Data annotation is simply the process of labeling information so that machines can use it. It is especially useful for supervised machine learning (ML), where the system relies on labeled datasets to process, understand, and learn from input patterns to arrive at desired outputs. In ML, data annotation occurs before the information gets fed to a system. The process can be likened to using flashcards to teach children. A flashcard with the picture of an apple and the word "apple" would tell the children how an apple looks and how the word is spelled.

The question seems somewhat ironic if you think about it. Are the creations--intelligent systems--set to replace or displace their creators--programmers--in the future? It sounds like the plot of a sci-fi movie, doesn't it? The more important question, though, is: Can it be true? A recent future of employment study predicts that nearly 50% of the jobs in the U.S. are likely to be automated by 2030.

Introducing a new technology often leads to unintended consequences. The development of the Dutch fluyt in the 16th century, for example, resulted in the discovery of coal tar. This, in turn, led to the development of a rubber substitute called "nylon." Nylon, in turn, ushered in the era of plastics, which helps sustain our industries, but also pollute our seas and release toxic chemicals into the environment as they decompose. Now we are on the cusp of unleashing another technology that has the potential to change lives and the power to redirect the course of human civilization, perhaps in an even more impactful way than the fluyt did.