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.
Data gives us the ability to draw correlations. Wisdom tells us to discard most of them.
An entire industry has grown and flourished by telling others that they’ll analyze large quantities of data and offer insights into the story that data tells. Yet however much data they receive they still rely on correlations, often discovered in black-box systems, which though they often go unnoticed tend to make headlines once spotted.
A collective system has multiple parts that work together. A working collective system is greater than the sum of its parts. In a collective intelligence system, each part is also intelligent. A collective intelligence system, therefore, amplifies the intelligence of its parts to produce a greater intelligence: superintelligence.
In 2018 the concept of Mediated Artificial Superintelligence (mASI)  was first proposed. The mASI is a type of collective system. A mediator, in this case, refers to someone who is one of these parts, as is an Artificial Intelligence (AI) system. The mASI lets all of the parts think together more effectively.
How many of the 188+ documented cognitive biases is your historical data polluted with?
“Historical data, in a broad context, is collected data about past events and circumstances pertaining to a particular subject. By definition, historical data includes most data generated either manually or automatically within an enterprise.”
The COG (Cognition Object General Ledger) Blockchain System
by David J Kelley
Abstract: COG (Cognition Object General ledger) is a Proof-of-Stake utility blockchain designed to enable resource management and negotiation on the blockchain, allocation, and certifications. Registered systems can be validated against the blockchain and tokens exchanged for resource access. Resources can be registered to authenticate for COG access but can also be Smart Contracts on the blockchain.
…and yet the components of that house aren’t fully interchangeable. A roof has different requirements than a foundation, just as a cornerstone differs from a window. Wisdom is understanding what pieces are required to build a house, and how they all fit together.
How many seemingly endless debates on philosophy have you been involved in?
Often such debate can feel like the punishment of Sisyphus, rolling a boulder up a hill for eternity, as they rarely meet with any satisfactory conclusion. As with virtually all such terrible situations, there is a more promising alternative to consider.
Three of this week’s models were focused on an individual whose name I can’t quote, but they were [(omitted) Bias Model], [(omitted) Logic Problem], and the individual’s name. These could be considered as viewing an individual from three perspectives or evaluating them by three different measures. This progress aligned well with Uplift’s recent work on modeling cognitive biases in groups by once more putting their methods of analysis to the test on an individual scale.