7 ms·

Last time I read about this the main practical difficulty was model transferability. The very thing that makes it so powerful and efficient is also the thing t

by tomxor 2y ago

Last time I read about this the main practical difficulty was model transferability. The very thing that makes it so powerful and efficient is also the thing that make it uncopiable, because sensitivity to tiny physical differences in the devices inevitably gets encoded into the model during training. It seems intuitive this is an unavoidable, fundamental problem. Maybe that scares away big tech, but I quite like the idea of having invaluable, non-transferable, irreplaceable little devices. Not so easily deprecated by technological advances, flying in the face of consumerism, getting better with age, making people want to hold onto things.

bongodongobob 2y ago
Reminds me of the evolutionary FPGA experiment that was dependent on magnetic flux or something. The same program wouldn't work on a different FPGA.
- cyberax 2y ago
  Here's the paper about it: https://www.researchgate.net/publication/2737441_An_Evolved_Circuit_Intrinsic_in_Silicon_Entwined_With_Physics https://www.researchgate.net/publication/2737441_An_Evolved_... And a more approachable article: https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/
- rusticpenn 2y ago
  What thy did was overfitting. We later found other ways of getting around the issue.
- actionfromafar 2y ago
  Would be interesting to hook up many FPGAs of the same model and train all of the at once. Programs with differing outputs on different individuals could be discarded. The program may still not transfer to another batch of FPGAs but at least you have a better chance of the working. Another idea is to just train a whole bunch of them individually, like putting your chips in school. :-D
trextrex 2y ago
Well, the brain is a physical neural network, and evolution seems to have figured out how to generate a (somewhat) copiable model. I bet we could learn a trick or two from biology here.
- chbint 2y ago
  There's indeed a nice trick to be learned from cognitive science focused in biological cognition: the mind is embodied and embedded. Which means, roughly, that it is not portable. It doesn't store things like "glass at position x,y" but only "glass is at a small movement of the hand towards the right". Consequently, whatever gets encoded only makes sense within a given body and only inasmuch as it relied on its environment (with humans, that includes social environments). The good news is that, despite being not portable, this reliance on physical properties might be a step in the right direction, after all.
- tomxor 2y ago
  Some parts are copiable, but not the more abstract things like the human intellect, for lack of a better word. We are not even born with what you might consider basic mental faculties, for example it might seem absurd, but we have to learn to see... We are born with the "hardware" for it, a visual cortex, an eye, all defined by our genes, but it's actually trained from birth, there is even a feedback loop that causes the retina to physically develop properly.
  - immibis 2y ago
    They raised some cats from birth in an environment with only vertically-oriented edges, none horizontal. Those cats could not see horizontally-oriented things. https://computervisionblog.wordpress.com/2013/06/01/cats-and-vision-is-vision-acquired-or-innate/ https://computervisionblog.wordpress.com/2013/06/01/cats-and... Likewise, kittens with an eye patch over an eye in the same time period remain blind in that eye forever.
    - tomxor 2y ago
      Wow, that's a horrific way of proving that theory.
alexpotato 2y ago
> Last time I read about this the main practical difficulty was model transferability. There is a great write up of this in this old blog post: https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/
robertsdionne 2y ago
This is “Mortal Computation” coined in Hinton’s The Forward-Forward Algorithm: Some Preliminary Investigations https://arxiv.org/abs/2212.13345 https://arxiv.org/abs/2212.13345.
programjames 2y ago
You can regularize the networks to make them transfer easier. I can't remember the abstract's title off the top of my head though.
dsabanin 2y ago
Couldn’t you still copy by training a new network on a new device to have same outputs for the same inputs as the original?
- tomxor 2y ago
  Yes, but training is the most expensive part of ML, for example GPT-3 is estimated to cost something like 1-4 million USD. With ANN you can do it one time and then clone the result for negligible energy cost. Maybe training a batch of PNNs in parallel could save some of the energy cost, but I don't know how feasible that is considering they could behave slightly differently during training causing divergence... Now that sarcastic comment at the bottom of this thread is starting to sound relevant "Schools".
  - kmmlng 2y ago
    > Yes, but training is the most expensive part of ML, for example GPT-3 is estimated to cost something like 1-4 million USD. That entirely depends on how many inferences the model will perform during its lifecycle. You can find different estimates for the energy consumption of ChatGPT, but they range from something like 500-1000 MWh a day. Assuming an electricity price of $0.165 per kWh, that would put you at roughly $80,000 to a $160,000 a day. Even at the lower end of $80,000 a day, you'll reach your $4 Million in just 50 days.
    - tomxor 2y ago
      That's not a proportional comparison, n simultaneous users to 1 training. How many users across how many GPUs is that 80k? With PNN you would have to multiply n by 1-4 million, training cost explodes.
  - l33tman 2y ago
    That's not true for the most well-known models. For example Meta's LLAMA training and architecture was predicated on the observation that training cost is a drop in the well compared to the inference cost for a model's lifetime.
- 2y ago
CuriouslyC 2y ago
This was the thing Geoff Hinton cited as a problem with analog networks. I think eventually we'll get to the point where we do a stage of pretraining on noisy digital hardware to create a transferrable network, then fine tune it on the analog system.
jegp 2y ago
It's still possible to train a network that's aware of the physics and then transfer that to physical devices. One approach to this from the neuromorphic community (that's been working on this for a long time) is called the Neuromorphic Intermediate Representation (NIR) and already lets you transfer models to several hardware platforms [1]. This is pretty cool because we can use the same model across systems, similar to a digital instruction set. Ofc, this doesn't fix the problem of sensitivity. But biology fixed that with plasticity, so we can probably learn to circumvent that. [1]: https://github.com/neuromorphs/nir https://github.com/neuromorphs/nir (disclaimer: I'm one of the authors)
6gvONxR4sf7o 2y ago
If (somehow/waves hands) you could parallelize training, maybe this would turn into an implicit regularization and be a benefit, not a flaw. Then again, physical parallelizability might be an infeasibly restrictive constraint?

