No Ghost in the Machine
It is desirable to guard against the possibility of exaggerated ideas that might arise as to the powers of the Analytical Engine. In considering any new subject, there is frequently a tendency, first, to overrate what we find to be already interesting or remarkable; and, secondly, by a sort of natural reaction, to undervalue the true state of the case, when we do discover that our notions have surpassed those that were really tenable. The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us in making available what we are already acquainted with.
—Augusta Ada King, the Countess of Lovelace, 1843
The first words uttered on a controversial subject can rarely be taken as the last, but this comment by British mathematician Lady Lovelace, who died in 1852, is just that—the basis of our understanding of what computers are and can be, including the notion that they might come to acquire artificial intelligence, which here means “strong AI,” or the ability to think in the fullest sense of the word. Her words demand and repay close reading: the computer “can do whatever we know how to order it to perform.” This means both that it can do only what we know how to instruct it to do, and that it can do all that we know how to instruct it to do. The “only” part is what most earlier writings on the subject have been concerned with, but the “all” part is just as important—it explains (for those open to explanation) why the computer keeps performing feats that seem to show that it’s thinking, while critics, like me, continue to insist that it is doing no such thing.
The amazing feats achieved by computers demonstrate our progress in coming up with algorithms that make the computer do valuable things for us. The computer itself, though, does nothing more than it ever did, which is to do whatever we know how to order it to do—and we order it to do things by issuing instructions in the form of elementary operations on bits, the 1s and 0s that make up computer code. The natural-language instructions offered by higher-order programming systems seem to suggest that computers understand human language. Such a delusion is common among those who don’t know that such higher-level instructions must first be translated into the computer’s built-in machine language, which offers only operations on groups of bits, before the computer can “understand” and execute them. If you can read Dante only in English translation, you cannot be said to understand Italian.
New software is often credited with being AI when it first appears, but these supposed breakthroughs are regularly demoted when their novelty wears off.
Adding to the confusion surrounding claims of AI is the lack of agreement on what technical developments deserve the name. In practice, new software created by an institution with and since 2017, the president of Cornell University. “As soon as we solve a problem, instead of looking at the solution as AI, we come to view it as just another computer system,” she told . One sympathizes with Pollack, who is no doubt familiar with the last of science-fiction writer Arthur C. Clarke’s three laws about the future: “Any sufficiently advanced technology is indistinguishable from magic.” But she may not have appreciated that “is indistinguishable from” is a symmetric relation, just as readily yielding Halpern’s Corollary: “Any sufficiently explained magic is indistinguishable from technology.”
