One interesting feature of the AGI community, alluded to above, is that it does not currently agree on any single definition of the AGI concept – though there is broad agreement on the general intuitive nature of AGI, along the lines I've summarized above; and broad agreement that some form of the core AGI hypothesis is true. There is a mature theory of general intelligence in the psychology field, and a literature in the AGI field on the formal mathematical definition of intelligence; both of these will be reviewed below; however, none of the psychological nor mathematical conceptions of general intelligence are accepted as foundational in their details, by more than a small plurality of the AGI community. Rather, the formulation of a detailed and rigorous theory of "what AGI is", is a small but significant part of the AGI community's ongoing research. The bulk of the emerging AGI community's efforts is devoted to devising and implementing designs for AGI systems, and developing theories regarding the best way to do so; but the fleshing out of the concept of "AGI" is being accomplished alongside and in synergy with these other tasks.

It must be noted, however, that the term "AI" also has many different meanings within the AI research community, with no clear agreement on the definition. George Lugar's popular AI textbook famously defined it as "that which AI practitioners do." The border between AI and advanced algorithmics is often considered unclear. A common joke is that, as soon as a certain functionality has been effectively achieved by computers, it's no longer considered AI. The situation with the ambiguity of "AGI" is certainly no worse than that with the ambiguity of the term "AI" itself.

In terms of basic semantics, the term "AGI" has been variously used to describe • a property of certain systems ("AGI" as the intersection of "artificial" (i.e. synthetic) and "generally intelligent") • a system that displays this property (an "AGI" meaning "an AGI system") • the field of endeavor pursuing the creation of AGI systems, and the study of the nature of AGI

AGI is related to many other terms and concepts. Joscha Bach has elegantly characterized it in terms of the quest to create "synthetic intelligence." One also finds communities of researchers working toward AGI-related goals under the labels "computational intelligence", "natural intelligence", "cognitive architecture", "biologically inspired cognitive architecture" (BICA), and many others. Each of these labels was introduced with a certain underlying purpose, and has a specific collection of concepts and approaches associated with it; each corresponds to a certain perspective or family of perspectives. The specific purpose underlying the concept and term "AGI" is to focus attention on the general scope and generalization capability of certain intelligent systems, such as humans, theoretical system like AIXI, and a subset of potential future synthetic intelligences. That is, roughly speaking, an AGI system is a synthetic intelligence that has a general scope and is good at generalization across various goals and contexts.

The ambiguity of the concept of "AGI" relates closely to the underlying ambiguity of the concepts of "intelligence" and "general intelligence." The AGI community has embraced, to varying extents, a variety of characterizations of general intelligence, finding each of them to contribute different insights to the AGI quest. Legg and Hutter wrote a paper summarizing and organizing over 70 different published definitions of "intelligence", most oriented toward general intelligence, emanating from researchers in a variety of disciplines. In the rest of this section I will overview the main approaches to defining or characterizing general intelligence taken in the AGI field.

One key distinction to be kept in mind as we review the various approaches to characterizing AGI, is the distinction between AGI and the related concept of "human-level AI" (which is usually used to mean, in effect: human-level, reasonably human-like AGI). AGI is a fairly abstract notion, which is not intrinsically tied to any particular characteristics of human beings. Some properties of human general intelligence may in fact be universal among all powerful AGIs, but given our current limited understanding of general intelligence, it's not yet terribly clear what these may be. The concept of "human-level AGI", interpreted literally, is confusing and ill-defined. It's difficult to place the intelligences of all possible systems in a simple hierarchy, according to which the "intelligence level" of an arbitrary intelligence can be compared to the "intelligence level" of a human. Some researchers have proposed universal intelligence measures that could be used in this way; but currently the details and utility of such measures are both quite contentious. To keep things simpler, here I will interpret "human-level AI" as meaning "human-level and roughly human-like AGI," a restriction that makes the concept much easier to handle. For AGI systems that are supposed to operate in similar sorts of environments to humans, according to cognitive processes vaguely similar to those used by humans, the concept of "human level" is relatively easy to understand.

The concept of "AGI" appears more theoretically fundamental than "human-level AGI"; however, its very breadth can also be problematic. "Human-level AGI" is more concrete and specific, which lets one take it in certain directions more easily than can be done with general AGI. In our discussions on evaluations and metrics below, for example, we will restrict attention to human-level AGI systems, because otherwise creating metrics to compare qualitatively different AGI systems becomes a much trickier problem.

