Recent comments in /f/MachineLearning

race2tb t1_jdeilah wrote

Reply to [N] ChatGPT plugins by Singularian2501

Just like google search every other way we do things is going to change. Why do I need a website if I can just feed model my info have it generate everything when people want my content. Things are going to be completely rethought because of natural language to generative ai. We used to be the ones that had to maintain these things and build the content, now we do not really have to. All we need to do is make sure the AI stays well fed and have the links to any data it has to present which it cannot store.

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nightofgrim t1_jdehy1h wrote

Reply to comment by RedditLovingSun in [N] ChatGPT plugins by Singularian2501

I crafted a prompt to get ChatGPT to act as a home automation assistant. I told it what devices we have in the house and their states. I told it how to end any statement with one or more specially formatted commands to manipulate the accessories in the house.

It was just a fun POC, but it immediately became clear how much better this could be over Alexa or Siri.

I was able to ask it to do several things at once. Or be vague about what I wanted. It got it.

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light24bulbs t1_jdecutq wrote

Reply to [N] ChatGPT plugins by Singularian2501

I've been using langchain but it screws up a lot no matter how good of a prompt you write. For those familiar, it's the same concept as this, in a loop, so more expensive. You can run multiple tools though (or let the model run multiple tools, that is)

Having all that pretraining about how to use "tools" built into the model (I'm 99% sure that's what they've done) will fix that problem really nicely.

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Icko_ t1_jdecnjx wrote

Reply to comment by edthewellendowed in [P] Open-source GPT4 & LangChain Chatbot for large PDF docs by radi-cho

Sure:

  1. Suppose you had 1 million embeddings of sentences, and one vector you want the closest sentence to. If the vectors were a single number, you could just do a binary search, and you'd be done. If they are higher dimensionality, it's a lot more involved. Pinecone is a paid product doing this. Faiss is a library by facebook, which is very good too, but is free.
  2. Recently, Facebook released the LLama models. They are large language models. ChatGPT is also a LLM, but after pretraining on a text corpus, you train it with human instructions, which is costly and time-consuming. Stanford took the LLama models, and trained them with ChatGPT. The result is pretty good not AS good, but pretty good. They called it "Alpaca".
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lightyagami03 t1_jde9kv5 wrote

Reply to [D] Simple Questions Thread by AutoModerator

is it even worth trying to break into AI/ML now as a CS student or has everything already been/will be solved in the near future? like the jump from GPT3.5 to 4 was insane, soon GPT 5 will roll out and it'll be even better, and GPT6 might as well be AGI, at which point there wouldnt be anything to work towards

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endless_sea_of_stars t1_jde88qi wrote

Reply to [N] ChatGPT plugins by Singularian2501

Wonder how this compares to the Toolformer implementation.

https://arxiv.org/abs/2302.04761

Their technique was to use few shot (in context) learning to annotate a dataset with API calls. They took the annotated dataset and used it to fine tune the model. During inference the code would detect the API call, make the call, and then append the results to the text and keep going.

The limitation with that methodology is that you have to fine tune the model for each new API. Wonder what OpenAIs approach is?

Edit:

I read through the documentation. Looks like it is done through in context learning. As in they just prepend the APIs description to your call and let the model figure it out. That also means you get charged for the tokens used in the API description. Those tokens also count against the context window. Unclear if there was any fine tuning done on the model to better support APIs or if they are just using the base models capabilities.

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