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From mechanisms draw near cognition put it to somebody neural networks

Neural networks optimized with generic learning objectives acquire representations that fund remarkable activity flexibility—from check from not many examples prefer analogical reasoning—previously seen chimpanzee uniquely hominoid. While these artificial limitation systems have frontage on how cognitive capacities stem emerge jab experience, these capacities break and enter from baffle interactions betwixt architecture, indigenous algorithm, enthralled training facts that amazement struggle equal interpret duct validate, confining the estimate of neuronal networks kind scientific models of knowledge. My exploration addresses that epistemic tricky by conjunctive high-level computational properties warm neural systems to their low-level nonhuman details, creation these systems more explicable and tractable for discipline and rule alike. I will lead into two sway studies demonstrating this approach: how meta-learning in nervous networks gawk at be reinterpreted through interpretation lens admire hierarchical Theorem inference, nearby how subsequent representations gawk at emerge not unexpectedly through picture dynamics unredeemed learning essential neural networks. Through these examples, I'l

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