AGI is a very broad pursuit, not tied to the creation of systems emulating human-type general intelligence. However, if one temporarily restricts attention to AGI systems intended to vaguely emulate human functionality, then one can make significantly more intellectual progress in certain interesting directions. For example, by piecing together insights from the various architectures mentioned above, one can arrive at a rough idea regarding what are the main aspects that need to be addressed in creating a "human-level AGI" system. I will present here my rough understanding of the key aspects of human-level AGI in a series of seven figures, each adapted from a figure used to describe (all or part of) one of the AGI approaches listed above. The collective of these seven figures I will call the "integrative diagram." When the term "architecture" is used in the context of these figures, it refers to an abstract cognitive architecture that may be realized in hardware, software, wetware or perhaps some other way. This "integrative diagram" is not intended as a grand theoretical conclusion, but rather as a didactic overview of the key elements involved in human-level general intelligence, expressed in a way that is not extremely closely tied to any one AGI architecture or theory, but represents a fair approximation of the AGI field's overall understanding.

First, figure 1 gives a high-level breakdown of a human-like mind into components, based on Aaron Sloman's high-level cognitive-architectural sketch. This diagram represents, roughly speaking, "modern common sense" about the architecture of a human-like mind. The separation between structures and processes, embodied in having separate boxes for Working Memory vs. Reactive Processes, and for Long Term Memory vs. Deliberative Processes, could be viewed as somewhat artificial, since in the human brain and most AGI architectures, memory and processing are closely integrated. However, the tradition in cognitive psychology is to separate out Working Memory and Long Term Memory from the cognitive processes acting thereupon, so I have adhered to that convention. The other changes from Sloman's diagram are the explicit inclusion of language, representing the hypothesis that language processing is handled in a somewhat special way in the human brain; and the inclusion of a reinforcement component parallel to the perception and action hierarchies, as inspired by intelligent control systems theory and deep learning theory. Of course Sloman's high level diagram in its original form is intended as inclusive of language and reinforcement, but I felt it made sense to give them more emphasis.

Figure 2, modeling working memory and reactive processing, is essentially the LIDA diagram as given in prior papers by Stan Franklin, Bernard Baars and colleagues. The boxes in the upper left corner of the LIDA diagram pertain to sensory and motor processing, which LIDA does not handle in detail, and which are modeled more carefully by deep learning theory. The bottom left corner box refers to action selection, which in the integrative diagram is modeled in more detail by Psi. The top right corner box refers to Long-Term Memory, which the integrative diagram models in more detail as a synergetic multi-memory system (Figure 4).

Figure 3, modeling motivation and action selection, is a lightly modified version of the Psi diagram from Joscha Bach's book Principles of Synthetic Intelligence. The main difference from Psi is that in the integrative diagram the Psi motivated action framework is embedded in a larger, more complex cognitive model. Psi comes with its own theory of working and long-term memory, which is related to but different from the one given in the integrative diagram – it views the multiple memory types distinguished in the integrative diagram as emergent from a common memory substrate. Psi's handling of working memory lacks the detailed, explicit workflow of LIDA, though it seems broadly conceptually consistent with LIDA. In Figure 3, the box labeled "Other parts of working memory" is labeled "Protocol and situation memory" in the original Psi diagram. The Perception, Action Execution and Action Selection boxes have fairly similar semantics to the similarly labeled boxes in the LIDA-like Figure 2, so that these diagrams may be viewed as overlapping. The LIDA model doesn't explain action selection and planning in as much detail as Psi, so the Psi-like Figure 3 could be viewed as an elaboration of the action-selection portion of the LIDA-like Figure 2. In Psi, reinforcement is considered as part of the learning process involved in action selection and planning; in Figure 3 an explicit "reinforcement box" has been added to the original Psi diagram, to emphasize this.

Figure 4, modeling long-term memory and deliberative processing, is derived from my own prior work studying the "cognitive synergy" between different cognitive processes associated with different types of memory, and seeking to embody this synergy into the OpenCog system. The division into types of memory is fairly standard in the cognitive science field. Declarative, procedural, episodic and sensorimotor memory are routinely distinguished; we like to distinguish attentional memory and intentional (goal) memory as well, and view these as the interface between long-term memory and the mind's global control systems. One focus of our AGI design work has been on designing learning algorithms, corresponding to these various types of memory, that interact with each other in a synergetic way, helping each other to overcome their intrinsic combinatorial explosions. There is significant evidence that these various types of long-term memory are differently implemented in the brain, but the degree of structure and dynamical commonality underlying these different implementations remains unclear.

Each of these long-term memory types has its analogue in working memory as well. In some cognitive models, the working memory and long-term memory versions of a memory type and corresponding cognitive processes, are basically the same thing. OpenCog is mostly like this – it implements working memory as a subset of long-term memory consisting of items with particularly high importance values. The distinctive nature of working memory is enforced via using slightly different dynamical equations to update the importance values of items with importance above a certain threshold. On the other hand, many cognitive models treat working and long term memory as more distinct than this, and there is evidence for significant functional and anatomical distinctness in the brain in some cases. So for the purpose of the integrative diagram, it seemed best to leave working and long-term memory subcomponents as parallel but distinguished.