The pragmatic approach to conceptualizing general intelligence is typified by the AI Magazine article "Human Level Artificial Intelligence? Be Serious!", written by Nils Nilsson, one of the early leaders of the AI field. Nilsson's view is ... that achieving real Human Level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has minimal, although extensive, built-in capabilities. These would have to include the ability to improve through learning along with many other abilities.

In this perspective, once an AI obsoletes humans in most of the practical things we do, it's got general Human Level intelligence. The implicit assumption here is that humans are the generally intelligent system we care about, so that the best practical way to characterize general intelligence is via comparison with human capabilities. The classic Turing Test for machine intelligence – simulating human conversation well enough to fool human judges – is pragmatic in a similar sense to Nilsson. But the Turing test has a different focus, on emulating humans. Nilsson isn't interested in whether an AI system can fool people into thinking it's a human, but rather in whether an AI system can do the useful and important practical things that people can do.

The psychological approach to characterizing general intelligence also focuses on human-like general intelligence; but rather than looking directly at practical capabilities, it tries to isolate deeper underlying capabilities that enable these practical capabilities. In practice it encompasses a broad variety of sub-approaches, rather than presenting a unified perspective. Viewed historically, efforts to conceptualize, define, and measure intelligence in humans reflect a distinct trend from general to specific (it is interesting to note the similarity between historical trends in psychology and AI). Thus, early work in defining and measuring intelligence was heavily influenced by Spearman, who in 1904 proposed the psychological factor g (the "g factor", for general intelligence). Spearman argued that g was biologically determined, and represented the overall intellectual skill level of an individual. A related advance was made in 1905 by Binet and Simon, who developed a novel approach for measuring general intelligence in French schoolchildren. A unique feature of the Binet-Simon scale was that it provided comprehensive age norms, so that each child could be systematically compared with others across both age and intellectual skill level. In 1916, Terman introduced the notion of an intelligence quotient or IQ, which is computed by dividing the test-taker's mental age (i.e., their age-equivalent performance level) by their physical or chronological age.

In subsequent years, psychologists began to question the concept of intelligence as a single, undifferentiated capacity. There were two primary concerns. First, while performance within an individual across knowledge domains is somewhat correlated, it is not unusual for skill levels in one domain to be considerably higher or lower than in another (i.e., intra-individual variability). Second, two individuals with comparable overall performance levels might differ significantly across specific knowledge domains (i.e., inter-individual variability). These issues helped to motivate a number of alternative theories, definitions, and measurement approaches, which share the idea that intelligence is multifaceted and variable both within and across individuals. Of these approaches, a particularly well-known example is Gardner's theory of multiple intelligences, which proposes eight distinct forms or types of intelligence: (1) linguistic, (2) logical-mathematical, (3) musical, (4) bodily-kinesthetic, (5) spatial, (6) interpersonal, (7) intrapersonal, and (8) naturalist. Gardner's theory suggests that each individual's intellectual skill is represented by an intelligence profile, that is, a unique mosaic or combination of skill levels across the eight forms of intelligence.

Another approach to understanding general intelligence based on the psychology literature, is to look at the various competencies that cognitive scientists generally understand humans to display. The following list of competencies was assembled at the 2009 AGI Roadmap Workshop via a group of 12 experts, including AGI researchers and psychologists, based on a review of the AI and psychology literatures. The list is presented as a list of broad areas of capability, each one then subdivided into specific sub-areas: • Perception — Vision (image and scene analysis and understanding); Hearing (identifying sounds associated with common objects; understanding which sounds come from which sources in a noisy environment); Touch (identifying common objects and carrying out common actions using touch alone); Crossmodal (integrating information from various senses); Proprioception (sensing and understanding what its body is doing) • Actuation — Physical skills (manipulating familiar and unfamiliar objects); Tool use, including the flexible use of ordinary objects as tools; Navigation, including in complex and dynamic environments • Memory — Implicit (memory the content of which cannot be introspected); Working (short-term memory of the content of current/recent experience); Episodic (memory of a first-person experience attributed to a particular instance of the agent); Semantic (memory regarding facts or beliefs); Procedural (memory of sequential/parallel combinations of actions, often habituated) • Learning — Imitation (spontaneously adopt new behaviors seen from others); Reinforcement (learn from positive/negative reinforcement); Interactive verbal instruction; Learning from written media; Learning via experimentation • Reasoning — Deduction, Induction, Abduction, Causal reasoning, Physical reasoning (based on naive physics), Associational reasoning (based on spatiotemporal associations), all from uncertain premises observed in the world • Planning — Tactical; Strategic; Physical; Social • Attention — Visual Attention; Social Attention; Behavioral Attention • Motivation — Subgoal creation; Affect-based motivation; Control of emotions • Emotion — Expressing Emotion; Perceiving/Interpreting Emotion • Modeling Self and Other — Self-Awareness; Theory of Mind; Self-Control; Other-Awareness; Empathy • Social Interaction — Appropriate Social Behavior; Communication about social relationships; Inference about social relationships; Group interactions (e.g. play) • Communication — Gestural communication; Verbal communication using natural language; Pictorial communication; Language acquisition; Cross-modal communication • Quantitative — Counting sets of objects; Simple, grounded arithmetic; Comparison of observed entities regarding quantitative properties; Measurement using simple tools • Building/Creation — Physical (creative constructive play); Conceptual invention (concept formation); Verbal invention; Social construction

