The “neuro” part of neuro-symbolic A.I. refers to deep learning neural networks. Neural nets are the brain-inspired type of computation which has driven many of the A.I. breakthroughs seen over the past decade. A.I. that can drive cars? Neural nets. A.I. which can translate text into dozens of different languages? Neural nets. A.I. which helps the smart speaker in your home to understand your voice? Neural nets are the technology to thank.

Neural networks work differently to symbolic A.I. because they’re data-driven, rather than rule-based. To explain something to a symbolic A.I. system means explicitly providing it with every bit of information it needs to be able to make a correct identification. As an analogy, imagine sending someone to pick up your mom from the bus station, but having to describe her by providing a set of rules that would let your friend pick her out from the crowd. To train a neural network to do it, you simply show it thousands of pictures of the object in question. Once it gets smart enough, not only will it be able to recognize that object; it can make up its own similar objects that have never actually existed in the real world.

“For sure, deep learning has enabled amazing advances,” David Cox told Digital Trends. “At the same time, there are concerning cracks in the wall that are starting to show.”

One of these so-called cracks relies on exactly the thing that has made today’s neural networks so powerful: data. Just like a human, a neural network learns based on examples. But while a human might only need to see one or two training examples of an object to remember it correctly, an A.I. will require many, many more. Accuracy depends on having large amounts of annotated data with which it can learn each new task. Read On: