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 Duration 14 hours

Course Outline

Introduction to AI in Defense Applications

  • Autonomous platforms, UAVs, and live surveillance capabilities.
  • Application of AI in defense contexts: navigation, tracking, and reconnaissance.
  • Overview of adapting AI models for mission-critical operational environments.

Data Preparation for Fine-Tuning

  • Handling sensor data streams: lidar, radar, thermal imaging, and video feeds.
  • Developing labeling strategies for object detection and target identification.
  • Data augmentation and anonymization techniques within military contexts.

Fine-Tuning AI Models for Perception and Control

  • Vision models focused on real-time object detection and segmentation.
  • Fusion models designed to integrate multi-sensor inputs.
  • Policy adjustments for autonomous navigation and obstacle avoidance.

Security, Safety, and Redundancy in AI Models

  • Constructing resilient models utilizing adversarial defense strategies.
  • Designing fail-safes and detecting anomalies during the inference phase.
  • Protecting model pipelines from tampering and spoofing attempts.

Testing and Simulation in Defense Environments

  • Leveraging synthetic data and digital twins for rigorous validation.
  • Stress-testing under adversarial and extreme operational conditions.
  • Executing sim-to-real transfers in operational simulations.

Compliance and Defense Standards

  • AI assurance frameworks specific to defense deployments.
  • Addressing security and ethical considerations in autonomous defense applications.
  • Documenting compliance with operational and legal mandates.

Field Deployment and Monitoring

  • Optimizing on-device inference and edge AI performance.
  • Managing telemetry, feedback loops, and continuous model updates.
  • Reviewing case studies from real-world defense AI implementations.

Conclusion and Future Steps

Requirements

  • Foundational knowledge of deep learning structures and computer vision frameworks.
  • Practical experience in training and evaluating AI models using libraries such as TensorFlow or PyTorch.
  • Familiarity with defense-grade system requirements and established security protocols.

Target Audience

  • Defense-focused AI engineers.
  • Developers in military technology.
  • Architects specializing in autonomous systems and surveillance platforms.

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