The Age of Spiritual Machines: When Computers Exceed Human Intelligence

  Turing and his colleagues constructed the world’s first operational computer from telephone relays and named it Robinson,5 after a popular cartoonist who drew “Rube Goldberg” machines (very ornate machinery with many interacting mechanisms). The group’s own Rube Goldberg succeeded brilliantly and provided the British with a transcription of nearly all significant Nazi messages. As the Germans added to the complexity of their code (by adding additional coding wheels to their Enigma coding machine), Turing replaced Robinson’s electromagnetic intelligence with an electronic version called Colossus built from two thousand radio tubes. Colossus and nine similar machines running in parallel provided an uninterrupted decoding of vital military intelligence to the Allied war effort.

  Use of this information required supreme acts of discipline on the part of the British government. Cities that were to be bombed by Nazi aircraft were not forewarned, lest preparations arouse German suspicions that their code had been cracked. The information provided by Robinson and Colossus was used only with the greatest discretion, but the cracking of Enigma was enough to enable the Royal Air Force to win the Battle of Britain.

  Thus fueled by the exigencies of war, and drawing upon a diversity of intellectual traditions, a new form of intelligence emerged on Earth.

  The Birth of Artificial Intelligence

  The similarity of the computational process to the human thinking process was not lost on Turing. In addition to having established much of the theoretical foundations of computation and having invented the first operational computer, he was instrumental in the early efforts to apply this new technology to the emulation of intelligence.

  In his classic 1950 paper, Computing Machinery and Intelligence, Turing described an agenda that would in fact occupy the next half century of advanced computer research: game playing, decision making, natural language understanding, translation, theorem proving, and, of course, encryption and the cracking of codes.6 He wrote (with his friend David Champernowne) the first chess-playing program.

  As a person, Turing was unconventional and extremely sensitive. He had a wide range of unusual interests, from the violin to morphogenesis (the differentiation of cells). There were public reports of his homosexuality, which greatly disturbed him, and he died at the age of forty-one, a suspected suicide.

  The Hard Things Were Easy

  In the 1950s, progress came so rapidly that some of the early pioneers felt that mastering the functionality of the human brain might not be so difficult after all. In 1956, Al researchers Allen Newell, J. C. Shaw, and Herbert Simon created a program called Logic Theorist (and in 1957 a later version called General Problem Solver), which used recursive search techniques to solve problems in mathematics. 7 Recursion, as we will see later in this chapter, is a powerful method of defining a solution in terms of itself. Logic Theorist and General Problem Solver were able to find proofs for many of the key theorems in Bertrand Russell and Alfred North Whitehead’s seminal work on set theory, Principia Mathematica,8 including a completely original proof for an important theorem that had never been previously solved. These early successes led Simon and Newell to say in a 1958 paper, entitled Heuristic Problem Solving: The Next Advance in Operations Research, “There are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until—in a visible future—the range of problems they can handle will be coextensive with the range to which the human mind has been applied.”9 The paper goes on to predict that within ten years (that is, by 1968) a digital computer would be the world chess champion. A decade later, an unrepentant Simon predicts that by 1985, “machines will be capable of doing any work that a man can do.” Perhaps Simon was intending a favorable comment on the capabilities of women, but these predictions, decidedly more optimistic than Turing’s, embarrassed the nascent Al field.

  The field has been inhibited by this embarrassment to this day, and AI researchers have been reticent in their prognostications ever since. In 1997, when Deep Blue defeated Gary Kasparov, then the reigning human world chess champion, one prominent professor commented that all we had learned was that playing a championship game of chess does not require intelligence after all.10 The implication is that capturing real intelligence in our machines remains far beyond our grasp. While I don’t wish to overstress the significance of Deep Blue’s victory, I believe that from this perspective we will ultimately find that there are no human activities that require “real” intelligence.

  During the 1960s, the academic field of AI began to flesh out the agenda that Turing had described in 1950, with encouraging or frustrating results, depending on your point of view. Daniel G. Bobrow’s program Student could solve algebra problems from natural English-language stories and reportedly did well on high-school math tests.11 The same performance was reported for Thomas G. Evans’s Analogy program for solving IQ-test geometric-analogy problems.12 The field of expert systems was initiated with Edward A. Feigenbaum’s DENDRAL, which could answer questions about chemical compounds.13 And natural-language understanding got its start with Terry Winograd’s SHRDLU, which could understand any meaningful English sentence, so long as you talked about colored blocks.14

  The notion of creating a new form of intelligence on Earth emerged with an intense and often uncritical passion simultaneously with the electronic hardware on which it was to be based. The unbridled enthusiasm of the field’s early pioneers also led to extensive criticism of these early programs for their inability to react intelligently in a variety of situations. Some critics, most notably existentialist philosopher and phenomenologist Hubert Dreyfus, predicted that machines would never match human levels of skill in areas ranging from the playing of chess to the writing of books about computers.

