Technology

Our approach to AGI is built on carefully selected computational technologies that combine biological plausibility with practical efficiency. These are the core technologies we've identified as essential for building genuine general intelligence.

Vector Symbolic Architectures (VSA), also known as Hyperdimensional Computing (HDC), represent a fundamentally different approach to computation inspired by the high-dimensional, distributed representations found in biological neural systems. Pioneered by researchers like Pentti Kanerva, Ross Gayler, and Tony Plate in the 1990s, VSAs encode information in high-dimensional vectors (typically 10,000+ dimensions) where semantic relationships are preserved through geometric relationships in the vector space. Recent developments have accelerated interest in this field: researchers at UC Berkeley and IBM have demonstrated neuromorphic hardware implementations achieving orders of magnitude improvements in energy efficiency, while work from ETH Zurich and MIT has shown VSAs matching transformer performance on certain tasks while using a fraction of the computational resources. The compositional algebra of VSAs—supporting operations like binding, bundling, and permutation—provides a natural mathematical framework for representing structured knowledge and relationships.

The inherent properties of VSAs directly address multiple AGI building blocks we've identified. Their compositional nature and algebraic operations naturally support analogical reasoning through structure-preserving transformations, allowing patterns learned in one domain to transfer seamlessly to others. As a formal language for internal representation, VSAs offer mathematical precision combined with biological plausibility—the brain's neural codes share many properties with distributed high-dimensional representations. Perhaps most critically, when implemented on appropriate hardware (neuromorphic chips, in-memory computing architectures, or even analog circuits), VSA operations can be extraordinarily energy efficient, consuming milliwatts rather than kilowatts. This efficiency is essential for enabling autonomous thought—continuous cognitive processing becomes tractable when individual operations cost orders of magnitude less than transformer inference. VSAs provide the representational foundation upon which memory systems, reasoning architectures, and learning mechanisms can be built efficiently and at scale.

Vector Symbolic Architectures

Sparse Distributed Memory

Sparse Distributed Memory (SDM), introduced by Pentti Kanerva in his seminal 1988 work, models memory as a vast address space where information is stored in a distributed fashion across many physical locations. Inspired by the structure of the cerebellum and the observation that biological memory operates through sparse activation patterns, SDM provides content-addressable storage where retrieval occurs through similarity rather than exact address matching. Recent research has renewed interest in SDM architectures: neuroscience studies continue to validate sparse coding as a fundamental principle of biological memory, while computer science research has demonstrated efficient hardware implementations. Work from Numenta, inspired by Jeff Hawkins' research on cortical algorithms, has shown how sparse distributed representations enable rapid learning and generalization. The mathematical properties of SDM—particularly its noise tolerance, graceful degradation, and natural handling of similarity-based retrieval—make it an ideal complement to other biologically-inspired computing paradigms.

SDM directly addresses the memory building block essential for AGI, providing automatic encoding and retrieval of information based on relevance and similarity. Unlike database lookups or vector similarity search in RAG systems, SDM operates through genuine content-addressable memory where similar patterns naturally activate related memories without explicit search algorithms. When combined with Vector Symbolic Architectures, SDM becomes extraordinarily powerful: VSA representations serve as natural addresses in the SDM space, and the high-dimensional nature of both systems ensures robust, noise-tolerant storage and retrieval. This combination also offers dramatic computational efficiency advantages—reading from and writing to SDM can be implemented in neuromorphic hardware with minimal energy expenditure, enabling the kind of persistent memory access necessary for continuous autonomous thought. The biological plausibility of SDM suggests we're not just building efficient systems but architectures that mirror successful solutions evolution has already discovered for general intelligence.

Evolutionary and Genetic Algorithms

Evolutionary and genetic algorithms, formalized by John Holland in the 1970s and expanded by researchers like David Goldberg and John Koza, apply the principles of biological evolution—selection, mutation, crossover, and fitness evaluation—to computational problem solving. These algorithms have demonstrated remarkable success in optimization, design, and adaptive system development across countless domains, from antenna design to game-playing strategies to molecular engineering. Recent developments have shown renewed vigor in evolutionary approaches: OpenAI's work on evolution strategies for reinforcement learning, POET (Paired Open-Ended Trailblazer) creating increasingly complex environments and solutions, and quality-diversity algorithms like MAP-Elites discovering diverse solution repertoires rather than single optima. The field has also seen interesting crossover with deep learning, where neuroevolution and evolutionary architecture search have produced novel neural network designs that human engineers might never conceive.

For AGI development, evolutionary algorithms provide a path toward complex adaptive systems that mirrors nature's most successful approach to creating intelligence. While gradient descent optimizes within a differentiable landscape, evolution explores radically different architectures and strategies, potentially discovering solutions that gradient-based methods cannot reach. This is particularly relevant for the self-improvement building block—evolutionary processes operating on AGI architectures themselves could discover novel cognitive capabilities and computational structures beyond those we explicitly design. Evolution also addresses aspects of alignment naturally: fitness functions can encode desired behaviors and values, and the gradual nature of evolutionary change allows for continuous evaluation and course correction. When combined with our other technologies—evolving VSA binding schemes, SDM addressing strategies, or neural network topologies—evolutionary algorithms become tools for discovering optimal implementations of cognitive primitives. Evolution doesn't just optimize parameters; it discovers fundamentally new architectural solutions to intelligence challenges.

