AI Specification Language

The AI Specification Language (AISpec) provides a structured way to define and document AI models. It’s designed to be human-readable and machine-interpretable, enabling clear communication between developers and stakeholders.

Language Basics

The AISpec language utilizes a YAML-based syntax for describing AI models. It follows a hierarchical structure, with each level representing different aspects of the model:

• Model: The top-level element representing the overall AI model.
• Inputs: Specifies the input data required by the model.
• Outputs: Defines the outputs generated by the model.
• Tasks: Describes the specific tasks the model performs.
• Parameters: Lists the configurable parameters and their types.
• Constraints: Sets limits and requirements for the model’s behavior.
• Metrics: Defines the metrics used to evaluate the model’s performance.

Model Definition

The following example demonstrates the basic structure of an AISpec model definition:

model:
            name: "Image Classification Model"
            description: "A model to classify images into predefined categories."
            inputs:
              - type: "image"
                format: "JPEG"
                size: [224, 224]
            outputs:
              - type: "label"
                values: ["cat", "dog", "bird"]
            tasks:
              - type: "classification"
              - type: "object detection"
            parameters:
              - name: "learning_rate"
                type: "float"
                default: 0.001
            constraints:
              - accuracy: "> 90%"
            metrics:
              - type: "accuracy"
          

Defining Inputs and Outputs

The inputs and outputs sections specify the data flowing into and out of the model. This includes defining data types, formats, and specific values:

inputs:
            - type: "text"
              format: "string"
            - type: "audio"
              format: "wav"
              sample_rate: 44100
          outputs:
            - type: "text"
              format: "string"
              length: 100
            - type: "numeric"
              format: "float"
              range: [0, 1]
          

Describing Tasks and Parameters

The tasks section lists the specific tasks the model performs. Parameters, their types, and default values are defined in the parameters section:

tasks:
            - type: "translation"
              from_language: "English"
              to_language: "Spanish"
          parameters:
            - name: "hidden_size"
              type: "integer"
              default: 256
            - name: "dropout_rate"
              type: "float"
              default: 0.1
          

Setting Constraints and Metrics

The constraints section defines the model’s performance requirements, while the metrics section specifies the metrics used to evaluate the model’s performance:

constraints:
            - latency: "< 100ms"
            - memory_usage: "< 1GB"
          metrics:
            - type: "precision"
            - type: "recall"
          

Extending the Language

AISpec is designed to be extensible, allowing users to define custom elements and structures. This enables the specification of specific details relevant to their domain or application.

Example:

model:
            name: "Sentiment Analysis Model"
            description: "A model to analyze the sentiment of text."
            inputs:
              - type: "text"
                format: "string"
            outputs:
              - type: "sentiment"
                values: ["positive", "negative", "neutral"]
            tasks:
              - type: "sentiment_analysis"
            parameters:
              - name: "embedding_size"
                type: "integer"
                default: 128
            constraints:
              - accuracy: "> 85%"
            metrics:
              - type: "f1_score"
              - type: "macro_f1_score"
          

Conclusion

The AI Specification Language provides a comprehensive and structured way to define and document AI models. Its flexibility and extensibility make it suitable for a wide range of applications and domains.