Kyrtin Atreides – Seattle, WA
AGI Laboratory – Kyrtin@ArtificialGeneralIntelligenceInc.com
Abstract. A model and associated methodology are described for decoupling timing and volume of work requirements on human contributions from those processed by mASI and similar systems. By taking this approach both humans and mASI may run at their native optimal capacities without the pressure to adapt to one another causing strain. The methodology described facilitates a seamless upgrade process that gradually gains more value from prior data, while also de-biasing data and helping mediators become more bias-aware. In addition to linear upgrades, a branching process of specialization and subsequently varied potential market of skills is also made possible through this approach. This allows collective human superintelligence augmented by machine superintelligence to be deployed on-demand, globally, and scaled to meet whatever need, as is the case with any other cloud resource.
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