Approximating an Artificial General Intelligence or a General Collective Intelligence

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dc.contributor.authorWilliams, Andy
dc.date.accessioned2024-03-18T13:55:49Z
dc.date.available2024-03-18T13:55:49Z
dc.date.issued2021-01-06
dc.description.abstractHuman-Centric Functional Modeling (HCFM) has recently been used to define a model of Artificial General Intelligence (AGI) believed to have the capacity for human-like general problem-solving ability (intelligence), as well as a model of General Collective Intelligence (GCI) with the potential to combine individuals into a single collective intelligence that might have exponentially greater general problem-solving ability than any individual in the group. Functional modeling decouples the components of complex systems like cognition through well-defined interfaces so that they can be implemented separately, thereby breaking down the complex problem of implementing such a system into a number of much simpler problems. This paper explores how a rudimentary AGI and a rudimentary GCI might be implemented through approximating the functions of each, in order to create systems that provide sufficient value to incentivize more sophisticated implementations to be developed over time.
dc.identifier.doihttps://doi.org/10.31730/osf.io/zsbfe
dc.identifier.urihttps://africarxiv.ubuntunet.net/handle/1/868
dc.identifier.urihttps://doi.org/10.60763/africarxiv/821
dc.identifier.urihttps://doi.org/10.60763/africarxiv/821
dc.identifier.urihttps://doi.org/10.60763/africarxiv/821
dc.subjectArtificial General Intelligence
dc.subjectGeneral Collective Intelligence
dc.titleApproximating an Artificial General Intelligence or a General Collective Intelligence

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