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.
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
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.
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".
> 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.
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.
Having to do that in each instance is still really cumbersome for cheap mass deployment compared to just making a digital-style exact copy, but then again I guess a main argument for wanting these systems is that they'd be doing things unachievable in practice on digital computers.
In some cases one might be able to distill to digital arithmetic after the heavy parts of the optimization are done, for replication, distribution, better access for software analysis, etc.
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.
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?
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.
The way the brain does it is by giving users a largely untrained model that they themselves have to train over the next 20 years for it to be of any use.
I suspect there may be trade off undergoing evolutionary selection here, where for some organisms a behaviour is more important from the offset, it's worth encoding more of the behaviour into genes, at what cost I wonder?
It's also possible there is some other mechanism going on at an embryonic stage, a kind of pre-training.
I suspect some of the division is also defined by how complex the task is, or how sensitive the model is to it's own neurons (kind of like PNN). I don't have a well rounded argument, but my instinct is that encoding or pre-training walking is far easier than seeing. Not to mention basic quadrupedal walking/standing is far easier than bipedal, they can learn the more complex coordinated movements after.
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.
We should also consider the effects of trauma on those brains. If you’ve ever spent time around people with extreme trauma they are very much in their own heads and can’t focus outside themselves long enough to focus enough to learn anything. It definitely impacts intellectual capacity. Humans are social animals and anyone raised without proper socializing and intimacy and nurturing will inevitably end up traumatized.
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.
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.