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June 8, 2014 / like2think

A Gaming Perspective

Imagine a kid playing a game called “Guess The Word!”. There is a pile of cards, with a sequence of seven coherent words written on each of them, the first and the last three on one side, the middle one on the other side. He picks a card, read the six words on the front and makes a guess about the skipped one. Then he flips the card and sees the correct answer.

A funny developing game, isn’t it? Requires, however, a certain language command to start. If the kid is properly motivated to play a lot, for example if he competes with his friends, it might improve his language skills.

My point is that this is exactly the way current machine leaning works. The process described above repeats a recent method of word representation learning.

In machine learning we choose a game with simple rules, we design a simple player that can learn to play better just by playing a lot. We leave it to play the game all night long, and in the morning we want to see it doing much better. And finally, we also expect that it will not only be good in this particular game, but also learn something useful, like a child did.

An interesting aspect of this comparison is that it justifies the pretraining phase. Because an infant can not play “Guess The Word!”. And also a savage from an isolated island can not play it. Or rather he can, but in a weird way: he might develop his own weird explanation of what these paintings mean and why “the” is always prepended to best. Make it a question of survival for this poor guy, and he would play not worse than me. But most probably he would never understand that these “words” stands for objects, colors, actions, etc.

So this is not a game you play with your 1-year old baby. Usually you find something a way simpler to “pretrain” it. The games you usually play with it involve input from different modalities: it hears, sees, touches. Another thing is that sometimes it even plays itself, stimulated by so well-known to everybody hate of boredom…

The challenge for machine learning is to organize such games.

To be continued.


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