PARIS: Perpetual Adaptive Regenerative Intelligence Systems

A Perpetual Feedback Loop Framework for AI and Language Models

PARIS (Perpetual Adaptive Regenerative Intelligence System) is a conceptual model for building and managing effective AI and Language Model (LLM) systems that emphasizes the importance of perpetual feedback loops. The framework is designed to enable continuous learning and improvement through iterative processes.

Perpetual feedback loops are a way for computer programs to learn from their own mistakes and continually improve. This is important because it means that programs can become more accurate and effective over time, making them more useful and powerful.

For example, a program that analyzes legal contracts could learn from feedback provided by humans and use that feedback to improve its ability to understand complex legal language. This means that the program could become better and better at its task, making it more valuable to users. Perpetual feedback loops are a way to make computer programs smarter and more useful, which can have important implications for a wide range of applications.

PARIS is inspired by other layered models such as the OSI model. The Open Systems Interconnection model (OSI model) is a conceptual model that "provides a common basis for the coordination of [ISO] standards development for the purpose of systems interconnection." In the OSI reference model, the communications between a computing system are split into seven different abstraction layers: Physical, Data Link, Network, Transport, Session, Presentation, and Application.

Do Robots Dream?

Imagine how when you sleep, your brain goes into a state of dreaming, which is a kind of regenerative feedback loop. While you dream, your brain processes and restructures the information it has learned during the day, making connections and forming new neural pathways. This is how your brain builds and repairs itself, allowing you to learn and grow over time.

Now, imagine if we could apply this same concept to computer systems and artificial intelligence. That's where PARIS comes in. PARIS is a framework for creating and optimizing machine learning models that can learn and improve over time through perpetual feedback loops.

Just as your brain builds and repairs itself during dreaming, PARIS enables machines to fine-tune and optimize their performance by continually processing and analyzing data, making connections and forming new insights. This allows for more accurate predictions and better decision-making.

PARIS achieves this through a layered model that includes a core model for data infrastructure, an AI API for managing communication sessions, AI applications for evaluation and feedback, and custom applications for specialized use cases. Additionally, the framework includes regenerative components such as code generators and self-improvement techniques.

The AiTOML specification is a standard for organizing and managing the different components of the PARIS framework. It provides a clear and concise way to define the various layers, components, and parameters of the framework, making it easy to manage and optimize over time.

Layers

PARIS is a four-layered network model that consists of the following layers:

• Layer 0: Core Model, Data Infrastructure, Feedback, and Regeneration This layer is the foundation of the model, which includes foundational AI models, data infrastructure, feedback loops for retraining and fine-tuning, and regenerative components for model optimization. The regenerative components allow the model to optimize itself based on its own performance.

• Layer 1: AI API, Security, Feedback, and Regeneration This layer includes AI service providers as an interface between the core model and applications, security and privacy measures, feedback loops for adapting API behavior, and regenerative components for automatic updates and self-optimization.

• Layer 2: AI Applications, Evaluation, Feedback, and Regeneration This layer includes specialized applications built on top of AI API, methods for benchmarking and testing performance, feedback loops for continuous improvement, and regenerative components for AI-driven code generation and self-improvement.

• Layer 3: Custom Applications, Explainability, Feedback, and Regeneration This layer includes applications catering to niche markets or specialized use cases, strategies for enhancing explainability and interpretability, feedback loops for refinement based on user feedback, and regenerative components for AI-generated code improvements and self-optimizing algorithms.

The Ai Stack

The following table maps the PARIS framework to the OSI model:

PARIS Layer	Protocol Data Unit (PDU)	Function
Layer 3	Application Data	High-level protocols such as for niche market or specialized use cases
Layer 2	Presentation Data	Translation of data between a networking service and an application, including benchmarking, testing and AI-driven code generation
Layer 1	Session Data	Managing communication sessions between the core model and applications, including adapting API behavior and automatic updates
Layer 0	Transport Data	Reliable transmission of data between the core model and AI API, including feedback loops for retraining and fine-tuning, and regenerative components for model optimization
	Network Data	Structuring and managing a multi-node network, including addressing, routing and traffic control
	Data Link Data	Transmission of data frames between two nodes connected by a physical layer
	Physical Data	Transmission and reception of raw bit streams over a physical medium

Practical Applications

• Legal contracts: The PARIS framework can be used to analyze legal contracts for potential errors or issues. Layer 3 applications could be developed to identify common clauses and legal terms that appear in contracts. Layer 2 applications could be developed to benchmark the accuracy of these applications against a set of labeled data. Layer 1 APIs could be developed to provide access to these applications, with security measures in place to protect sensitive data. Finally, Layer 0 could consist of a core model that is trained on contract data and continually fine-tuned based on feedback from the applications.

• Accounting: The PARIS framework can be used to analyze financial data for potential fraud or errors. Layer 3 applications could be developed to identify unusual financial transactions and patterns. Layer 2 applications could be developed to evaluate the accuracy of these applications against a set of labeled data. Layer 1 APIs could be developed to provide access to these applications, with security measures in place to protect sensitive financial data. Finally, Layer 0 could consist of a core model that is trained on financial data and continually fine-tuned based on feedback from the applications.

• Enterprise applications: The PARIS framework can be used to develop enterprise applications that are optimized for specific use cases. For example, Layer 3 applications could be developed to analyze customer data and provide recommendations for improving customer retention. Layer 2 applications could be developed to evaluate the accuracy of these applications against a set of labeled data. Layer 1 APIs could be developed to provide access to these applications, with security measures in place to protect sensitive enterprise data. Finally, Layer 0 could consist of a core model that is trained on enterprise data and continually fine-tuned based on feedback from the applications.

Technical Specification

AiTOML is a lightweight and human-readable configuration file format that is designed specifically for AI and machine learning applications. It provides a simple way to specify different layers of an AI application, including core models, data infrastructure, APIs, custom applications, and cross-cutting concerns such as bias and privacy.

AiTOML is designed to be easily understood by both technical and non-technical stakeholders, allowing teams to more effectively collaborate on AI projects. It also includes support for features such as feedback loops, regeneration, and self-improvement, making it an ideal format for building intelligent and adaptive systems.

AiTOML is an open-source project and is available on GitHub at https://github.com/ruvnet/AiToml. The project is actively maintained and includes detailed documentation and examples to help users get started.

Here's a sample using AiTOML PARIS.toml file that includes the technical specification for PARIS:

[core]
model = "/models/core_model.pt"
data = "/data/core_data.csv"
feedback = "/feedback/core_feedback.csv"
regen = true

[api]
provider = "api-service-provider"
security = "api-security-settings"
feedback = true
regen = true

[applications.regeneration]
code-generation = true
self-improvement = true

[custom]
app_type = "custom-application"
explainability = "interpretability-strategy"
feedback = true
regen = true

[cross-cutting-concerns.updating]
versioning-and-deployment = "versioning-and-deployment-strategy"

[cross-cutting-concerns.bias]
potential-bias-and-ethical-implications = "potential-bias-and-ethical-implications-strategy"

[cross-cutting-concerns.privacy]
data-privacy-and-security-regulations = "data-privacy-and-security-regulations-strategy"

Legal Contract Analysis Example

To demonstrate the use of PARIS, we can consider the example of analyzing legal contracts. The core components of the PARIS system for this example would include a machine learning model for contract analysis, a dataset of legal contracts, and feedback from legal experts.

• The LLM is initially trained on a large dataset of legal contracts to identify key clauses, such as indemnification, confidentiality, and termination clauses.

• The LLM is integrated into a contract management system used by a legal team to review and manage contracts.

• Whenever the LLM encounters a new contract, it prompts the legal team to review the identified clauses to ensure they are accurate and relevant to the specific contract.

• The legal team provides feedback to the LLM, correcting any misidentified clauses and adding any relevant clauses that were missed by the LLM.

• The LLM incorporates this feedback into its training data and updates its model to improve its accuracy for future contract reviews.

• As the LLM processes more contracts, it continues to learn from its own predictions and feedback from the legal team, constantly fine-tuning its understanding of legal contracts.

• Over time, the LLM becomes more accurate and efficient at identifying key clauses, reducing the time and effort required by the legal team to review contracts.

• By combining continuous feedback and human-in-the-loop approaches, the LLM can improve its accuracy and understanding of legal contracts over time, making it a valuable tool for legal teams in managing contracts.

