Turbulance DSL Integration

The Turbulance Domain-Specific Language provides semantic processing capabilities for structured visual understanding requirements. This integration allows users to express complex visual analysis tasks through structured scientific experiment syntax.

Overview

Turbulance syntax enables users to:

• Define hypotheses for visual understanding tasks
• Specify semantic validation requirements
• Express probabilistic reasoning constraints
• Structure complex analysis workflows

Syntax Elements

Hypothesis Definition

hypothesis ImageAnalysisTask:
    claim: "Image contains specific features"
    semantic_validation:
        - feature_understanding: "can identify target features"
        - contextual_understanding: "can understand feature relationships"
    requires: "validated_visual_comprehension"

Item Processing

item analysis = understand_image("input.jpg", 
    confidence_threshold: 0.95,
    reconstruction_validation: true
)

Conditional Processing

given analysis.understanding_level >= "excellent":
    perform_detailed_analysis(analysis)
otherwise:
    delegate_to_autobahn(analysis)

Python Integration

Script Execution

import helicopter.turbulance as turb

# Execute Turbulance script
results = turb.execute_script("analysis.trb")

# Access results
understanding_level = results['understanding_level']
quality_metrics = results['quality_metrics']

Direct API Integration

from helicopter.turbulance import TurbulanceCompiler

compiler = TurbulanceCompiler()
compiled_task = compiler.compile_hypothesis(
    claim="Medical scan analysis",
    validation_requirements=["anatomical_accuracy", "pathological_detection"]
)

results = engine.execute_compiled_task(compiled_task)

Use Cases

Medical Image Analysis

hypothesis RadiologyAnalysis:
    claim: "Chest X-ray contains pathological indicators"
    semantic_validation:
        - anatomical_recognition: "can identify lung structures"
        - pathology_detection: "can detect abnormalities"
        - diagnostic_reasoning: "can correlate findings"
    requires: "medical_grade_understanding"

item scan_analysis = understand_medical_image("chest_xray.dcm")
given scan_analysis.confidence >= 0.95:
    generate_diagnostic_report(scan_analysis)

Autonomous Vehicle Vision

hypothesis TrafficSceneAnalysis:
    claim: "Traffic scene contains navigation-relevant objects"
    semantic_validation:
        - object_detection: "can identify vehicles, pedestrians, signs"
        - spatial_understanding: "can determine object positions"
        - temporal_reasoning: "can predict object movements"
    requires: "safety_critical_understanding"

item scene = understand_traffic_scene("camera_feed.jpg")
given scene.safety_confidence >= "maximum":
    execute_navigation_decision(scene)
otherwise:
    request_human_intervention()

Compilation Process

The Turbulance compiler transforms semantic specifications into executable reconstruction tasks:

1. Parsing: Semantic syntax parsed into structured representations
2. Validation: Requirements checked against system capabilities
3. Compilation: Semantic requirements compiled to reconstruction objectives
4. Execution: Compiled tasks executed through Helicopter engines
5. Validation: Results validated against semantic requirements