Rebooting AI: Building Artificial Intelligence We Can Trust by Ernest Davis

Author:Ernest Davis [Davis, Ernest]
Language: eng
Format: epub
Amazon: B07MYLGQLB
Publisher: Pantheon
Published: 2019-09-09T23:00:00+00:00

9. CAUSAL RELATIONS ARE A FUNDAMENTAL ASPECT OF UNDERSTANDING THE WORLD.

As Turing Award winner Judea Pearl has emphasized, a rich understanding of causality is a ubiquitous and indispensable aspect of human cognition. If the world was simple, and we had full knowledge of everything, perhaps the only causality we would need would be physics. We could determine what affects what by running simulations; if I apply a force of so many micronewtons, what will happen next?

But as we will discuss in detail below, that sort of detailed simulation is often unrealistic; there are too many particles to track in the real world, and too little time.

Instead, we often use approximations; we know things are causally related, even if we don’t know exactly why. We take aspirin, because we know it makes us feel better; we don’t need to understand the biochemistry. Most adults know that having sex can lead to babies, even if they don’t understand the exact mechanics of embryogenesis, and can act on that knowledge even if it is incomplete. You don’t have to be a doctor to know that vitamin C can prevent scurvy, or a mechanical engineer to know that pressing a gas pedal makes the car go faster. Causal knowledge is everywhere, and it underlies much of what we do.

In Lawrence Kasdan’s classic film The Big Chill, Jeff Goldblum’s character jokes that rationalizations are even more important than sex. (“Have you ever gone a week without a rationalization?” he asks.) Causal inferences are even more important than rationalizations; without them, we just wouldn’t understand the world, even for an hour. We are with Pearl in thinking that few topics in AI could be more important; perhaps nothing else so important has been so neglected. Pearl himself has developed a powerful mathematical theory, but there is much more to explore about how we manage to learn the many causal relationships that we know.

That’s a particularly thorny problem, because the most obvious route to causal knowledge is so fraught with trouble. Almost every cause that we know leads to correlations (cars really do tend to go faster when you press the gas pedal, so long as the engine is running and the emergency brake hasn’t been deployed), but a lot of correlations are not in fact causal. A rooster’s crow reliably precedes dawn; but any human should be able to tell you that silencing a rooster will not stop the sun from rising. The reading on a barometer is closely correlated with the air pressure, but manually moving a barometer needle with your hands will not change the air pressure.

Given enough time, it’s easy to find all kinds of purely coincidental correlations, like this one from Tyler Vigen, correlating per capita cheese consumption and death by bedsheet tangling, sampled from the years 2000–2009.

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