As appropriate for an early-stage research field, there is a wide variety of different approaches to AGI in play. Fairly comprehensive reviews have been provided by Wlodek Duch's review paper from the AGI-08 conference; and Alexei Samsonovich's BICA review paper, which compares a number of biologically inspired cognitive architectures in terms of a feature checklist, and was created collaboratively with the creators of the architectures. Hugo de Garis and I also wrote two review papers, one focused on biologically-inspired cognitive architectures and the other on computational neuroscience systems with AGI ambitions. Here I will not try to review the whole field in detail; I will be content with describing the main categories of approaches, and briefly citing a few illustrative examples of each one.

Duch's survey divides existing approaches into three paradigms – symbolic, emergentist and hybrid. Whether this trichotomy has any fundamental significance is somewhat contentious, but it is convenient given the scope of approaches currently and historically pursued, so I will use it to help structure the present brief review of AGI approaches. But I will deviate from Duch in a couple ways: I add one additional category ("universalist"), and I split the emergentist category into multiple subcategories.

A venerable tradition in AI focuses on the physical symbol system hypothesis, which states that minds exist mainly to manipulate symbols that represent aspects of the world or themselves. A physical symbol system has the ability to input, output, store and alter symbolic entities, and to execute appropriate actions in order to reach its goals. Generally, symbolic cognitive architectures focus on "working memory" that draws on long-term memory as needed, and utilize a centralized control over perception, cognition and action. Although in principle such architectures could be arbitrarily capable (since symbolic systems have universal representational and computational power, in theory), in practice symbolic architectures tend to be weak in learning, creativity, procedure learning, and episodic and associative memory. Decades of work in this tradition have not compellingly resolved these issues, which has led many researchers to explore other options.

Perhaps the most impressive successes of symbolic methods on learning problems have occurred in the areas of Genetic Programming (GP), Inductive Logic Programming, and probabilistic learning methods such as Markov Logic Networks (MLN). These techniques are interesting from a variety of theoretical and practical standpoints. For instance, it is notable that GP and MLN have been usefully applied to high-level symbolic relationships, and also to quantitative data resulting directly from empirical observations, depending on how one configures them and how one prepares their inputs. Another important observation one may make about these methods is that, in each case, the ability to do data-driven learning using an underlying symbolic representation, comes along with a lack of transparency in how and why the learning algorithms come up with the symbolic constructs that they do.

Indeed, the complex, somewhat "emergentist" nature of "symbolic" algorithms like GP and MLN provides a worthwhile reminder that the "symbolic vs. subsymbolic" dichotomy, while heuristically valuable for describing the AI and AGI approaches existent at the current time, is not necessarily a clear, crisp, fundamentally grounded distinction. It is utilized here more for its sociological descriptive value, as for its core value as a scientific, mathematical or philosophical distinction.

A few illustrative symbolic cognitive architectures are: • ACT-R is fundamentally a symbolic system, but Duch classifies it as a hybrid system because it incorporates connectionist-style activation spreading in a significant role; and there is an experimental thoroughly connectionist implementation to complement the primary mainly-symbolic implementation. Its combination of SOAR-style "production rules" with large-scale connectionist dynamics allows it to simulate a variety of human psychological phenomena. • Cyc is an AGI architecture based on predicate logic as a knowledge representation, and using logical reasoning techniques to answer questions and derive new knowledge from old. It has been connected to a natural language engine. Cyc's most unique aspect is the large database of commonsense knowledge that Cycorp has accumulated (millions of pieces of knowledge, entered by specially trained humans in predicate logic format); part of the philosophy underlying Cyc is that once a sufficient quantity of knowledge is accumulated in the knowledge base, the problem of creating human-level general intelligence will become much less difficult. • EPIC, a cognitive architecture aimed at capturing human perceptual, cognitive and motor activities through several interconnected processors working in parallel. The system is controlled by production rules for cognitive processor and a set of perceptual and motor processors operating on symbolically coded features. It has been connected to SOAR for problem solving, planning and learning. • ICARUS, an integrated cognitive architecture for physical agents, with knowledge specified in the form of reactive skills, each denoting goal-relevant reactions to a class of problems. It includes a perceptual system, a planning system, an execution system, and several memory systems. • SNePS (Semantic Network Processing System) is a logic, frame and network-based knowledge representation, reasoning, and acting system that has undergone over three decades of development. • SOAR, a classic example of expert rule-based cognitive architecture designed to model general intelligence. It has recently been extended to handle sensorimotor functions and reinforcement learning.

