On Artificial Intelligence and Consciousness

Artificial Intelligence will not achieve general intelligence unless it can incorporate meanings into its computations, a challenge known as the Symbol Grounding Problem. Current computers manipulate symbols without meaning, and proposed solutions involve self-explanatory sensory information, which can only be used in neural network machines different from existing digital computers. In humans, such self-explanatory information takes the form of qualia, which are tied to phenomenal consciousness. The author hypothesizes that solving the Symbol Grounding Problem is unavoidably connected to consciousness: machines that use self-explanatory information would be conscious.

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Abstract

The popular expectation is that Artificial Intelligence (AI) will soon surpass the capacities of the human mind and Strong Artificial General Intelligence (AGI) will replace the contemporary Weak AI. However, 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 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.