7.3 Toward a Formal Characterization of Real-World General Intelligence 135

7.3 Toward a Formal Characterization of Real-World General Intelligence 135 7.3 Toward a Formal Characterization of Real-World General Intelligence Having defined what we mean by an agent acting in an environment, we now turn to the question of what it means for such an agent to be “intelligent.” As we have reviewed extensively in Chapter 2 above, “intelligence” is a commonsense, “folk psychology” concept, with all the imprecision and contextuality that this generally entails. One cannot expect any compact, elegant formalism to capture all of its meanings. Even in the psychology and AI research communities, divergent definitions abound; Legg and Hutter [L107a] lists and organizes 70+ definitions from the literature. Practical study of natural intelligence in humans and other organisms, and practical de- sign, creation and instruction of artificial intelligences, can proceed perfectly well without an agreed-upon formalization of the “intelligence” concept. Some researchers may conceive their own formalisms to guide their own work, others may feel no need for any such thing. But nevertheless, it is of interest to seek formalizations of the concept of intelligence, which capture useful fragments of the commonsense notion of intelligence, and provide guidance for practical research in cognitive science and AI. A number of such formalizations have been given in recent decades, with varying degrees of mathematical rigor. Perhaps the most carefully- wrought formalization of intelligence so far is the theory of “universal intelligence” presented by Shane Legg and Marcus Hutter in [LI07b], which draws on ideas from algorithmic information theory. Universal intelligence captures a certain aspect of the “intelligence” concept very well, and has the advantage of connecting closely with ideas in learning theory, decision theory and computation theory. However, the kind of general intelligence it captures best, is a kind which is in a sense more general in scope than human-style general intelligence. Universal intelligence does capture the sense in which humans are more intelligent than worms, which are more intelligent than rocks; and the sense in which theoretical AGI systems like Hutter’s AIXI or AIXI® [Hut05] would be much more intelligent than humans. But it misses essential aspects of the intelligence concept as it is used in the context of intelligent natural systems like humans or real-world AI systems. Our main goal in this section is to present variants of universal intelligence that better capture the notion of intelligence as it is typically understood in the context of real-world natural and artificial systems. The first variant we describe is pragmatic general intelligence, which is inspired by the intuitive notion of intelligence as “the ability to achieve complex goals in complex environments,” given in [Goe93a]. After assuming a prior distribution over the space of possible environments, and one over the space of possible goals, one then defines the pragmatic general intelligence as the expected level of goal-achievement of a system relative to these distributions. Rather than measuring truly broad mathematical general intelligence, pragmatic general intelligence measures intelligence in a way that’s specifically biased toward certain environments and goals. Another variant definition is then presented, the efficient pragmatic general intelligence, which takes into account the amount of computational resources utilized by the system in achieving its intelligence. Some argue that making efficient use of available resources is a defining characteristic of intelligence, see e.g. [Wan06]. A critical question left open is the characterization of the prior distributions corresponding to everyday human reality; we give a semi-formal sketch of some ideas on this in Chapter 9 below, where we present the notion of a “communication prior,” which assigns a probability HOUSE_OVERSIGHT_013051