One of the first things I bought when I moved to college was a rice cooker. It was simple, the kind that costs 20 bucks at any old appliance or homeware store and has exactly two settings: on and off. Sure, it was hard to clean the gummy, burned rice off the bottom of the bowl in my dorm room’s thimble of a bathroom sink. And there was no eating the gelatinous, unevenly cooked mess that the machine tried to pass off as brown rice. But it served me well through two years of dorm living.
A few years out of college, a cousin decided that it was time for me to officially cross over into adulthood. She graduated me to a new kind of rice cooker, one that to this day is the smartest thing in my kitchen.
It serenades me when I turn it on with A through P of the alphabet song and croons an air called “Amaryllis,” by Louis XIII, when it’s done. But it’s the math this one runs on, not the adorable music, that makes it so special. The rice cooker of my adulthood is built on fuzzy logic, a field of computing that tries to make rational decisions in a world of imprecision. By mimicking our gray matter’s ability to reconcile gray information, this frivolous gadget has become one of the most essential items in my kitchen.
Fuzzy logic is very different from the walls of 1s and 0s that are the foundation for so many computers and electronics. Those 1s and 0s are rooted in Boolean binary — an expression of Boolean logic, where every value or action is reduced to an answer of true or false. Fuzzy logic was an attempt to formalize a radically different approach, one that more closely resembles the human mind’s ability to find reason and rationality among incomplete information. Many important questions can’t be narrowed down to a yes or no; lots of things don’t fall into discrete categories. At the stroke of midnight on someone’s 18th birthday, is she an adult or a child? Is that periwinkle shirt blue? Where do you put a spork in your kitchen drawer — the spoon slot or the fork slot?
Fuzzy logic was first proposed in 1965 by Lotfi Zadeh, a computer scientist who is now retired from the University of California, Berkeley. It was controversial for decades. As Zadeh wrote in the foreword to a 2013 edition of an academic journal dedicated to fuzzy logic, his use of words instead of numbers, as well as the attempt to incorporate imprecision, was heresy to many of his colleagues. “Almost all real-life applications of fuzzy logic involve the use of linguistic variables,” Zadeh wrote, adding, “In science, there is a deep-seated tradition of according much more respect for numbers than for words. In fact, scientific progress is commonly equated to progression from the use of words to the use of numbers.”
When Zadeh attended conferences, one of his Berkeley colleagues, William Kahan, often shadowed him to give public rebuttals, recalled Heidar Malki, a professor of electrical and computer engineering at the University of Houston who specializes in fuzzy logic. One encounter between the men was chronicled in a 2002 journal series: “Fuzzy theory is wrong — wrong and pernicious,” Kahan said. “The danger of fuzzy theory is that it will encourage the sort of imprecise thinking that brought us to so much trouble.” Such thinking, he and other mathematicians lamented, didn’t require the rigor demanded by probability theory, the kind of logic we most often use here at FiveThirtyEight. That approach was seen as the only path to true knowledge.
Indeed, the very word fuzzy often has a negative connotation in the U.S. (see fuzzy math in politics), and goes against Western notions of logic, which are mostly built around the Aristotelian law of the excluded middle: in lay terms, the idea that a statement cannot be true and false at the same time.
Malki, however, says that fuzzy logic’s ability to incorporate gray into what was once a black and white world is what makes it so powerful. These gray areas, along with the use of language rather than just numbers, also explain why it is a foundation of artificial intelligence. “This is exactly how we as human beings think and make decisions,” Malki said. “If you ask someone how the temperature is, we don’t say 82.3 degrees. We say it’s warm.”
So what does all of this have to do with consistently perfect rice?
The Aristotle-inspired rice cooker I had in college would heat until the temperature of the rice rose above 212 degrees Fahrenheit, at which point all of the water would have been absorbed. As the temperature rose past this point, a magnet was activated by a thermostat and the machine would shut off. The appliance was either on or off, and it did but one thing while it was on. In my current fuzzy-logic cooker, however, I tell the machine what kind of rice I’m using and how long it has been soaking. It takes that information and decides what temperature it should reach, and for how long. Generally using what are essentially if/then statements, it can fine-tune the process. For example, it can take into account the surrounding air temperature and turn the heating element up or down to compensate. The rice isn’t cooked or uncooked; the fuzzy-logic machine wants it to be cooked correctly.
Take brown rice, which is the same as white rice except it hasn’t had all of its bran layer and germ removed. In a magnetic cooker, you deal with the hard exterior by adding more water, which breaks down the outside but often leaves the rice mushy. In a fuzzy-logic cooker, the brown rice kernels are cooked at a lower temperature for a much longer period, which allows the rice to cook through without turning into a pulpy paste.
I asked Marilyn Matsuba, a marketing manager for Zojirushi, a Japanese company known for its technologically advanced household products, why rice cookers are so much more intelligent than other kitchen devices. She pointed out that they are the natural child of two of Japan’s obsessions: rice and robots. Fuzzy logic is a subset of the artificial intelligence used in robots, and rice is so important in the diet that manufacturers are constantly looking for better ways to cook it.
Compare that with some other popular kitchen gadgets. A handheld frother feels like a magic wand the way it whips billowy foam out of a cup of milk, but it’s really just a fast-moving whisk. A food processor can have attachments that do everything from kneading dough to shredding cheese, but the cook tells it when to turn on and off, and how fine to julienne the carrots. By contrast, innumerable gadgets and electronics outside the kitchen use fuzzy logic, among them the Sendai subway system in Japan, some fancy facial pattern recognition software, antilock brakes and air conditioners.
“The funny thing is, the more research they do, the more they realize that the way they used to cook it with fire was the best way,” Matsuba told me. She says the more advanced technology is in many ways just better at mimicking a very old cooking technique that involves a vessel called a mushikamado, which looks something like an igloo with the top cut off. A rice pot was suspended inside, and then a lid was placed on the vessel, with a stone on top. Below the pot was fire. The cook would use high heat at first, then lower the temperature. Judging by the amount of steam, he would know when the rice was ready.
There’s something wondrous about this seemingly simple device tapping into generations of cooking knowledge, using humanlike judgment skills to turn out an ever more perfect iteration of one of humanity’s staple foods. Fuzzy-logic rice cookers are a luxury (online they range in price from $50 to more than $700), but the awe mine inspires nearly matches the quality of the rice. Now if only someone would figure out how to get pasta perfectly al dente every time.
Though there is a natural break in the song after P, the abrupt ending leaves me hanging. Perhaps the tune is better described as the first two lines of “Twinkle, Twinkle, Little Star,” which would appropriately leave us suspended in a state of wonder.