## 7 Open Problems in Defining Intelligence  
A fundamental and long standing difficultly in the field of artificial intelligence is that (generic) intelligence itself is not well defined. It is an anomaly that nowadays most AI researchers avoid discussingintelligence, which is caused by several factors: It is a difficult old subject, it is politically charged, it is not necessary for narrow AI which focusses on specific applications, AI research is done mainly by computer scientists who mainly care about algorithms rather than philosophical foundations, and the popular belief that general intelligence is principally unamenable to a mathematical definition. These reasons explain but only partially justify the low effort in trying to define intelligence. Assume we had a definition, ideally a formal, objective, non-anthropocentric, and direct method of measuring intelligence, or at least a very general intelligence-like performance measure that could serve as an adequate substitute. This would bring the higher goals of the field into tight focus and allow us to objectively compare different approaches and judge the overall progress. Indeed, formalizing and rigorously defining a previously vague concept usually constitutes a quantum leap forward in the field: Cf. set theory, logical reasoning, infinitesimal calculus, energy, temperature, etc. Of course there is (some) work on defining [165] and testing [166] intelligence (see [32] for a comprehensive list of references):  
The famous Turing test [167, 168, 169] involves human interaction, so is unfortunately informal and anthropocentric, others are large \"messy\" collections of existing intelligence tests [170, 171] (\"shotgun\" approaches), which are subjective and lack a clear theoretical grounding, and are potentially too narrow. There are some more elegant solutions based on classical [172] and algorithmic [173] information theory (\"C-Test\" [174, 175, 176]), the latter closely related to [PERSON]'s [37] \"perfect\" inductive inference model. The simple program in [177] reached good IQ scores on some of the more mathematical tests. One limitation of the C-Test however is that it only deals with compression and (passive) sequence prediction, while humans or machines face reactive environments where they are able to change the state of the environment through their actions. AIXI generalizes [PERSON] to reactive environments, which suggested an extremely _general_, _objective_, _fundamental_, and _formal_ performance measure [178, 56]. This so-called Intelligence Order Relation (IOR) [27] even attracted the popular scientific press [179, 180], but the theory surrounding it has not yet been adequately explored. Here I only describe three non-technical open problems in defining intelligence. * **General and specific performance measures. ** Currently it is only partially understood how the IOR theoretically compares to the myriad of other tests of intelligence such as conventional IQ tests or even other performance tests proposed by AI other researchers. Another open question is whether the IOR might in some sense be too general. One may narrow the IOR to specific classes of problems [139] and compare how the resulting IOR measures compare to standard performance measures for each problem class. This could shed light on aspects of the IOR and possibly also establish connections between seemingly unrelated performance metrics for different classes of problems. * **Practical performance measures. ** A more practically orientated line of investigation would be to produce a resource bounded version of the IOR like the one in [27, Sec.7], or perhaps some of its special cases. This would allow one to define a practically implementable performance test, similar to the way in which the C-Test has been derived from incomputable definitions of compression using \(Kt\) complexity [175]. As there are many subtle kinds of resource bounded complexity [41], the advantages and disadvantages of each in this context would need to be carefully examined.