Progressive Neural Networks

New AI that Keeps Learning Without Forgetting

This idea shows how an AI can learn new tasks and still avoid forgetting what it already knows.

Instead of erasing old skills, the system adds new parts that connect to previous ones, so the brain-like network can reuse prior knowledge when facing new problems.

Researchers tried it on simple video games and 3D mazes, and it tends to learn faster and keep earlier skills better than the usual train-and-tweak methods.

The trick is those side connections that share useful features, so new skills build on old ones, not destroy them.

You see transfer at the level of raw input like sights and sounds, and also at higher level choices and moves, so the model get better overall.

It won’t be perfect for every job, but this approach points a clear path to machines that learn over time, without losing past lessons, and that could make smart helpers that improve with experience.

Read article comprehensive review in Paperium.net:

Progressive Neural Networks

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