I train neural networks, which are a type of machine learning program that imitates the way human brains learn. (Technically, they’re artificial neural networks, to differentiate them from the biological sort). Unlike traditional computer programming, where a human programmer comes up with a long set of rules for a computer to follow, with neural networks, the computer learns by example and comes up with its own rules.
Most of us encounter neural networks every day - they power face recognition, automatic language translation, object recognition, and self-driving cars. The neural networks I train, though, are for more modest, and sillier purposes - inventing new paint colors (like Burf Pink and Stanky Bean) or new names for guinea pigs (Popchop and Fuzzable, for example).
Today’s experiment: computer algorithms.
Not the algorithms themselves, mind you - that sounds difficult. Just their names. 2045 of them from the Wikipedia list of computer algorithms (big thanks to Joshua Schachter for extracting the data). I gave them to a simple char-rnn neural network, thinking it would be very interesting to find out what one computer program decides to name another.
The results do sound pretty algorithmic - you might be able to get away with recommending these to a programmer who’s stuck on a problem, if you leave before they google it.
Vandical time algorithm
Speedated heeling tree
Jusi tree Shamer
These are a bit less plausible (especially the inevitable fart algorithms. For some reason, the word “fart” often comes up in this neural network’s results.)
Prebabel strung parser
Stani computer somplerity farter estimator
Random damplestremptic ferchion
Aromatic pashering contex algorithm
Farter-hear srial fecty optimization
And these prove that basic and simple does not necessarily mean more believable. Don’t suggest using these. You will not sound smart.
Jashen computer statistication
Code forting algorithms
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