A caricature of some common attitudes for and against the symbolic approach to AGI would be: • For: Symbolic thought is what most strongly distinguishes humans from other animals; it's the crux of human general intelligence. Symbolic thought is precisely what lets us generalize most broadly. It's possible to realize the symbolic core of human general intelligence independently of the specific neural processes that realize this core in the brain, and independently of the sensory and motor systems that serve as (very sophisticated) input and output conduits for human symbol-processing. • Against: While these symbolic AI architectures contain many valuable ideas and have yielded some interesting results, they seem to be incapable of giving rise to the emergent structures and dynamics required to yield humanlike general intelligence using feasible computational resources. Symbol manipulation emerged evolutionarily from simpler processes of perception and motivated action; and symbol manipulation in the human brain emerges from these same sorts of processes. Divorcing symbol manipulation from the underlying substrate of perception and motivated action doesn't make sense, and will never yield generally intelligent agents, at best only useful problem-solving tools.

Another species of AGI design expects abstract symbolic processing – along with every other aspect of intelligence – to emerge from lower-level "subsymbolic" dynamics, which sometimes (but not always) are designed to simulate neural networks or other aspects of human brain function. Today's emergentist architectures are sometimes very strong at recognizing patterns in high-dimensional data, reinforcement learning and associative memory; but no one has yet compellingly shown how to achieve high-level functions such as abstract reasoning or complex language processing using a purely subsymbolic, emergentist approach. There are research results doing inference and language processing using subsymbolic architectures; but these mainly involve relatively simplistic problem cases. The most broadly effective reasoning and language processing systems available are those utilizing various forms of symbolic representations, though often also involving forms of probabilistic, data-driven learning.

A few illustrative subsymbolic, emergentist cognitive architectures are: • DeSTIN is a hierarchical temporal pattern recognition architecture, with some similarities to HTM but featuring more complex learning mechanisms. It has been integrated into the CogPrime architecture to serve as a perceptual subsystem; but is primarily being developed to serve as the center of its own AGI design, assisted via action and reinforcement hierarchies. • Hierarchical Temporal Memory (HTM) is a hierarchical temporal pattern recognition architecture, presented as both an AI/AGI approach and a model of the cortex. So far it has been used exclusively for vision processing, but a conceptual framework has been outlined for extension to action and perception/action coordination. • SAL, based on the earlier and related IBCA, is a large-scale emergent architecture that seeks to model distributed information processing in the brain, especially the posterior and frontal cortex and the hippocampus. So far the architectures in this lineage have been used to simulate various human psychological and psycholinguistic behaviors, but haven't been shown to give rise to higher-level behaviors like reasoning or subgoaling. • NOMAD automata and its successors are based on Edelman's "Neural Darwinism" model of the brain, and feature large numbers of simulated neurons evolving by natural selection into configurations that carry out sensorimotor and categorization tasks. • Ben Kuipers and his colleagues have pursued an extremely innovative research program which combines qualitative reasoning and reinforcement learning to enable an intelligent agent to learn how to act, perceive and model the world. Kuipers' notion of "bootstrap learning" involves allowing the robot to learn almost everything about its world, including for instance the structure of 3D space. • Tsvi Achler has demonstrated neural networks whose weights adapt according to a different methodology than the usual, combining feedback and feedforward dynamics in a particular way, with the result that the weights in the network have a clear symbolic meaning. This provides a novel approach to bridging the symbolic-subsymbolic gap.

There has also been a great deal of work relevant to these sorts of architectures, done without explicit reference to cognitive architectures, under labels such as "deep learning" – e.g. Andrew Ng's well known work applying deep learning to practical vision processing problems, and the work of Tomasso Poggio and his team which achieves deep learning via simulations of visual cortex. And there is a set of emergentist architectures focused specifically on developmental robotics, which we will review below in a separate subsection, as all of these share certain common characteristics. A caricature of some common attitudes for and against the emergentist approach to AGI would be: • For: The brain consists of a large set of simple elements, complexly self-organizing into dynamical structures in response to the body's experience. So, the natural way to approach AGI is to follow a similar approach: a large set of simple elements capable of appropriately adaptive self-organization. When a cognitive faculty is achieved via emergence from subsymbolic dynamics, then it automatically has some flexibility and adaptiveness to it (quite different from the "brittleness" seen in many symbolic AI systems). The human brain is actually very similar to the brains of other mammals, which are mostly involved in processing high-dimensional sensory data and coordinating complex actions; this sort of processing, which constitutes the foundation of general intelligence, is most naturally achieved via subsymbolic means. • Against: The brain happens to achieve its general intelligence via self-organizing networks of neurons, but to focus on this underlying level is misdirected. What matters is the cognitive "software" of the mind, not the lower-level hardware or wetware that's used to realize it. The brain has a complex architecture that evolution has honed specifically to support advanced symbolic reasoning and other aspects of human general intelligence; what matters for creating human-level (or greater) intelligence is having the right information processing architecture, not the underlying mechanics via which the architecture is implemented.

