It is terrible, they literally can't explain what they mean. They don't say what it means to "compute the A.I. network". Makes me think it is a bogus story, some academic runoff.
To "compute a neural network" is a long-established way to say "train a neural network", which in turn is a long-established way to say "find a set of weights for the neural network that maximises its accuracy".
The idea is that a neural net is a kind of data structure used in AI, like a decision tree or a decision list (like a decision tree but it's a list). There are different algorithms that can "compute", i.e. construct, a decision tree from data. In modern parlance we say that the decision tree is "trained". Same goes for neural nets, except the network itself is typically constructed beforehand, and manually (we refer to it as the "architecture" of the neural net) and only its weights need to be tweaked until it has a good accuracy- at which point we say the training algorithm has "converged".
It's all a bit confusing because in common parlance there is little distinction made between a neural net's network (its architecture), the algorithm that trains the neural net by finding the weights that minimise its error (backpropagation) and the neural net with trained weights (the "model"). Sometimes I wonder if this distinction is clear in the minds of people who actually train those things.
Btw, the study is solid and meaningful. It's a theoretical result. More of those are needed in machine learning, we got plenty of empirical results.
Thanks. What reason do they give that these network weights cannot be computed?
I am pretty hip to modern AI workings, and that is what I assumed they meant by compute. What I know also tells me that "compute" as a concept is lacking for what we might later call holistic AI, and if, for instance, they are conjecturing strictly SGD proved networks, then I maintain my position that they are merely noodling, with AI and terminology alike.
Intelligence is not entirely computable; nor is occurrence entirely probabilistic. Respectively, there is randomness at work, and dynamic systems that produce novelty.
The idea is that a neural net is a kind of data structure used in AI, like a decision tree or a decision list (like a decision tree but it's a list). There are different algorithms that can "compute", i.e. construct, a decision tree from data. In modern parlance we say that the decision tree is "trained". Same goes for neural nets, except the network itself is typically constructed beforehand, and manually (we refer to it as the "architecture" of the neural net) and only its weights need to be tweaked until it has a good accuracy- at which point we say the training algorithm has "converged".
It's all a bit confusing because in common parlance there is little distinction made between a neural net's network (its architecture), the algorithm that trains the neural net by finding the weights that minimise its error (backpropagation) and the neural net with trained weights (the "model"). Sometimes I wonder if this distinction is clear in the minds of people who actually train those things.
Btw, the study is solid and meaningful. It's a theoretical result. More of those are needed in machine learning, we got plenty of empirical results.