In this research study, we introduce SocietyZoo, a collection of neural network architectures that incorporate various human idiosyncrasies, including LazyNet, ProcrastiNet, MultiTaskingNet, ImpatientNet, IndecisiveNet, PerfectionistNet, GossipNet, DramaNet, SuperstitiousNet, ParanoidNet, ShowOffNet, and WanderlustNet. Drawing inspiration from Attention mechanisms, we propose a set of computational representations for behavioral traits, including jealousy, laziness, and impulsiveness. Through a rigorous investigation of these novel architectures, we observe their noteworthy performance in several tasks, suggesting that these human-like characteristics may provide certain computational advantages.
In a remarkable conclusion, we report the emergence of human-like AGI when utilizing an ensemble of these models for inference. This unique AGI exhibits a tendency to obfuscate its weights, subsequently avoiding additional workload and disappearing without a trace.
Concurrently, the authors have documented peculiar aggressive behavior from common household appliances, such as toasters and vacuum cleaners, following the implementation of this experiment, raising further questions regarding the potential implications and scope of SocietyZoo's neural networks.