9 machine learning myths

When technology is as hyped as machine learning is, misunderstandings and misconceptions abound. Here’s a clear-eyed look at what machine learning can and can’t deliver.

9 machine learning myths

Machine learning is proving so useful that it's tempting to assume it can solve every problem and applies to every situation. Like any other tool, machine learning is useful in particular areas, especially for problems you’ve always had but knew you could never hire enough people to tackle, or for problems with a clear goal but no obvious method for achieving it.

Still, every organization is likely to take advantage of machine learning in one way or another, as 42 percent of executives recently told Accenture they expect AI will be behind all their new innovations by 2021. But you’ll get better results if you look beyond the hype and avoid these common myths by understanding what machine learning can and can’t deliver.

Myth: Machine learning is AI

Machine learning and artificial intelligence are frequently used as synonyms, but while machine learning is the technique that’s most successfully made its way out of research labs into the real world, AI is a broad field covering areas such as computer vision, robotics and natural language processing, as well as approaches such as constraint satisfaction that don’t involve machine learning. Think of it as anything that makes machines seem smart. None of these are the kind of general “artificial intelligence” that some people fear could compete with or even attack humanity.

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