Generative Cooperative Network (GCN) AI Framework

The second native AI service is what we call the Generative Cooperative Network (GCN). This framework uses principles of coevolution to combine crowdsourced models into models that get better and better at solving longevity challenges. By coevolution we mean that agents learn from and adjust to each other until no agent can change for the better anymore. Emergence can happen as when relations of the agents to each other develop into self reinforcing patterns.

The GCN itself is expected to increase in complexity and sophistication as more models are integrated into it. In its more advanced versions, it will leverage principles of consensus from symbolic interactionism in natural social systems to coevolve the micro and macro layers of a multiresolutional simulation.

This technique is particularly good for modeling biological signal coordination that explains “omics” data. For instance such principles can help GCN to differentiate user contributed models into biological systems made of modules that are useful in solving a number of challenges. Presenting a GCN with challenges that require it to generate approximations of real world data from other data and models of underlying causal processes will direct the GCN toward producing a mimic of the human body for the multiresolutional simulation, as well as of particular human bodies in different states of health.