AI And Human Collective Cognition (CC)

Artificial intelligence can only look into the past and recognise the patterns for which it was “rewarded” (//trained). This means, however, that the patterns of the past must resemble those of the future in order for the AI system to generate valuable information. In disruptive situations the AI does most probably not perform because it is not trained for it – would be an interesting task to design an AI for disruptive situations.

The more tasks the AI takes on, the more it weanes their users from following their own intuitions and thus sharpening their own senses. We observe this effect when we use computer navigation to find our way. We can’t memorize the paths we use as well as if we asked for them or searched for them ourselves. Human collective cognition systems on the other hand train people to become processors in a collective cognition process. They are training their individual cognition.

The more cognitive performances are taken over by an AI, the less the subjective perspectives of individual people play a role as AI is normally an abstraction of human group behaviour. An AI cannot well anticipate the specific needs of an individual human being. People’s life situations are far too complex and individual for this. Therefore AI systems cannot formulate what people need, people have to do it themselves and they do it best in collective cognition systems. By the way, every exchange between people forms such a system. We just want to build an infrastructure that ideally supports this exchange and scales it worldwide.

AI use their training sessions in data sets to try to identify general abstractions of behavioral and value patterns and apply them to other data sets. Repeatability always plays a role here, which is in the nature of pattern recognition. In humane collective cognition systems like Quote, Reddit or in future DIVERSUS it is the other way around. The systems ideally do reward differences, the uniqueness of information in its context. At this level, collective human cognition is even the opposite of an AI abstraction derived from repetitive training units in search of similarities of group behaviour. In the spirit of Hararis Dataismus, human collective cognition systems (HCCF) identify the network nodes that reassemble information in a new and unique way. This is an existential difference to the developments set in motion by AI, where differences in information processing are rather levelled out.

Nevertheless, AI can recognise valuable patterns in the mass of data that people simply cannot see due to its complexity. Therefore, the two systems – human collective cognition and artificial intelligence – are supposed to be complementary.

Why Is Collective Cognition Happening?

Nature has been developing collective cognition in the animal kingdom for millions of years. The humanoid systems are only the temporary highlight of this development. Which physical laws of nature are at work here? Why does dead matter tend to live? Why does it tend to form increasingly complex cognitive systems?

A convincing answer can be found in the second law of thermodynamics, that the enthropy of the whole system increases over time. If life at the particle level is a forbidden entropy reversal, why is it formed with such force? Where is the loss of entropy compensated? The answer is simple: in the cognition of biological systems. Their perceptions, intuitions and mental processes are also physical processes of electromagnetic nature, and they produce a stunningly higher entropy than if the physical particles of the physical cognition system were simply scattered.

Therefore, human collective cognition systems correspond to cosmological development. The more information entropy they allow, the more value they generate for evolution.

The dialectical predisposition of DIVERSUS in this sense generates a higher entropy than previous systems, since the link between two nodes is not one-dimensional, but can have an infinite number of dialectical qualities. This opens up the two-dimensional nature of the Internet’s linking structure into a vertical, third dimension. Entropy grows exponentially. The predictability of the information process is approaching zero.

Those who are looking for an answer to the exponentially growing complexity of hypercivilization, which is currently stalling the biological system, can continue their research with us here.

What is Collective Cognition?

In principle, the word is self-explanatory. After more than 15 years of even best sellers with the buzz word “Collective Intelligence” (The Swarm, Frank Schätzing), it’s time to diversify and deepen the concept. This happens currently with concepts like shared, distributed or collective cognition.

The Santa Fe Institute did the interesting experiment and threw the term into a group of thinkers to see how it was digested. Here is a list of the answers:

• • • Decisions that are made by a large set of nonlinear subunits.
    • Group based information transforming actions of adaptive systems.
    • Distributed computation about knowledge.
    • Multiple spatio-temporal timescale adaptive learning
    • Solving problems better together
    • Problem solving by groups of individuals
    • Mental processes distributed across multiple agents
    • Spatial, temporal, or social structure, collective effects of components, estimating regularities to reduce uncertainty

Here is a nice baseline study on Collective Cognition