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In recent years broad community of researchers has emerged, focusing on the original ambitious goals of the AI field - the creation and study of software or hardware systems with general intelligence comparable to, and ultimately perhaps greater than, that of human beings. This paper surveys this diverse community and its progress. Approaches to defining the concept of Artificial General Intelligence (AGI) are reviewed including mathematical formalisms, engineering, and biology inspired perspectives. The spectrum of designs for AGI systems includes systems with symbolic, emergentist, hybrid and universalist characteristics. Metrics for general intelligence are evaluated, with a conclusion that, although metrics for assessing the achievement of human-level AGI may be relatively straightforward (e.g. the Turing Test, or a robot that can graduate from elementary school or university), metrics for assessing partial progress remain more controversial and problematic.

“Cognitive synergy”– a dynamic in which multiple cognitive processes, cooperating to control the same cognitive system, assist each other in overcoming bottlenecks encountered during their internal processing. – has been posited as a key feature of real-world general intelligence, and has been used explicitly in the design of the OpenCog cognitive architecture. Here category theory and related concepts are used to give a formalization of the cognitive synergy concept. Cognitive synergy is proposed to correspond to a certain inequality regarding the relative costs of different paths through certain commutation diagrams. Applications of this notion of cognitive synergy to particular cognitive phenomena, and specific cognitive processes in the PrimeAGI design, are discussed.

The PSI theory of Dietrich Dorner touches a number of questions, especially about knowledge representation, perception and bounded rationality. However, since it is formulated within psychology, it has relatively little impact on the discussion of emotion modeling within computer science. This paper introduces a computational model for emotion generation and function by formalizing part of Doner's PSI theory. We also borrowed some technical ideas from MicroPSI, one of the concrete implementations of PSI theory by Joscha Bach. Based on this computational model, a number of simulation experiments have been performed and evaluated. The experimental results show that the emotions of agents controlled by our proposed model can emerge from the interaction between the agents and the environment. Then the dynamics of this computational model are studied using Lewis's dynamic theory of emotions. We successfully found hints of phase transitions in the emotional changes, including trigger, self-amplification and self-stabilization phases, as suggested by Lewis. Based on these simulation results, we argue that this computational model is a quite promising approach of modeling both emotion emergence and dynamics.

The concept of glocal memory (i.e. memory involving systematic coordination between localized memory traces and globalized dynamical-attractor-based memory traces) is reviewed, and is argued to be a critical principle for the design of artificial brains and artificial general intelligence systems. Some exploratory experiments are reviewed, involving introduction of glocal memory into Hopfield neural networks, and also into economic attention networks as utilized in the OpenCog and Novamente integrated AI architectures.

Driven by rapid ongoing advances in computer hardware, neuroscience and computer science, Artificial Brain research and development are blossoming. This article constitutes the first half of a two-part world survey of artificial brain projects: this part dealing with large-scale brain simulations, and the second part with biologically inspired cognitive architectures (BICAs). The large-scale brain simulations we consider in depth here include those by Markram, Modha, Boahen, Horwitz, Edelman, Izhikevich, and Just. As well as reviewing the particulars of these simulation projects, we position them in a broader perspective, comparing at the different underlying definitions of the concept of ''simulation,'' noting that in many ways the projects are modeling neurosystems at different levels as well as using different methodologies.

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

A novel category of theories is proposed, providing a potential explanation for the representation of complex knowledge in the human (and, more generally, mammalian) brain. Firstly, a "glocal" representation for concepts is suggested, involving localized representations in a sparse network of "concept neurons" in the Medial Temporal Lobe, coupled with a complex dynamical attractor representation in other parts of cortex. Secondly, it is hypothesized that a combi-natory logic like representation is used to encode abstract relationships without explicit use of variable bindings, perhaps using systematic asynchronization among concept neurons to indicate an analogue of the combinatory-logic operation of function application. While unraveling the specifics of the brain's knowledge representation mechanisms will require data beyond what is currently available, the approach presented here provides a class of possibilities that is neurally plausible and bridges the gap between neurophysiological realities and mathematical and computer science concepts.

When writing the first draft of this book, some years ago, we had the idea to explain CogPrime by aligning its various structures and processes with the ones in the “standard architecture diagram” of the human mind.

Procedural knowledge must be learned, an often subtle and difficult process—but it must also be enacted. Procedure enaction is not as tricky a topic as procedure learning, but still is far from trivial, and involves the real-time interaction of procedures, during the course of execution, with other knowledge. In this brief chapter we explain how this process may be most naturally and flexibly carried out, in the context of CogPrime’s representation of procedures as programs (“Combo trees”).