Map-Seeking Circuits

Map-Seeking Circuits (MSC), developed by David W. Arathorn in 2002, provide a biologically-inspired computational mechanism for discovering transformation mappings between patterns. The core innovation relies on the "superposition ordering property," a mathematical principle that solves the combinatorial explosion problem that plagued previous approaches to visual computation. Rather than exhaustively testing millions of possible transformations, MSC creates superpositions—essentially multiple exposures of patterns under different transformations—and iteratively converges on the correct mapping through a circuit-based selection process. The algorithm has been formalized as a discrete dynamical system that converges to either a unique transformation mapping or signals "no match found." Arathorn demonstrated MSC's explanatory power across diverse domains including limb-motion planning, perceptual deficits associated with schizophrenia, scene segmentation, and shape determination from view displacement. Recent implementations have shown that MSC can run at real-time rates on graphics processing units, making transformation-invariant object recognition practical for industrial applications.

For perceptually-grounded AGI, Map-Seeking Circuits provide a crucial bridge between raw sensory streams and symbolic cognitive representations. MSC's applicability to any sensory modality—with implementations possible in neuronal form, algorithmic procedures, and analog electronics—means a single architectural principle can handle diverse perceptual inputs. The transformation invariance MSC achieves naturally supports perceptual grounding: objects can be recognized regardless of position, scale, rotation, or viewing angle, mirroring human perceptual capabilities. The combination of MSC with VSA/HDC and SDM creates a powerful pipeline: MSC extracts transformation-invariant features and discovers the mappings between sensory patterns, these features get encoded into VSA representations that preserve relational structure, and SDM stores the patterns for content-addressable retrieval based on similarity. This architecture transforms raw sensory data—pixels, pressure waves, proprioceptive signals—into compositional symbolic structures suitable for analogical reasoning and causal understanding. By combining multiple sensor inputs into unified conceptual representations through MSC processing, we enable the kind of multimodal, transformation-invariant understanding that characterizes biological intelligence.

Reinforcement Learning

Reinforcement learning, formalized by Richard Sutton, Andrew Barto, and others building on earlier work in optimal control and animal learning theory, provides a framework for agents to learn behaviors through interaction with environments and feedback signals. From its theoretical foundations in Markov Decision Processes through modern deep reinforcement learning achievements like AlphaGo and robotic control, RL has demonstrated remarkable success in learning complex behaviors without explicit programming. Recent developments include model-based RL approaches that learn world models for planning, hierarchical RL for temporal abstraction and skill composition, and multi-agent RL for learning in social contexts. Work on intrinsic motivation and curiosity-driven learning has shown how RL agents can autonomously set goals and explore without external reward engineering. The integration of RL with large language models and other architectures has opened new possibilities for combining symbolic reasoning with trial-and-error learning.

Reinforcement learning integrates naturally with our technology stack and addresses multiple AGI building blocks. RL provides the learning mechanism for perceptually-grounded systems—agents learn by interacting with physical environments, developing causal understanding through observing consequences of actions rather than statistical patterns in text. When combined with VSA/HDC representations, RL can operate in compositional state and action spaces where generalization occurs through structural similarity rather than raw perceptual similarity. SDM provides episodic memory for RL agents, storing trajectories and outcomes for rapid retrieval when similar situations arise. The marriage of RL with evolutionary algorithms is particularly powerful: evolution discovers architectures and learning rules while RL optimizes behaviors within those architectures, combining phylogenetic and ontogenetic learning as biology does. For autonomous thought, RL enables internal planning and mental simulation—the agent can "think" by simulating actions and outcomes in learned world models. The reward structures in RL also connect to alignment: properly designed reward functions guide learning toward behaviors consistent with human values, while the continuous feedback loop allows for ongoing refinement and course correction as understanding deepens.

Graph Knowledgebases

Graph-based knowledge representation has a rich history in AI, from semantic networks in the 1960s through modern knowledge graphs like ConceptNet, Cyc, and commercial systems from Google and Microsoft. Graphs provide natural structures for representing entities, concepts, and their relationships, with reasoning occurring through graph traversal and pattern matching. Recent developments in graph neural networks and attention mechanisms have shown how learning and reasoning can operate directly on graph structures, while knowledge graph completion techniques help fill gaps in represented knowledge. Work by researchers like Pedro Domingos on Markov Logic Networks and probabilistic reasoning on graphs has demonstrated how uncertainty can be handled in structured knowledge representations. The field has seen renewed interest as researchers recognize limitations of pure neural approaches and seek ways to incorporate explicit structured knowledge.

For AGI, graph knowledgebases provide the formal language for representing semantic knowledge and supporting causal reasoning. The crucial innovation, as outlined by John Holland and colleagues in "Induction," is the ability to traverse massive graphs using minimal working memory—loading only active nodes into RAM while keeping the vast majority on disk storage. This architecture enables AGI systems with petabytes of knowledge to operate on hardware with mere megabytes of active memory, solving the scalability problem that plagues current approaches. Graph representations align naturally with VSA encodings: nodes and relationships can be represented as high-dimensional vectors, graph traversal becomes vector similarity operations, and SDM can store frequently accessed subgraphs for rapid retrieval. This combination supports all our cognitive building blocks—graphs explicitly represent causal relationships, analogical reasoning operates through structural similarity in graph patterns, and memory becomes navigating through a vast semantic space. The graph structure also supports autonomous thought: background processes can explore connections, discover implications, and identify novel relationships without external prompting. For self-improvement, the AGI can represent and reason about its own architecture as a graph, enabling introspection and modification.

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