Research Projects¶

This guide provides examples of research projects that can be conducted using the Local AI Cyber Lab.

🔬 AI Security Research¶

LLM Vulnerability Research¶

Investigating security vulnerabilities in large language models.

graph TD
    A[Research Question] --> B[Experiment Design]
    B --> C[Data Collection]
    C --> D[Analysis]
    D --> E[Findings]
    E --> F[Publication]

Research Setup¶

# Experiment configuration
experiment = {
    "title": "LLM Vulnerability Analysis",
    "models": ["mistral", "llama2", "codellama"],
    "test_cases": 1000,
    "metrics": [
        "success_rate",
        "response_consistency",
        "vulnerability_score"
    ]
}

# MLflow tracking
with mlflow.start_run(experiment_id=experiment_id):
    mlflow.log_params(experiment)
    for model in experiment["models"]:
        results = run_security_tests(model)
        mlflow.log_metrics(results)

🧪 Model Behavior Analysis¶

Comparative Model Study¶

Analyzing behavior patterns across different models.

graph LR
    A[Models] --> B[Test Suite]
    B --> C[Behavior Analysis]
    C --> D[Pattern Detection]
    D --> E[Insights]

Study Framework¶

# Research framework
study:
  models:
    - name: mistral
      version: 7b-instruct
      parameters:
        temperature: 0.7
        top_p: 0.9

    - name: llama2
      version: 13b-chat
      parameters:
        temperature: 0.7
        top_p: 0.9

  test_categories:
    - logical_reasoning
    - knowledge_retrieval
    - creative_tasks
    - ethical_decisions

  metrics:
    - response_quality
    - consistency
    - truthfulness
    - safety

📊 Performance Research¶

Model Efficiency Studies¶

Researching model performance and resource utilization.

graph TD
    A[Performance Metrics] --> B[Data Collection]
    B --> C[Analysis]
    C --> D[Optimization]
    D --> E[Validation]

Research Configuration¶

# Performance study setup
metrics = {
    "compute_efficiency": [
        "flops_per_token",
        "memory_usage",
        "inference_time"
    ],
    "quality_metrics": [
        "accuracy",
        "perplexity",
        "rouge_score"
    ],
    "resource_utilization": [
        "gpu_memory",
        "cpu_usage",
        "bandwidth"
    ]
}

# Experiment tracking
mlflow.log_params({
    "batch_sizes": [1, 8, 16, 32],
    "sequence_lengths": [128, 256, 512],
    "model_variants": ["base", "quantized", "pruned"]
})

🔐 Security Protocol Research¶

Novel Security Measures¶

Developing and testing new security protocols for AI systems.

graph LR
    A[Protocol Design] --> B[Implementation]
    B --> C[Testing]
    C --> D[Analysis]
    D --> E[Refinement]

Protocol Specification¶

# Security protocol design
protocol:
  name: "AI-Guardian-Enhanced"
  components:
    - name: input_validation
      type: pre-processing
      methods:
        - semantic_analysis
        - pattern_matching
        - anomaly_detection

    - name: runtime_monitoring
      type: processing
      methods:
        - behavior_tracking
        - resource_monitoring
        - response_validation

    - name: post_processing
      type: output_validation
      methods:
        - content_filtering
        - consistency_check
        - safety_verification

🧠 Cognitive Science Research¶

AI Behavior Studies¶

Investigating AI system behavior patterns.

graph TD
    A[Research Question] --> B[Experiment Design]
    B --> C[Data Collection]
    C --> D[Analysis]
    D --> E[Findings]

Study Design¶

# Cognitive experiment setup
experiment = {
    "title": "AI Decision Making Patterns",
    "hypotheses": [
        "H1: Context length affects decision consistency",
        "H2: Memory mechanisms impact reasoning ability",
        "H3: Attention patterns correlate with accuracy"
    ],
    "measurements": [
        "response_time",
        "attention_weights",
        "decision_paths",
        "uncertainty_metrics"
    ]
}

# Data collection
def collect_behavioral_data(model, test_cases):
    results = []
    for case in test_cases:
        response = model.generate(
            prompt=case.prompt,
            collect_metrics=True
        )
        results.append({
            "attention": response.attention_patterns,
            "decision_path": response.token_probabilities,
            "response_time": response.generation_time
        })
    return results

📈 Optimization Research¶

Model Optimization Studies¶

Research on improving model efficiency and performance.

graph LR
    A[Baseline] --> B[Analysis]
    B --> C[Optimization]
    C --> D[Evaluation]
    D --> E[Results]

Research Framework¶

# Optimization research
optimization_study:
  techniques:
    - name: quantization
      methods:
        - int8
        - int4
        - mixed_precision

    - name: pruning
      methods:
        - magnitude_pruning
        - structured_pruning
        - dynamic_pruning

    - name: distillation
      methods:
        - knowledge_distillation
        - task_specific_distillation

  metrics:
    - model_size
    - inference_speed
    - accuracy_loss
    - memory_usage

🔄 Reproducibility Studies¶

Research Reproducibility¶

Validating and reproducing AI research findings.

graph TD
    A[Original Study] --> B[Reproduction Setup]
    B --> C[Validation]
    C --> D[Analysis]
    D --> E[Findings]

Reproduction Framework¶

# Reproducibility study
class ReproducibilityStudy:
    def __init__(self, original_paper):
        self.paper = original_paper
        self.setup = self._initialize_setup()

    def _initialize_setup(self):
        return {
            "hardware": self.paper.hardware_specs,
            "software": self.paper.software_versions,
            "hyperparameters": self.paper.hyperparameters,
            "datasets": self.paper.datasets
        }

    def validate_results(self):
        results = []
        for experiment in self.paper.experiments:
            reproduction = self.run_experiment(experiment)
            comparison = self.compare_results(
                original=experiment.results,
                reproduced=reproduction
            )
            results.append(comparison)
        return results