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The Roots of Artificial Intelligence
Two Months and Ten Men at Dartmouth
The dream of creating an intelligent machine—one that is as smart as or smarter than humans—is centuries old but became part of modern science with the rise of digital computers. In fact, the ideas that led to the first programmable computers came out of mathematicians’ attempts to understand human thought—particularly logic—as a mechanical process of “symbol manipulation.” Digital computers are essentially symbol manipulators, pushing around combinations of the symbols 0 and 1. To pioneers of computing like Alan Turing and John von Neumann, there were strong analogies between computers and the human brain, and it seemed obvious to them that human intelligence could be replicated in computer programs.
Most people in artificial intelligence trace the field’s official founding to a small workshop in 1956 at Dartmouth College organized by a young mathematician named John McCarthy.
In 1955, McCarthy, aged twenty-eight, joined the mathematics faculty at Dartmouth. As an undergraduate, he had learned a bit about both psychology and the nascent field of “automata theory” (later to become computer science) and had become intrigued with the idea of creating a thinking machine. In graduate school in the mathematics department at Princeton, McCarthy had met a fellow student, Marvin Minsky, who shared his fascination with the potential of intelligent computers. After graduating, McCarthy had short-lived stints at Bell Labs and IBM, where he collaborated, respectively, with Claude Shannon, the inventor of information theory, and Nathaniel Rochester, a pioneering electrical engineer. Once at Dartmouth, McCarthy persuaded Minsky, Shannon, and Rochester to help him organize “a 2 month, 10 man study of artificial intelligence to be carried out during the summer of 1956.”1 The term artificial intelligence was McCarthy’s invention; he wanted to distinguish this field from a related effort called cybernetics.2 McCarthy later admitted that no one really liked the name—after all, the goal was genuine, not “artificial,” intelligence—but “I had to call it something, so I called it ‘Artificial Intelligence.’”3
The four organizers submitted a proposal to the Rockefeller Foundation asking for funding for the summer workshop. The proposed study was, they wrote, based on “the conjecture that every aspect of learning or any other feature of intelligence can be in principle so precisely described that a machine can be made to simulate it.”4 The proposal listed a set of topics to be discussed—natural-language processing, neural networks, machine learning, abstract concepts and reasoning, creativity—that have continued to define the field to the present day.
Even though the most advanced computers in 1956 were about a million times slower than today’s smartphones, McCarthy and colleagues were optimistic that AI was in close reach: “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”5
Obstacles soon arose that would be familiar to anyone organizing a scientific workshop today. The Rockefeller Foundation came through with only half the requested amount of funding. And it turned out to be harder than McCarthy had thought to persuade the participants to actually come and then stay, not to mention agree on anything. There were lots of interesting discussions but not a lot of coherence. As usual in such meetings, “Everyone had a different idea, a hearty ego, and much enthusiasm for their own plan.”6 However, the Dartmouth summer of AI did produce a few very important outcomes. The field itself was named, and its general goals were outlined. The soon-to-be “big four” pioneers of the field—McCarthy, Minsky, Allen Newell, and Herbert Simon—met and did some planning for the future. And for whatever reason, these four came out of the meeting with tremendous optimism for the field. In the early 1960s, McCarthy founded the Stanford Artificial Intelligence Project, with the “goal of building a fully intelligent machine in a decade.”7 Around the same time, the future Nobel laureate Herbert Simon predicted, “Machines will be capable, within twenty years, of doing any work that a man can do.”8 Soon after, Marvin Minsky, founder of the MIT AI Lab, forecasted that “within a generation … the problems of creating ‘artificial intelligence’ will be substantially solved.”9
Definitions, and Getting On with It
None of these predicted events have yet come to pass. So how far do we remain from the goal of building a “fully intelligent machine”? Would such a machine require us to reverse engineer the human brain in all its complexity, or is there a shortcut, a clever set of yet-unknown algorithms, that can produce what we recognize as full intelligence? What does “full intelligence” even mean?
