OpenCog Hyperon

Atomspace MetaGraph

The Atomspace is a flexible and dynamic knowledge repository, which enables Hyperon to organize and access a variety of knowledge of various types (declarative , linguistic, mathematical, procedural, attentional, goal-related, sensory, motoric, emotional, intuitive, etc.) The Atomspace Metagraph is a weighted labeled graph-like structure used by Hyperon to represent and store data. Atomspace lives in RAM for use in real-time cognitive processing, and also lives on disk and in distributed form across multiple machines, thanks to the Distributed Atomspace implementation.

MeTTa Programming Language

MeTTa (Meta Type Talk) is a novel programming language designed specifically to meet the needs of AGI. It is intended both for humans to script portions of AGI cognitive processes, and for the programming activity of AGI-related learning and reasoning algorithms themselves.

MeTTa programs combine elements of functional, logical and process-calculus-based programming. Each MeTTa program is represented as a subgraph of an Atomspace metagraph, and carries out its business centrally by querying and rewriting portions of Atomspaces.

MeTTa handles highly abstract constructs like run-time self-modifying code simply and naturally, but is also designed for efficiency both in running its own natively implemented AGI-related algorithms, and in interfacing with external processes invoking non-OpenCog AI algorithms such as deep neural nets or SAT solvers.

PRIMUS

​PRIMUS is a cognitive architecture proposed within the OpenCog AGI framework, aiming to create a human-level Artificial General Intelligence (AGI) capable of self-modification towards Artificial Superintelligence (ASI). It serves as a complex and coherent design to overcome challenges in developing AGI systems.

The architecture has partially motivated the development of OpenCog Hyperon, an infrastructure designed to support such advanced cognitive systems.

While PRIMUS provides a comprehensive proposal, its complexity means that some aspects are only partially specified, leading to ongoing research and development to implement its components effectively.​ Source: ActPC-Chem: Discrete Active Predictive Coding

for Goal-Guided Algorithmic Chemistry as a Potential Cognitive Kernel for Hyperon & PRIMUS-Based AGI

Probabilistic Logic Networks (PLNs)

Economic Attention Network (ECAN)

An Economic Attention Network (ECAN) is a conceptual framework within the field of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) that seeks to optimize the allocation of attention—a finite cognitive resource—within economic systems. The idea is to manage and direct attention efficiently to enhance decision-making, productivity, and overall economic well-being.​

Key Aspects of ECAN in AGI and ASI Contexts:

• Attention as an Economic Resource: Recognizes attention as a scarce commodity in the digital age, necessitating intelligent systems to prioritize and manage information effectively.​
• Integration with AGI Systems: Incorporates AGI capabilities to analyze vast amounts of data, identify patterns, and direct human attention to areas of high importance or interest, thereby improving efficiency.​

• Personalization and Relevance: Utilizes AGI to tailor information delivery based on individual preferences and behaviors, ensuring that users receive content most pertinent to their needs.​

• Ethical Considerations: Addresses concerns about manipulation and privacy by implementing transparent algorithms and allowing user control over attention-directing mechanisms.​

By leveraging AGI, an ECAN aims to create a more balanced and effective distribution of attention across various sectors, enhancing both individual experiences and collective economic outcomes.

Meta-Optimizing Semantic Evolutionary Search (MOSES)

Non-Axiomatic Reasoning System (NARS)

​A Non-Axiomatic Reasoning System (NARS) is an approach to artificial intelligence that aims to enable systems to reason and learn in environments characterized by insufficient knowledge and resources. Unlike traditional AI systems that rely on predefined axioms and complete information, NARS is designed to function effectively under uncertainty and incomplete data, making it a potential framework for developing Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI).​

Key Features of NARS in AGI and ASI Development:

• Adaptive Learning: NARS continuously updates its knowledge base and reasoning strategies based on new information, allowing it to adapt to changing environments—a crucial aspect for AGI systems that must operate across diverse domains.​
• Unified Framework: By integrating various cognitive functions such as learning, reasoning, and decision-making into a single system, NARS aligns with the goals of AGI to replicate the breadth of human intelligence.​
• Resource-Bounded Reasoning: NARS operates effectively within limited computational resources, a necessary feature for scalable AGI and ASI systems.​

Incorporating NARS into AGI and ASI research offers a pathway toward creating systems capable of autonomous learning and reasoning in real-world scenarios, moving beyond the constraints of traditional, axiom-based AI methodologies.

Sources: Modeling Arbitrarily Applicable Relational Responding with the Non-Axiomatic Reasoning System: A Machine Psychology Approach, Machine Psychology: Integrating Operant Conditioning with the Non-Axiomatic Reasoning System for Advancing Artificial General Intelligence Research

Scalable

Fit for rapid, vertical growth without sacrificing performance or consuming additional resources.

UX-Focused

Opencog Hyperon offers high usability and a short learning curve, aiming to make your job as easy as possible.

Elegant

Stylish, compact, and simple to optimize your work, save you time, and improve the final result.

AGI-Specific

Designed exclusively to help developers achieve human-level artificial intelligence and beyond.

Versatile

Doesn’t matter what you’re building — OpenCog can offer you the tools to do it.

Exhaustive