Here is a summary of an overview of the field of Artificial General Intelligence. Generative AI (Neural Networks) likely need enhancements from the other disciplines of AGI. Here a summary of the views of Ben Geortzel who has been working on these problems for decades and has had led funded AI companies working to achieve these goals.
The General Theory of General Intelligence: A Pragmatic Patternist Perspective. (2021)
The relation between formal theory, conceptual theory and experimentation in AI has historically been subtle and dialectical, as in many disciplines where engineering is allied with frontier science.
• Genetic algorithms were a case where strong conceptual analogies to biology led to robust experimentation, which was followed only significantly later by useful formal theoretical understanding came only later.
• Deep neural nets were an example where weak analogies to biology were followed by fairly useful formal theory (e.g. regarding hierarchical neural approaches to function approximation and reinforcement learning), which then for decades led only to toy-scale and relatively unimpressive practical examples, until supporting technologies matured enough that the real-world power of the ideas could be realized experimentally.
• Logic-based AI has been strong on theory for quite some time, and there is an increasing suspicion that it’s going to finally come into its practical prime over the next 5 years with the rise of neural-symbolic systems. Modern work on ML-based guidance of theorem-proving combines empirical experimentation with formal theory in a fascinatingly intricate way.
Range of AGI approaches
At the one extreme, there has been the approach of starting with a general theory of AGI and then deriving practical systems from this theory and implementing them. Marcus Hutter and his students have been the best example of this approach, with Hutter’s Universal AI theory serving as a credible (though debatable in many respects) general theoretical AGI approach and a number of relatively practical proto-AGI systems emerging from it. Arthur Franz’s work has perhaps gone the furthest toward building a practical bridge between Hutter’s universal AGI theory and the realm of practically usable AGI systems.
At the other extreme, there is the currently more common approach of working toward AGI by creating more and more powerful practical ML and RL systems, experimenting with them and seeing what they can do, and then working out theoretical explanations of observed AGI system behaviors as needed. The various attempts underway to work toward AGI by creating more and more powerful neural net architectures, incorporating e.g. deep and reinforcement learning networks combined and end-to-end trained using backpropagation, are this trial and error approach. There is not much of an attempt to derive the details of an AGI architecture from an overall conception of what a mind is.
Ben’s approach to AGI over the last several decades has been on the whole more theoretically than experimentally driven – with an integrative “cognitive systems theory” approach including mathematics along with other disciplinary influences, rather than a primarily mathematical approach a la Hutter.
Summary of Key Points
1. The “patternist philosophy of mind”, in which the aspects of intelligence most relevant from an engineering perspective are viewed in terms of the understanding of a mind as the set of patterns associated with an intelligent system.
2. General aspects of intelligent function like evolution and self-organization, and aspects of cognitive network structure and dynamics, are conceived in a patternist way.
3. A formalization of the concept of “pattern”, grounding pattern in a formal theory of complexity/simplicity that embraces algorithmic information theory but also frames the concepts more generally in terms of “combination systems” of simple elements that combine to produce other elements in the manner of an abstract algorithmic chemistry
4. G. Spencer Brown’s Laws of Form and related thinking regarding “distinction graphs” is introduced as a more foundational ontological and phenomenological layer within which the formalization of pattern, simplicity, combination, function application, process execution and related concepts can be situated
5. Distinction graphs are seen to naturally extend into distinction metagraphs, with typed nodes and links including e.g. types related to temporal relationships. These metagraphs can be taken as a foundational knowledge representation and meta-representation scheme for AGI theory and practice.
6. Paraconsistent, probabilistic and fuzzy logic can be grounded naturally in distinction metagraphs and their symmetries and emergent properties
7. Execution and analysis of programs in appropriate languages can be grounded in distinction metagraphs via Curry-Howard correspondences between these languages and logics that are grounded in distinction metagraphs
8. Intelligence in general must be considered as an open-ended phenomenon without any single scalar or vectorial quantification. However, intelligent systems can be quantified in multiple respects, including e.g. joy, growth and choice, and also including goal-achievement skill.
9. Formalization of the “goal-achievement skill” aspect of intelligence in terms of algorithmic information theory is interesting in multiple respects, including the simple formal models of extraordinarily intelligent though physically infeasible agents (e.g. AIXI푡푙 and the Godel Machine) that it naturally corresponds to
10. The activity of these impractical formal extraordinarily intelligent agents can be associated with formal models of the world constructed according to elegant information-theoretic principles like “Maximal Algorithmic Caliber”
11. Achievement of reasonably high degrees of general intelligence under conditions of constrained resources relies heavily on “cognitive synergy” – the property via which different sorts of learning processes associated with different kinds of practically relevant knowledge are able to share intermediate internal state and help each other out of learning dead-ends and bottlenecks
12. Approximation of impractical formal models of extraordinarily intelligent agents in terms of practically achievable Discrete Decision Systems (DDSs) seeking incremental reward maximization via sampling and inference guided action selection is a worthwhile approach to practical AGI design. These DDSs can often be executed in terms analyzable as greedy algorithms or approximate stochastic dynamic programming.
