability is also restricted. Any thoughts about it?

Isn't this similar to your idea? https://github.com/openai/openai-python/commit/75c90a71e88e4...

Correct me if I am wrong, but the way I understand is that, when LLMs have to process a certain text, every word will get tokenized into some vector representation. So, if you insert the new special token and wrap data around, it is not the fact that you can ignore the entire prompt. Because as soon as you have to prompt the model, you will be using the entire tokenized sentence. This would mean that even if there is a special token somewhere, the model will not be able to ignore the token before/after that special token. So what will happen to the model if somewhere there is prompt that overrides this special token?

I am using sofle v2. It costs less than 200!