Yet people think the feats like those of Equation Group could ONLY be state-sponsored (Iâ€™m not saying it isnâ€™t). Individuals will engage in superhuman effort for even the most trivial of reasons.

No no no. He may have a nifty algorithm, but there are only a small, finite number of Waldo pictures. Consciously or not, heâ€™s memorized them. Itâ€™s like when youâ€™ve played too much Trivial Pursuit - somebody says, â€śIn 1066â€¦â€ť and you shout out, â€śWilliam the Conqueror!â€ť

What happens when you create a new picture and stick Waldo in the top left corner?

Wally, dammit!

Now we know where to place Waldo so that people using this algorithm canâ€™t ever find him.

This is a text-book example of overfitting.

There are only a limited number of Whereâ€™s Waldo puzzles. Itâ€™s very easy to generate an algorithm that â€śsolvesâ€ť the puzzle, but which in essence is just memorizing where they are on those several-dozen images.

The fact that Waldo never appears in the bottom right is simply random noise â€“ and thatâ€™s precisely what an overfitted model will appear to represent as a â€śpatternâ€ť rather than â€śrandom chance.â€ť

Reducing the variables in the model to general, not specific, qualities and trying to see how many match will help avoid the problem of overfitting. For example, â€śnot around the edgesâ€ť is a much more general quality, and may well represent a real pattern in the data.

This â€śpath,â€ť by the way, is even sillier, now that I look at the method. Itâ€™s simply every single possible Waldo location, and then the approximate shortest line between all points. â€śStart hereâ€ť doesnâ€™t actually represent the optimal place to start â€“ the path would be identical in all ways if you followed it backwards.

Donâ€™t jump the gun.

â€śIn 1066â€¦â€ť

â€śWilliam the Conqueror!â€ť

â€śâ€¦who was the Jewish vizier crucified at the Granada Massacre?â€ť

No longer necessary to look: http://media-cache-ak0.pinimg.com/736x/e9/bc/d2/e9bcd255a60ccef732811fd547d7fb64.jpg

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