MIND|CONSTRUCT is a leading developer in Artificial General Intelligence (AGI), historically also known as 'Strong AI'. Having developed a scientifically fully vetted model for 'machine consciousness', we are now in the process of building the actual machine that will prove our concept. Our system is mainly aimed at controlling humanoid robotics for fully autonomous operation (first target being Health Care robots), but can also be implemented in other areas where a high level of autonomous operation and/or human-like machine interaction is needed.

The MIND|CONSTRUCT organization is the culmination of several years of scientific research in the realm of Machine Cognition and the so called 'hard-problems' in AI-research. By leveraging extensive experience in (human) knowledge-management, we have designed a generic system that is capable of not only 'learning', but actually 'understanding' and having 'experiences'.

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).