Don’t you get the same effect from adding an orderly grid of dots?
In that particular example, yes. Because the image is static, as is the static.
If the static could change over time, you could get a better sense of where the image lies. It’s cheaper and easier—and thus ‘better’ - to let natural randomness produce this static, especially since significant resources would have to be expended to eliminate the random noise.
What about from aligning the dots along the lines of the image?
If we knew where the image was, we wouldn’t need the dots.
To be precise, in every case where the environment only cares about your actions and not what algorithm you use to produce them, any algorithm that can be improved by randomization can always be improved further by derandomization.
It’s clear this is what you’re saying.
It is not clear this can be shown to be true. ‘Improvement’ depends on what is valued, and what the context permits. In the real world, the value of an algorithm depends on not only its abstract mathematical properties but the costs of implementing it in an environment for which we have only imperfect knowledge.
If the static could change over time, you could get a better sense of where the image lies. It’s cheaper and easier—and thus ‘better’ - to let natural randomness produce this static, especially since significant resources would have to be expended to eliminate the random noise.
If we knew where the image was, we wouldn’t need the dots. It’s clear this is what you’re saying.It is not clear this can be shown to be true. ‘Improvement’ depends on what is valued, and what the context permits. In the real world, the value of an algorithm depends on not only its abstract mathematical properties but the costs of implementing it in an environment for which we have only imperfect knowledge.