Scientists at Scripps Research have developed an artificial intelligence (AI) called MovieNet that can understand moving images like a human brain.

This AI, described in a study published in PNAS, works by mimicking how neurons process real-time scenes. Instead of focusing on still images, MovieNet deals with complex, changing visuals. This could change how we do things like medical diagnosis or drive cars autonomously.

MovieNet is more accurate and better for the environment than older AI models. Scripps researcher Hollis Cline explains that our brains don’t just see pictures; they understand a continuous story. By watching how tadpole neurons react to moving images, the scientists found neurons in a visual processing region of the tadpole brain that detect changes like brightness or rotation. These neurons put together bits of moving images into a whole scene, much like solving a puzzle where each piece is a moment in time.

The new AI learned to spot these moving patterns by looking at short clips, from 100 to 600 milliseconds long, rather than single frames. When tested with videos of swimming tadpoles, MovieNet could tell normal from abnormal movements with 82.3% accuracy, beating both human observers and other AI like GoogLeNet by a significant margin.

Better results with less data and power

MovieNet doesn’t just work better; it uses less energy. Old AI needs lots of data and power, but MovieNet simplifies data, making it eco-friendly. This could make AI more sustainable and affordable for many uses.

In medicine, MovieNet might help spot subtle signs of diseases early, like Parkinson’s, by noticing tiny changes in movement. It could also improve drug testing by observing how cells react over time, rather than just at certain moments.

The scientists plan to keep improving MovieNet, making it adaptable for more uses. By learning from biology, they think AI can become more efficient and useful in ways we haven’t even thought of yet.

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