There’s an old joke about three construction workers arguing about the greatest invention ever. The first worker nominates the telephone: “Now we can hear people from miles away!” The second worker points out that with television we can see them, too. “No,” the third worker insists, “you’re both wrong. The greatest invention is the thermos.”
“Sure. On a cold day, it keeps my soup hot. On a warm day, it keeps my lemonade cold.”
“How does it know??”
Laughable though it is, this question is surprisingly insightful from the perspective of computer science. Computation is all about what a system knows and what it can learn from that: how can your laptop, armed with just a wireless radio, find out what www.google.com looks like? How can a security camera infer what’s in an image from a blurry bunch of pixels? Almost every classic problem in computer science amounts to some form of manipulating information.
It’s not just computers, though, that can be thought of as knowing things; every physical system encodes and transforms information. Applying the concept of information beyond the digital world may seem odd, but this lens on the world turns out to be remarkably versatile: it yields tantalizing questions and even the occasional explanation about everything from slime molds to thermoses.
My earliest recollection of thinking this way is a deep sense of puzzlement at age 6 about cars: when the road rises at the foot of a hill, how does the car know to follow it? I later learned that the car rises because the rising road compresses the suspension, and that what keeps the road and tire apart is repulsion between the electrons in their respective atoms. But even then, I remained unsatisfied: after all, how do the tire’s electrons know the road’s electrons are there?
In fact, we could ask the same thing about many physical phenomena we take for granted. Consider the humble fridge magnet. We all know the magnet will pull out of your hand as you bring it close to the refrigerator. But how does it know the fridge is there at all? Furthermore, if you drop the magnet further from the fridge, it will simply fall to the floor – somehow aware of the tug of the Earth’s hulking mass. How such “action at a distance” could occur befuddled physicists for centuries, with no less than Isaac Newton declaring it an “inconceivable…absurdity” that objects could affect each other without contact. Through the lens of computation, we can express the objection more precisely: there appears to be no protocol of information exchange by which the magnet can discover the presence of the fridge.
This enigma has only partially been resolved. Physicists have determined that electrons are constantly sending short-lived photons (known as “virtual” photons) back and forth. These photons act like little postcards, exchanging information between the electrons – in other words, the car knows the road is there because the road sent it a message saying so. This theory, called quantum electrodynamics, has been wildly successful at explaining electricity and magnetism, but gravity’s still a bit of a mystery: it’s an open question whether an analogous particle swap explains gravity, too. Either way, though, both the questions and the answers we have so far about the fundamental workings of our universe turn out to be best understood in terms of exchanging information.
Physics has particularly deep connections to the concept of information (see, for example, Radiolab’s episode on what slinkies know). But the same lens raises some intriguing questions in other domains, too.
Biology seems to be especially full of information mysteries. Embryonic cells somehow learn enough about where they are in the embryo to know what type of cell to mature into. A growing community of plant biologists believe that plants are so clever at integrating chemical signals from soil, other plants, etc. that we should start thinking of them more like brains. And let’s not forget slime molds, which can figure out incredibly efficient food transportation networks despite not knowing the landscape in advance (a finding that earned an Ig Nobel prize). Our best hope for explaining these phenomena seems to lie in viewing each system as a biological computer, integrating information from diverse signals to compute seemingly unknowable information.
This computational viewpoint does sometimes yield profound explanations. The construction worker’s thermos is a prime example. Normally, the way your cool lemonade finds out that it’s hot outside is that the bottle’s surface passes heat from the air (in the form of tiny molecular vibrations) through to the other side and into your drink. A thermos, however, has a layer of vacuum between the inside and outside surfaces, so there’s nothing for the outside surface to pass along those tiny vibrations to. In other words, the vacuum creates a barrier to information, preventing the inside of the thermos from ever finding out what temperature it is outside.
The computational lens is a handy tool for studying the world, but for me, its greatest utility is ultimately the wonder it provides. I imagine the world as an enormous computer, where information of all sorts is taken in and derived and transmitted and destroyed among myriad components, from particles to people. On every level you care to examine, it truly is a world of information.