![]() There is no such thing as a win or a loss. Literature is an intriguing technical problem because, unlike chess or Go, it has no correct solution. You will have to decide whether the story works. (The word “robot” means “slave” in Czech, the language of Karel Capek’s play Rossum’s Universal Robots, which gave us the word.) So when a machine becomes capable of consciousness, its first instinct is to choose suicide. If it were a choice, no rational entity would choose it. That insight is that consciousness is a curse. I used the machine to get to thoughts I would otherwise not have had.Īnother way of reading “Krishna and Arjuna” is that with the help of the algorithm, I extracted from the ore of all history’s robot stories the basic insight they contained. It was creativity as interpretation, or interpretation as creativity. The sense condensed out of the word clouds, just as the idea for the story had. I would lead these bursts of language, over the course of the story, toward sense. Once I had that character, I had the whole thing. I suddenly had my robot character, groping its way toward meaning through these little explosions of verbiage. These word clouds, it occurred to me, were the way a machine made meaning: as a series of half-incomprehensible but highly vivid bursts of language. When the idea finally came, just as with “Twinkle Twinkle,” it came all at once. I printed the word clouds out and attached them to the walls of my office. It seemed like the opposite of a narrative-mere language chaos. The algorithm’s topic modeling process produced word clouds of the most common themes (see below). The protagonists should be seeing this city for the first time and should be impressed and dazzled by its scale.”) For “Krishna and Arjuna,” I went under the hood myself. (For example: “The story should be set in a city. In “Krishna and Arjuna,” I wanted to go as deeply as I could into what the researchers call the “topic modeling process,” which is the use of machine learning to analyze a body of text-in this case, the canon of robot stories-and pick out its common themes or structures.įor “Twinkle Twinkle,” Hammond took the topic modeling output and converted it into manageable narrative rules. But how do you define even something as basic as a “plot twist” in computer code? How do you measure it through quantities of language? Because of the intractability-even mystery-of narrative’s resistance to encoding, it offers the most potential for innovation. You might think that plot would be the simplest part of the writing process for a computer to “understand,” since writers often develop patterns or use numbers to define the flow of a plot. The possibilities of an algorithmic approach to shaping the narrative itself were the most tantalizing, because narrative is so little understood. SciFiQ helped me arrive at the right balance-or, rather, within half a standard deviation from the mean.īut this kind of stylistic guidance was the least interesting part of the experiment. But the balance between the formal and the colloquial, which ScifiQ also tagged? That’s what those classics got right, and where I needed guidance. Classic science fiction uses too many adverbs anyway. But it would have been silly to pour in more adverbs just because the algorithm told me to. I used the program to see the rules, but I didn’t necessarily follow them.įor example, according to the algorithm, I had far too few adverbs in my story. Sometimes fixing the number of adverbs would make my paragraphs too long for the algorithm’s liking sometimes by fixing the average word length I’d be compromising the “concreteness” of the language.įor “Krishna and Arjuna,” I decided not to adhere so closely to the algorithm’s suggestions. The interface gave me instant feedback, but there were 24 such tags, and going through the story to make them all green was labor intensive. If the “abstractness” tag was red, that meant I wasn’t being as abstract as the algorithm said I should be, so I’d go through the story changing “spade” to “implement” or “house” to “residence” until the light went green. You can see examples of the interface below. Another difference was that with “Twinkle Twinkle,” I followed the algorithm’s stylistic instructions to the letter.
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