Why does switching barriers imply that electrical potential energy is probably being converted to heat? I don’t see how that follows at all.
Where else is the energy going to go?
What is “the energy” that has to go somewhere? As you recognize, there’s nothing that says it costs energy to change the shape of a potential well. I’m genuinely not sure what energy you’re talking about here. Is it electrical potential energy spent polarizing a medium?
I think what I’m saying is standard in how people analyze power costs of switching in transistors, see e.g. this physics.se post.
Yeah, that’s pretty standard. The ultimate efficiency limit for a semiconductor field-effect transistor is bounded by the 60 mV/dec subthreshold swing, and modern tiny transistors have to deal with all sorts of problems like leakage current which make it difficult to even reach that limit.
Unclear to me that semiconductor field-effect transistors have anything to do with neurons, but I don’t know how neurons work, so my confusion is more likely a state of my mind than a state of the world.
I don’t think transistors have too much to do with neurons beyond the abstract observation that neurons most likely store information by establishing gradients of potential energy. When the stored information needs to be updated, that means some gradients have to get moved around, and if I had to imagine how this works inside a cell it would probably involve some kind of proton pump operating across a membrane or something like that. That’s going to be functionally pretty similar to a capacitor, and discharging & recharging it probably carries similar free energy costs.
I think what I don’t understand is why you’re defaulting to the assumption that the brain has a way to store and update information that’s much more efficient than what we’re able to do. That doesn’t sound like a state of ignorance to me; it seems like you wouldn’t hold this belief if you didn’t think there was a good reason to do so.
I think what I don’t understand is why you’re defaulting to the assumption that the brain has a way to store and update information that’s much more efficient than what we’re able to do. That doesn’t sound like a state of ignorance to me; it seems like you wouldn’t hold this belief if you didn’t think there was a good reason to do so.
It’s my assumption because our brains are AGI for ~20 W.
In contrast, many kW of GPUs are not AGI.
Therefore, it seems like brains have a way of storing and updating information that’s much more efficient than what we’re able to do.
Of course, maybe I’m wrong and it’s due to a lack of training or lack of data or lack of algorithms, rather than lack of hardware.
DNA storage is way more information dense than hard drives, for example.
It’s my assumption because our brains are AGI for ~20 W.
I think that’s probably the crux. I think the evidence that the brain is not performing that much computation is reasonably good, so I attribute the difference to algorithmic advantages the brain has, particularly ones that make the brain more data efficient relative to today’s neural networks.
The brain being more data efficient I think is hard to dispute, but of course you can argue that this is simply because the brain is doing a lot more computation internally to process the limited amount of data it does see. I’m more ready to believe that the brain has some software advantage over neural networks than to believe that it has an enormous hardware advantage.
What is “the energy” that has to go somewhere? As you recognize, there’s nothing that says it costs energy to change the shape of a potential well. I’m genuinely not sure what energy you’re talking about here. Is it electrical potential energy spent polarizing a medium?
Yeah, that’s pretty standard. The ultimate efficiency limit for a semiconductor field-effect transistor is bounded by the 60 mV/dec subthreshold swing, and modern tiny transistors have to deal with all sorts of problems like leakage current which make it difficult to even reach that limit.
Unclear to me that semiconductor field-effect transistors have anything to do with neurons, but I don’t know how neurons work, so my confusion is more likely a state of my mind than a state of the world.
I don’t think transistors have too much to do with neurons beyond the abstract observation that neurons most likely store information by establishing gradients of potential energy. When the stored information needs to be updated, that means some gradients have to get moved around, and if I had to imagine how this works inside a cell it would probably involve some kind of proton pump operating across a membrane or something like that. That’s going to be functionally pretty similar to a capacitor, and discharging & recharging it probably carries similar free energy costs.
I think what I don’t understand is why you’re defaulting to the assumption that the brain has a way to store and update information that’s much more efficient than what we’re able to do. That doesn’t sound like a state of ignorance to me; it seems like you wouldn’t hold this belief if you didn’t think there was a good reason to do so.
It’s my assumption because our brains are AGI for ~20 W.
In contrast, many kW of GPUs are not AGI.
Therefore, it seems like brains have a way of storing and updating information that’s much more efficient than what we’re able to do.
Of course, maybe I’m wrong and it’s due to a lack of training or lack of data or lack of algorithms, rather than lack of hardware.
DNA storage is way more information dense than hard drives, for example.
I think that’s probably the crux. I think the evidence that the brain is not performing that much computation is reasonably good, so I attribute the difference to algorithmic advantages the brain has, particularly ones that make the brain more data efficient relative to today’s neural networks.
The brain being more data efficient I think is hard to dispute, but of course you can argue that this is simply because the brain is doing a lot more computation internally to process the limited amount of data it does see. I’m more ready to believe that the brain has some software advantage over neural networks than to believe that it has an enormous hardware advantage.