One commonsensical approach to AGI, falling conceptually under the "emergentist" umbrella, would be to use computational neuroscience to create a model of how the brain works, and then to use this model as an AGI system. If we understood the brain more fully, this would be an extremely effective approach to creating the world's first human-level AGI. Given the reality of our currently limited understanding of the brain and how best to digitally simulate it, the computational neuroscience approach to AGI is no panacea, and in fact is almost impossible to pursue – but this is an interesting direction nonetheless. To understand the difficulty of taking this approach to AGI, consider some illustrative examples of contemporary large-scale computational neuroscience projects: • Markram's IBM "Blue Brain Project", which used a "Blue Gene" supercomputer to simulate (at ion channel level of detail) the neural signaling of a cortical column of the rat brain. The long-term goal of the project, now continuing in the EU with a large sum of government funding under the label "Human Brain Project", is to "be able to simulate the full cortex of the human brain". • Modha's IBM "Cognitive Computation Project", aimed at "reverse engineering the structure, function, dynamics and behavior of the human brain, and then delivering it in a small compact form factor consuming very low power". The best publicized achievement has been a simulation of a neural network the size of the "cortex of a cat", with 10⁹ neurons, and 10¹³ synapses. • Boahen's "Neurogrid Project" (at Stanford), involving the creation of custom integrated circuits that emulate the way neurons compute. So far his "neuromorphic engineering" research group has built a silicon retina and a self organizing chip that emulates the way a developing brain wires itself up. • Horwitz's "Large-Scale Brain Modeling" (at the US NIH) initiative, involving simulation of the dynamic assemblage of neural subnetworks performing cognitive tasks, especially those associated with audition and language. Horwitz's simulation work is guided closely by data gathered from brain imaging using fMRI, PET, and MEG. • Izhikevich's and Edelman's "Large Scale Model of Thalamocortical Systems", a simulation on a scale similar to that of the full human brain itself. By simulating the spiking and plasticity features of the neural cortex, they managed to reproduce certain special features of the brain, such as initial states sensitivity, brain wave propagation, etc. Their model was used to simulate a million spiking neurons consisting of multiple compartments, joined by a half billion synapses. • Just's "4CAPS" cognitive architecture, a hybrid of a computational neuroscience model and a symbolic AI system, intended to explain both behavioral and neuroimaging data.

These are all fantastic projects; however, they embody a broad scope of interpretations of the notion of "simulation" itself. Different researchers are approaching the task of large-scale brain simulation with very different objectives in mind, e.g. 1. Creating models that can actually be connected to parts of the human brain or body, and can serve the same role as the brain systems they simulate. 2. Creating a precise functional simulation of a brain subsystem, i.e. one that simulates the subsystem's internal dynamics and its mapping of inputs to outputs with adequate fidelity to explain exactly what the brain subsystem does to control the organism. 3. Creating models that quantitatively simulate the generic behavior and internal dynamics of a certain subsystem of the brain, but without precisely functionally simulating that subsystem. 4. Creating models that qualitatively simulate brain subsystems or whole brains at a high level, without simulating the particular details of dynamics or I/O, but with a goal of exploring some of the overall properties of the system. 5. Creating models that demonstrate the capacity of hardware to simulate large neural models based on particular classes of equations, but without any claims about the match of the models to empirical neuroscience data.

