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I used to design low-precision hardware to train AIs. > The actually important thing is (a) getting a result out, and (b) being able to nudge the weights to ge

I used to design low-precision hardware to train AIs. > The actually important thing is (a) getting a result out, and (b) being able to nudge the weights to get the result closer to what you want That is accurate. > That doesn't have to be built on "math" or "numbers" but could be built on bits directly so long as the operations satisfy the goals Some people have published research where the parameters or activations are binary, sometimes in a stochastic fashion. Typically either the AI doesn't converge or it converges to a rather suboptimal solution. Also, just because the weights or activations are binary it doesn't mean there's no math involved. You need some sort of a mathematical model to figure out how to tweak the parameters of the model in order to improve its fitness to the data.

sigmoid10 3y ago
The keyword the original comment was missing is differentiability. This is what a) and b) imply because it is how you ensure that you can train your model using gradient descent or whatever. So you need a particular kind of math to train these models using the methods we have today. There's quite a bit of research into other methods for training neural networks that don't come with this constraint, because it's obvious the human brain doesn't work that way. But this is early research and very far from being as useful.
- stevenhuang 3y ago
  I wouldn't say it's obvious the brain doesn't work this way. See the free energy principle, one of the leading theories of how our brain works. > The free energy principle is a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input. It highlights the brain's objective of aligning its internal model with the external world to enhance prediction accuracy Making predictions and then correcting for them in some way is a pretty low bar and it's often achieved through differentiability. I wouldn't be surprised if relevant neurological structures exist in our brains that do this.
  - sigmoid10 3y ago
    There are reasons to believe it could work similarly, but they are related to computational efficiency and out lack of knowledge regarding more efficient methods. But I would not bet on our own ignorance as an explanation.

sigmoid10 3y ago

The keyword the original comment was missing is differentiability. This is what a) and b) imply because it is how you ensure that you can train your model using gradient descent or whatever. So you need a particular kind of math to train these models using the methods we have today. There's quite a bit of research into other methods for training neural networks that don't come with this constraint, because it's obvious the human brain doesn't work that way. But this is early research and very far from being as useful.

stevenhuang 3y ago
I wouldn't say it's obvious the brain doesn't work this way. See the free energy principle, one of the leading theories of how our brain works. > The free energy principle is a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input. It highlights the brain's objective of aligning its internal model with the external world to enhance prediction accuracy Making predictions and then correcting for them in some way is a pretty low bar and it's often achieved through differentiability. I wouldn't be surprised if relevant neurological structures exist in our brains that do this.
- sigmoid10 3y ago
  There are reasons to believe it could work similarly, but they are related to computational efficiency and out lack of knowledge regarding more efficient methods. But I would not bet on our own ignorance as an explanation.