After a moment, Anna answered for him: “The University of Tokyo will want to license any technology or applications that are based on what Masayuki’s equipment has made possible, I’m sure. If there are spontaneously emerging cellular automata in the background of the Web, there may be commercial applications for them — in cryptography, in distributed computing, in random-number generation, and so on. The cellular automata might be patentable, and certainly the method for accessing them is.”
“Dr. Kuroda?” said Caitlin. “Is that what you’re thinking?”
“Such thoughts have crossed my mind, yes. My university owns the research, and I’ve got an obligation to help them monetize it where possible.”
“But it’s my websight!”
“Which website?” Anna asked.
“No, no. My websight, s-i-g-h-t — my ability to see the Web. They can’t patent that! If anything, we should open-source it, or put it out under a Creative Commons license.”
There was an awkward silence. At last, Kuroda said, “Well.”
Caitlin crossed her arms in front of her chest. Well, indeed!
Chapter 29
The atmosphere in the basement was still chilly, and not just because of the temperature. Caitlin’s dad must have swiveled his chair slightly; she heard it squeak. “Look,” he said, his tone conciliatory, “the cellular automata are probably just an epiphenomenon.”
Oh you silver-tongued devil! thought Caitlin. Only her dad could try smoothing over a tense moment with bafflegab. Still, that he was speaking up of his own volition meant that even he recognized that she was pissed off. But the fact that she didn’t know what an epiphenomenon was just made her even more angry. She didn’t say anything, but perhaps Kuroda read something in her expression — whatever the hell that meant!
“He means he thinks they’re just a random by-product of something else,”
Kuroda said gently. “Like foam, which is an epiphenomenon of waves: it doesn’t mean anything; it just occurs.”
She got it: her dad was saying, hey, see, nothing here worth fighting about; if the cellular automata are meaningless, there’s probably nothing of value to patent anyway. But that hardly excused Kuroda even thinking about making a buck — a yen! — off something that she was doing. Yes, yes, his hardware was feeding her the signals, but it was her brain that was interpreting them. Websight wasn’t just hers, it was her.
“You may be right, Malcolm,” said Anna Bloom, over the webcam link from Haifa. Caitlin was still fuming, and wondered if Anna really knew the mood here. She was seeing a very limited view through the camera, no doubt, and the crappy computer mike probably wasn’t picking up subtlety of tone.
Anna went on: “One bit does affect the next, at least in copper wire; the magnetic fields do overlap, after all. So maybe some sort of … I don’t know, constructive interference, perhaps … could accidentally give rise to cellular automata.”
“But they would still just be noise,” her dad said.
“You’re probably right,” Kuroda replied. “But um, what is it you like to say, Miss Caitlin? You’re ‘an empiricist at heart.’”
He was trying to cajole her, to include her, she knew, but she remained angry. Kuroda worked with computers all day long, for crying out loud — didn’t he know that information wants to be free?
Caitlin was still leaning against the worktable. The street-hockey game continued outside: someone just scored.
“Miss Caitlin?” said Kuroda. “Testing what your father just suggested will involve some cool maths…”
“Like what?” she said, her tone petulant.
“Perhaps a Zipf plot…”
Caitlin didn’t know what that was, either, but to her great surprise her father said a very enthusiastic, “Yes!” That was enough to make her curious, but she wasn’t ready to give in just yet. “Is there empty room on this table?” she said, patting its surface. “And do you think it’ll hold me?”
“Sure,” said Kuroda after a pause, presumably to give her father a chance to answer first. “Everything to the left — your left — of the computer is clear.”
Caitlin boosted herself up onto the table, the folding legs groaning slightly as she did so, and she sat cross-legged on it. “Okay,” she said, her tone still not very cheery. “I’ll bite. What’s a Zipf plot?”
“It’s a way of finding out if there’s any information in a signal, even if you can’t decode the signal,” Kuroda said.
Caitlin frowned. “Information? In the cellular automata?”
“Could be,” said Kuroda in a tone that sounded like it should be accompanied by a shrug.
“But, um, can cellular automata contain information?” Caitlin asked.
“Oh, yes,” said Anna. “In fact, Wolfram wrote a paper about encoding information into them for cryptographic purposes as far back as, um, 1986, I think. And a bunch of people have tried to develop public-key cryptography systems using them.”
“Anyway,” Kuroda said, “George Zipf was a linguist at Harvard. In the 1930s, he noticed something fascinating: in any language, the frequency with which a word is used is inversely proportional to its rank in a table of the frequency of use of all words in the language. That means—”
You don’t have to spoon-feed Calculass! “That means,” she said, “the second most-common word is used one-half as often as the first most-common, the third most-common is used one-third as often as the first most-common, the fourth most-common is used one quarter as often, and so on.” She frowned. “But is that really true?”
“Yes,” said Kuroda. “In English, the most-common word is ‘the,’ then ‘of,’ then ‘to,’ then … um, I think it’s ‘in.’ And, yes, ‘in,’ or whatever it is, is used one-quarter as often as ‘the.’”
“But surely that’s just a quirk of English, isn’t it?” said Caitlin, shifting slightly on the table.
“No, it’s the same in Japanese.” He rattled off some words in that language.
“Those are the four most common, and they appear in the same inverse ratio.”
“And it’s true for Hebrew, too,” said Anna.
“But what’s really amazing,” said Kuroda, “is that it doesn’t apply just to words. It applies equally well to letters: the fourth most-common in English, which is O, is used one-quarter as much as the first most-common, E. And it applies to phonemes, too — the smallest building blocks of speech — and, again, in all languages, from Arabic to…” He trailed off, clearly trying to think of a language that started with Z.
“Zulu?” offered Caitlin, deciding to be helpful.
“Exactly, thanks.”
She thought about this. It was indeed pretty cool.
“Everything Masayuki said is right,” Anna said, “but you know what’s even more interesting, Caitlin? This inverse ratio applies to dolphin songs, too.”
Well, that was awesome. “Really?” she said.
“Yes,” said Kuroda. “In fact, this technique can be used to determine if there is information in the noise any animal makes. If there is, it will obey Zipf’s law, so that if you plot the frequency of use of the components on a logarithmic scale, you get a line with a slope of negative one.”
Caitlin nodded. “A line going diagonally from the upper left down to the lower right.”
“Exactly,” said Kuroda. “And when you plot dolphin vocalizations you do get a negative-one slope. But if you take, say, the sounds made by squirrel monkeys, you get a slope, at best, of -0.6, because what they make is just random noise. Even the SETI people — Search for Extraterrestrial Intelligence — are doing Zipf plots now, because the inverse-relationship is a property of information, not of any particularly human approach to language.”