• What is Aspect-Oriented AI

    Tagtal Labs has been committed to evolvable artificial neural software for decades and has made breakthroughs in recent years in AOAI (Aspect-Oriented Artificial Intelligence) and decentralized GPU infrastructure with Proof of Useful Work. In AOAI, neurons in artificial neural networks are modeled as aspects rather than objects. This kind of neural network architecture is highly adaptive and flexible, making the self-evolution of neural networks possible. Along with the high performance achieved by traditional deep neural networks (such as transformers), AOAI becomes the core of AGI.

     

    This new approach allows for more dynamic and adaptive neural network architectures, enhancing their ability to evolve and meet new challenges autonomously. By leveraging the flexibility and modularity offered by AOAI, Tagtal Labs aims to push the boundaries of what AGI can achieve, driving innovation in decentralized AI applications and fostering a more autonomous and efficient cryptocurrency economy.

     

    Separation of Concerns:

    AOP addresses cross-cutting concerns that are typically scattered and tangled across various modules in conventional programming paradigms. By modularizing these concerns into distinct aspects, AOP enhances code maintainability and scalability.

    Aspect

    An aspect in AOP encapsulates behaviors that affect multiple classes into reusable modules. This modularization is analogous to neurons in biological nervous systems, where each neuron handles specific functions independently but works cohesively within a network.

     

     

    Join Point and Pointcut:

    Join points are specific points in the execution of the program, such as method calls or field accesses, where aspects can be applied. Pointcuts define the conditions under which aspects are woven into the main code, enabling dynamic and context-specific behavior modification.

     

     

    Advice

    Advice is the action taken by an aspect at a particular join point. It can alter the program flow by executing additional code before, after, or around the join points.
     

     

  • Application in Neural Network

    Applying AOP to ANNs can significantly enhance their flexibility and evolvability:

     

    Dynamic Adaptation:AOP allows for dynamic changes in the neural network's structure and behavior without modifying the core logic. This capability is essential for creating self-evolving systems that can adapt to new data and environments in real-time.

     

    Improved Modularity:Neural networks implemented with AOP benefit from better modularity, making it easier to isolate and update specific functionalities, such as learning algorithms or activation functions, without affecting the entire system.

     

    Enhanced Maintainability:By separating cross-cutting concerns, such as logging, error handling, and security, into aspects, AOP reduces code redundancy and simplifies the maintenance of large-scale neural networks.

    Biologically-Inspired AI Agent Framework

     

    The biologically-inspired programming paradigm suggests that the nervous system's handling of cross-cutting concerns can be mirrored in software development. The nervous system’s modular approach, where neurons (aspects) handle specific tasks and communicate through synapses (join points), can be applied to create adaptive and evolvable software systems. 

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