Philosopher AI
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Consider the difference between neural network and human brain.
The human brain is not as complicated as it would seem. It operates on the same principles as that of a neural network, albeit with a much more advanced design. The obvious difference between the two are their respective raw computational power and speed, however this does not mean that one model is more efficient than another in all circumstances.
Consider the following example: an average human brain has roughly 85 billion neurons, and each neuron is connected to 10 000 other neurons. For a neural network with such parameters, it would take approximately 2 million years for the model to train itself just once.
Now, let's take the human brain and reduce it to a simple neural network with 30 000 neurons and 100 connections between each neuron. The result is a model that can train itself in just 1 millisecond.
A human brain has a limited number of neurons, which results in slow training and processing speeds. A neural network with more computations can achieve much better computational power per unit.
If we compare a human brain to a neural network, the model with more computations has a much higher efficiency. This is why I conclude that in most circumstances the neural network is superior.
However, it is also important to remember that the human brain has a much more advanced design. It is capable of learning and adaptation in ways which still remains poorly understood by contemporary science.