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.
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.
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.
The search space is all off too wide, difficult to parameterize, and there is a wide gap between effective and ineffective architectures - ie: a very small change can make a network effectively DOA.
Notably architecture search was popular for small vision nets where the cost of many training runs was low enough. I suspect some of the train-then-prune approaches will come back, but even there only by the best funded teams.
https://en.wikipedia.org/wiki/Neural_architecture_search
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.
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.
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.
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Program synthesis is a generalization of this. I’m not sure that many ML researchers have thought about the connections yet.
The search space is all off too wide, difficult to parameterize, and there is a wide gap between effective and ineffective architectures - ie: a very small change can make a network effectively DOA.
Notably architecture search was popular for small vision nets where the cost of many training runs was low enough. I suspect some of the train-then-prune approaches will come back, but even there only by the best funded teams.