All of the above are validly called "large scale brain simulations", yet they constitute very different forms of research. Simulations in the first and fifth category are adequate to serve as components of AGI systems. Simulations in the other categories are useful for guiding neuroscience or hardware development, but are less directly useful for AGI. Now, any one of these simulations, if advanced a little further in the right direction, could become more robustly functional and hence more clearly "AGI" rather than just computational neuroscience. But at the present time, our understanding of neuroscience isn't quite advanced enough to guide the creation of computational neuroscience systems that actually display interesting intelligent behaviors, while still displaying high neural fidelity in their internal structures and dynamics. The bottleneck here isn't really the computational simulation side, but more the neuroscience side – we just haven't gathered the neuroscience data needed to spawn the creation of the neuroscience knowledge and understanding we'd need to drive this sort of AGI approach effectively yet.

Summing up, a caricature of some common attitudes for and against computational neuroscience as an approach to AGI would be: • For: The brain is the only example we have of a system with a high level of general intelligence. So, emulating the brain is obviously the most straightforward path to achieving AGI. Neuroscience is advancing rapidly, and so is computer hardware; so, putting the two together, there's a fairly direct path toward AGI by implementing cutting-edge neuroscience models on massively powerful hardware. Once we understand how brain-based AGIs work, we will likely then gain the knowledge to build even better systems. • Against: Neuroscience is advancing rapidly but is still at a primitive stage; our knowledge about the brain is extremely incomplete, and we lack understanding of basic issues like how the brain learns or represents abstract knowledge. The brain's cognitive mechanisms are well-tuned to run efficiently on neural wetware, but current computer hardware has very different properties; given a certain fixed amount of digital computing hardware, one can create vastly more intelligent systems via crafting AGI algorithms appropriate to the hardware than via trying to force algorithms optimized for neural wetware onto a very different substrate.

Another potential emergentist approach to AGI is to simulate a different type of biology: not the brain, but the evolving ecosystem that gave rise to the brain in the first place. That is: to seek AGI via artificial life. Although Alife itself is a flourishing field, the artificial organisms created so far have been quite simplistic, more like simplified bugs or microscopic organisms than like creatures typically thought of as displaying a high level of general intelligence. Further, given the state of the art, each Alife simulation tends to reach an upper limit of complexity relatively soon; no one has yet managed to emulate the open-ended nature of biological ecosystems. Bruce Damer's Evogrid attempts to break through this logjam directly, via a massive distributed-computing powered use of chemistry simulations, in which evolutionary algorithms are used in an effort to evolve the best possible chemical soups; but this is still early-stage, though initial results are promising. The main limitation of this approach is computational resource related: An ecosystem obviously requires a lot more computing resources than an individual brain or body. At present it's unclear whether we have sufficient computational resources to realize individual human-level minds at feasible cost; simulating a whole ecosystem may be out of reach until a few more Moore's Law doublings have occurred. Although, this isn't a definitive objection, because it may be possible to craft artificial life-forms making exquisitely efficient use of digital computer architecture, or even of quantum computers or other radical new computing fabrics. At any rate, the Alife approach is not a major force in the AGI community at present, but it may surge as readily available computational power increases.

Finally, one subset of emergentist cognitive architectures that I consider particularly important is the developmental robotics architectures, focused on controlling robots without significant "hard-wiring" of knowledge or capabilities, allowing robots to learn (and learn how to learn etc.) via their engagement with the world. A significant focus is often placed here on "intrinsic motivation," wherein the robot explores the world guided by internal goals like novelty or curiosity, forming a model of the world as it goes along, based on the modeling requirements implied by its goals. Many of the foundations of this research area were laid by Juergen Schmidhuber's work in the 1990s, but now with more powerful computers and robots the area is leading to more impressive practical demonstrations.

I mention here a handful of the illustrative initiatives in this area: • Juyang Weng's Dav and SAIL projects involve mobile robots that explore their environments autonomously, and learn to carry out simple tasks by building up their own world-representations through both unsupervised and teacher-driven processing of high-dimensional sensorimotor data. The underlying philosophy is based on human child development, the knowledge representations involved are neural network based, and a number of novel learning algorithms are involved, especially in the area of vision processing. • FLOWERS, an initiative at the French research institute INRIA, led by Pierre-Yves Oudeyer, is also based on a principle of trying to reconstruct the processes of development of the human child's mind, spontaneously driven by intrinsic motivations. Kaplan has taken this project in a practical direction via the creation of a "robot playroom." Experiential language learning has also been a focus of the project. • IM-CLEVER, a new European project coordinated by Gianluca Baldassarre and conducted by a large team of researchers at different institutions, which is focused on creating software enabling an iCub humanoid robot to explore the environment and learn to carry out human childlike behaviors based on its own intrinsic motivations. A caricature of some common attitudes for and against the developmental robotics approach to AGI would be: • For: Young human children learn, mostly, by unsupervised exploration of their environment – using body and mind together to adapt to the world, with progressively increasing sophistication. This is the only way that we know of, for a mind to move from ignorance and incapability to knowledge and capability. • Against: Robots, at this stage in the development of technology, are extremely crude compared to the human body, and thus don't provide an adequate infrastructure for mind/body learning of the sort a young human child does. Due to the early stage of robotics technology, robotics projects inevitably become preoccupied with robotics particulars, and never seem to get to the stage of addressing complex cognitive issues. Furthermore, it's unclear whether detailed sensorimotor grounding is actually necessary in order to create an AGI doing human-level reasoning and learning.

