I think this is not a good idea to message the AC.

What do you think they can do? They cannot force the reviewers to ask questions. Then they will just lose time with your request while being very busy by the multiple submissions they are managing. It would only make you look impatient or unprofessional which will not help.

theres a waitlist

Not all premium users have the 'plugin' option in the web interface. (I do not)

I don't know if it available if using the API instead.

Per default when you close the session everything about it is forgotten when you have next session. (The past sessions will must certainly be used to train next version of GPT though)

Idk, I've never heard of it.

https://arxiv.org/abs/2303.12712

If you dont want to read 154 pages, here is an awsome summary:

https://youtu.be/Mqg3aTGNxZ0

Link/citation please

Yeah you're probably right. Thanks a lot for your kind advice! My reviewers' comments are insightful and very helpful to improve our manuscript so I just want to respond to their feedback in the best way that I can.

Yeah this is like looking at the linux kernel binary and seeing patterns of clouds in it. It makes zero sense to psychoanalyze a bunch of optimized vectors and to pretend to be Shamans or medieval alchemists. We better stick to scientific arguments about it

DV3? Darth Vader III? I knew it

Neither was LLaMA before Alpaca.

This a good reminder for everyone to delete comments from tex files before uploading to arxiv ... especially, if they were not meant to be public.

Depends on the quality of the reviews. In theory, the AC could completely ignore all reviews. However, 7&8 are very good and you definitely have a high chance. Be proud of your work!

Adress this! Ask them why they did not increase their score if all concerns were addressed.

If I understand correctly, the model is optimised to effectively predict the next word. That says nothing of its internal representations or lack thereof. It could well be forming internal representations as an efficient strategy to predict the next word. As Sam Altman pointed out, we’re optimised to reproduce and nothing else, yet look at the complexity of living organisms.

EDIT: Just to add, it’s not quite the same thing, but another way of thinking of “most probable next word” is “word that a person would be most likely to write next” (assuming the training data is based on human writings). One way to get really good at approximating what a human would likely write given certain information would be to actually approximate human cognitive structures internally.

That's a very good school project! Good job!

>but LLMs really have an uncanny ability to generate output that looks "intelligent".

Like many of these internet gurus and intellectuals e.g. Eric Weinstein

Yes

Are they public now?

There is no point arguing with the "it just predicts next word" crowd. They only look at what an LLM technically does, and while they are of course technically correct, they completely ignore emergent capabilities, speed of progress and societal impact.

The next discussion to have is not whether we have achieved early stages of AGI, but whether it matters. As long as we're not pretending that a system is sentient (which is a separate issue from whether it has AGI properties) it ultimately doesn't matter how it reliably solves a multitude of problems as if it had general intelligence; it only matters that it does.

Microsoft edge chat/Bing chat but its nerfed and not multimodal. Also has some odd behaviour I asked it if it could analyze images it said yes and to upload to an image site and give it the link. It seemed to be processing it then just froze. I tried again and it said "no I am not able to analyze images"

An open source library with abstractions for different LLM providers, and modular components for chaining together LLM-based steps. A bit like the ChatGPT plugins it includes integrations for the LLM to interact with things like Google search, python REPL, calculator etc.

That's true, but the outputs it produces are eerily persuasive. I'm firmly in the "LLMS are impressive but not AGI" camp. Still, the way it used Java to draw a picture in the style of Kandinsky blew me away. Obviously, a text2image model would be able to do that. But here they prompted GPT-4 to generate code that would generate a picture in a specific style. Which requires an extra level of abstraction and I can't really understand how that came about given that you would not expect a task like this in the training data. (page 12 for reference: https://arxiv.org/pdf/2303.12712.pdf)

I agree that a transformer really should not be considered "intelligent" or AGI, but LLMs really have an uncanny ability to generate output that looks "intelligent". Granted, that's what we built them to do, but still.

I heard of people talking about using ANNOY for approximate nearest neighbor search. How is ANNOY compared to pinecone and faiss? Are pinecone and faiss self-hostable?

There's a pretty nice demo implementation of this concept at https://toolformerzero.com/