Whole-brain emulation has two difficult bottlenecks

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 6:05

   matters why not try to do literal whole brain emulation like we have we have proof of concept for this with much smaller animals we had the sea elegance worm and now the fruit fly i have so many robin hansen has written about this you know age of m one of our favorite books of all time so like why not just do this so i love this stuff i think it's really cool i have many friends are working on this you should get my friend love chish off on the show if you want to see some really really crazy sci-fi as for whole brain emulation stuff i i think this is a really noble path but maybe it's slightly misguided insofar as there's two hard parts with whole brain emulation the first is actually much much harder than the second but the second is also really hard the first is how do you get the data of what the heck's going on in the brain

2. 6:43

   our best scanners are woefully inadequate here whenever you get mri or pt or whatever this is really quite high resolution it's great and yet temporally everything's smeared to death because it's really the blood that you're measuring right and if you look at a device like a neural link a device that has immense potential to you know raise the floor for the human experience and create the transhumanist future like okay you can get high resolution neuron activations but localized to a given area and maybe that company will move towards getting more coverage and this of thing in the limit i don't know we'll see where they go but even then you're not going to cover every single neuron you're not going to get the full dynamics of the brain like that and so you have a really hard time even just in a really naive way measuring what's going on and then

3. 7:20

   secondly if you were to actually have the measurements how do you run the thing if you think about number of parameters the brain has it's a lot and they're not necessarily parameters the same way that you might get a neural network they're kind of constrained some of their connectivity and things it's there's you cannot just analogy like 100 billion yeah so the question you're talking about like neurons or synapses or what have you um but if you were to try to model them in the completely exact same way like with like a hodgepain hodgepain type thing it might take like 200x more compute i think something rough like that you should actually be on this this is something i'm not totally sure off the top of my head uh it's not actually a thing that you can map one-to-one in this clean way because your brain is taking advantage of physics it's

4. 7:56

   taking advantage of these really really weird dynamics that make a lot of sense When you have the substrate that's kind of evolving forward and it's really grainy and actually doesn't really have a good analog when it comes to our computational substrates. And so I think we should come with algorithms that are not like literally exactly what the brain is doing, but algorithms which are, well, for one, probably much better because we have a way more bandwidth, right?

5. 8:14

   We can send a lot more information around. We can actually be a lot greedier. The amount of information exchange in your model is so much more than your brain really can do, at least, you know, per millisecond. And then these new algorithms... What was the other thing? The second thing is actually running the thing, like I said. So there's like getting the data about what is going on, and then there's running the thing faithfully, both of which are really, really hard, nearly intractable problems.