💡 Why AI Still Has a Long Way to Go With Human Movement — a must‑read from Forbes that challenges the hype.
On paper, today’s AI models can label images and map joints at scale. But in the real world — gyms, clinics, factories, and physical work settings — they still misinterpret fundamental human movement patterns, with meaningful consequences for safety, performance, and recovery. Why? Because movement isn’t static — it’s dynamic, contextual, and shaped by force, intent, fatigue, and environment — and most AI wasn’t built to understand that.
This piece digs into the real obstacles: lack of realistic data, contextual understanding, and the limitations of models trained primarily on still images. It paints a thoughtful picture of what it will take for AI to move beyond recognition to true movement intelligence.
A great read for anyone in AI, robotics, sports science, or health tech — and a reminder that the “next frontier” of AI won’t just be bigger models, but deeper human understanding.
👉 Worth sharing with your network: https://shorturl.at/raFbh
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