13. Combinatory Function Optimization (COFO) systems – which seek to maximize functions via guiding function-evaluation using sampling and inference guided
selection of combinations within a combination system – are introduced as a species of DDS particularly useful within AGI architectures.
14. Practical cognitive systems are viewed as recursive DDSs aimed at carrying out organismic goals (like pursuing joy, growth, choice, survival, discovery of new things, etc.), via choosing actions via methods that rely on COFO systems oriented toward various function-optimization subgoals.
15. Key practical cognitive algorithms like probabilistic logical inference, evolutionary and probabilistic program learning, agglomerative clustering, greedy pattern mining and activation spreading based attention allocation (used e.g. in the OpenCog AGI design) are represented as COFO systems.
16. The formalization of these key cognitive algorithms in COFO terms is driven by the representation of e.g. logical inference rules, program execution steps and clustering steps as operations within, upon and by distinction metagraphs. This common representation is critical for the practical achievement of cognitive synergy.
17. Practical COFO systems implementing these key cognitive algorithms can be approximatively represented using Galois connections, which – as shown by theorems summarized here – allows them to be approximatively implemented in software via chronomorphisms (folds and unfolds) over typed metagraphs.
18. Algebraic associativity properties of combinatory operations (as represented by edges in typed metagraphs interpreted as programmatic metagraph transformations) play a key role in enabling practical general intelligence given realistic resource constraints. Cost-associativity of combinatory operations underlying cognition is critical for construction of subpattern hierarchies (hierarchical knowledge representation), whereas associativity of combinatory operations underlying COFO representations of cognitive processes is critical for mapping these COFO dynamics into chronomorphisms.
19. The cognitive architecture of human-like intelligences, as articulated via various theories and researches within the cognitive science discipline (and illustrated here in a series of cognitive architecture diagrams), can be viewed as a way of arranging these key cognitive algorithms in an overall DDS configured to operate within the sorts of resource constraints characterizing human brains and bodies
20. Essential properties of AGI knowledge representations and programming languages can be derived from these considerations – this is part of the design process currently being undertaken regarding OpenCog Hyperon.
21. “Consciousness” in AGI systems may be understood as a holistic phenomenon characterized by a number of different properties; human-like consciousness is a particular manifestation of general consciousness which is driven by key properties of human-like cognitive architecture including cognitive synergy and attention focusing.
22. Ethics in AGI systems will take different manifestations as these systems mature in their cognitive capabilities; advanced self-reflecting and self-modifying AGI systems, if appropriately designed and educated, should be able to achieve a level of “reflective ethics” beyond what is possible within human brain/mind architecture.
23. Achieving advanced reflective ethics will require the right cognitive architecture (e.g. the GOLEM framework) but also the right situations and interactions during the system’s growth phase, e.g. focus on broadly beneficial goals rather than narrow goals primarily benefiting particular parties.
Conclusion and Future Directions
Ben’s paper reviewed a deep and diverse body of theoretical exploration, carried out over multiple decades in conjunction with a considerable amount of related practical experimentation. The goal of this body of work has been twofold: To understand what general intelligence is and how it works, and to guide the practical implementation of advanced, benevolent generally intelligent software systems. Clearly neither of these goals is quite fulfilled as yet, but we believe we have made significant progress on the theoretical level which – coupled with our extensive prototype experimentation and concurrent advances in compute hardware and scalable data accessibility – may well put us in a position for accelerated coupled progress on the theoretical and implementation/deployment/education levels during the coming years.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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It’s becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman’s Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990’s and 2000’s. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I’ve encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there’s lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar’s lab at UC Irvine, possibly. Dr. Edelman’s roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461
Having been one of the fools who gave Ben money and time, sorry Brian it is not viable it is not coherent it is fancy word soup. NO practical anything has emerged from Ben’s ideas. Lots of money time and talent have been wasted in pursuit of Ben’s AGI webmind. As a 1960’s TV show put it DANGER WILL ROBINSON DANGER.
Discussing the different forms of intelligence is helpful in clarifying the difference between human intelligence and machine intelligence and the difficulty and time required to achieve convergence. Chat GPT is a low level verbal intelligence. We have all experienced university professors who were high on verbal intelligence but could not tie their shoes or pump their own gas. The different forms of intelligence are high lighted by an old adage.
Those who can, do.
Those who can’t, teach.
Those who can’t teach, teach teachers.