When I was first introduced to neural networks, they were considered algorithms with extremely expensive computational requirements. Even the most trivial network required a high-end PC with lots of memory and floating-point math capability.

Of course, at the time a high-end PC processor ran at 90 megahertz, 32 megabytes of RAM is considered a lot, and floating point math required a separate (and expensive) floating-point co-processor.

Now the cell phones we have in our pockets have faster processor and more memory than those powerful PCs of old. Every current processor has floating-point math capability, no extra chip required.

Which means what used to be the domain of specialized programmers, running on expensive hardware, is now possible everywhere: running in a web browser like the TensorFlow playground.

But it’s still hard for a human to grasp what’s going on inside a neural network as it learns and adjusts. While the accessibility of the technology (meaning how easy it is to obtain) has improved, the accessibility of the knowledge (meaning how easy it is to understand) hasn’t.

Computer brains have made great advances in the past years…

Human brains have not.