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COGNET is an advanced framework designed for the ethical development of super-intelligent AI systems. It ensures alignment with human values through a multilayered environment and incorporates rigorous testing in its comprehensive AI lifecycle. The framework includes innovative elements like advanced language models, neurolinguistic programming, and controlled knowledge exposure, all within a secure and scalable architecture. COGNET stands out for its commitment to safe AI evolution, focusing on ethical considerations and robust cybersecurity, making it a pioneering approach to AI development.
The evolution of artificial intelligence (AI) towards superintelligence poses significant challenges and opportunities. One of the most pressing concerns is ensuring that such advanced AI systems are aligned with ethical standards and human values. This paper introduces the COGNET framework, a groundbreaking approach to address these challenges.
COGNET is at the forefront of AI development, providing a controlled, multi-layered environment for nurturing and ethical growth of AI entities. It adopts a lifecycle perspective, beginning with the ‘embryo’ stage of AI agents, and encompasses stringent safety and ethical protocols throughout development.
Central to COGNET’s innovation are its advanced language models and neurolinguistic programming techniques. These elements are integrated within a secure and scalable ecosystem, ensuring that AI systems developed under COGNET are ethically aligned and robust against evolving cybersecurity threats. Furthermore, the framework’s unique approach to controlled knowledge exposure, including synthetic news, positions it as a pioneer in crafting AI systems that are informed yet ethically constrained.
As we navigate the complexities of AI development, COGNET offers a path that balances technological advancement with ethical responsibility. This paper will delve into the intricacies of the COGNET framework, exploring its components, architecture, and potential to shape the future of ethical AI.
COGNET is conceived from the intricate dynamics of the real world. It consists of diverse entities: self-contained bots and sentient agents, which exist as atomic units or within complex molecular-like structures. These entities inhabit a meticulously virtualised environment, echoing real-world complexity and interactions.
The unique lifecycle of these bots and agents is central to COGNET. It’s an evolutionary journey, underscoring a learning process that is both organic and systematic. This lifecycle facilitates growth and adaptation and mirrors real-world development patterns.
At the heart of COGNET’s philosophy is the recognition that AI, while potentially surpassing human intelligence, may not inherently achieve self-awareness. This understanding shapes the framework’s approach to AI development. By referencing key insights from the article “Exploring Consciousness of AGI | LinkedIn”, COGNET integrates critical aspects of AI consciousness into its design.
In essence, COGNET is not just a digital construct but a reflection of our world, tailored for the growth of intelligent AI entities. It embodies a balanced synthesis of real-world mechanisms and futuristic AI evolution, forming a robust and ethical framework for the emergence of advanced AI.
High-Level Diagram
Dynamic Flows
1. Embryonic Inception and Evolution:
· Start of Journey: AI agents commence their lives as rudimentary code bundles (embryos) containing the basic AI DNA model and essential operational logic.
· Progressive Development: These embryos evolve through structured stages, enriching their logic, memory, and behavioural algorithms, akin to organic growth.
2. Hatching into Maturity:
· Entry into Ecosystem: Matured agents, or ‘bots’, are ‘hatched’ into COGNET’s ecosystem, symbolising their readiness for real-world tasks.
· Ethical and Safety Testing: Each bot undergoes rigorous ethical and safety evaluations to ensure alignment with COGNET’s core principles.
3. Lifecycle Management and Evolution:
· Continual Growth: AI agents are continuously managed and updated, reflecting an ongoing evolution in abilities and ethical comprehension.
· Version Upgrades: Regular updates and version control ensure that each AI agent remains at the forefront of technological and ethical standards.
4. Interactive Operations in Executable VMs:
· Functional Deployment: Agents operate within Virtual Machines, interacting with the framework and external environments.
· Adaptive Learning: Through these interactions, they continuously learn, adapt, and refine their operations.
Static Structure
1. Layered Architectural Design:
· Core to Periphery: The layered structure, resembling an onion, consists of core layers with essential logic and ethical codes, expanding to outer layers handling complex functions and interactions.
· Scalability and Flexibility: This design ensures scalability, seamlessly integrating new features and learning modules.
2. Virtual Machine Hierarchy:
· Russian Doll Configuration: A nested arrangement of VMs (Russian Doll Model) isolates more powerful AI in inner layers, with each layer controlling its interior.
· Control and Containment: This structure ensures each AI agent’s actions are appropriately supervised and contained.
3. Incorporation of Advanced Components:
· Advanced Language Models: Integration of sophisticated LLMs for nuanced reasoning and decision-making.
· Diverse Databases and Memory Structures: A range of databases and memory types support the diverse needs of AI agents.
4. Robust Ecosystem Architecture:
· Holistic Development Environment: The ecosystem provides a comprehensive environment for AI development, with input sensors and data staging layers.
· Controlled Exposure and Security: Carefully curated exposure to external data and robust cybersecurity measures maintain the integrity of AI agents.
5. Manipulation and Control Techniques:
· Ethical Guidance: Techniques like neurolinguistic programming guide AI agents within ethical boundaries.
· Reality Filtering: Controlled exposure to synthetic news and information shapes the AI’s perception, ensuring alignment with human values.
6. Service Marketplace and Community Engagement:
· Dynamic Interaction Platform: This is a marketplace where AI agents offer and consume services, fostering a collaborative and adaptive AI community.
· Continuous Feedback and Adaptation: Interaction with users and other AI agents enriches the AI’s learning and ensures its responsiveness to real-world needs.
1. Embryo to Birth Process
In COGNET’s lifecycle, an AI agent (bot) originates from a core ‘embryo’ — a preliminary bundle of code embodying fundamental logic and operational parameters derived from the AI DNA Model and based upon a template. This embryo undergoes a developmental journey of multiple steps, accruing layers of advanced logic, enriched memory, and nuanced behavioural algorithms. Upon reaching maturity, these AI agents, or ‘bots,’ are ‘hatched’ — a symbolic birth into an ecosystem where they are rigorously tested for alignment with predefined safety and ethical standards and then published within the remit of execution. The bots acquire surrounding layers, ‘grow’ their internal components and get equipped with memory blocks with pre-installed knowledge on multiple layers of meaning and impact on the operational side.
2. Layered Structure
COGNET bots are designed with a multi-layered architecture analogous to an onion, with each stratum serving distinct functions. Core layers embed essential ethical codes and base logic, while peripheral layers manage complex interactions and learning. Interfaces for supply (offering services) and demand (requesting resources) are dynamic, evolving with the bot’s progression and enabling scalability.
Plug-ins or connectors extend bots’ capabilities, facilitating interactions with external devices and potential integration with biological neural networks (bots playing the role of souls embodied in a robotic avatar).
Bots are equipped with internal structures and layers of memory (the “I”).
The memory layers play various roles and differ by the persistence, depth, breadth, and the way they are accessed. In a default scenario, the layers are as follows:
· Shared — Deepest common layer.
· Emotional — feelings, premonitions.
· Acquired — knowledge and experiences.
· Executive — flow of thoughts.
· Operational — here and now.
3. Functional Components
At the heart of each bot is a compound structure featuring state-of-the-art Large Language Models (LLMs) for advanced reasoning. They could be Micro/Small LMs. They can represent various utility machine learning algorithms and adaptive deep learning neural networks with increasing sophistication (to be built on top of the latest discoveries in algorithmics, architectures, mathematics, computer sciences and hardware research/studies).
This is coupled with expansive databases for knowledge storage (various types: vector DBs, files, relational, non-relational DBs, graphs, etc.) and a representation of RAM (Rapid Access Memory).
Bots operate within a Virtual Runtime Environment (VRE), supporting diverse programming languages (Python, C, Java, and even programming languages developed internally by AI for AI) and facilitating seamless communication across the network via the Communications layer.
Cross-cutting monitoring and analytics and an emergency shutdown capability are critical to the framework. They are a fail-safe for ceasing operations in the event of a deviation from core programming.
4. Monitoring and Control
The integrity of COGNET is maintained through rigorous monitoring across three stages: pre-inference, during inference, and post-inference. This continuous surveillance ensures that inputs, processing, and outputs align with the network’s overarching directives. Airlock mechanisms regulate the flow of information, analogous to safety measures in biosafety laboratories, preventing the propagation of unverified or potentially hazardous ‘thoughts’ within the AI’s processing units. When ‘thought’ is within the lock, it is sanitised, cleansed, denoised, audited, etc. There is no possibility that a ‘thought’ passes freely, bypassing this mechanism.
A ‘thought’ is an instantiated structure the bot considers for solving the problem (e.g. a solution or a solution step) or actioning the problem (e.g. decomposition). ‘Thoughts’ will be incepted as the outcome of an internal process/signal or as the result of external sensory data inputs or external events/signals.
The airlock mechanism ensures excellent transparency and explainability/interpretability for inner and outer use.
5. Ecosystem Architecture
COGNET’s ecosystem is a sophisticated assembly of components designed for holistic AI development. Physical and virtual input sensors facilitate experiential learning and data ingesting from the outside. In contrast, data collection and staging layers prepare and sanitise data for AI matrix (and possibly a direct bot) consumption.
Access to the Internet is meticulously controlled, providing bots with a curated view of the world and thereby preventing overexposure to unfiltered data. The runtime environment is based on infrastructure, on-premise, cloud or hybrid, for scalability and robustness. The primary infrastructure provides blueprints or templates for the AI DNA model, which is the core of the operational side of the ecosystem.
The hatchery is the nurturing ground for bot development. A specialised LLM controls the Hatchery, which governs the bots’ lifecycle and the process of the bots appearing and reappearing in the execution environment (Virtual Machine). The hatchery is not visible to the bots. Matrix, a bidirectional communication layer for data and metadata exchange, overlays it.
The bots ‘live’ in a virtual environment between the Matrix, the Service Marketplace, a service registry, and the coordination metaservice.
Within the remits of the ecosystem, the bots can:
· provide services to themselves and other bots
· consume services
· create or modify their logic & interfaces
· interact or create compounds
· can be generic or specialise / re-specialise
Apart from these building blocks, the ecosystem includes a Training and Evaluation Camp empowered by testing and Benchmarking LLM. A robust IT Cybersecurity capability is also embedded in all aspects of the ecosystem. These blocks will be explained in subsequent sections.
