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0 pointsActorNightly1y ago0 comments

There has been some work, but the problem is that its such a massive search space. Philosophically speaking, if you look at how humans came into existence, you could make an argument that the process of evolution from basic lifeforms can be represented as one giant compute per minute across of all of earth, where genetic selection happens and computation proceeds to the next minute. Thats a fuckload of compute.

In more practical terms, you would imagine that an advanced model contains some semblance of a CPU to be able to truly reason. Given that CPUs can be all NAND gates (which take 2 neurons to represent), and are structured in a recurrent way, you fundamentally have to rethink how to train such a network, because backprop obviously won't work to capture things like binary decision points.

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baanist1y ago

I thought the whole point of neural networks was that they were good at searching through these spaces. I'm pretty sure OpenAI is pruning their models behind the scenes to reduce their costs because that's the only way they can keep reducing the cost per token. So their secret sauce at this point is whatever pruning AI they're using to whittle the large computation graphs into more cost efficient consumer products.

spencerchubb1y ago

When you train a neural network, it is not search, it is descending through a curve.

If you were to search for billions of parameters by brute force, you literally could not do it in the lifespan of the universe.

A neural network is differentiable, meaning you can take the derivative of it. You train the parameters by taking finding gradient with respect to each parameter, and going in the opposite direction. Hence the name of the popular algorithm, gradient descent.

bob10291y ago

A biological neural network is certainly not differentiable. If the thing we want to build is not realizable with this technique, why can't we move on from it?

Gradient descent isn't the only way to do this. Evolutionary techniques can explore impossibly large, non-linear problem spaces.

Being able to define any kind of fitness function you want is sort of like a super power. You don't have to think in such constrained ways down this path.

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