Innovative AI-technology
Traditionally, AI-systems are developed using one of two major paradigms: Symbolic AI on one side, and Artificial Neural Networks (ANN) on the other side. Both methods have severe practical barriers that have been attacked for decades now and neither have managed to overcome their respective problems.
Symbolic AI tries to develop algorithms for each and every problem that the perceived system must be able to handle in 'reality', and clearly fails because 'reality' is hard to predict.
The ANN approach on the other hand needs so much CPU-power, that any 'human level' results will be impossible for many decades to come. Besides that, ANNs are 'black box' systems; they are capable of 'learning' to recognize specific 'patterns', but we have no clue what a certain ANN actually has learned or recognizes, when it works.
The MIND|CONSTRUCT model borrows the strong points from both methods (generic approach, symbolic (semantic) knowledge modeling), while staying away from the obvious pitfalls of both methods (emulating low-level brain systems, trying to describe every functionality on its own). Our 'model' has both 'symbolic' alike properties, and similarities to Neural Networks like 'weighted connections' and 'forward/backward propagation' (although the actual propagation is different from ANNs).