we can do image segmentation, but segmentation uncertainties are a bit iffy. we can do pixel-wise uncertainties, but that really is not what we want because neighbouring pixels are not independent. e.g., if you have a detect-and-segment task, then with an uncertain detection, your segmentation masks should reflect that sometimes "nothing" is detected and thus there is nothing to segment. i think we have not progressed there beyond ising model variations.

Embrace extend extinguish.

I believe going through azure to access the openai models, Microsoft has said they would delete data after 30 days. You could also consider using open source models like Flan-T5, but that means you need to host/setup the model.

Thanks for details.

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I went ahead and asked ChatGPT your question for you:

No, Google is not a language transformer like ChatGPT. While Google has developed language models like BERT and GPT-3, it is primarily a search engine that uses various algorithms to deliver search results to users. Google's language models are used to improve search results and to power services like Google Assistant, but they are not the primary focus of the company.

As for ChatGPT, it is true that its main purpose is to generate human-like text based on prompts provided by users. However, it is not just a search engine that can talk back. It is a complex machine learning model that has been trained on vast amounts of text data and uses advanced natural language processing techniques to generate responses.

While it is true that the development of more algorithms and computing power is necessary for further advancements in AI, ChatGPT and other similar models have already made significant strides in the field of natural language processing. They have the potential to be used in a wide range of applications, including language translation, content creation, and customer service. However, it is unlikely that they will completely replace human jobs, as they are still limited by their inability to understand and reason about the world in the way that humans do.

First author is a high-school student. Impressive.

Thank you for your interest, but the downvotes and basic attitude of the sub make me not feel welcome here. My lack of financial security also compels me not to freely share technical details of what could be a breakthrough worth a lot of money (if only in energy and time savings) to a subreddit that is downvoting me for agreeing that they should be more inviting. Once I check the next few things off the to do list maybe I'll post a demo.

This is a hobby to me, I don't have research funding or anything that is compelling me to potentially advance the field just for the sake of it, especially when the community is bitter to newcomers. I recognize ai is most likely going to be a cornerstone of the economy, and if my architecture scales like I think it will, it will be worth something to someone, and you'll see a demo in a few weeks or months once I take it as far as I want to. I think most people understand not wanting to have one's ideas be borrowed for free when one is struggling.

Thanks for being one of apparently 5 people who's curiosity is at least as strong as their skepticism.

Good luck in your endeavors.

Just linked it in a top-level comment.

Paper: https://aclanthology.org/2022.icnlsp-1.2/
GitHub: https://github.com/radi-cho/RSTOD

Link?

sorry from what I understand it goes something like this:

LLM processes prompt, formats output as per the initial few shot demos.

This output is an intermediary step in plain text including keywords that then get picked up by Toolformer

Toolformer goes off does the search things and returns predefined chunks formatted from the search results

The prompt is then stuffed with those chunks and asked the question again with the added retrieved search context

(and I'm sure there is more pixie dust sprinkled in somewhere. )

It's not a new model. It's davinci-003.

Basically the model begins generating. Once it hits an API request, the request is received and sent and the result of the request is pasted back into text and sent back to open AI to generate again and gpt continues generating until it hits another request and the process is repeated till it's done generating.

Thanks for the link!

I mean I guess there was nothing too surprising about the rules, given how these systems work (essentially trying to predict the end of a user input text). But the rest, seems so ridiculously dramatic that I wouldn’t be shocked if he specifically prompted it to be that dramatic and hid that part. I’m probably being paranoid, since at least the rules part is true, but it seems like the perfect conversation to elicit every single fear people have about AI.

Really? As in the order of operations is: token parsing => Toolformer => LLM?

Genuine question, is the text/token parsing for queries to an LLM (eg chatgpt) performed separately and beforehand to the actual LLM being leveraged, or is the text/token parsing a part of the LLM? I figured it was the latter and you couldn’t just insert a tool there

Edit: I think this is a new model for this purpose, rather than reusing an existing LLM (eg ChatGPT) as I first assumed, which makes more sense

Edit 2: I actually read the paper and the LM itself is taught to reach out to tools as a part of its response operations, it’s not something separate

but theory of mind is not sentience. it is also not clear whether what we measured here is theory of mind.

Microsoft has confirmed the rules are real:

>We asked Microsoft about Sydney and these rules, and the company was happy to explain their origins and confirmed that the secret rules are genuine.

The rest, who knows. I never got access before they fixed it. But there are many screenshots from different people of it acting quite unhinged.

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Really? As in the order of operations is: token parsing => Toolformer => LLM?

Genuine question, is the text/token parsing for queries to an LLM (eg chatgpt) performed separately and beforehand to the actual LLM being leveraged, or is the text/token parsing a part of the LLM? I figured it was the latter and you couldn’t just insert a tool there

I think classification tasks (like image or face recognition) is really useful, but is more niche. We had image recognition before, NNs just do it better. They don’t open up new use cases for recognition.

Same for speech to text and text to speech.

Translation is another huge one, that’s true.

I don’t think NN code autocomplete is a “big real life use case” as we have perfectly correct autocomplete as is and for anything beyond simple programs, I have seen any model give good suggestions. Plus not everyone writes code.

Natural language “understanding” is a weird one. I’m not convinced (yet) that we have models that “understand” language, just models that are good at guessing the next word.

ChatGPTs tendency to be flat out wrong or give nonsensical answers to very niche and specific questions suggests that it isn’t doing any kind of critical thinking about a question, it’s just generating statistically probable following tokens. It just generates convincing prose as it was trained to do.

maybe for people who play video games all day, this is the most real life use case

The guts of the Google search engine are basically like a big table that turns your search question into a ranking of websites.

The guts of ChatGPT are like a colossal equation that takes the history of your conversation with it and computers the next response.

Both work on standard computing architecture, not quantum computers.

> The models are larger because there's maybe 100x the information in a high res image than a paragraph of text.

That's actually not true. Today's LLMs are 175B parameters, Stable Diffusion is 890 million.

Images contain a lot of pixels, but most of those pixels are easy to predict and don't contain much high-level information. A paragraph of text can contain many complex abstract ideas, while an image usually only contains a few objects with simple relationships between them.

In many image generators (like Imagen), the language model they use to understand the prompt is several times bigger than the diffuser they use to generate the image.

Depends how you define "biggest" but running an ML physics sim per-frame per-character in a AAA title would add up to a hell of a lot of inference.

ML is a mathematical discipline. You have to read books to dive into it. Collaboration is possible after you become usefull. Try "Grocking deep learning" for simple introduction to neural networks. Also check classical ml tasks in regression/classification/trees and drill them. This is hard work wich can not be substituted by being part of some community.

Update: Before it you better learn basics of python programming language. Find lectures with homeworks which are not connected with ML itself (16 hours + 40 hours will be enough)