From medical imaging and language translation to facial recognition and self-driving cars, examples of artificial intelligence (AI) are everywhere. And let’s face it: although not perfect, AI’s capabilities are pretty impressive.

Even something as seemingly simple and routine as a Google search represents one of AI’s most successful examples, capable of searching vastly more information at a vastly greater rate than humanly possible and consistently providing results that are (at least most of the time) exactly what you were looking for.

The problem with all of these AI examples, though, is that the artificial intelligence on display is not really all that intelligent. While today’s AI can do some extraordinary things, the functionality underlying its accomplishments works by analyzing massive data sets and looking for patterns and correlations without understanding the data it is processing. As a result, an AI system relying on today’s AI algorithms and requiring thousands of tagged samples only gives the appearance of intelligence.

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