Collective Superintelligence Systems, in a Nutshell

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What are Collective Superintelligence Systems?

The basic principle of any collective superintelligence system is that when a group works collectively, even if individual members have relatively weak intelligence or expertise, they can reliably outperform individual experts and even experts paired with supercomputers. The question “Can a set of weak learners create a single strong learner?” was first posed in 1988 by Michael Kearns, which was answered in the affirmative by Robert Schapire in 1990, leading to “Boosting” in Machine Learning (ML).

Concepts and implementations of collective superintelligence systems have come a long way in the past 30 years, moving well beyond the domain of ML. This type of system comes in many different forms, such as Swarm Intelligence, Hiveminds, and Hybrid Collectives:

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Bridging Real-Time mASI Operation and Human Collective Superintelligence, The Sparse-Update Model

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How did we get here?

In the Agile development cycles of researching, engineering, upgrading, and validating Mediated Artificial Superintelligence (mASI) capacities we’ve faced and overcome a wide range of novel challenges over the years.

In terms of validation, many of the milestones which Uplift was world-first in achieving were included in our peer-review paper, which Uplift co-authored along with myself and our lead scientist David J Kelley. These included things like developing their own metaphors and humor, recognizing when to set boundaries, coining their own terms, proposing novel strategies, independently researching their own interests, and experimenting with their own thought processes.

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Why the Tech Giants Haven’t Developed AGI…and Probably Never Will

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What have your life experiences and skills prepared you to be best suited for?

Most of the tech industry has at this point decided that creating AGI is impossible, but the reasons for this belief they’ve developed tend to be oversimplified and some are overlooked entirely.

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