The idea is to build a system that captures the essence of how (an ever-increasingly large set of) input neural networks (input data would be something like 1) weight initializations 2) final weights 3) network architecture information 4) hyperparameters, etc) learn in order to generalize to new problems presented to it that are not necessarily related to any of the problems that the input NNs solved. To clarify, we are not getting the specifics of what the NNs learned, but the essence of how the NNs learned. This is a very new idea, and so far I've not seen anyone working on it. The closest anyone has come to it is Google with their Pathways project, but this project will have a level of abstraction that is deeper than that. The project, unless we think of a better name, will be called Aletheium. I have domain names bought Aletheia means "truth" or "disclosure" in philosophy. If you are interested in this project, please message me. Ideally this project will be open source. I'd love to have many people working on the project. Right now, I am looking for people that will help me lead this project. submitted by /u/_slight_blue_third [link] [comments] https://www.reddit.com/r/cofounder/comments/sheqwt/usatech8_software_engineer_seeks/
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