In other words, there are no "rabbit holes" or "dark tunnels of the mind."

The great success of neural networks is due in large part to the surprising ease in which gradient-based methods are used to train them. Neural networks are often trained using a “loss function” which computes the error rate between predicted and observed values from its environment. The predictions are calculated based on an extremely large and complex function of millions or billions of parameters (the network itself). Since the loss function is merely a simple function between these predictions (outputs) and the values received from the environment, the loss function itself is an extremely high-dimensional function.

Gradient descent, the training method used with neural networks, can be intuitively thought of as “hill climbing” (actually hill descending, but this is isomorphic to the other). In this metaphor, the “parameters” of the model would be represented by the X and Y axes, and the “loss” would be represented by the Z axis, on a typical 3-dimensional graph. The problem, of course, is that the parameters of the model are typically far, far greater than two in number.

That doesn’t stop people from being able to visualize the network training using this metaphor, anyway.^[1] Researchers have wanted to understand why and how gradient descent works so well, given that gradient updates are characterized by small, incremental “steps” down the hill. What if there are many hills? What if taking a step down the hill is in the overall wrong direction, if there is a longer, but less steep way to move down in the opposite direction?

Most gradient-descent methods make use of momentum, which means that if the incremental updates have been in largely the same direction for the past several iterations, the speed (magnitude) of the update is allowed to increase. This essentially allows for lots of hills, but assumes that the overall correct direction would mainly be in the same general direction - imagine walking down a large crater with lots of little bumps and rocks all over the slope.

In the early days of neural networks, they were widely thought to be difficult to train.^[2] This was largely due to the belief that optimization processes would converge to “local minima” with error rates far higher than the global minimum. This belief persisted for quite some time, until empirical evidenc...

Analogies From AI Applied To Rationality

Analogies From AI Applied To Rationality