Augmented Artificial Intelligence: a Conceptual Framework
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All artificial Intelligence (AI) systems make errors. These errors are unexpected, and differ often from the typical human mistakes ("non-human" errors). The AI errors should be corrected without damage of existing skills and, hopefully, avoiding direct human expertise. This paper presents an initial summary report of project taking new and systematic approach to improving the intellectual effectiveness of the individual AI by communities of AIs. We combine some ideas of learning in heterogeneous multiagent systems with new and original mathematical approaches for non-iterative corrections of errors of legacy AI systems. New stochastic separation theorems demonstrate that the corrector technology can be used to handle errors in data flows with very general probability distributions and far away from the classical i.i.d. hypothesis.In particular, in the analysis of mathematical foundations of AI non-destructive correction, we answer one general problem published by Donoho and Tanner in 2009.
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