Artificial intelligence: Can we teach computers what “truth” means?

Can We Really Teach Computers What “Truth” Means?

Can We Really Teach Computers What “Truth” Means?

The citizen’s guide to the future.
Feb. 26 2013 7:46 AM

Can We Teach Computers What “Truth” Means?

It’s harder than it sounds.

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Computer scientists have worked to come up with formal descriptions of the everyday world. Here is a short list, taken from the Stanford Encyclopedia of Philosophy of some of scenarios they’ve tried to encode:

The Baby Scenario, the Bus Ride Scenario, the Chess Board Scenario, the Ferryboat Connection Scenario, the Furniture Assembly Scenario, the Hiding Turkey Scenario, the Kitchen Sink Scenario, the Russian Turkey Scenario, the Stanford Murder Mystery, the Stockholm Delivery Scenario, the Stolen Car Scenario, the Stuffy Room Scenario, the Ticketed Car Scenario, the Walking Turkey Scenario, and the Yale Shooting Anomaly.

Let’s take the last of these—the Yale Shooting Anomaly, which aims to formally codify the fact that an unloaded shotgun, if loaded and then shot at a person, would kill the person. Classical logic dealt with things like “1+1=2” which are true, (or false, like 1=0) for all time. They were true, are true, and always will be true. It doesn’t allow for things to happen. But to encode common- sense knowledge, computer scientists need a way to allow for events to take place. They also need ways to encode spatial locations.


Some of this had been worked out in a rigorous but limited way, in what philosophers call modal logic, which was first enunciated by C.I. Lewis in 1918. But modal logic was too limited for computer scientists to use in semireal world systems. In the languages that computer scientists have come up with, as in the Yale Shooting Anomaly, they were unable to preclude the possibility that the shotgun would spontaneously unload itself. It’s not that computer scientists think that that will happen; it’s that they struggle to formalize how it can’t. (Since the Yale Shooting Anomaly was first stated in 1986, many solutions have been proposed, but it remains an area of research.)

A central challenge computer scientists face is what’s called the ramification problem: How to codify that fact that if I walk into a room, my shirt does, too. This is paralleled by the “frame problem,” first enunciated by McCarthy in 1969, which is the “problem of efficiently determining which things remain the same in a changing world.” These problems are considerably harder than careless cheerleaders like Kurzweil make them out to be.

The central result of logicians in the 20th century was that, in the end, it will always be necessary to extend your axioms—things you just assume to be true without proving them—if you are to extend your idea of truth. This brings us to our second idea about truth—that men are created equal and entitled to life, liberty, and the pursuit of happiness. Thomas Jefferson’s insight (without getting into the abominable hypocrisy of the fact that slavery was legal at the time) was that these truths were not provable from some more basic system of logic, but must themselves be assumed.

The sense in which artificial intelligence research has eroded the distinction between such moral truths and mathematical truths is not a rigorous philosophical identification of the two, but just a sense that truths of the first sort are not as absolute as they seem (at the end the fights between logicians come down to opinion and taste) while truths of the second sort can be, grudgingly and with struggle, written down in a form that outwardly resembles the simpler-seeming truths of mathematics.

Konstantin Kakaes is a fellow at New America, where he writes about the evolving uses of drones. He is the author of The Pioneer Detectives, an e-book.