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Will Artificial Intelligence soon surpass the capacities of the human mind and will Strong Artificial General Intelligence replace the contemporary Weak AI? It might appear to be so, but there are certain fundamental issues that have to be addressed before this can happen. There can be no intelligence without understanding, and there can be no understanding without getting meanings. Contemporary computers manipulate symbols without meanings, which are not incorporated in the computations. This leads to the Symbol Grounding Problem; how could meanings be incorporated? The use of self-explanatory sensory information has been proposed as a possible solution. However, self-explanatory information can only be used in neural network machines that are different from existing digital computers and traditional multilayer neural networks. In humans self-explanatory information has the form of qualitative sensory experiences, qualia. To have reportable qualia is to be phenomenally conscious. This leads to the hypothesis about an unavoidable connection between the solution of the Symbol Grounding Problem and consciousness. If, in general, self-explanatory information equals to qualia, then machines that utilize self-explanatory information would be conscious. The author presents the associative neural architecture HCA as a solution to these problems and the robot XCR-1 as its partial experimental verification.

DOI: 10.1142/9789811276675_0004