Exercise: Being Random
Used in Workroom PlayTime 027
Randomness is hard to assess. While testing, we commonly need random data – sometimes to be meaningless, sometimes to be more 'real', sometimes to avoid disrupting behaviour with unintended patterns.
This exercise lets us explore different ways that patterns can be present and absent in a string of text.
More to read and play with
See Randomness Test for information on how to check for randomness, and play with https://mzsoltmolnar.github.io/random-bitstream-tester/ (in the exercise below)
Machine-made randomness and human-made randomness are different. Humans can make meaningful strings that algorithms judge to be random, while human attempts at randomness often fail algorithmic assessments. Algorithms can make endless random output, but that randomness may be constrained to work only within the bounds of the algorithm. See Moravec's paradox for more on tasks that algorithms find hard but people find easy
Play with the ARC-AGI daily puzzle for direct experience with something that is designed for people to manage but which AIs find tricky.
The exercise involves assessing and producing random text.
Examples / questions
How is abbabbbbababbababbbbabababaaababababbabba random?
Compare with or
how about ?
how about ropemediumfillsadtrout? Or азсекасбамджеймс?
Let's talk about these examples, and reflect on what we understand by random.
Exercise
Use https://mzsoltmolnar.github.io/random-bitstream-tester/ . get rid of the video overlay, chose "manual ... input" and start hammering 1s and 0s. Hit "start now" to test.
Try to type in a random string of 1s and 0s
Reflect
What insights can you share?
You've used randomness in testing – did it matter, to the tests, whether 'random' was random? How did you check?
?[placeholder for now – needs instructions, text input, submission mechanism, analysis]
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JL Notes: We need random data to data that is effectively meaningless to act as a control (i.e. text field content), distributed evenly but not regularly across a range (, or distributed around some value (as contrasted to being bang on)
JL Notes: We test for randomness with
JL notes: we generate randomness with (entropy, algorithms, keyboard bashing). We need to be aware of distribution, of sample size, of
Type a random string in the box below. Hit assess to see an algorithmic assessment of how random it is.
Exercise 1
Try to make a string that seems random to the assessor
Exercise 2
Try to make a string that seems random to the assessor, but is clearly not random. You're welcome to explain the trick that makes it deterministic to you.
Exercise 3
What randomnesses, and what patterns, is the assessor looking for? Explore to discover, share your findings and examples.
Comments
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