The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent.
Prokopenko M (2014) Grand challenges for computational intelligence. Front. Robot. AI 1:2. http://journal.frontiersin.org/Journal/10.3389/frobt.2014.00002/full