In procedure learning as elsewhere in cognition, it’s not enough to use the right algorithm, one has to use it in the right way based on the data and context and affordances available.

While it’s widely accepted that human beings carry out both symbolic and subsymbolic processing, as integral parts of their general intelligence, the precise definition of “symbolic” versus “subsymbolic” is a subtle issue, which different AI researchers will approach in different ways depending on their differing overall perspectives on AI.

One of the most powerful metaphors we’ve found for understanding minds is to view them as networks—i.e. collections of interrelated, interconnected elements. The view of mind as network is implicit in the patternist philosophy, because every pattern can be viewed as a pattern in something, or a pattern of arrangement of something—thus a pattern is always viewable as a relation between two or more things. A collection of patterns is thus a pattern-network. Knowledge of all kinds may be given network representations; and cognitive processes may be represented as networks also; for instance via representing them as programs, which may be represented as trees or graphs in various standard ways. The emergent patterns arising in an intelligence as it develops may be viewed as a pattern network in themselves; and the relations between an embodied mind and its physical and social environment may be viewed in terms of ecological and social networks.

In this chapter and the next we describe one particular incarnation of the above ideas on semantic CSDLNs in more depth: the integration of CogPrime with the DeSTIN architecture reviewed in Chap. 5 of Part 1.

Creating AGI, we have said, is not only about having the right structural and dynamical possibilities implemented in the initial version of one’s system—but also about the environment and embodiment that one’s system is associated with, and the match between the system’s internals and these externals. Another key aspect is the long-term time-course of the system’s evolution over time, both in its internals and its external interaction—i.e., what is known as development.

Intelligence is not just about what happens inside a system, but also about what happens outside that system, and how the system interacts with its environment. Real-world general intelligence is about intelligence relative to some particular class of environments, and human-like general intelligence is about intelligence relative to the particular class of environments that humans evolved in (which in recent millennia has included environments humans have created using their intelligence). In Chap. 4, we reviewed some specific capabilities characterizing human-like general intelligence; to connect these with the general theory of general intelligence from the last few chapters, we need to explain what aspects of human-relevant environments correspond to these human-like intelligent capabilities. We begin with aspects of the environment related to communication, which turn out to tie in closely with cognitive synergy. Then we turn to physical aspects of the environment, which we suspect also connect closely with various human cognitive capabilities. Finally we turn to physical aspects of the human body and their relevance to the human mind. In the following chapter we present a deeper, more abstract theoretical framework encompassing these ideas.

In the last chapter we discussed human intelligence from a fairly down-to-earth perspective, looking at the particular intelligent functions that human beings carry out in their everyday lives. And we strongly feel this practical perspective is important: Without this concreteness, it’s too easy for AGI research to get distracted by appealing (or frightening) abstractions of various sorts.

CogPrime, a comprehensive architecture for embodied Artificial General Intelligence, is reviewed, covering the core architecture and algorithms, the underlying conceptual motivations, and the emergent structures, dynamics and functionalities expected to arise in a completely implemented CogPrime system once it has undergone appropriate experience and education. A qualitative argument is sketched, in favor of the assertion that a completed CogPrime system, given a modest amount of experience in an embodiment enabling it to experience a reasonably rich human-like world, will give rise to human-level general intelligence (with significant difference from humans, and with potential for progress beyond this level).

Human cognition is portrayed as involving a highly flexible, self-organizing "cognitive network", closely coupled with a number of more specific intelligent "body-system networks" --- e.g. those associated with the perceptual and motor systems, the heart, the digestive system, the liver, and the immune and endocrine systems, all of which have been shown to have their own adaptive intelligence. These specialized intelligent networks provide the general-purpose cognitive network with critical structural and dynamical inductive biasing. It is argued that early-stage AGI systems must involve roughly comparable inductive biasing, though not necessarily achieved in the same way.

Bridging the gap between symbolic and subsymbolic representations is a --- perhaps the --- key obstacle along the path from the present state of AI achievement to human-level artificial general intelligence. One approach to bridging this gap is hybridization --- for instance, incorporation of a subsymbolic system and a symbolic system into a integrative cognitive architecture. Here we present a detailed design for an implementation of this approach, via integrating a version of the DeSTIN deep learning system into OpenCog, an integrative cognitive architecture including rich symbolic capabilities. This is a "tight" integration, in which the symbolic and subsymbolic aspects exert detailed real-time influence on each others' operations. An earlier technical report has described in detail the revisions to DeSTIN needed to support this integration, which are mainly along the lines of making it more "representationally transparent," so that its internal states are easier for OpenCog to understand.

