This is an automated archive.
The original was posted on /r/singularity by /u/East-Print5654 on 2024-01-14 07:59:36+00:00.
Ok I’m gonna try to keep this brief but I just randomly thought of this. What if you ran two relatively competent AGIs in tandem with one another. One as a “supervisor”, and the other a generator.
The framework is as follows, instead of Mixture of experts models, this idea uses two relatively competent models, say GPT 4 and model P. GPT 4 in this scenario has been trained to utilize the web, can manipulate docs and spreadsheets, and other basic things. Model P, is an AI trained on the specialization of whatever industry a specific firm belongs to, plus the aggregate of all relevant proprietary data.
The algorithm is as follows: A ceo at an accounting firm types into the prompt that he wants a tax spreadsheet on one of his clients done of the last 12 months. This question first gets handed off to model P, who knows what needs to be done, but doesn’t know how to do it himself, so he goes to chatgpt 4, and asks “hey, can you generate a spreadsheet about X Y and Z about this specific client?” Gpt4 says yes, and returns its final work to model P, but it’s flawed. Model P reprompts gpt4 a couple of times again, specifying each time what needs to be fixed until the desired outcome is received, then outputs the finished work to the ceo.
This process kind of reminds me of CGP Greys video on how the YouTube algorithm works.
But the difference here, isn’t that you’re giving one ai one prompt and expecting it to do all the work in one go, model P is constantly reprompting it and changing it, and improving the output each time.
So why couldn’t you make a variation of this sort of algorithm for every relevant industry?
I’m asking out of curiosity because I am by no means an expert in ML.