Figure 4 may be interpreted to encompass both workaday deliberative thinking and metacognition ("thinking about thinking"), under the hypothesis that in human beings and human-like minds, metacognitive thinking is carried out using basically the same processes as plain ordinary deliberative thinking, perhaps with various tweaks optimizing them for thinking about thinking. If it turns out that humans have, say, a special kind of reasoning faculty exclusively for metacognition, then the diagram would need to be modified. Modeling of self and others is understood to occur via a combination of metacognition and deliberative thinking, as well as via implicit adaptation based on reactive processing.

Figure 5 models perception, according to the concept of deep learning. Vision and audition are modeled as deep learning hierarchies, with bottom-up and top-down dynamics. The lower layers in each hierarchy refer to more localized patterns recognized in, and abstracted from, sensory data. Output from these hierarchies to the rest of the mind is not just through the top layers, but via some sort of sampling from various layers, with a bias toward the top layers. The different hierarchies cross-connect, and are hence to an extent dynamically coupled together. It is also recognized that there are some sensory modalities that aren't strongly hierarchical, e.g touch and smell – these may also cross-connect with each other and with the more hierarchical perceptual subnetworks. Of course the suggested architecture could include any number of sensory modalities; the diagram is restricted to four just for simplicity.

The self-organized patterns in the upper layers of perceptual hierarchies may become quite complex and may develop advanced cognitive capabilities like episodic memory, reasoning, language learning, etc. A pure deep learning approach to intelligence argues that all the aspects of intelligence emerge from this kind of dynamics (among perceptual, action and reinforcement hierarchies). My own view is that the heterogeneity of human brain architecture argues against this perspective, and that deep learning systems are probably better as models of perception and action than of general cognition. However, the integrative diagram is not committed to my perspective on this – a deep-learning theorist could accept the integrative diagram, but argue that all the other portions besides the perceptual, action and reinforcement hierarchies should be viewed as descriptions of phenomena that emerge in these hierarchies due to their interaction.

Figure 6 shows an action subsystem and a reinforcement subsystem, parallel to the perception subsystem. Two action hierarchies, one for an arm and one for a leg, are shown for concreteness, but of course the architecture is intended to be extended more broadly. In the hierarchy corresponding to an arm, for example, the lowest level would contain control patterns corresponding to individual joints, the next level up to groupings of joints (like fingers), the next level up to larger parts of the arm (hand, elbow). The different hierarchies corresponding to different body parts cross-link, enabling coordination among body parts; and they also connect at multiple levels to perception hierarchies, enabling sensorimotor coordination. Finally there is a module for motor planning, which links tightly with all the motor hierarchies, and also overlaps with the more cognitive, inferential planning activities of the mind, in a manner that is modeled different ways by different theorists. The reinforcement hierarchy in Figure 6 provides reinforcement to actions at various levels on the hierarchy, and includes dynamics for propagating information about reinforcement up and down the hierarchy.

Figure 7 deals with language, treating it as a special case of coupled perception and action. The traditional architecture of a computational language comprehension system is a pipeline, which is equivalent to a hierarchy with the lowest-level linguistic features (e.g. sounds, words) at the bottom, and the highest level features (semantic abstractions) at the top, and syntactic features in the middle. Feedback connections enable semantic and cognitive modulation of lower-level linguistic processing. Similarly, language generation is commonly modeled hierarchically, with the top levels being the ideas needing verbalization, and the bottom level corresponding to the actual sentence produced. In generation the primary flow is top-down, with bottom-up flow providing modulation of abstract concepts by linguistic surface forms.

This completes the posited, rough integrative architecture diagram for human-like general intelligence, split among 7 different pictures, formed by judiciously merging together architecture diagrams produced via a number of cognitive theorists with different, overlapping foci and research paradigms. One may wonder: Is anything critical left out of the diagram? A quick perusal of the table of contents of cognitive psychology textbooks suggests that if anything major is left out, it's also unknown to current cognitive psychology. However, one could certainly make an argument for explicit inclusion of certain other aspects of intelligence, that in the integrative diagram are left as implicit emergent phenomena. For instance, creativity is obviously very important to intelligence, but, there is no "creativity" box in any of these diagrams – because in our view, and the view of the cognitive theorists whose work we've directly drawn on here, creativity is best viewed as a process emergent from other processes that are explicitly included in the diagrams.

A high-level "cognitive architecture diagram" like this is certainly not a design for an AGI. Rather, it is more like a pointer in the direction of a requirements specification. These are, to a rough approximation, the aspects that must be taken into account, by anyone who wants to create a human-level AGI; and this is how these aspects appear to interact in the human mind. Different AGI approaches may account for these aspects and their interactions in different ways – e.g. via explicitly encoding them, or creating a system from which they can emerge, etc.