The significance of “edge cases” and the cost of imperfection as it pertains to AI adoption

With the advent of new tools and technologies, it’s tempting to think that the rules of work have changed or that old problems can be forgotten. This is often true, but as we use new technology, we see new manifestations of ancient problems.

For the want of a nail the shoe was lost,

For the want of a shoe the horse was lost,

For the want of a horse the rider was lost,

For the want of a rider the battle was lost,

For the want of a battle the kingdom was lost,

And all for the want of a horseshoe nail.

— Benjamin Franklin (Poor Richards Almanack)

The above poem uses a dated reference, for modern militaries don’t often rely on horseshoe nails. However, the spirit of this poem remains true.

Small problems often have large effects.

We see this in many applications of AI. The degree to which this applies informs which AI applications can have widespread adoption, and which remain experimental.

Edge cases

AI has broad capabilities, with varying levels of adoption. Each application of AI inevitably encounters scenarios in which the systems do not perform as required or as expected. We call these scenarios “edge cases,” as illustrated below.

Friend or Food?

Deep learning and convolutional neural networks are two AI techniques used extensively to perform image classification.

Humans can look at a picture and identify what the subject of the image is. Machines don’t fare too badly — in fact, Google’s “Show and Tell” algorithm can caption an image with over 93% accuracy!

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