Teaching Me Softly

Machine learning is teaching us the secret to teaching

By Alan S. Brown

When Pyotr Stolyarsky died in 1944, he was considered Russia’ s greatest violin teacher. He counted among his pupils a coterie of stars, including David Oistrakh and Nathan Milstein, and a school for gifted musicians in his native Odessa was named after him in 1933. But Stolyarsky couldn’t play the violin anywhere near as well as his best students. What he could do was whisper metaphors into their ears. He might lean over and explain how his mother cooked Sabbath dinner. His advice gave no specific information on what angle the bow should describe, or how to move the fingers across the frets to create vibrato. Instead, it distilled his experience of the music into metaphors his students could understand.

When Vladimir Vapnik teaches his computers to recognize handwriting, he does something similar. While there’s no whispering involved, Vapnik does harness the power of “privileged information.” Passed from student to teacher, parent to child, or colleague to colleague, privileged information encodes knowledge derived from experience. That is what Vapnik was after when he asked Natalia Pavlovich, a professor of Russian poetry, to write poems describing the numbers 5 and 8, for consumption by his learning algorithms. The result sounded like nothing any programmer would write. One of her poems on the number 5 read,

All told, Pavlovich wrote 100 such poems, each on a different example of a handwritten 5 or 8, as shown in the figure to the right. Some had excellent penmanship, others were squiggles. One 5 was, “a regular nice creature. Strong, optimistic and good,” while another seemed “ready to rush forward and attack somebody.” Pavlovich then graded each of the 5s…

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