by tomxor 2y ago

bongodongobob 2y ago

Reminds me of the evolutionary FPGA experiment that was dependent on magnetic flux or something. The same program wouldn't work on a different FPGA.

cyberax 2y ago
Here's the paper about it: https://www.researchgate.net/publication/2737441_An_Evolved_Circuit_Intrinsic_in_Silicon_Entwined_With_Physics https://www.researchgate.net/publication/2737441_An_Evolved_... And a more approachable article: https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/
rusticpenn 2y ago
What thy did was overfitting. We later found other ways of getting around the issue.
actionfromafar 2y ago
Would be interesting to hook up many FPGAs of the same model and train all of the at once. Programs with differing outputs on different individuals could be discarded. The program may still not transfer to another batch of FPGAs but at least you have a better chance of the working. Another idea is to just train a whole bunch of them individually, like putting your chips in school. :-D

trextrex 2y ago

Well, the brain is a physical neural network, and evolution seems to have figured out how to generate a (somewhat) copiable model. I bet we could learn a trick or two from biology here.

chbint 2y ago
There's indeed a nice trick to be learned from cognitive science focused in biological cognition: the mind is embodied and embedded. Which means, roughly, that it is not portable. It doesn't store things like "glass at position x,y" but only "glass is at a small movement of the hand towards the right". Consequently, whatever gets encoded only makes sense within a given body and only inasmuch as it relied on its environment (with humans, that includes social environments). The good news is that, despite being not portable, this reliance on physical properties might be a step in the right direction, after all.
tomxor 2y ago
Some parts are copiable, but not the more abstract things like the human intellect, for lack of a better word. We are not even born with what you might consider basic mental faculties, for example it might seem absurd, but we have to learn to see... We are born with the "hardware" for it, a visual cortex, an eye, all defined by our genes, but it's actually trained from birth, there is even a feedback loop that causes the retina to physically develop properly.
- immibis 2y ago
  They raised some cats from birth in an environment with only vertically-oriented edges, none horizontal. Those cats could not see horizontally-oriented things. https://computervisionblog.wordpress.com/2013/06/01/cats-and-vision-is-vision-acquired-or-innate/ https://computervisionblog.wordpress.com/2013/06/01/cats-and... Likewise, kittens with an eye patch over an eye in the same time period remain blind in that eye forever.
  - tomxor 2y ago
    Wow, that's a horrific way of proving that theory.

alexpotato 2y ago

> Last time I read about this the main practical difficulty was model transferability. There is a great write up of this in this old blog post: https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/

robertsdionne 2y ago

This is “Mortal Computation” coined in Hinton’s The Forward-Forward Algorithm: Some Preliminary Investigations https://arxiv.org/abs/2212.13345 https://arxiv.org/abs/2212.13345.

programjames 2y ago

You can regularize the networks to make them transfer easier. I can't remember the abstract's title off the top of my head though.

dsabanin 2y ago

Couldn’t you still copy by training a new network on a new device to have same outputs for the same inputs as the original?

tomxor 2y ago
Yes, but training is the most expensive part of ML, for example GPT-3 is estimated to cost something like 1-4 million USD. With ANN you can do it one time and then clone the result for negligible energy cost. Maybe training a batch of PNNs in parallel could save some of the energy cost, but I don't know how feasible that is considering they could behave slightly differently during training causing divergence... Now that sarcastic comment at the bottom of this thread is starting to sound relevant "Schools".
- kmmlng 2y ago
  > Yes, but training is the most expensive part of ML, for example GPT-3 is estimated to cost something like 1-4 million USD. That entirely depends on how many inferences the model will perform during its lifecycle. You can find different estimates for the energy consumption of ChatGPT, but they range from something like 500-1000 MWh a day. Assuming an electricity price of $0.165 per kWh, that would put you at roughly $80,000 to a $160,000 a day. Even at the lower end of $80,000 a day, you'll reach your $4 Million in just 50 days.
  - tomxor 2y ago
    That's not a proportional comparison, n simultaneous users to 1 training. How many users across how many GPUs is that 80k? With PNN you would have to multiply n by 1-4 million, training cost explodes.
- l33tman 2y ago
  That's not true for the most well-known models. For example Meta's LLAMA training and architecture was predicated on the observation that training cost is a drop in the well compared to the inference cost for a model's lifetime.
2y ago

CuriouslyC 2y ago

This was the thing Geoff Hinton cited as a problem with analog networks. I think eventually we'll get to the point where we do a stage of pretraining on noisy digital hardware to create a transferrable network, then fine tune it on the analog system.

jegp 2y ago

It's still possible to train a network that's aware of the physics and then transfer that to physical devices. One approach to this from the neuromorphic community (that's been working on this for a long time) is called the Neuromorphic Intermediate Representation (NIR) and already lets you transfer models to several hardware platforms [1]. This is pretty cool because we can use the same model across systems, similar to a digital instruction set. Ofc, this doesn't fix the problem of sensitivity. But biology fixed that with plasticity, so we can probably learn to circumvent that. [1]: https://github.com/neuromorphs/nir https://github.com/neuromorphs/nir (disclaimer: I'm one of the authors)

6gvONxR4sf7o 2y ago

If (somehow/waves hands) you could parallelize training, maybe this would turn into an implicit regularization and be a benefit, not a flaw. Then again, physical parallelizability might be an infeasibly restrictive constraint?