How can we best conceptualize and approach the original problem regarding which the AI field was founded: the creation of thinking machines with general intelligence comparable to, or greater than, that of human beings? The standard approach of the AI discipline (Russell and Norvig, 2010), as it has evolved in the 6 decades since the field's founding, views artificial intelligence largely in terms of the pursuit of discrete capabilities or specific practical tasks. But while this approach has yielded many interesting technologies and theoretical results, it has proved relatively unsuccessful in terms of the original central goals of the field.

Ray Kurzweil (Kurzweil, 2005) has used the term "narrow AI" to refer to the creation of systems that carry out specific "intelligent" behaviors in specific contexts. For a narrow AI system, if one changes the context or the behavior specification even a little bit, some level of human reprogramming or reconfiguration is generally necessary to enable the system to retain its level of intelligence. This is quite different from natural generally intelligent systems like humans, which have a broad capability to self-adapt to changes in their goals or circumstances, performing "transfer learning" (Taylor, Kuhlmann, and Stone, 2008) to generalize knowledge from one goal or context to others. The concept of "Artificial General Intelligence" has emerged as an antonym to "narrow AI", to refer to systems with this sort of broad generalization capability.

A system need not possess infinite generality, adaptability and flexibility to count as "AGI". Informally, AGI may be thought of as aimed at bridging the gap between current AI programs, which are narrow in scope, and the types of AGI systems commonly seen in fiction – robots like R2D2, C3PO, HAL 9000, Wall-E and so forth; but also general intelligences taking non-robotic form, such as the generally intelligent chat-bots depicted in numerous science fiction novels and films. And some researchers construe AGI much more broadly than even the common science fictional interpretations of AI would suggest, interpreting it to encompass the full gamut of possible synthetic minds, including hypothetical ones far beyond human comprehension, such as uncomputable minds like AIXI (Hutter, 2005). The precise definition or characterization of AGI is one of the subjects of study of the AGI research field.

In recent years, a somewhat broad community of researchers united by the explicit pursuit of AGI has emerged, as evidenced for instance by conference series like AGI, BICA (Biologically Inspired Cognitive Architectures) and Advances in Cognitive Systems, and numerous special tracks and symposia on Human-Level Intelligence, Integrated Intelligence and related themes. The "AGI community", consisting e.g. of the attendees at the AGI-related conferences mentioned above, is a fuzzy set containing researchers with various interpretations of, and varying levels of commitment to, the AGI concept. This paper surveys the key ideas and directions of the contemporary AGI community.

But what is this "general intelligence" of what we speak? A little later, I will review some of the key lines of thinking regarding the precise definition of the GI concept. Qualitatively speaking, though, there is broad agreement in the AGI community on some key features of general intelligence: • General intelligence involves the ability to achieve a variety of goals, and carry out a variety of tasks, in a variety of different contexts and environments. • A generally intelligent system should be able to handle problems and situations quite different from those anticipated by its creators. • A generally intelligent system should be good at generalizing the knowledge it's gained, so as to transfer this knowledge from one problem or context to others. • Arbitrarily general intelligence is not possible given realistic resource constraints. • Real-world systems may display varying degrees of limited generality, but are inevitably going to be a lot more efficient at learning some sorts of things than others; and for any given real-world system, there will be some learning tasks on which it is unacceptably slow. So real-world general intelligences are inevitably somewhat biased toward certain sorts of goals and environments. • Humans display a higher level of general intelligence than existing AI programs do, and apparently also a higher level than other animals. • It seems quite unlikely that humans happen to manifest a maximal level of general intelligence, even relative to the goals and environment for which they have been evolutionarily adapted.

There is also a common intuition in the AGI community that various real-world general intelligences will tend to share certain common properties; though there is less agreement on what these properties are!

Another point broadly shared in the AGI community is confidence in what I would venture to call the "core AGI hypothesis," i.e. that Core AGI hypothesis: the creation and study of synthetic intelligences with sufficiently broad (e.g. human-level) scope and strong generalization capability, is at bottom qualitatively different from the creation and study of synthetic intelligences with significantly narrower scope and weaker generalization capability.

This "core AGI hypothesis" is explicitly articulated in English for the first time here in this review paper (it was presented previously in Japanese in Goertzel (2014)). I highlight it because it is something with which nearly all researchers in the AGI community agree, regardless of their different conceptualizations of the AGI concept and their different architectural, theoretical, technical and engineering approaches. If this core hypothesis is correct, then distinguishing AGI as a separate pursuit and system class and property from the "narrow AI" that has come to constitute the main stream of the AI field, is a sensible and productive thing to do.

Note, the core AGI hypothesis doesn't imply there is zero commonality between narrower-scope AI work and AGI work. For instance, if a researcher is engineering a self-driving car via a combination of specialized AI techniques, they might use methods from the field of transfer learning to help each component of the car's control system (e.g. the object recognition system, the steering control system, etc.) better able to deal with various diverse situations it might encounter. This sort of transfer learning research, having to do with generalization, might have some overlap with the work one would need to do to make a generalized "AGI driver" that could, on its own, adapt its operations flexibly from one vehicle or one environment to another. But the core AGI hypothesis proposes that, in order to make the latter sort of AGI driver, additional architectural and dynamical principles would be required, beyond those needed to aid in the human-mediated, machine learning aided creation of a variety of narrowly specialized AI driving systems.

Within the scope of the core AGI hypothesis, a number of different approaches to defining and characterizing AGI are under current study, encompassing psychological, mathematical, pragmatic and cognitive architecture perspectives. This paper surveys the contemporary AGI field in a fairly inclusive way. It also discusses the question of how much evidence exists for the core AGI hypothesis – and how the task of gathering more evidence about this hypothesis should best be pursued. The goal here is not to present any grand new conclusions, but rather to summarize and systematize some of the key aspects AGI as manifested in current science and engineering efforts.

It is argued here that most contemporary approaches to designing AGI systems fall into four top-level categories: symbolic, emergentist, hybrid and universalist. Leading examples of each category are provided, and the generally perceived pros and cons of each category are summarized. Not all contemporary AGI approaches seek to create human-like general intelligence specifically. But it is argued here, that, for any approach which does, there is a certain set of key cognitive processes and interactions that it must come to grips with, including familiar constructs such as working and long-term memory, deliberative and reactive processing, perception, action and reinforcement learning, metacognition and so forth.

A robust theory of general intelligence, human-like or otherwise, remains elusive. Multiple approaches to defining general intelligence have been proposed, and in some cases these coincide with different approaches to designing AGI systems (so that various systems aim for general intelligence according to different definitions). The perspective presented here is that a mature theory of AGI would allow one to theoretically determine, based on a given environment and goal set and collection of resource constraints, the optimal AGI architecture for achieving the goals in the environments given the constraints. Lacking such a theory at present, researchers must conceive architectures via diverse theoretical paradigms and then evaluate them via practical metrics.

Finally, in order for a community to work together toward common goals, environments and metrics for evaluation of progress are necessary. Metrics for assessing the achievement of human-level AGI are argued to be fairly straightforward, including e.g. the classic Turing test, and the test of operating a robot that can graduate from elementary school or university. On the other hand, metrics for assessing partial progress toward human-level AGI are shown to be more controversial and problematic, with different metrics suiting different AGI approaches, and with the possibility of systems whose partial versions perform poorly on commonsensical metrics, yet whose complete versions perform well. The problem of defining agreed-upon metrics for incremental progress remains largely open, and this constitutes a substantial challenge for the young field of AGI moving forward.