Different researchers have different views about which of the above competency areas is most critical, and as you peruse the list, you may feel that it over or under emphasizes certain aspects of intelligence. But it seems clear that any software system that could flexibly and robustly display competency in all of the above areas, would be broadly considered a strong contender for possessing human-level general intelligence.

Complementing the above perspectives, Laird et al have composed a list of "requirements for human-level intelligence" from the standpoint of designers of cognitive architectures. Their own work has mostly involved the SOAR cognitive architecture, which has been pursued from the AGI perspective, but also from the perspective of accurately simulating human cognition: R0. FIXED STRUCTURE FOR ALL TASKS (i.e., explicit loading of knowledge files or software modification should not be done when the AGI system is presented with a new task) R1. REALIZE A SYMBOL SYSTEM R2. REPRESENT AND EFFECTIVELY USE MODALITY-SPECIFIC KNOWLEDGE R3. REPRESENT AND EFFECTIVELY USE LARGE BODIES OF DIVERSE KNOWLEDGE R4. REPRESENT AND EFFECTIVELY USE KNOWLEDGE WITH DIFFERENT LEVELS OF GENERALITY R5. REPRESENT AND EFFECTIVELY USE DIVERSE LEVELS OF KNOWLEDGE R6. REPRESENT AND EFFECTIVELY USE BELIEFS INDEPENDENT OF CURRENT PERCEPTION R7. REPRESENT AND EFFECTIVELY USE RICH, HIERARCHICAL CONTROL KNOWLEDGE R8. REPRESENT AND EFFECTIVELY USE META-COGNITIVE KNOWLEDGE R9. SUPPORT A SPECTRUM OF BOUNDED AND UNBOUNDED DELIBERATION R10. SUPPORT DIVERSE, COMPREHENSIVE LEARNING R11. SUPPORT INCREMENTAL, ONLINE LEARNING

As Laird et al note, there are no current AI systems that plainly fulfill all these requirements (although the precise definitions of these requirements may be open to a fairly broad spectrum of interpretations). It is worth remembering, in this context, Stan Franklin's careful articulation of the difference between a software "agent" and a mere "program": An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future. Laird and Wray's requirements do not specify that the general intelligence must be an autonomous agent rather than a program. So, their requirements span both "agent AI" and "tool AI". However, if we piece together Franklin's definition with Laird and Wray's requirements, we get a reasonable stab at a characterization of a "generally intelligent agent", from the perspective of the cognitive architecture designer.

In contrast to approaches focused on human-like general intelligence, some researchers have sought to understand general intelligence in general. The underlying intuition here is that • Truly, absolutely general intelligence would only be achievable given infinite computational ability. For any computable system, there will be some contexts and goals for which it's not very intelligent • However, some finite computational systems will be more generally intelligent than others, and it's possible to quantify this extent This approach is typified by the recent work of Legg and Hutter, who give a formal definition of general intelligence based on the Solomonoff-Levin prior. Put very roughly, they define intelligence as the average reward-achieving capability of a system, calculated by averaging over all possible reward-summable environments, where each environment is weighted in such a way that more compactly describable programs have larger weights.

According to this sort of measure, humans are nowhere near the maximally generally intelligent system. However, humans are more generally intelligent than, say, rocks or worms. While the original form of Legg and Hutter's definition of intelligence is impractical to compute, a more tractable approximation has recently been developed (Legg and Veness, 2013). Also, Achler has proposed an interesting, pragmatic AGI intelligence measurement approach explicitly inspired by these formal approaches, in the sense that it explicitly balances the effectiveness of a system at solving problems with the compactness of its solutions. This is similar to a common strategy in evolutionary program learning, where one uses a fitness function comprising an accuracy term and an "Occam's Razor" compactness term.

