Why brains and GPUs aren’t built the same way

6:26all sorts of very very cool things. So that's how we've mostly done artificial neural networks. Right now how are how is the brain different from these artificial neural networks? That's something that we should focus on here because these guys are trying to use biological neurons. If the artificial neural network is just that good, why would we want to try to use biological neural networks? There is an advantage that cortical labs and other people doing this technology say the idea is silicon models which are the things in Nvidia and things like that they use the vonoman architecture of computing. The biological brain is

7:07just the biological brain. The vonoman architecture is the following. Your processor is separate from your memory. >> Mhm. >> Right. The memory is where you store stuff. The processing is where you compute. Those are two separate things in a conventional computer. Right? You've got your like RAM and then you've got your CPU. And over the years, there's a huge gap between how fast you can compute things in your CPU or your GPU and how fast you can access the memory where you store stuff in the brain. Both are colllocated. >> Okay? >> They're the same thing. They're all stored in the synapses, the weights of these things. The memory itself is stored in the weights. And the way that

7:49you process stuff is you go from one neuron to the other in this neural network. But in a artificial neural network, there's that difference. Mhm. >> Right. >> Mhm. >> So the idea is that perhaps this gives the brain an advantage. Another advantage that the brain has is that it's massively parallel and it only uses like 12 to 20 watts of power for 100 billion neurons. On the other hand, state-of-the-art GPUs and neural nets use gawatt of power for computing, right? So there's a clear difference also in the amount of energy needed to create that computing. So the the the the differences between the two, one is efficiency. We've created a simplified

8:31abstraction of what the brain does >> and it takes a lot more energy to even do some of the things that the brain does. So it's a highly inefficient >> as compared to the brain naturally. Yeah. Artificial intelligence is highly inefficient or highly energy consumptive. >> Yes. Um the also the way in which the brain is structured, it can do significantly larger amounts of parallel processing uh which enables that energy efficiency in part. >> Yeah. And also like each neural each neuron itself is like highly efficient as a computational unit, right? It doesn't require a lot of energy. We

9:11think you know our brain does consume 20% of the energy of our body but still that's not actually a lot when you think about things right so perhaps the idea is we can use this natural biological wetwware >> in place of our vonoman architecture hardware the GPUs that take up all that energy that's the argument okay but in order to do that we need to be able to read and write to biological neurons >> right and also where do these biological neurons exist >> yeah yeah where do they come from They're usually in our brain, right? So, let's get into how we actually read and write to real biological neurons in a dish. This was actually um pioneered in 1972 by Thomas G. Pine at Caltech when

9:54he developed the first multi-elerode array. Here's the idea. So, neurons produce electricity when they talk to one another. That's how they talk. They use ion channels which are little holes in their membranes, the boundary between the inside and the outside of the cell. And when those little membrane proteins, those channels open up or close, that facilitates the transfer of ions like potassium ions, chlorine ions, sodium ions from the inside to the outside of the cell. Ions are charged atoms in some sense. And so when they move around, they're going to create electricity. They're going to create electric fields. And we can sense that if we have an electrode array, which is just a bunch

10:36of tips of metal that is connected to some kind of amplifier. And as each neuron produces a little electric field blip, I can sense that with my electrode array, right? I can also supply electricity to then influence the behavior of the neurons near my electrode. Like if I've got a little metal tip here and I supply electricity, that is going to change the electric field around that metal tip, which is then going to influence the ions and thus the proteins on that neuron that are nearby that metal tip. And so I can now toggle between on and off based on that stimulation. So I've got a read, which is when the neurons fire, they're going to release a little

11:16electric field, you know, spike that I can read. And then I can write by supplying electricity and that's going to influence the neurons around it. >> I have a quick clarifying question. Yeah. When you talked about the spike just now that you're referring to this the concept you talked about earlier of the action potential. Yes. >> This sort of like surge of of of of electrical signal. >> Yeah. >> That then the neuron utilizes to do its next action or its next step. And so when we have the ability, we have the ability to provide that uh energy or source material necessary for the neuron to trigger its action

11:57potential artificially. It's not only uh cap it's not only a capability that is >> u not reproducible artificially. >> Yes. Exactly. Yeah. It's something that we can we can provide a signal to whatever metal contact is near that neuron and then you know that's going to create a change in electric field which is going to then drive ions around that neuron. It's going to drive the proteins around that neuron and then the neurons will be sensitive to that change in the electric field, right? They're going to think that it's another neuron somewhere that's like doing that. >> So we've to to to just make a fine point on it. We can translate into the language that these neurons speak using

12:38this kind of methodology. >> Yes. Exactly. Yeah. So now we can read and write. >> Yes. >> Okay. So can we do biomputing? Well, early biomp computing actually started in the late 1990s, early 2000s. There was this guy Steve M. Potter. He was at