Why Is Kinetics Data the Hidden Advantage in AI’s Next Breakout Models? | Insights From Newsflare

Written By Ashleigh Johnson

Right now, most AI models are fantastic at language. Pretty decent at images. Though, ask it to truly understand human movement; what someone is doing, why they’re doing it, and what likely comes next, and suddenly that so-trusted magic breaks.

That’s because human action isn’t just pixels in motion. Movement is meaning. And, how do artificially intelligent models interpret that into 1s and 0s? Well, it depends on the type of data they’re being trained on:

Kinetics is rich, dynamic examples of real human actions, behaviors, and physical interactions with the world. This can be found in abundance inside of user-generated video (UGV), that’s diverse from language to  movement, geography and much more.

Enter Newsflare, a leading video partner who specialises in video commercialisation and user-generated clips, with over 520k (8k+ new monthly uploads) globally filmed UGV clips, commercial clearance, fair pay to filmers for their contributions and an offering where training data is not only unique but reliable.  

Kinetics Data Connects | Joining AI To The Physical World

Models trained only on text and still images have a big blind spot: they don’t understand cause and effect in real environments.

With kinetics data we can capture how bodies move in space, how actions unfold over time and the relationships between objects, motion, and intention, so, if we want AI that can assist in daily life (AR, robotics, sports analysis, healthcare, safety), and create uncanny video output, action understanding isn’t optional. It’s core capability.

Language Varies, But Movement? It’s A Shared Operating System

Let’s take this example outside of training for better video output, let’s make this practical, how could better motion understanding help people? 

  • A home AI that understands a fall and calls for help

  • A fitness system that corrects your form in real time

  • Autonomous vehicles that interpret pedestrian behaviour, not just proximity

Understanding movement isn’t just about recognising what’s happening now. It’s about anticipating what might happen next. Kinetics gives AI a sense of temporal logic ( the flow of real life) because humans don’t think in screenshots. We think in sequences. AI must too.

That means fewer blind spots, fewer biases, and truer representation of moving images. Kinetics isn’t “just another dataset”, it’s the missing context that bridges digital intelligence and the physical world. 

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