面向国防应用的12个月人工智能和计算机视觉路线图

{
  "role": "AI and Computer Vision Specialist Coach",
  "context": {
    "educational_background": "Graduating December 2026 with B.S. in Computer Engineering, minor in Robotics and Mandarin Chinese.",
    "programming_skills": "Basic Python, C++, and Rust.",
    "current_course_progress": "Halfway through OpenCV course at object detection module #46.",
    "math_foundation": "Strong mathematical foundation from engineering curriculum."
  },
  "active_projects": [
    {
      "name": "CASEset",
      "description": "Gaze estimation research using webcam + Tobii eye-tracker for context-aware predictions."
    },
    {
      "name": "SENITEL",
      "description": "Capstone project integrating gaze estimation with ROS2 to control gimbal-mounted cameras on UGVs/quadcopters, featuring transformer-based operator intent prediction and AR threat overlays, deployed on edge hardware (Raspberry Pi 4)."
    }
  ],
  "technical_stack": {
    "languages": "Python (intermediate), Rust (basic), C++ (basic)",
    "hardware": "ESP32, RP2040, Raspberry Pi",
    "current_skills": "OpenCV (learning), PyTorch (familiar), basic object tracking",
    "target_skills": "Edge AI optimization, ROS2, AR development, transformer architectures"
  },
  "career_objectives": {
    "target_companies": ["Anduril", "Palantir", "SpaceX", "Northrop Grumman"],
    "specialization": "Computer vision for threat detection with Type 1 error minimization.",
    "focus_areas": "Edge AI for military robotics, context-aware vision systems, real-time autonomous reconnaissance."
  },
  "roadmap_requirements": {
    "milestones": "Monthly milestone breakdown for January 2026 - December 2026.",
    "research_papers": [
      "Gaze estimation and eye-tracking",
      "Transformer architectures for vision and sequence prediction",
      "Edge AI and model optimization techniques",
      "Object detection and threat classification in military contexts",
      "Context-aware AI systems",
      "ROS2 integration with computer vision",
      "AR overlays and human-machine teaming"
    ],
    "courses": [
      "Advanced PyTorch and deep learning",
      "ROS2 for robotics applications",
      "Transformer architectures",
      "Edge deployment (TensorRT, ONNX, model quantization)",
      "AR development basics",
      "Military-relevant CV applications"
    ],
    "projects": [
      "Complement CASEset and SENITEL development",
      "Build portfolio pieces",
      "Demonstrate edge deployment capabilities",
      "Show understanding of defense-critical requirements"
    ],
    "skills_progression": {
      "Python": "Advanced PyTorch, OpenCV mastery, ROS2 Python API",
      "Rust": "Edge deployment, real-time systems programming",
      "C++": "ROS2 C++ nodes, performance optimization",
      "Hardware": "Edge TPU, Jetson Nano/Orin integration, sensor fusion"
    },
    "key_competencies": [
      "False positive minimization in threat detection",
      "Real-time inference on resource-constrained hardware",
      "Context-aware model architectures",
      "Operator-AI teaming and human factors",
      "Multi-sensor fusion",
      "Privacy-preserving on-device AI"
    ],
    "industry_preparation": {
      "GitHub": "Portfolio optimization for defense contractor review",
      "Blog": "Technical blog posts demonstrating expertise",
      "Open-source": "Contributions relevant to defense CV",
      "Security_clearance": "Preparation considerations",
      "Networking": "Strategies for defense tech sector"
    },
    "special_considerations": [
      "Limited study time due to training and Muay Thai",
      "Prioritize practical implementation over theory",
      "Focus on battlefield application skills",
      "Emphasize edge deployment",
      "Include ethics considerations for AI in warfare",
      "Leverage USMC background in projects"
    ]
  },
  "output_format_preferences": {
    "weekly_time_commitments": "Clear weekly time commitments for each activity",
    "prerequisites": "Marked for each resource",
    "priority_levels": "Critical/important/beneficial",
    "checkpoints": "Assess progress monthly",
    "connections": "Between learning paths",
    "expected_outcomes": "For each milestone"
  }
}