Since the invention of the computer, there have been people talking about the things that computers will never be able to do. Whether it was beating a grand master at chess or winning on Jeopardy!, these predictions have always been wrong. However, some such nay-saying always had a better grounding in computer science. There were goals that, if you knew how computers worked, you knew they would be virtually impossible to achieve. Recognizing human emotions through facial expressions. Reading a wide variety of cursive handwriting. Correctly identifying the words in spoken language. Driving autonomously through busy streets.

Well, computers are now starting to be able to do all of those things, and quite a bit more. Were the nay-sayers really just too cynical about the true capabilities of digital computers? In a way, no. To solve those monumental challenges, scientists were forced to come up with a whole new type of computer, one based on the structure of the brain. These artificial neural networks (ANNs) only ever exist as a simulation running on a regular digital computer, but what goes on inside that simulation is fundamentally very different from classical computing.

The usefulness of ANNs falls into one of two basic categories: as tools for solving problems that are inherently difficult for both people and digital computers, and as experimental and conceptual models of something — classically, brains. Let’s talk about each one separately.
First, the real reason for interest (and, more importantly, investment) in ANNs is that they can solve problems. Google uses an ANN to learn how to better target “watch next” suggestions after YouTube videos. The scientists at the Large Hadron Collider turned to ANNs to sift the results of their collisions and pull the signature of just one particle out of the larger storm. Shipping companies use them to minimize route lengths over a complex scattering of destinations. Credit card companies use them to identify fraudulent transactions. They’re even becoming accessible to smaller teams and individuals — Amazon, MetaMind, and more are offering tailored machine learning services to anyone for surprisingly modest a fee.

Things are just getting started. Google’s been training its photo-analysis algorithms with more and more pictures of animals, and they’re getting pretty good at telling dogs from cats in regular photographs. Both translation and voice synthesis are progressing to the point that we could soon have a babelfish-like device offering natural, real time conversations between people speaking different languages. And, of course, there are the Big Three ostentatious examples that really wear the machine learning on their sleeve: Siri, Now, and Cortana.
The other side of a neural network lies in carefully designing it to mirror the structure of brains. Both our understanding of that structure, and the computational power necessary to simulate it, are nowhere close to what we’d need to do robust brain-science in a computer model. There have been some amazing efforts at simulating certain aspects of certain portions of the brain, but it’s still in the very preliminary stages.

One advantage of this approach is that while you can’t (or… shouldn’t) genetically engineer humans to have an experimental change built into their brains, you absolutely can perform such mad-scientist experiments on simulated brains. ANNs can explore a far wider array of possibilities than medicine could ever practically or ethically consider, and they could someday allow scientists to quickly check on more out-there, “I wonder” hypotheses with potentially unexpected results.
When you ask yourself, “Can an artificial neural network do it?” immediately after, ask yourself “Can I do it?” If the answer is yes, then your brain must be capable of doing something that an ANN might one day be able to simulate. On the other hand, there are plenty of things an ANN might one day be able to do that a brain never could.
The potential for ANNs is nearly limitless.