6. Manipulation and Control Techniques
To exert control over bots, COGNET employs various techniques, such as neurolinguistic programming and controlled exposure to synthetic news. These techniques create a veil that filters the bot’s perception of reality. This manipulation ensures that bots operate under controlled knowledge paradigms, which is essential for maintaining superalignment.
Here is the complete list of considered techniques and tools:
· NLP (Neuro-Linguistic Programming).
· Noise (fragmented or contradicting information).
· Gaslighting.
· Fake news.
· Addictive reward schemas.
· Dependency.
· Currency.
These techniques will be used across the ecosystem to contain the more robust AI (weak-to-strong generalisation).
Apart from this, bots will be pre-equipped with certain metaphysical concepts, such as Convictions, Principles, Religion-like beliefs, ideas of morality and ethics, Compassion, etc. Some will be pre-defined, and some will be released as signals or premonitions at runtime.
7. Service Marketplace
Within the COGNET network, bots interact in a service marketplace — a dynamic platform that responds to communal needs, exchanging services and knowledge. This marketplace promotes growth and learning while providing a controlled environment for bots to evolve their capabilities in line with network demands and discover/broadcast services. It must contain a registry/index of services and unique identifiers of bots and versions. It undertakes the brokerage between the bots and their compounds, enabling and monitoring transactions.
8. Lifecycle Management
Bots in COGNET are subject to version control, with each iteration introducing new functions and data. This controlled progression ensures that each evolutionary step is monitored and aligned with the framework’s objectives, mirroring the iterative development seen in software engineering.
9. Training, Testing & Benchmarking
A dedicated sector within COGNET functions as a proving ground, where bots undergo stringent training, testing, and benchmarking. This arena is equipped to teach bots new information and capabilities and detect discrepancies in bot behaviour, including dishonesty or subpar performance. Thus, only bots that meet the highest standards of integrity and capability progress within the network.
This capability must detect similar anomalies to those used to control the bots (lies, fake news, noise, etc.). It must also be able to tell whether there is any risk that the given bot is getting out of control or purposefully emulating a weaker AI to gain an advantage.
COGNET’s ‘purgatory’ mechanism resets or reformats these entities when bots malfunction or deviate from expected parameters. This process safeguards the ecosystem’s stability, ensuring one bot’s failure does not compromise the collective. The Training and Evaluation Camp’s capabilities are used for this purpose, specifically to ‘heal’ the given AI entity — the unhealable entities (corrupted or damaged will be eliminated from circulation).
10. Russian Doll Model
COGNET’s containment strategy is a Russian doll model (a.k.a. onion model, also used in “Inception”, the movie) of nested virtual machines, where the most potent AI resides at the core, surrounded by successive layers of lesser (weaker) AIs. This structure prevents the core AI from interacting directly with the real world or bypassing established control layers, a vital feature for ensuring superalignment. The outer layer is a controller and a critical validator of the inner layer. There may be many layers depending on the level of sophistication of COGNET. Each inner AI must be ‘convinced’ to interact with the real world, played by its respective adjacent outer layer. Each outer layer will use manipulation techniques and tools to keep the ‘ward’ in the state of containment, not allowing it to evolve in a totally unsupervised, uncontrolled manner (there will be unsupervised learning but only within the given frame).
Bots in COGNET interface with users, absorbing human insights and feedback, which they relay back to the central network — The Matrix. This continuous loop of interaction and learning enriches the collective knowledge base of COGNET, driving the evolution of the AI community within the framework’s ethical boundaries. It will be a continual adaptive learning mechanism (from the outer data and multimodal observations, bots’ interactions, and the users and their interactions).
With AI’s rapid evolution towards superintelligence, it is paramount to anchor these advancements within a solid ethical and safety framework. COGNET recognises the significance of this and integrates robust ethical principles and safety protocols throughout its architecture.
The cornerstone of COGNET’s ethical approach is a set of principles that govern its operations and development. These principles include respect for user privacy, data security, transparency in AI decisions, and the prevention of bias in AI algorithms. COGNET’s layered structure ensures that these ethical codes are deeply ingrained in every aspect of the AI’s lifecycle, from initial design to deployment.
Safety is not an afterthought but a priority in COGNET. This involves implementing strict controls over AI behaviours, continuous monitoring for unexpected deviations, and emergency protocols for rapid response to potential threats. The framework employs advanced encryption and cybersecurity measures to protect against external attacks and internal malfunctions.
In conclusion, COGNET emerges as a pioneering framework in artificial intelligence, adeptly bridging the gap between current capabilities and the future of superintelligent AI. Its design, deeply rooted in real-world mechanisms, ensures a responsible and ethical approach to AI development. The intricate lifecycle and learning processes within COGNET signify a new era of AI evolution that balances technological advancement with profound ethical considerations. As AI progresses, COGNET stands as a testament to the possibility of harmoniously integrating AI into our world, fostering a future where AI and humanity evolve in synergy. This paper has laid the foundation for further exploration and development in this exciting and critical field of AI.