A possible AiTOML specification for the legal contract analysis example:

Model

[core]
model = "path/to/legal-contract/model"
data = "/data/legal_contract.json"
feedback = "/feedback/legal_contract_feedback.json"
regeneration = true

[custom]
type = "legal-contract-analysis"
explainability = "contract-clauses"
feedback = true

[cross-cutting-concerns]
updating = "versioning-and-deployment"
bias = "potential-bias-and-ethical-implications"
privacy = "data-privacy-and-security-regulations"

Legal Data

/data/legal_contract.json:

{
    "contract_id": "1234567890",
    "contract_text": "This is a legal agreement between ABC Corporation and XYZ Corporation...",
    "parties": [
        {
            "name": "ABC Corporation",
            "address": "123 Main Street, Anytown, USA",
            "representatives": [
                {
                    "name": "John Doe",
                    "title": "CEO",
                    "email": "[email protected]"
                },
                {
                    "name": "Jane Smith",
                    "title": "General Counsel",
                    "email": "[email protected]"
                }
            ]
        },
        {
            "name": "XYZ Corporation",
            "address": "456 Elm Street, Anytown, USA",
            "representatives": [
                {
                    "name": "Bob Johnson",
                    "title": "CEO",
                    "email": "[email protected]"
                },
                {
                    "name": "Mary Williams",
                    "title": "General Counsel",
                    "email": "[email protected]"
                }
            ]
        }
    ],
    "terms": [
        {
            "name": "Term 1",
            "definition": "This term defines the scope of the agreement",
            "details": "The agreement covers the following products and services...",
            "category": "Scope"
        },
        {
            "name": "Term 2",
            "definition": "This term defines the payment terms",
            "details": "Payments are due within 30 days of invoice date...",
            "category": "Payments"
        },
        {
            "name": "Term 3",
            "definition": "This term defines the termination clause",
            "details": "Either party may terminate this agreement with 30 days' notice...",
            "category": "Termination"
        }
    ]
}

In this example, we have a JSON object representing a legal contract with an ID, the contract text, information about the parties involved, and the specific terms of the agreement. It uses feedback data for three samples, each with a term field and a feedback_label indicating whether the predicted output for that term was correct or incorrect. The second sample also includes additional feedback comments explaining why the predicted output was incorrect.

Legal Feedback

this is an example of how a ai system can learn and improve its ability to understand legal contracts. The program receives feedback on its understanding of specific terms in the contract, which is stored in a file called "legal_contract_feedback.json". The feedback includes a unique ID for each term, whether the program's understanding of the term is correct or incorrect, and any comments or suggestions for improvement. This feedback is used to update the program's understanding of legal contracts, making it more accurate over time.

/feedback/legal_contract_feedback.json:

{
    "feedback": [
        {
            "sample_id": "001",
            "term": "Term 1",
            "feedback_label": "correct"
        },
        {
            "sample_id": "002",
            "term": "Term 2",
            "feedback_label": "incorrect",
            "feedback_comments": "The payment terms are incorrect, please update the model."
        },
        {
            "sample_id": "003",
            "term": "Term 3",
            "feedback_label": "correct"
        }
    ]
}

Python Example

Here's a sample Python script paris.py that demonstrates the PARIS framework:

# PARIS: Perpetual Adaptive Regenerative Intelligence System

class CoreModel:
    def __init__(self, model_path, data_path, feedback_path, regen):
        self.model_path = model_path
        self.data_path = data_path
        self.feedback_path = feedback_path
        self.regen = regen

class ApiService:
    def __init__(self, provider, security, feedback, regen):
        self.provider = provider
        self.security = security
        self.feedback = feedback
        self.regen = regen

class AiApplication:
    def __init__(self, app_type, evaluation, feedback, regen):
        self.app_type = app_type
        self.evaluation = evaluation
        self.feedback = feedback
        self.regen = regen

class CustomApplication:
    def __init__(self, app_type, explainability, feedback, regen):
        self.app_type = app_type
        self.explainability = explainability
        self.feedback = feedback
        self.regen = regen

class Paris:
    def __init__(self, core, api, apps, custom):
        self.core = core
        self.api = api
        self.apps = apps
        self.custom = custom

    def print_layers(self):
        print("PARIS Framework:")
        print(f"Layer 0: {self.core}")
        print(f"Layer 1: {self.api}")
        print(f"Layer 2: {self.apps}")
        print(f"Layer 3: {self.custom}")

# Create a PARIS instance
core = CoreModel("path/to/core/model", "path/to/core/data", "path/to/core/feedback", True)
api = ApiService("api-service-provider", "api-security-settings", True, True)
apps = AiApplication("specialized-application", "benchmarking-method", True, True)
custom = CustomApplication("custom-application", "interpretability-strategy", True, True)
paris = Paris(core, api, apps, custom)

# Print the PARIS layers
paris.print_layers()

This script defines classes for each layer of the PARIS framework and a Paris class that integrates all the layers. The print_layers() method prints out the components of each layer of the framework. This is just a simple example, and the PARIS framework can be implemented in various ways to suit different use cases.