Putting the “intelligent” machine in its place

South Korean Go game fans watch a television screen broadcasting live footage of the Google DeepMind Challenge Match, at the Korea Baduk Association in Seoul on March 9, 2016. A 3,000-year-old Chinese board game was the focus of a very 21st century showdown as South Korean Go grandmaster Lee Se-Dol kicked off his highly anticipated clash with the Google-developed supercomputer, AlphaGo. / AFP / JUNG YEON-JE (Photo credit should read JUNG YEON-JE/AFP/Getty Images)

It’s rare for a data scientist to be so reflective and critical on the limitations (and therefore the real opportunities) of their discipline, as Katherine Bailey has managed in a short article on Techcrunch.

Recent issues around machine learning biases and ethics make it clear that math and data can only take us so far. The recent fake news debacle and the efforts of some top researchers in natural language processing to address it show that sometimes even just defining the problem you’re trying to solve is the hardest part. We need human intelligence to decide how and when to use machine intelligence, and the more sophisticated the uses we make of machine intelligence, the more critically we need human intelligence to ensure it’s deployed sensibly and safely.

It’s time we started exalting critical thinking skills the way we do math skills. While we can entrust machines with mathematical calculations, we can’t entrust them with critical thinking, nor will we be able to any time soon. Reasoning about moral issues and identifying which types of problems are solvable with math are skills unique to humans.

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