In response to the complementary strengths and weaknesses of the symbolic and emergentist approaches, in recent years a number of researchers have turned to integrative, hybrid architectures, which combine subsystems operating according to the two different paradigms. The combination may be done in many different ways, e.g. connection of a large symbolic subsystem with a large subsymbolic system, or the creation of a population of small agents each of which is both symbolic and subsymbolic in nature. Nils Nilsson expressed the motivation for hybrid AGI systems very clearly in his article at the AI-50 conference. While affirming the value of the "Physical Symbol System Hypothesis" (PSSH) that underlies classical symbolic AI, he argues that the PSSH explicitly assumes that, whenever necessary, symbols will be grounded in objects in the environment through the perceptual and effector capabilities of a physical symbol system. Thus, he continues, "I grant the need for non-symbolic processes in some intelligent systems, but I think they supplement rather than replace symbol systems. I know of no examples of reasoning, understanding language, or generating complex plans that are best understood as being performed by systems using exclusively non-symbolic processes.... AI systems that achieve human-level intelligence will involve a combination of symbolic and non-symbolic processing."

Hybrid architectures are often designed to leverage (hypothesized or empirically observed) "whole is greater than the sum of the parts" phenomena arising when multiple components are appropriately connected. This is philosophically related to the emergence phenomena at the conceptual heart of many subsymbolic architectures. The concept of "cognitive synergy" is formulated to capture this idea; it is conjectured that human-level AGI intrinsically depends on the synergetic interaction of multiple components (for instance, as in the CogPrime design, multiple memory systems each supplied with its own learning process). A few illustrative hybrid cognitive architectures are: • CLARION is a hybrid architecture that combines a symbolic component for reasoning on "explicit knowledge" with a connectionist component for managing "implicit knowledge." Learning of implicit knowledge may be done via neural net, reinforcement learning, or other methods. Learning in the symbolic and subsymbolic portions is carried out separately rather than dynamically coupled. • CogPrime, an AGI approach developed by Goertzel and colleagues, and being implemented within the OpenCog open source AI software platform. CogPrime integrates multiple learning algorithms associated with different memory types, using a weighted labeled hypergraph knowledge representation and making heavy use of probabilistic semantics. The various algorithms are designed to display "cognitive synergy" and work together to achieve system goals. • DUAL is arguably the most impressive system to come out of Marvin Minsky's "Society of Mind" paradigm. It features a population of agents, each of which combines symbolic and connectionist representation, utilizing population-wide self-organization to collectively carry out tasks such as perception, analogy and associative memory. • LIDA is a comprehensive cognitive architecture heavily based on Bernard Baars' "Global Workspace Theory". It articulates a "cognitive cycle" integrating various forms of memory and intelligent processing in a single processing loop. It deals most thoroughly with "lower level" aspects of intelligence; the handling of more advanced aspects like language and reasoning in LIDA has not yet been worked out in detail. • MicroPsi is an integrative architecture based on Dietrich Dorner's Psi model of motivation, emotion and intelligence. Similar to LIDA, MicroPsi currently focuses on the "lower level" aspects of intelligence, not yet directly handling advanced processes like language and abstract reasoning. • PolyScheme integrates multiple methods of representation, reasoning and inference schemes for general problem solving. Each Polyscheme "specialist" models a different aspect of the world using specific representation and inference techniques, interacting with other specialists and learning from them. • Shruti is a biologically-inspired model of human reflexive inference, which uses a connectionist architecture to represent relations, types, entities and causal rules using focal-clusters. • James Albus's 4D/RCS robotics architecture shares a great deal with some of the emergentist architectures, e.g. it has the same hierarchical pattern recognition structure as DeSTIN and HTM, and shares with the developmental robotics architectures a focus on real-time adaptation to the structure of the world. However, 4D/RCS is not foundationally learning-based but relies on hard-wired architecture and algorithms.