  It turned out that the problems we thought were difficult—solving mathematical theorems, playing respectable games of chess, reasoning within domains such as chemistry and medicine—were easy, and the multithousand-instructions-per-second computers of the 1950s and 1960s were often adequate to provide satisfactory results. What proved elusive were the skills that any five-year-old child possesses: telling the difference between a dog and a cat, or understanding an animated cartoon. We’ll talk more about why the easy problems are hard in Part II.

  Waiting for Real Artificial Intelligence

  The 1980s saw the early commercialization of artificial intelligence with a wave of new AI companies forming and going public. Unfortunately, many made the mistake of concentrating on a powerful but inherently inefficient interpretive language called LISP, which had been popular in academic AI circles. The commercial failure of LISP and the AI companies that emphasized it created a backlash. The field of AI started shedding its constituent disciplines, and-companies in natural-language understanding, character and speech recognition, robotics, machine vision, and other areas originally considered part of the AI discipline now shunned association with the field’s label.

  Machines with sharply focused intelligence nonetheless became increasingly pervasive. By the mid-1990s, we saw the infiltration of our financial institutions by systems using powerful statistical and adaptive techniques. Not only were the stock, bond, currency, commodity, and other markets managed and maintained by computerized networks, but the majority of buy-and-sell decisions were initiated by software programs that contained increasingly sophisticated models of their markets. The 1987 stock market crash was blamed in large measure on the rapid interaction of trading programs. Trends that otherwise would have taken weeks to manifest themselves developed in minutes. Suitable modifications to these algorithms have managed to avoid a repeat performance.

  Since 1990, the electrocardiogram (EKG) has come complete with the computer’s own diagnosis of one’s cardiac health. Intelligent image-processing programs enable doctors to peer deep into our bodies and brains, and computerized bioengineering technology enables drugs to be designed on biochemical simulators. The disabled have been particularly fortunate beneficiaries of
the age of intelligent machines. Reading machines have been reading to blind and dyslexic persons since the 1970s, and speech-recognition and robotic devices have been assisting hands-disabled individuals since the 1980s.

  Perhaps the most dramatic public display of the changing values of the age of knowledge took place in the military. We saw the first effective example of the increasingly dominant role of machine intelligence in the Gulf War of 1991. The cornerstones of military power from the beginning of recorded history through most of the twentieth century—geography, manpower, firepower, and battle-station defenses—have been largely replaced by the intelligence of software and electronics. Intelligent scanning by unstaffed airborne vehicles, weapons finding their way to their destinations through machine vision and pattern recognition, intelligent communications and coding protocols, and other manifestations of the information age have transformed the nature of war.

  Invisible Species

  With the increasingly important role of intelligent machines in all phases of our lives—military, medical, economic and financial, political—it is odd to keep reading articles with titles such as Whatever Happened to Artificial Intelligence? This is a phenomenon that Turing had predicted: that machine intelligence would become so pervasive, so comfortable, and so well integrated into our information-based economy that people would fail even to notice it.

  It reminds me of people who walk in the rain forest and ask, “Where are all these species that are supposed to live here?” when there are several dozen species of ant alone within fifty feet of them. Our many species of machine intelligence have woven themselves so seamlessly into our modern rain forest that they are all but invisible.

  Turing offered an explanation of why we would fail to acknowledge intelligence in our machines. In 1947, he wrote: “The extent to which we regard something as behaving in an intelligent manner is determined as much by our own state of mind and training as by the properties of the object under consideration. If we are able to explain and predict its behavior we have little temptation to imagine intelligence. With the same object, therefore, it is possible that one man would consider it as intelligent and another would not; the second man would have found out the rules of its behavior.”

  I am also reminded of Elaine Rich’s definition of artificial intelligence, as the “study of how to make computers do things at which, at the moment, people are better.”

  It is our fate as artificial intelligence researchers never to reach the carrot dangling in front of us. Artificial intelligence is inherently defined as the pursuit of difficult computer-science problems that have not yet been solved.

  “I think you should be more explicit here in step two.”

  THE FORMULA FOR INTELLIGENCE

  The computer programmer is a creator of universes for which he alone is the lawgiver.... No playwright, no stage director, no emperor, however powerful, has ever exercised such absolute authority to arrange a stage or a field of battle and to command such unswervingly dutiful actors or troops.

  —Joseph Weizenbaum

  A beaver and another forest animal are contemplating an immense man-made dam. The beaver is saying something like “No, I didn’t actually build it. But it’s based on an idea of mine. ”

  —Edward Fredkin

  Simple things should be simple; complex things should be possible.

  —Alan Kay

  What Is Intelligence?

