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Simple things that don't seem impressive to you, but are impressive to others, may be because you haven't experienced the first hand difficulty of doing that th

Simple things that don't seem impressive to you, but are impressive to others, may be because you haven't experienced the first hand difficulty of doing that thing pre-transformers/GPT. For example, I get _unbelievably_ excited with knowledge extraction and question answering demos on PDFs. Why? Because i've built similar systems for over a decade and know how difficult it is to build on top of messy archival data. Now, with very little code, i'm getting SOTA results. If you didn't have experience with this, you'd probably thing "Huh, that's not impressive, XYZ does this already". But it's the moving of the baseline that's what's really impressive. === AI hype-beasts aside of course - the breathless pontificating of "influencers" and former crypto bros is cringe.

CharlesW 3y ago
Hey, would you mind sharing the tools and other resources you’ve found helpful? I’m really interested in trying this but am not sure where to start.
- celestialcheese 3y ago
  This tutorial is a good overview of the rough systems behind most of this "Chat your data" application you're seeing now. https://www.pinecone.io/learn/langchain-retrieval-augmentation/ https://www.pinecone.io/learn/langchain-retrieval-augmentati... Langchain / Llama indexes are both toolboxes that abstract away a lot of the plumbing for doing this kind of thing, and Pinecone is one of dozens of vector databases. Personally, i'd try out langchain and chromadb and go through some of the examples langchain has in their docs, then be prepared to completely abandon langchain and just work with the LLM APIs directly. Start with openai, get on the waitlist for GPT-4 tokens, and also get on Anthropics Claude waitlist for the 100k-1.3. It's _very_ good for knowledge retrieval. Langchain tries to do too much in extracting away the prompts, and the prompts are really what matter in getting interesting stuff out of your own data. Use langchain, llamaindex pieces but build from scratch for most things as your tinkering. It's really not hard if you have a background in programming, and it's _so_ much fun. You'll feel like you have superpowers once you get a scraping interface hooked into an LLM. All of a sudden you can automate some really complex pipelines very quickly
- ukuina 3y ago
  Here's a packaged solution! https://github.com/imartinez/privateGPT https://github.com/imartinez/privateGPT
surgical_fire 3y ago
I am kind of in the same boat as you. I sit in a weird spot of thinking the current trend of AI is really impressive, but also thinking people are massively over-hyping it (with a smaller counter wave of some people really undermining it). Having tangled with natural language processing and transformation in the past (always with dismal results), I can say it's one of the most annoying problems to tackle in computation, because natural languages have the horrible tentency of being very irregular (i.e.: they are a fucking mess). ChatGPT capabilities to parse and generate fluent language never ceases to amaze me. At the same time I don't think it's going to take over the world. It's more like a game-changer productivity tool (with all the upheaval that comes along when those appear) than the birth of Skynet.
- Freebytes 3y ago
  I had thought about a translation system that sought to translate into a different, more coherent language, pass that into an AI model, get a response in that language, and then translate the results back into English. Early OpenAI responses did not seem very impressive to me; however, that was before ChatGPT came along and blew me away. And GPT-4 gives even better responses! I am not sure if the intermediate language solution is necessary or viable at this point because you would need a powerful LLM to perform the translation. But, there might be value in symbolic logic being part of the machine learning vernacular as part of the early tokenization and training process.
gatkinso 3y ago
fair, mostly I'm unimpressed with visual outputs - AI making 'art' etc. PDF extraction is extremely cool and useful and the results are fantastic. Fully agree re influencers. Seems endemic these days.

CharlesW 3y ago

Hey, would you mind sharing the tools and other resources you’ve found helpful? I’m really interested in trying this but am not sure where to start.

celestialcheese 3y ago
This tutorial is a good overview of the rough systems behind most of this "Chat your data" application you're seeing now. https://www.pinecone.io/learn/langchain-retrieval-augmentation/ https://www.pinecone.io/learn/langchain-retrieval-augmentati... Langchain / Llama indexes are both toolboxes that abstract away a lot of the plumbing for doing this kind of thing, and Pinecone is one of dozens of vector databases. Personally, i'd try out langchain and chromadb and go through some of the examples langchain has in their docs, then be prepared to completely abandon langchain and just work with the LLM APIs directly. Start with openai, get on the waitlist for GPT-4 tokens, and also get on Anthropics Claude waitlist for the 100k-1.3. It's _very_ good for knowledge retrieval. Langchain tries to do too much in extracting away the prompts, and the prompts are really what matter in getting interesting stuff out of your own data. Use langchain, llamaindex pieces but build from scratch for most things as your tinkering. It's really not hard if you have a background in programming, and it's _so_ much fun. You'll feel like you have superpowers once you get a scraping interface hooked into an LLM. All of a sudden you can automate some really complex pipelines very quickly
ukuina 3y ago
Here's a packaged solution! https://github.com/imartinez/privateGPT https://github.com/imartinez/privateGPT

surgical_fire 3y ago

I am kind of in the same boat as you. I sit in a weird spot of thinking the current trend of AI is really impressive, but also thinking people are massively over-hyping it (with a smaller counter wave of some people really undermining it). Having tangled with natural language processing and transformation in the past (always with dismal results), I can say it's one of the most annoying problems to tackle in computation, because natural languages have the horrible tentency of being very irregular (i.e.: they are a fucking mess). ChatGPT capabilities to parse and generate fluent language never ceases to amaze me. At the same time I don't think it's going to take over the world. It's more like a game-changer productivity tool (with all the upheaval that comes along when those appear) than the birth of Skynet.

Freebytes 3y ago
I had thought about a translation system that sought to translate into a different, more coherent language, pass that into an AI model, get a response in that language, and then translate the results back into English. Early OpenAI responses did not seem very impressive to me; however, that was before ChatGPT came along and blew me away. And GPT-4 gives even better responses! I am not sure if the intermediate language solution is necessary or viable at this point because you would need a powerful LLM to perform the translation. But, there might be value in symbolic logic being part of the machine learning vernacular as part of the early tokenization and training process.

gatkinso 3y ago

fair, mostly I'm unimpressed with visual outputs - AI making 'art' etc. PDF extraction is extremely cool and useful and the results are fantastic. Fully agree re influencers. Seems endemic these days.