By exploring the relationships between different AGI architectures, one can work toward a holistic cognitive model of human-level intelligence. In this vein, here an integrative architecture diagram for human-like general intelligence is proposed, via merging of lightly modified version of prior diagrams including Aaron Sloman’s high-level cognitive model, Stan Franklin and the LIDA group’s model of working memory and the cognitive cycle, Joscha Bach and Dietrich Dorner’s Psi model of motivated action and cognition, James Albus’s three-hierarchy intelligent robotics model, and the author’s prior work on cognitive synergy in deliberative thought and metacognition, along with ideas from deep learning and computational linguistics.

In this chapter we consider the specific question of how the ideas of the previous chapters contribute to carrying out reasoning regarding the potential causes of salient changes in large knowledge stores.

Ideas from computational psychology and the theory of self-organizing evolution are used to explore the relationship between neural processes and evolutionary processes. I argue that, although mind/brains are very different from ecosystems, there are two important dynamical processes which are common to both: namely, evolution by natural selection, and form creation by sexual reproduction.

Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper summarizes the presentations of a set of 14 position papers in the ICCI'09 Panel on Cognitive Informatics and Cognitive Computing contributed from invited panelists who are part of the world's renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

Do there exist general principles, which any system must obey in order to achieve advanced general intelligence using feasible computational resources? Here we propose one candidate: “cognitive synergy,” a principle which suggests that general intelligences must contain different knowledge creation mechanisms corresponding to different sorts of memory (declarative, procedural, sensory/episodic, attentional, intentional); and that these different mechanisms must be interconnected in such a way as to aid each other in overcoming memory-type-specific combinatorial explosions.

OpenCogPrime (OCP), a comprehensive architecture for artificial general intelligence (AGI) is briefly overviewed. Aimed in the long term at AGI at the human level and beyond, the current partial implementation of OCP is being used for applications such as controlling virtual pets in virtual worlds and inferring novel conclusions from sets of semantic relations extracted from natural language. The key aspects of OCP are described here in the context of the theoretical foundation of “cognitive synergy theory”; and the current implementation status is briefly reviewed.

We outline a general conceptual definition of real-world general intelligence that avoids the twin pitfalls of excessive mathematical generality, and excessive anthropomorphism.. Drawing on prior literature, a definition of general intelligence is given, which defines the latter by reference to an assumed measure of the simplicity of goals and environments. The novel contribution presented is to gauge the simplicity of an entity in terms of the ease of communicating it within a community of embodied agents (the so-called Embodied Communication Prior or ECP). Augmented by some further assumptions about the statistical structure of communicated knowledge, this choice is seen to lead to a model of intelligence in terms of distinct but interacting memory and cognitive subsystems dealing with procedural, declarative, sensory/episodic, attentional and intentional knowledge. A sister paper then extends these ideas to yield a “Cognitive Synergy Theory” that suggests specific conclusions for the architecture of artificial general intelligences, based on the ECP.

Publisher Summary The World Wide Brain—a hybrid human–digital intelligent network, spanning the globe and carrying out information processing different in extent and nature from anything that has come before—is as yet little more than a dream and a little less than a reality. It is coming into being, bit by bit, each year. This process of emergence is, as all Net-aholics know, a wonder to behold, and growing more wondrous all the time. This is an exploration in which human psychology and sociology interact in a fascinating way, with the psychology of an emerging, nonhuman organism. It is an exploration in which mundane technical issues such as groupware and server–server communication software rub up against concepts from transpersonal psychology, such as the Collective Unconscious and the Hierarchy of Being. It is, therefore, an exploration that not only transcends disciplinary boundaries but pushes the boundaries of human thought itself. The increasing integration of human activity with World Wide Brain operations may ultimately occur via body-modifying or body-obsolescing technologies a la Moravec, or it may occur without them, through the advent of more sophisticated noninvasive interfaces. One way or another, it will fuse the global Web.

This edited volume gives the first-ever book-length presentation of contemporary research in the domain of Artificial General Intelligence (AGI). What distinguishes AGI research from run-of-the-mill AI research is that it is explicitly focused on engineering general intelligence ? autonomous, self-reflective, self-improving, commonsensical intelligence ? in the short term. Bringing the diverse body of AGI research together in a single volume reveals the common themes among various researchers? work, and makes clear what the big open questions are in this vital and critical area of research, as well as highlighting relationships between AI and related fields such as philosophy, neuroscience, linguistics, psychology, biology, sociology, anthropology and engineering.

