5 ms·

Before finishing my read, I need to register an objection to the opening which reads to me so as to imply it is the only means: > Researchers simulate a weird

Before finishing my read, I need to register an objection to the opening which reads to me so as to imply it is the only means: > Researchers simulate a weird type of pseudo-neural-tissue, “reward” it a little every time it becomes a little more like the AI they want, and eventually it becomes the AI they want. This isn't the only way. Back propagation is a hack around the oversimplification of neural models. By adding a sense of location into the network, you get linearly inseparable functions learned just fine. Hopfield networks with Hebbian learning are sufficient and are implemented by the existing proofs of concept we have.

janalsncm 3y ago
This is true. We use backpropagation not because it’s the only way or because it’s biologically plausible (the brain doesn’t have any backward passes) but because it works. Neural networks aren’t special because of any sort of connection to the brain, we use them because we have hardware (GPUs) which can train them pretty quickly. I feel the same way about transformers vs RNNs: even if RNNs are more “correct” in some sense of having theoretically infinite memory it takes forever to train them so transformers won. And then we developed techniques like Long LoRA which make theoretical disadvantages functionally irrelevant.
- bilsbie 3y ago
  > developed techniques like Long LoRA which make theoretical disadvantages functionally irrelevant. How’s that?
  - janalsncm 3y ago
    Context windows used to be tiny e.g. 768 or 1024. That meant sliding windows of limited context if you had any reasonably sized input. If your context window is 32k or even 100k, a lot of inputs will fit into the context window entirely. The reason huge context windows weren't possible in the past is that memory requirements were quadratic with input length. Long LoRA lets us use less memory for our context windows, or use the same memory footprint for larger context windows.
- erikerikson 3y ago
  I agree that doing effective things is effective and we should do effective things when they prove valuable. These things here are generally simplifications or even distillations which can be faster and more efficient which are excellent attributes of solutions. My objection isn't that we develop great systems but that we don't forget that other branches in the solution space exist. Particularly here because the more rich enough may help yield the next generation of solutions.
dekhn 3y ago
I think it would be really exciting if somebody could show that ANNs that more resembled biological neurons could learn function approximation as well as (or better than!) current DNNs. However, my understanding of math and engineering suggests that for the time being, the mechanisms we currently use and invest so much time and effort into will exceed more biologically inspired neurons, for utterly banal reasons.
- erikerikson 3y ago
  Yet brains remain superior in many regards. Still, by all means, let's celebrate the advances we make!
blovescoffee 3y ago
Back propagation isn't a hack. It's a triumph. It's powering the revolution we're experiencing.
- erikerikson 3y ago
  It has yield some excellent results that we should celebrate and use. Yet the consensus of the field, when I was properly informed on such things, was that we developed back propagation while under a misunderstanding that linearly inseparable functions (e.g. XOR) could not be learned by Hebbian learning in Hopfield networks. This was a correct result in the context of his assumptions but too limited and/or over applied conclusion from Minsky's work.

janalsncm 3y ago

This is true. We use backpropagation not because it’s the only way or because it’s biologically plausible (the brain doesn’t have any backward passes) but because it works. Neural networks aren’t special because of any sort of connection to the brain, we use them because we have hardware (GPUs) which can train them pretty quickly. I feel the same way about transformers vs RNNs: even if RNNs are more “correct” in some sense of having theoretically infinite memory it takes forever to train them so transformers won. And then we developed techniques like Long LoRA which make theoretical disadvantages functionally irrelevant.

bilsbie 3y ago
> developed techniques like Long LoRA which make theoretical disadvantages functionally irrelevant. How’s that?
- janalsncm 3y ago
  Context windows used to be tiny e.g. 768 or 1024. That meant sliding windows of limited context if you had any reasonably sized input. If your context window is 32k or even 100k, a lot of inputs will fit into the context window entirely. The reason huge context windows weren't possible in the past is that memory requirements were quadratic with input length. Long LoRA lets us use less memory for our context windows, or use the same memory footprint for larger context windows.
erikerikson 3y ago
I agree that doing effective things is effective and we should do effective things when they prove valuable. These things here are generally simplifications or even distillations which can be faster and more efficient which are excellent attributes of solutions. My objection isn't that we develop great systems but that we don't forget that other branches in the solution space exist. Particularly here because the more rich enough may help yield the next generation of solutions.

dekhn 3y ago

I think it would be really exciting if somebody could show that ANNs that more resembled biological neurons could learn function approximation as well as (or better than!) current DNNs. However, my understanding of math and engineering suggests that for the time being, the mechanisms we currently use and invest so much time and effort into will exceed more biologically inspired neurons, for utterly banal reasons.

erikerikson 3y ago
Yet brains remain superior in many regards. Still, by all means, let's celebrate the advances we make!

blovescoffee 3y ago

Back propagation isn't a hack. It's a triumph. It's powering the revolution we're experiencing.

erikerikson 3y ago
It has yield some excellent results that we should celebrate and use. Yet the consensus of the field, when I was properly informed on such things, was that we developed back propagation while under a misunderstanding that linearly inseparable functions (e.g. XOR) could not be learned by Hebbian learning in Hopfield networks. This was a correct result in the context of his assumptions but too limited and/or over applied conclusion from Minsky's work.