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This is: Babble, published by alkjash on the AI Alignment Forum.
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This post is an exercise in "identifying with the algorithm." I'm a big fan of the probabilistic method and randomized algorithms, so my biases will show.
How do human beings produce knowledge? When we describe rational thought processes, we tend to think of them as essentially deterministic, deliberate, and algorithmic. After some self-examination, however, I've come to think that my process is closer to babbling many random strings and later filtering by a heuristic. I think verbally, and my process for generating knowledge is virtually indistinguishable from my process for generating speech, and also quite similar to my process for generating writing.
Here's a simplistic model of how this works. I try to build a coherent sentence. At each step, to pick the next word, I randomly generate words in the category (correct part of speech, relevance) and sound them out one by one to see which continues the sentence most coherently. So, instead of deliberately and carefully generating sentences in one go, the algorithm is something like:
Babble. Use a weak and local filter to randomly generate a lot of possibilities. Is the word the right part of speech? Does it lie in the same region of thingspace? Does it fit the context?
Prune. Use a strong and global filter to test for the best, or at least a satisfactory, choice. With this word in the blank, do I actually believe this sentence? Does the word have the right connotations? Does the whole thought read smoothly?
This is a babble about embracing randomness.
Baby Babble
Research on language development suggests that baby babble is an direct forerunner to language. You might imagine that infants learn by imitation, and that baby babble is just an imperfect imitation of words the baby hears, and progress occurs as they physiologically adapt to better produce those sounds. You would be wrong.
Instead, infants are initially capable of producing all the phonemes that exist in all human languages, and they slowly prune out which ones they need via reinforcement learning. Based on the sounds that their parents produce and respond to, babies slowly filter out unnecessary phonemes. Their babbles begin to drift as they prune out more and more phonemes, and they start to combine syllables into proto-words. Babble is the process of generating random sounds, and looking for clues about which ones are useful. Something something reinforcement learning partially observable Markov decision process I'm in over my head.
So, we've learned that babies use the Babble and Prune algorithm to learn language. But this is quite a general algorithm, and evolution is a conservative force. It stands to reason that human beings might learn other things by a similar algorithm. I don't think it's a particularly controversial suggestion that human thought proceeds roughly by cheaply constructing a lot of low-resolution hypotheses and then sieving from them by allowing them to play out to their logical conclusions.
The point I want to emphasize is that the algorithm has two distinct phases, both of which can be independently optimized. The stricter and stronger your Prune filter, the higher quality content you stand to produce. But one common bug is related to this: if the quality of your Babble is much lower than that of your Prune, you may end up with nothing to say. Everything you can imagine saying or writing sounds cringey or content-free. Ten minutes after the conversation moves on from that topic, your Babble generator finally returns that witty comeback you were looking for. You'll probably spend your entire evening waiting for an opportunity to force it back in.
Your pseudorandom Babble generator can also be optimized, and in two different ways. On the one hand, you can...
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