Let's solidify the idea of using time-interval, dependent information to inform sequential predictions with another conceptual example, where such information is considered crucial for accurate predictions. Consider how a human drummer, for example, must execute a precise sequence of motor commands corresponding to a precise flow of rhythm. They time their actions and count their progressions in a sequentially dependent order. Here, the information representing patterns of generated sequences is, at least partially, conveyed through the time delays between these respective events. Naturally, we would be interested in artificially replicating the sophisticated sequence modeling task occurring in such interactions. In theory, we could even use such an approach to sample novel rhyming schemes from computer-generated poetry, or create robot athletes...
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