How DeepMind's Latest AI Hints at Machines That Think More Like Us

I once asked a deep learning researcher what he'd like for Christmas. Nerd jokes aside, the lack of so-called "labeled" training data in deep learning is a real problem. Deep learning relies on millions upon millions of examples to tell the algorithm what to look for--cat faces, vocal patterns, or humanoid things strolling on the street. A deep learning algorithm is only as good as the data it's trained on--"garbage in, garbage out"--so accurately gathering and labeling existing data is essential. For the human researchers tasked with the job, carefully parsing the training data is a horrendously boring and time-consuming process.