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It’s not Skynet yet: In machine learning, there’s still a role for humans

Even though a perception persists that machines can increasingly solve complex problems and process large amounts of data on their own, machine learning experts say humans still play a key role.

If you’ve ever seen any of The Terminator films, you’re familiar with Skynet, the self-aware computing system at odds with humanity. But, even though a perception persists that machines can increasingly solve complex problems and process large amounts of data on their own, machine learning experts say humans still play a very important role.

Human intervention is critical at multiple layers, from choosing the algorithms to apply to feature creation to crafting the entire structure within which a machine will learn, said Scott Brave, founder and CTO of Baynote, at GigaOM’s Structure: Data conference Wednesday.

Down the road, he said, there will be more opportunities for machine-man collaboration, as data scientists observe what the machines may be learning and then add new inputs and ideas to the system.

“A lot of times we forget that even though it’s big data, the amount of data that the machine has access to pales in comparison to the amount of data we’re absorbing and have access to,” he said. “We’re building intuitions and holistic pictures in our minds and we see these connections that the machine might not even have the possibility of seeing because it doesn’t have the right data.”

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