In this episode, Byron talks about the ability to prove or disprove superstitions via analysis of massive data sets.
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Itâ€™s no surprise that sports teams often take on a whole lot of superstitions.
In the grand scheme of things, they donâ€™t actually play that many games, so if player X stubs his toe and goes on to win, and then the next day stubs his toe again and wins, itâ€™s not unreasonable to assume that he should go around stubbing his toe before every game, and perhaps even encouraging his teammates to do the same.
The interesting thing is that humans are not the only creatures that behave with superstitions.
Pigeons, for instance, do it as well. If a pigeon does some behavior and is rewarded with food, maybe some elaborate dance or something, it becomes a superstition, and it does that dance again and again, expecting a reward.
And even when the superstition is proven to be unreliable like in our stubbed toe example, the pigeon continues to do so because of its limited grasp of reality, because we experience life at such a slow pace.
Computers would be in theory prone to superstitions, because their training algorithms are trained on limited data sets in the real world.
They can, in fact, have erroneous data sets. But if the data set is large enough and appropriately representative, all of the superstitions should be wiped out of the system.
I mean, think about it. If a baseball player stubbed his toe a hundred thousand times and won a hundred thousand games, and then didnâ€™t stub his toe a hundred thousand times and lost that same number of games, you could reasonably conclude that perhaps stubbing his toe was, in fact, a key constituent of winning the game.
I suspect that much of what we think we know about the world, however, is in one form or another, simply superstition.
We just deal in our minds with such limited data sets, very few things about our world have really been subjected to the enormous data sets required to get rid of superstition thanks to the law of large numbers.
In addition, right now probably training our machines to be every bit as superstitious as we are, but that as our datasets grow and as our computers get faster, this will no longer be the case in the future.
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