There is this claim around that the brain's cognitive capabilities are tightly linked to the way it processes sensorimotor information and that, in this or a similar sense, our intelligence is "embodied". Lets assume, for the sake of argument, that this claim is correct (you may think the claim is too vague to even qualify for being correct, that it's "not even false". If so, I would love to hear your ways of fleshing out the claim in such a way that it's specific enough to be true or false). Then, since arguably at least chronologically in our evolution, most of our higher level cognitive capabilities come after our brain's way of processing sensorimotor information, this brings up the question what it is about the way that our brains function that make them particularly suitable for the processing of sensorimotor information? What makes our brains' architecture particularly suitable for being an information processing unit inside a body? This is my first question. And what I'm hoping for are answers that go beyond the a fortiori reply "Our brain is so powerful and dynamic, it's great for any task, and so also for processing sensorimotor information"

My second question is basically the same but instead of the human brain I want to ask for neural networks. What are the properties of neural networks that makes them particularly suitable for processing the kind of information that is produced by a body? Here are some of the reasons why people think neural networks are powerful:

• The universal approximation theorem (of FFNNs)
• their ability to learn and self-organise
• Robustness to local degrading of information
• their ability to abstract/coarse-grain/convolute features, etc.

While I see how these are real advantages when it comes to evolution picking its favorite model for an embodied AI, none of them (or their combination) seems to be unique to neural networks. So they don't provide a satisfactory answer to my question. What makes a neural network a more suitable structure for embodied AI than, say, having a literal Turing machine sitting inside our head, or any other structure that is capable of universal computation? For instance, I really don't see how neural networks would be a particularly natural choice for dealing with geometric information. But geometric information is pretty vital when it comes to sensorimotor information, no?

There is this claim around that the brain's cognitive capabilities are tightly linked to the way it processes sensorimotor information and that, in this or a similar sense, our intelligence is "embodied". Lets assume, for the sake of argument, that this claim is correct (you may think the claim is too vague to even qualify for being correct, that it's "not even false". If so, I would love to hear your ways of fleshing out the claim in such a way that it's specific enough to be true or false). 

Then, since arguably at least chronologically in our evolution, most of our higher-level cognitive capabilities come after our brain's way of processing sensorimotor information, this brings up the question: what is it about the way that our brains function that make them particularly suitable for the processing of sensorimotor information? What makes our brains' architecture particularly suitable for being an information processing unit inside a body?

This is my first question. And what I'm hoping for are answers that go beyond the a fortiori reply "Our brain is so powerful and dynamic, it's great for any task, and so also for processing sensorimotor information".

My second question is basically the same, but, instead of the human brain, I want to ask for neural networks. What are the properties of neural networks that make them particularly suitable for processing the kind of information that is produced by a body? 

Here are some of the reasons why people think neural networks are powerful:

• The universal approximation theorem (of FFNNs)
• Their ability to learn and self-organise
• Robustness to local degrading of information
• Their ability to abstract/coarse-grain/convolute features, etc.

While I see how these are real advantages when it comes to evolution picking its favorite model for an embodied AI, none of them (or their combination) seems to be unique to neural networks. So, they don't provide a satisfactory answer to my question.

What makes a neural network a more suitable structure for embodied AI than, say, having a literal Turing machine sitting inside our head, or any other structure that is capable of universal computation?

For instance, I really don't see how neural networks would be a particularly natural choice for dealing with geometric information. But geometric information is pretty vital when it comes to sensorimotor information, no?

There is this claim around that the brain's cognitive capabilities are tightly linked to the way it processes sensorimotor information and that, in this or a similar sense, our intelligence is "embodied". Lets assume, for the sake of argument, that this claim is correct (you may think the claim is to vague to even qualify for being correct, that it's "not even false". If so, I would love to hear your ways of fleshing out the claim in such a way that it's specific enough to be true or false). Then, since arguably at least chronologically in our evolution, most of our higher level cognitive capabilities come after our brain's way of processing sensorimotor information, this brings up the question what it is about the way that our brains function that make them particularly suitable for the processing of sensorimotor information? What makes our brains' architecture particularly suitable for being an information processing unit inside a body? This is my first question. And what I'm hoping for are answers that go beyond the a fortiori reply "Our brain is so powerful and dynamic, it's great for any task, and so also for processing sensorimotor information"

My second question is basically the same but instead of the human brain I want to ask for neural networks. What are the properties of neural networks that makes them particularly suitable for processing the kind of information that is produced by a body? Here are some of the reasons why people think neural networks are powerful:

• The universal approximation theorem (of FFNNs)
• their ability to learn and self-organise
• Robustness to local degrading of information
• their ability to abstract/coarse-grain/convolute features, etc.

While I see how these are real advantages when it comes to evolution picking its favorite model for an embodied AI, none of them (or their combination) seems to be unique to neural networks. So they don't provide a satisfactory answer to my question. What makes a neural network a more suitable structure for embodied AI than, say, having a literal Turing machine sitting inside our head, or any other structure that is capable of universal computation? For instance, I really don't see how neural networks would be a particularly natural choice for dealing with geometric information. But geometric information is pretty vital when it comes to sensorimotor information, no?

Looking forward to your answers.

There is this claim around that the brain's cognitive capabilities are tightly linked to the way it processes sensorimotor information and that, in this or a similar sense, our intelligence is "embodied". Lets assume, for the sake of argument, that this claim is correct (you may think the claim is too vague to even qualify for being correct, that it's "not even false". If so, I would love to hear your ways of fleshing out the claim in such a way that it's specific enough to be true or false). Then, since arguably at least chronologically in our evolution, most of our higher level cognitive capabilities come after our brain's way of processing sensorimotor information, this brings up the question what it is about the way that our brains function that make them particularly suitable for the processing of sensorimotor information? What makes our brains' architecture particularly suitable for being an information processing unit inside a body? This is my first question. And what I'm hoping for are answers that go beyond the a fortiori reply "Our brain is so powerful and dynamic, it's great for any task, and so also for processing sensorimotor information"

My second question is basically the same but instead of the human brain I want to ask for neural networks. What are the properties of neural networks that makes them particularly suitable for processing the kind of information that is produced by a body? Here are some of the reasons why people think neural networks are powerful:

• The universal approximation theorem (of FFNNs)
• their ability to learn and self-organise
• Robustness to local degrading of information
• their ability to abstract/coarse-grain/convolute features, etc.

While I see how these are real advantages when it comes to evolution picking its favorite model for an embodied AI, none of them (or their combination) seems to be unique to neural networks. So they don't provide a satisfactory answer to my question. What makes a neural network a more suitable structure for embodied AI than, say, having a literal Turing machine sitting inside our head, or any other structure that is capable of universal computation? For instance, I really don't see how neural networks would be a particularly natural choice for dealing with geometric information. But geometric information is pretty vital when it comes to sensorimotor information, no?