We won’t find intelligence at the bottom of gradient descent.
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Do you have an alternative approach to learning that is more efficient than gradient-based optimization?
The point is that we need to find one. AI research has become too narrowly focused on one paradigm. Gradient descent is a local optimum, and we need to get out of it. Do you really think the cortex learns by GD?
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Tämä twiitti ei ole saatavilla.
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What evolution does hardly resembles gradient descent. I mean, multi-agent heterogenous parallel random search with several varying in time targets and feedback loops? Is it even optimization?
Keskustelun loppu
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Q1: Does the cortex learn? Q2: if it does, does it do it by minimizing some objective? Q3: if it does, does it do it by estimating some sort of gradient information, or something to that effect? Q4: if it doesn't, then what type of (gradient-free) optimization does it use?
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But what is intelligence?
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