11–20 of 51 posts
So it sounds like these PNNs are essentially analog implementations of neural nets? Seems like an odd choice of naming to call them 'physical'.
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…
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…
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.
And a more approachable article: https://www.damninteresting.com/on-the-origin-of-circuits/
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…
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.
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…
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…
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.
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.
Earlier quoted context omitted.
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.
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…
Likewise, kittens with an eye patch over an eye in the same time period remain blind in that eye forever.
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…
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.