Everyone knows something. Some people know a lot. But no human being knows as much, apparently, as Watson, the IBM computer that defeated the greatest champions of the television quiz show Jeopardy at their own game.
Faced with the prompt Aeolic, spoken in ancient times, was a dialect of this, Watson effortlessly answered Ancient Greek (or rather, using Jeopardy’s answers-as-questions format, What is Ancient Greek?). Confronted with Classic candy bar that’s a female Supreme Court justice, Watson shot back Baby Ruth Ginsburg. Very impressive. But does Watson understand what it’s talking about? Answering that question will point the way to the distinction between mere knowledge and true understanding.
When given This “insect” of a gangster was a real-life hit man for Murder Incorporated in the 1930s & ’40s, Watson answered James Cagney. Surely it knows that James Cagney was an actor, not a “real-life” gangster? And asked a question in the category U.S. Cities, Watson notoriously replied Toronto. So it knows that Aeolic is a kind of ancient Greek, but not that Toronto is in Canada?
As the commentators later explained, Watson does not figure out the answers to these questions in the way that humans do. Whereas we summon up a list of gangsters or U.S. cities and then ask ourselves whether they meet the other criteria explicitly imposed or implicitly suggested by the clue, Watson consults a sophisticated table of statistical associations between words, extracted from a massive stock of written material, from newspaper reports through encyclopedia articles. Aeolic, in the few places in which it appears, is linked closely to Ancient Greek. The same goes for Baby Ruth and candy bar and of course Ruth Bader Ginsburg and Supreme Court justice; Watson is then clever enough to see the overlap. Unfortunately, the same strong connection is found between James Cagney and various terms associated with organized crime. Watson has a great deal of information at its cybernetic fingertips about gangsterism, gangster movies, and the key figures in both, but it seems not to understand what it means to be a real-life gangster.
And how could it? How could a table of statistical associations comprehend the difference between fact and fiction, between murder and make-believe?
Watson knows a lot of things—assuming that knowledge is a matter of fast, reliable, retrieval of the facts. But it has little or no understanding of the things that it knows. It knows that Aeolic is a dialect of Ancient Greek, but it does not know what it is to be a dialect, or even—in spite of the fact that it is connected to the outside world only by words—what it is to be a language. It can talk (at least in the context of a quiz show) as informatively and as accurately as some of the most knowledgeable people on the planet, but its grasp on the facts it conveys is even less certain than that of a cocktail party habitué who has read all the latest reviews but has never glanced at a page of the books themselves.
What is Watson missing? I have given it a name—understanding. But what is that? There are two kinds: understanding language and understanding the world. Consider this sentence: Δέδυκε μεν ἀ σελάννα. You won’t understand it unless you read Aeolic Greek. But if I tell you it means “The moon has set”, you grasp immediately what the sentence is about: you know what it is for the moon to disappear below the horizon. You do not understand the sentence (unless you speak Aeolic), but only because you do not understand the language, not because you fail to understand its subject matter.
Watson’s problem is that it does not understand the world. (Some philosophers would argue that it does not genuinely understand language either, but I put that question aside.) It gives answers, but it has no grasp of what makes its answers correct.
You do not have to be a computer to find yourself knowing without understanding. There are some facts that real flesh and blood people know only in a Watson-like way. Perhaps you know that Bach wrote fugues, but you don’t understand what a fugue is—the more so, perhaps, if you are tone deaf. Another case: when I was a boy, intoxicated by science, I knew—in a trivia-contest-winning sort of way—that the hydrogen and oxygen molecules in water were held together by covalent bonds, but covalent was not much more to me than a glamorously technical word.
A little later I learned that a single electron could be in a superposition of two different places at once. All physicists know this, yet arguably, no one yet really understands what it means. We have the sentences, or mathematical formulae, to represent superposition, but we don’t know what, deep down, these sentences are talking about. And wouldn’t we love to? Knowledge is good, but isn’t understanding much better still?
I am, however, supposed to be analyzing, not acclaiming, understanding. What is it that Watson does not grasp about the movies, that the younger me did not grasp about covalent bonds, that no one perhaps grasps about quantum superposition?
One way to answer this question is to ask how we might distinguish facts that are truly understood from facts that are merely known Watson-style. It seems easy to make such distinctions from the inside, about our own knowledge. My bad conscience as a young boy told me that I was only feigning chemical expertise; the non-musical know-it-all is well aware that they don’t really understand what a fugue is.
We all know, by contrast, that we have a grip on the setting of the moon. Close your eyes and imagine: the familiar orb drifts steadily downward, is sucked into the horizon, is gone. Or think: as the Earth turns on its axis we stationary observers on its surface speed toward, then away from the moon; eventually our planet comes to occlude it entirely. Or feel: the setting of the moon measures the passing of time, the distance traveled by a departing lover, the passing of life.
Watson misses all of that, you might suppose. But so what? Watson has many peculiarities: it is blind, has no lovers, and is theoretically immortal. Surely none of this, however, stands in the way of understanding. We may relate to the moon through our senses and emotions, but might not other beings take a different but no less profound approach?
A different test for understanding investigates abilities rather than internal imagery, feelings, and thoughts. It is easy to discover that something important is missing in the youthful Michael’s grasp of covalent bonds. Ask me to define “covalent”, and I would have faltered. Or better, ask the younger me to explain how to solve problems in quantum chemistry, or ask someone who has just read Bach’s Wikipedia entry but who has no interest in music to tell a fugue from a passacaglia. The gap in understanding emerges soon enough.
Watson will not crack so easily. Imagine a more versatile version of Watson, proficient in answering questions generally, not just on Jeopardy—exactly the kind of expert system that IBM is using its Watson technology to build. Such a system would have no trouble defining covalent, fugue, or any other term that you throw at it. Presumably, it might learn to solve problem sets in a science class or to classify works of music, using the same statistical techniques that work so well in Jeopardy to distinguish the right moves from the wrong moves.
Why does the machine seem all the same not to achieve understanding? one answer is that its expertise is parasitic: it learns the right moves by examining the moves already made in the vast body of text that its programmers supply. Arguably, though, most of us require a similar degree of assistance—most of what we know we learn from others, rather than by figuring it all out for ourselves. A deeper answer is that there is something about Watson’s statistical ways of knowing that is incompatible with understanding.
Watson and you both answer questions by seeing connections between things. But they are different kinds of connections. Watson picks up from things it reads that there is a correlation between a sphere’s rotating and a fixed point on its surface having a constantly changing view of the rest of the world. You grasp why this correlation exists, seeing the connection between the opacity of the Earth, light’s traveling in straight lines, and geometry of the sphere itself. For you the statistics are a byproduct of what really matters, the physical and causal relations between things and people and what they do and say. Grasping those relations is what understanding consists in. Watson lives in a world where there are no such relations: all it sees are statistics. It can predict a lot and so it can know a lot, but what it never grasps is why its predictions come true.
Discussion Questions
1. Could a machine ever understand things in the way that we do?
2. Is understanding a matter of having knowledge of certain special facts, such as causal facts? Or is it a matter of having a special kind of knowledge of facts: transparent, deep, luminous, or something like that?
3. Many scientists believe that, at bottom, our thought is implemented in neural networks that make statistical associations. Does that mean that we are no better than Watson? That our sense of understanding is an illusion?