The nature of integration between components varies among the hybrid architectures. Some of them are in essence, multiple, disparate algorithms carrying out separate functions, encapsulated in black boxes and communicating results with each other. For instance, PolyScheme, ACT-R and CLARION all display this "modularity" property to a significant extent. On the other hand, architectures such as CogPrime, DUAL, Shruti, LIDA and MicroPsi feature richer integration – which makes their dynamics more challenging to understand and tune. A caricature of some common attitudes for and against the hybrid approach to AGI would be: • For: The brain is a complex system with multiple different parts, architected according to different principles but all working closely together; so in that sense, the brain is a hybrid system. Different aspects of intelligence work best with different representational and learning mechanisms. If one designs the different parts of a hybrid system properly, one can get the different parts to work together synergetically, each contributing its strengths to help overcome the others' weaknesses. Biological systems tend to be messy, complex and integrative; searching for a single "algorithm of general intelligence" is an inappropriate attempt to project the aesthetics of physics or theoretical computer science into a qualitatively different domain. • Against: Gluing together a bunch of inadequate systems isn't going to make an adequate system. The brain uses a unified infrastructure (a neural network) for good reason; when you try to tie together qualitatively different components, you get a brittle system that can't adapt that well, because the different components can't work together with full flexibility. Hybrid systems are inelegant, and violate the "Occam's Razor" heuristic.

A school of AGI research that doesn't fit neatly into any of the three categories reviewed above (symbolic, emergentist, hybrid) is what I call the "universalist approach". In this approach, one starts with AGI algorithms that would yield incredibly powerful general intelligence if supplied with massively, unrealistically much computing power; and then one tries to "scale them down," via adapting them to work using feasible computational resources. Historically, the roots of this approach may be traced to Solomonoff's pioneering work on the theory of induction. The paradigm case of a universalist AGI approach is Marcus Hutter's AIXI system, which is based on the following simple concepts: • An AGI system is going to be controlled by some program • Instead of trying to figure out the right program via human wizardry, we can just write a "meta-algorithm" to search program space, and automatically find the right program for making the AGI smart, and then use that program to operate the AGI • We can then repeat this meta-algorithm over and over, as the AGI gains more data about the world, so it will always have the operating program that's best according to all its available data

Marcus Hutter has proved that the AIXI system, which works basically as described in the above list, would be maximally generally intelligent, if the latter is defined appropriately in terms of maximizing computable reward functions in computable environments. The catch is that AIXI requires infinite processing power. But there's another version, AIXI(tl), that requires only an infeasibly massive finite amount of computing power. Juergen Schmidhuber's Goedel Machine operates differently in detail, but the concept is similar. At each step of the way, it takes the action that it can prove, according to its axiom system and its perceptual data, will be the best way to achieve its goals. Like AIXI, this is uncomputable in the most direct formulation, and computable but probably intractable in its most straightforward simplified formulations. These theoretical approaches suggest a research program of "scaling down from infinity", and finding practical, scalable ways of achieving AGI using similar ideas. Some promising results have been obtained, using simplified program space search to solve various specialized problems. But whether this approach can be used for human-level AGI, with feasible resource usage, remains uncertain. It's a gutsy strategy, setting aside particularities of the human mind and brain, and focusing on what's viewed as the mathematical essence of general intelligence.

A caricature of some common attitudes for and against the program search approach to AGI would be: • For: The case of AGI with massive computational resources is an idealized case of AGI, similar to assumptions like the frictionless plane in physics, or the large population size in evolutionary biology. Now that we've solved the AGI problem in this simplified special case, we can use the understanding we've gained to address more realistic cases. This way of proceeding is mathematically and intellectually rigorous, unlike the more ad hoc approaches typically taken in the field. And we've already shown we can scale down our theoretical approaches to handle various specialized problems. • Against: The theoretical achievement of advanced general intelligence using infinitely or unrealistically much computational resources, is a mathematical game which is only minimally relevant to achieving AGI using realistic amounts of resources. In the real world, the simple "trick" of exhaustively searching program space until you find the best program for your purposes, won't get you very far. Trying to "scale down" from this simple method to something realistic isn't going to work well, because real-world general intelligence is based on various complex, overlapping architectural mechanisms that just aren't relevant to the massive-computational-resources situation.