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Brain Inspired AI
Data Skeptic
Connections and Weight Changes in the Transformer Model
The transformer model has many interconnections between neurons which allows it to learn new knowledge by adjusting parameter weights. However, this process can make the neural network forget important information. To optimize the transformer model, we can apply rules from our brain to make it more efficient and sparse while maintaining its power. The advantage of the transformer model is that it can consider long-distance dependencies by gathering information from all inputs. Similar to our brain, short-term and long-term memories are stored in different locations.
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