I’m really excited to see this kind of stuff experimented with. I find it’s really useful of thinking of machine learning agent training in terms of creating a topology through balancing of the weights and connections that ends up being a model of a particular domain described by the data that it’s being fed. The agent learns patterns in the data it observes and creates an internal predictive model based on that. Currently, most machine learning systems seem to focus on either individual agents or small groups such as adding a supervisor. It would be interesting to see large graphs of such agents that interact in complex ways and where high level agents are only interacting with other agents and don’t even need to see any of the external inputs directly. One example would be to have a system trained on working with visual input and another with audio, and then have a high level system that’s responsible for integrating these inputs and doing the actual decision making.
I’m really excited to see this kind of stuff experimented with. I find it’s really useful of thinking of machine learning agent training in terms of creating a topology through balancing of the weights and connections that ends up being a model of a particular domain described by the data that it’s being fed. The agent learns patterns in the data it observes and creates an internal predictive model based on that. Currently, most machine learning systems seem to focus on either individual agents or small groups such as adding a supervisor. It would be interesting to see large graphs of such agents that interact in complex ways and where high level agents are only interacting with other agents and don’t even need to see any of the external inputs directly. One example would be to have a system trained on working with visual input and another with audio, and then have a high level system that’s responsible for integrating these inputs and doing the actual decision making.
and just ran across this https://arxiv.org/abs/2308.00352