Philosophy of mind tells us what it means to say a digital system like Webmind or the Internet is a mind. Complexity science gives us a more rigorous vocabulary for saying the same things. Neither of them tells us quite enough, though. To understand in detail what it will take to push current digital systems in the direction of mind-ness, one requires a much more detailed understanding of mental structure and dynamics. For this purpose, among others, over the last decade I have created a new way of thinking about intelligent systems, which I call the “psynet model of mind.”

Now it’s time to return to some of the more far-out ideas introduced in the Prologue, and pursue them a little more systematically. As you may recall, I proposed there that there are two Internet-related Metasystem Transitions in our future: first the transition to Internet Intelligence, and then the transition to the Global Brain. The first one will come soon: probably in the next decade or two, we will see a Metasystem transition involving the evolution of the Net into a full-fledged, largely autonomous, globally distributed intelligent system — a wild computing system. Phase Two Global Brain begins. And as this occurs, we will see this Internet AI network gradually wend itself further and further into human affairs, yielding, eventually, another Metasystem Transition, involving the emergence of a synergetic, symbiotic global intelligent system, incorporating machine and human intelligence into a single continuum of thought — a human-digital (and hopefully a human-digital-ecological) global brain. Phase Two Global Brain ends, Phase Three begins.

Nearly everyone who has seriously thought about the evolution of technology over the next few hundred years has come to the same conclusion: We live at a crucial point in history — an incredibly exciting and frightening point; a point that is stimulating to the point of excess, intellectually, philosophically, physically and emotionally. A number of really big technologies are brewing. Virtual reality, which lets us create synthetic worlds equal in richness to the physical world, thus making the Buddhist maxim “reality is illusion” a palpable technical fact. Biotechnology, allowing us to modify our bodies in various ways, customizing our genes and jacking our brains, organs and sense organs into computers and other devices. Nanotechnology, allowing us to manipulate molecules directly, creating biological, computational, micromechanical, and other kinds of systems that can barely be imagined today. Artificial intelligence, enabling mind, intelligence and reason to emerge out of computer systems — thinking machines built by humans. And advances in unified field theory in physics will in all likelihood join the party, clarifying the physical foundation of life and mind, and giving the nanotechnologists new tricks no one has even speculated about yet.

Philosophy can seem stodgy, boring, and old-fashioned, especially when compared to ultra-high-velocity, mega-sex-appeal disciplines like Internet AI engineering. But yet, the timelessness of philosophy is probably its greatest strength. The fundamental nature of the universe, which is what philosophy is about, doesn’t change over time. In a sense it even pre-exists the notion of time. As I see it, the only way to really understand the latest greatest thing — and the new new new thing, just around the corner — is to tie it in with the truths of the ages.

Now, finally, with the cognitive equation and the theory of belief systems under our belt, we are ready to return to the “crucial connections” of Chapter Six — to the intimate relationship between language, thought, reality, self and consciousness. In this chapter I will present several different views of the relationship between psychology and the external world.

I have already talked a little about deduction and its role in the mind. In this chapter, however, I will develop this theme much more fully. The relation between psychology and logic is important, not only because of the central role of deductive logic in human thought, but also because it is a microcosm of the relation between language and thought in general. Logic is an example of a linguistic system, and it reveals certain phenomena that are obscured by the sheer complexity of other linguistic systems.

To anyone trained in physical science, the overall impression made by psychology and neuroscience is one of incredible messiness. So many different chemical compounds, so many different neural subsystems, so many different psychic dysfunctions, so many different components of intelligence, perception, control.... And no overarching conceptual framework in which all aspects come together to form a unified whole. No underlying equation except those of physics and chemistry, which refer to a level incomprehensibly lower than that of thoughts, emotions and beliefs. No cognitive law of motion.

It is clear that analogy cannot work effectively without recourse to an effective method of storing patterns. However, I suggest that an even stronger statement holds: the nature of analogy actually dictates a particular type of memory structure. The only way analogy can work effectively is if it is coupled with a memory that is specifically structured so as to support analogical reasoning.

Synthesizing concepts and findings from a number of recent models of human consciousness, a unified model of the key properties characterizing human consciousness is outlined. Six key properties are emphasized: Dynamical representation of the focus of consciousness, Focusing of energetic resources and focusing of informational resources on a subset of system knowledge, Global Workspace dynamics as outlined by Bernard Baars in his cognitive theory of consciousness, Integrated Information as emphasized by Tononi, and correlation of attentional focus with self-modeling . It is proposed that the extent, and relative importance, of these properties may vary in different states of consciousness; and that any AI system displaying closely human-like intelligence will need to manifest these properties in its consciousness as well. The “hard problem” of consciousness is sidestepped throughout, via focusing on structures and dynamics posited to serve as neural or cognitive correlates of subjective conscious experience.