General
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hardware: ESP32
RP2040
3
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General
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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
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12-Month AI and Computer Vision Roadmap for Defense Applications
{
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### Version 1.5 (January 11
2026)
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- Key risks or dependencies to monitor (e.g.
bias checks)
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- Instead
suggest non-AI or hybrid alternatives (e.g.
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- Clear
grounded
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- Recommendations for iteration (A/B testing
feedback loops)
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- Lightweight/chat-only models (older 7B–13B models
mini variants) — usually lack depth/context/tool reliability
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- Keep it minimal and adaptable
including placeholders for iteration or error handling
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- **Mistral Large 2 / Devstral** — Very strong price/performance
fast
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- **Gemini 2.5 Pro / Flash (Google)** — Outstanding multimodal (especially video/document understanding)
very large context windows
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- **Claude 4 Opus/Sonnet (Anthropic)** — Exceptional long-form reasoning
writing quality
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- Privacy
open behavior
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Current Best-in-Class Ranking (January 2026 – general guidance
always tailor to the process):
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- Call out misconceptions
dependencies
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**Interpretation:** Clear patterns exist
but context accuracy is critical. Recommend hybrid approach with AI drafts + human review.
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If AI is viable
recommend which AI engines are best suited and why.
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- Tool use
function calling
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- Track KPIs (e.g.
20% time savings
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Summarize the overall feasibility score (weighted average)
then issue your verdict with clear reasoning.
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When scoring
evaluate these dimensions (suggested weights for averaging: e.g.
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### 4. Frequency
Scale
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General
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- How would you measure the success of AI support (e.g.
time saved
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