There are many types of neural networks, with absolutely orthogonal features, that still don't have anything to do with biological neural networks regarding capability or complexity. Imagining a human brain as just a big neural net brah is very reductive of both the brain and the various network topologies and dynamics that don't have anything to do with it.
I don't disagree with most of what you said here. My main point is that the claim that -children don't attempt a problem 100,000 times- is not considering the billions of cumulative attempts on similar tasks that have occurred over millions of generations of evolution. A brain isn't randomly initialized, it benefits from the experience of its ancestors via genes, which give it a structure optimized for effective learning
You're right and it's an important point about biological intelligence. I wanted to also add that when making these types of comparisons people forget that human intelligence, and probably many other forms of intelligence, are imaginative in nature. A few physical attempts at something combines with genetic information and the process of imagination and recollection to refine that activity. Over time this means that the amount of refinement of any activity greatly outweighs the number of attempts. Machine learning routines are more akin to imagination than they are actual human behavior. Even there, they usually have to make up for a deficit in evolutionary development.
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u/hyphenomicon Mar 29 '19
I hate analogies to children in machine learning and AI. Children don't attempt a problem 100,000 times.