In my last babble, I introduced the Babble and Prune model of thought generation: Babble with a weak heuristic to generate many more possibilities than necessary, Prune with a strong heuristic to find a best, or the satisfactory one. I want to zoom in on this model. If the last babble was colored by my biases as a probabilist, this one is motivated by my biases as a graph theorist.

First, I will speculate on the exact mechanism of Babble, and also highlight the fact Babble and Prune are independent systems that can be mocked out for unit testing.

Second, I will lather on some metaphors about the adversarial nature of Babble and Prune. Two people have independently mentioned Generative Adversarial Networks to me, a model of unsupervised learning involving two neural nets, Generator and Discriminator. The Artist and the Critic are archetypes of the same flavor - I have argued in the past the spirit of the Critic is Satan.

Babble is (Sampling From) PageRank

Previously, I suggested that a Babble generator is a pseudorandom word generator, weighted with a weak, local filter. This is roughly true, but spectacular fails one of the technical goals of a pseudorandom generator: independence. In particular, the next word you Babble is frequently a variation (phonetically or semantically) of the previous one.

PageRank, as far as I know, ranks web pages by the heuristic of "what is the probability of ending up at this page after a random walk with random restarts." That's why a better analogy for Babble is sampling from PageRank i.e. taking a weighted random walk in your Babble graph with random restarts. Jackson Pollock is visual Babble.

Imagine you're playing a game of Scrabble, and you have the seven letters JRKAXN. What does your algorithm feel like?

You scan the board and see an open M. You start Babbling letter combinations that might start with M: MAJR, MRAJ, MRAN, MARN, MARX (oops, proper noun), MARK (great!). That's the weighted random walk. You set MARK aside and look for another place to start.

Time for a restart. You find an open A before a Triple Word, that'd be great to get! You start Babbling combinations that end with A: NARA, NAXRA, JARA, JAKA, RAKA. No luck.

Maybe the A should be in the middle of the word! ARAN, AKAN, AKAR, AJAR (great!). You sense mean stares for taking so long, so you turn off the Babble and score AJAR for (1+8+1+...

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