  A goal may be survival—evade a foe, forage for food, find shelter. Or it might be communication—relate an experience, evoke a feeling. Or perhaps it is to partake in a pastime—play a board game, solve a puzzle, catch a ball. Sometimes it is to seek transcendence—create an image, compose a passage. A goal may be well defined and unique, as in the solution to a math problem. Or it may be a personal expression with no clearly right answer.

  My view is that intelligence is the ability to use optimally limited resources—including time—to achieve such goals. There is a plethora of other definitions. One of my favorites is by R. W Young, who defines intelligence as “that faculty of mind by which order is perceived in a situation previously considered disordered.” 15 For this definition, we will find the paradigms discussed below quite apropos.

  Intelligence rapidly creates satisfying, sometimes surprising plans that meet an array of constraints. The products of intelligence may be clever, ingenious, insightful, or elegant. Sometimes, as in the case of Turing’s solution to cracking the Enigma code, an intelligent solution exhibits all of these qualities. Modest tricks may accidentally produce an intelligent answer from time to time, but a true intelligent process that reliably creates intelligent solutions inherently goes beyond a mere recipe. Clearly, no simple formula can emulate the most powerful phenomenon in the Universe: the complex and mysterious process of intelligence.

  Actually, that’s wrong. All that is needed to solve a surprisingly wide range of intelligent problems is exactly this: simple methods combined with heavy doses of computation (itself a simple process, as Alan Turing demonstrated in 1936 with his conception of the Turing Machine,16 an elegant model of computation) and examples of the problem. In some cases, we don’t even need the latter; just one well-defined statement of the problem will do.

  How far can we go with simple paradigms? Is there a class of intelligent problems amenable to simple approaches, with another, more penetrating class that lies beyond its grasp? It turns out that the class of problems solvable with simple approaches is extensive. Ultimately, with sufficient computational brute force (which will be ample in the twenty-first century) and the right formulas in the right combination, there are few definable problems that fail to yield. Except perhaps for this problem: What is the complete set of unifying formulas that underlies intelligence?

  Evolution determined an answer to this problem in a few billion years. We’ve made a good start in a few thousand years. We are likely to finish the job in a few more decades.

  These methods, described briefly below, are discussed in more detail in the supplementary section in the back of this book “How to Build an Intelligent Machine in Three Easy Paradigms.”

  Let’s take a look at a few plain yet powerful paradigms. With a little practice, you, too, can build intelligent machines.

  The Recursive Formula: Just Carefully State the Problem

  A recursive procedure is one that calls itself. Recursion is a useful approach to generating all of the possible solutions to a problem, or, in the context of a game such as chess, all of the possible move-countermove sequences.

  Consider the game of chess. We construct a program called “Pick Best Move” to select each move. Pick Best Move starts by listing all of the possible moves from the current state of the board. This is where the careful statement of the problem comes in, because to generate all of the possible moves we need to precisely consider the rules of the game. For each move, the program constructs a hypothetical board that reflects what would happen if we made this move. For each such hypothetical board, we now need to consider what our opponent would do if we made this move. Now recursion comes in, because Pick Best Move simply calls Pick Best Move (that is, itself) to pick the best move for our opponent. In calling itself, Pick Best Move then lists all of the legal moves for our opponent.

  The program keeps calling itself, looking ahead to as many moves as we have time to consider, which results in the generation of a huge move-countermove tree. This is another example of exponential growth, because to look ahead an additional half-move requires multiplying the amount of available computation by about five.

  Key to the recursive formula is pruning this huge tree of possibilities, and ultimately stopping the recursive growth of the tree. In the game context, if a board looks hopeless for either side, the program can stop the expansion of the move-countermove tree from that point (called a “terminal leaf” of the tree), and consider the most recently considered move to be a likely win or loss.

  When all of these nested program calls are completed, the progra
m will have determined the best possible move for the current actual board, within the limits of the depth of recursive expansion that it had time to pursue.

  The recursive formula was good enough to build a machine—a specially designed IBM supercomputer—that defeated the world chess champion (although Deep Blue does augment the recursive formula with databases of moves from most of the grand-master games of this century). Ten years ago, in the Age of Intelligent Machines, I noted that while the best chess computers were gaining in chess ratings by forty-five points a year, the best humans were advancing by closer to zero points. That put the year in which a computer would beat the world chess champion at 1998, which turned out to be overly pessimistic by one year. Hopefully my predictions in this book will be more accurate. 17

  Our simple recursive rule plays a world-class game of chess. A reasonable question, then, is, What else can it do? We certainly can replace the module that generates chess moves with a module programmed with the rules of another game. Stick in a module that knows the rules of checkers, and you can also beat just about any human. Recursion is really good at backgammon. Hans Berliner’s program defeated the human backgammon champion with the slow computers we had back in 1980.18