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I feel like there is an inferential leap implied, to greatly simplify, from "A does X and B does X" to "A and B must operate relevantly similarly." For example,

I feel like there is an inferential leap implied, to greatly simplify, from "A does X and B does X" to "A and B must operate relevantly similarly." For example, walking and flying are both modes of transportation, but you can't really learn anything interesting about one from studying the other

sebzim4500 4y ago
>For example, walking and flying are both modes of transportation, but you can't really learn anything interesting about one from studying the other You can figure out newtonian mechanics entirely on the ground and that clearly helps you understand flight. By analogy, getting more understanding about what limits exist in ANNs could plausibly help understand how the brain works (and vice versa).
ChikkaChiChi 4y ago
Once you understand that walking gets you from where you are to where you want to be, you start to define the characteristics of motion. Then, seeing other forms of conveyance that are faster underpin the concept of efficiency. Walking and flying may not have a lot in common to you and I, but to a thing learning to crawl, there is a lot to be understood.
- sklargh 4y ago
  I assure you as the parent of a toddler, young children attempt to find the line of commonality between walking and flying often.
  - deleted 4y ago
    [deleted]
ggm 4y ago
Underrated comment. This is like a "correlation is not causation" side of "this ML worked so this must be how a brain works" needs to be hit on the head, time and time again. Maybe we can teach an ML to whack-a-mole until we see ideas pop out which the ML doesn't think are repetitive correlation/causation/model-is-working effects
- mjburgess 4y ago
  Even more severely, I think this is pseudoscience -- though a genuinely borderline case "with real value". All theyre doing is feeding in pixel patterns and object information for specific scenes, and then calling the models ability to detect general correlations in those scenes "solidity", "inertia", and the like. Any time where patterns in correlations are taken as an explantory model, we have pseudoscience. Yes, inertia/solidity/etc. is the right answer for why physical objects show those visual correlations. But pixel patterns in some images are NOT models of inertia, solidity, etc. Just as correlations in pixel patterns in the sky is not a model of gravity. The right model, eg., is F=GMm/r^2 which are variables not even present in the data. So no correlative model of a night sky will ever be a model of gravity.
robotresearcher 4y ago
Demonstrations almost always are about sufficiency, not necessity. If I make a machine that walks to the store, I have shown that walking is sufficient to get to the store. I have not ruled out the possibility that you bicycle to the store. But if there has been a long running passionate debate on whether walking could ever get you to the store, showing sufficiency can settle that.

sebzim4500 4y ago

>For example, walking and flying are both modes of transportation, but you can't really learn anything interesting about one from studying the other You can figure out newtonian mechanics entirely on the ground and that clearly helps you understand flight. By analogy, getting more understanding about what limits exist in ANNs could plausibly help understand how the brain works (and vice versa).

ChikkaChiChi 4y ago

Once you understand that walking gets you from where you are to where you want to be, you start to define the characteristics of motion. Then, seeing other forms of conveyance that are faster underpin the concept of efficiency. Walking and flying may not have a lot in common to you and I, but to a thing learning to crawl, there is a lot to be understood.

sklargh 4y ago
I assure you as the parent of a toddler, young children attempt to find the line of commonality between walking and flying often.
- deleted 4y ago
  [deleted]

ggm 4y ago

Underrated comment. This is like a "correlation is not causation" side of "this ML worked so this must be how a brain works" needs to be hit on the head, time and time again. Maybe we can teach an ML to whack-a-mole until we see ideas pop out which the ML doesn't think are repetitive correlation/causation/model-is-working effects

mjburgess 4y ago
Even more severely, I think this is pseudoscience -- though a genuinely borderline case "with real value". All theyre doing is feeding in pixel patterns and object information for specific scenes, and then calling the models ability to detect general correlations in those scenes "solidity", "inertia", and the like. Any time where patterns in correlations are taken as an explantory model, we have pseudoscience. Yes, inertia/solidity/etc. is the right answer for why physical objects show those visual correlations. But pixel patterns in some images are NOT models of inertia, solidity, etc. Just as correlations in pixel patterns in the sky is not a model of gravity. The right model, eg., is F=GMm/r^2 which are variables not even present in the data. So no correlative model of a night sky will ever be a model of gravity.

robotresearcher 4y ago

Demonstrations almost always are about sufficiency, not necessity. If I make a machine that walks to the store, I have shown that walking is sufficient to get to the store. I have not ruled out the possibility that you bicycle to the store. But if there has been a long running passionate debate on whether walking could ever get you to the store, showing sufficiency can settle that.