The unicist approach to artificial intelligence implies the integration of data-based AI and fundamentals-based AI.
On the one hand, the categories defined by fundamentals-based AI, Unicist AI, provide the autonomous universes that are needed to minimize the subjective biases of data-based AI.
On the other hand, data-based AI allows quantifying the specific structure of fundamentals-based AI to establish the aspects of the categories and segments of entities to build solutions.
When the quantity of data does not suffice to develop data-based AI, the use of non-destructive testing is used to provide the quantitative information to manage the categories and segments of Unicist AI.
The unicist artificial intelligence emulates the human reflection process to apprehend the concepts of complex adaptive systems and environments. It uses the rules of the unicist double dialectical logic and allows developing solutions and learning from the pilot tests of their implementation until their functionality has been confirmed.
The double dialectical logic is an emulation of the ontogenetic intelligence of nature that drives the functionality and evolution of complex adaptive systems and environments. The unicist artificial intelligence allows emulating the solutions of a complex adaptive system to build structural adaptive solutions.
The emulation of the reflection process of human intelligence requires two functions to make this possible: The learning function and the decision function.
- The learning function allows confirming the functionality of actions based on the feedback of pilot tests.
- The decision-making function of a UAI approach to reality, allows making automated decisions that work as conscious decisions based on the recycling though the learning function