Another perspective views general intelligence as closely tied to the environment in which it exists. Pei Wang has argued carefully for a conception of general intelligence as adaptation to the environment using insufficient resources (Wang, 2006). A system may be said to have greater general intelligence, if it can adapt effectively to a more general class of environments, within realistic resource constraints. In a 2010 paper, I sought to modify Legg and Hutter's mathematical approach in an attempt to account for the factors Wang's definition highlights: • The pragmatic general intelligence is defined relative to a given probability distribution over environments and goals, as the average goal-achieving capability of a system, calculated by weighted-averaging over all possible environments and goals, using the given distribution to determine the weights • The generality of a system's intelligence is defined in a related way, as (roughly speaking) the entropy of the class of environments over which the system displays high pragmatic general intelligence • The efficient pragmatic general intelligence is defined relative to a given probability distribution over environments and goals, as the average effort-normalized goal-achieving capability of a system, calculated by weighted-averaging over all possible environments and goals. The effort-normalized goal-achieving capability of a system is defined by taking its goal-achieving capability and dividing it by the computational effort the system must expend to achieve that capability

Lurking in this vicinity are some genuine differences of perspective with the AGI community, regarding the proper way to conceive general intelligence. Some theorists (e.g. Legg and Hutter) argue that intelligence is purely a matter of capability, and that the intelligence of a system is purely a matter of its behaviors, and is independent of how much effort it expends in achieving its behaviors. On the other hand, some theorists (e.g. Wang) believe that the essence of general intelligence lies in the complex systems of compromises needed to achieve a reasonable degree of generality of adaptation using limited computational resources. In the latter, adaptationist view, the sorts of approaches to goal-achievement that are possible in the theoretical case of infinite or massive computational resources, have little to do with real-world general intelligence. But in the former view, real-world general intelligence can usefully be viewed as a modification of infinite-resources, infinitely-general intelligence to the case of finite resources.

A close relative of the adaptationist approach, but with a very different focus that leads to some significant conceptual differences as well, is what we may call the embodiment approach to characterizing general intelligence. In brief this perspective holds that intelligence is something that physical bodies do in physical environments. It holds that intelligence is best understood via focusing on the modulation of the body-environment interaction that an embodied system carries out as it goes about in the world. Rodney Brooks is one of the better known advocates of this perspective. Pfeifer and Bonard summarize the view of intelligence underlying this perspective adroitly as follows: "In spite of all the difficulties of coming up with a concise definition, and regardless of the enormous complexities involved in the concept of intelligence, it seems that whatever we intuitively view as intelligent is always vested with two particular characteristics: compliance and diversity. In short, intelligent agents always comply with the physical and social rules of their environment, and exploit those rules to produce diverse behavior." For example, they note: "All animals, humans and robots have to comply with the fact that there is gravity and friction, and that locomotion requires energy... [A]dapting to these constraints and exploiting them in particular ways opens up the possibility of walking, running, drinking from a cup, putting dishes on a table, playing soccer, or riding a bicycle."

Pfeifer and Bonard go so far as to assert that intelligence, in the perspective they analyze it, doesn't apply to conventional AI software programs. "We ascribe intelligence only to ... real physical systems whose behavior can be observed as they interact with the environment. Software agents, and computer programs in general, are disembodied, and many of the conclusions drawn ... do not apply to them." Of course, this sort of view is quite contentious, and e.g. Pei Wang has argued against it in a paper titled "Does a Laptop Have a Body?" – the point being that any software program with any kind of user interface is interacting with the physical world via some kind of body, so the distinctions involved are not as sharp as embodiment-oriented researchers sometimes imply.

Philosophical points intersect here with issues regarding research focus. Conceptually, the embodiment perspective asks whether it even make sense to talk about human-level or human-like AGI in a system that lacks a vaguely human-like body. Focus-wise, this perspective suggests that, if one is interested in AGI, it makes sense to put resources on achieving human-like intelligence the way evolution did, i.e. in the context of controlling a body with complex sensors and actuators in a complex physical world. The overlap between the embodiment and adaptationist approaches is strong, because historically, human intelligence evolved specifically to adapt to the task of controlling a human body in certain sorts of complex environment, given limited energetic resources and subject to particular physical constraints. But, the two approaches are not identical, because the embodiment approach posits that adaptation to physical body-control tasks under physical constraints is key, whereas the adaptationist approach holds that the essential point is more broadly-conceived adaptation to environments subject to resource constraints.