“Define your terms … or we shall never understand one another.”10 This admonition from the eighteenth-century philosopher Voltaire is a challenge for anyone talking about artificial intelligence, because its central notion—intelligence—remains so ill-defined. Marvin Minsky himself coined the phrase “suitcase word”11 for terms like intelligence and its many cousins, such as thinking, cognition, consciousness, and emotion. Each is packed like a suitcase with a jumble of different meanings. Artificial intelligence inherits this packing problem, sporting different meanings in different contexts.
Most people would agree that humans are intelligent and specks of dust are not. Likewise, we generally believe that humans are more intelligent than worms. As for human intelligence, IQ is measured on a single scale, but we also talk about the different dimensions of intelligence: emotional, verbal, spatial, logical, artistic, social, and so forth. Thus, intelligence can be binary (something is or is not intelligent), on a continuum (one thing is more intelligent than another thing), or multidimensional (someone can have high verbal intelligence but low emotional intelligence). Indeed, the word intelligence is an over-packed suitcase, zipper on the verge of breaking.
For better or worse, the field of AI has largely ignored these various distinctions. Instead, it has focused on two efforts: one scientific and one practical. On the scientific side, AI researchers are investigating the mechanisms of “natural” (that is, biological) intelligence by trying to embed it in computers. On the practical side, AI proponents simply want to create computer programs that perform tasks as well as or better than humans, without worrying about whether these programs are actually thinking in the way humans think. When asked if their motivations are practical or scientific, many AI people joke that it depends on where their funding currently comes from.
In a recent report on the current state of AI, a committee of prominent researchers defined the field as “a branch of computer science that studies the properties of intelligence by synthesizing intelligence.”12 A bit circular, yes. But the same committee also admitted that it’s hard to define the field, and that may be a good thing: “The lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.”13 Furthermore, the committee notes, “Practitioners, researchers, and developers of AI are instead guided by a rough sense of direction and an imperative to ‘get on with it.’”
An Anarchy of Methods
At the 1956 Dartmouth workshop, different participants espoused divergent opinions about the correct approach to take to develop AI. Some people—generally mathematicians—promoted mathematical logic and deductive reasoning as the language of rational thought. Others championed inductive methods in which programs extract statistics from data and use probabilities to deal with uncertainty. Still others believed firmly in taking inspiration from biology and psychology to create brain-like programs. What you may find surprising is that the arguments among proponents of these various approaches persist to this day. And each approach has generated its own panoply of principles and techniques, fortified by specialty conferences and journals, with little communication among the subspecialties. A recent AI survey paper summed it up: “Because we don’t deeply understand intelligence or know how to produce general AI, rather than cutting off any avenues of exploration, to truly make progress we should embrace AI’s ‘anarchy of methods.’”14
But since the 2010s, one family of AI methods—collectively called deep learning (or deep neural networks)—has risen above the anarchy to become the dominant AI paradigm. In fact, in much of the popular media, the term artificial intelligence itself has come to mean “deep learning.” This is an unfortunate inaccuracy, and I need to clarify the distinction. AI is a field that includes a broad set of approaches, with the goal of creating machines with intelligence. Deep learning is only one such approach. Deep learning is itself one method among many in the field of machine learning, a subfield of AI in which machines “learn” from data or from their own “experiences.” To better understand these various distinctions, it’s important to understand a philosophical split that occurred early in the AI research community: the split between so-called symbolic and subsymbolic AI.
First let’s look at symbolic AI. A symbolic AI program’s knowledge consists of words or phrases (the “symbols”), typically understandable to a human, along with rules by which the program can combine and process these symbols in order to perform its assigned task.
I’ll give you an example. One early AI program was confidently called the General Problem Solver,15 or GPS for short. (Sorry about the confusing acronym; the General Problem Solver predated the Global Positioning System.) GPS could solve problems such as the “Missionaries and Cannibals” puzzle, which you might have tackled yourself as a child. In this well-known conundrum, three missionaries and three cannibals all need to cross a river, but their boat holds only two people. If at any time the (hungry) cannibals outnumber the (tasty-looking) missionaries on one side of the river … well, you probably know what happens. How do all six get across the river intact?
The creators of the General Problem Solver, the cognitive scientists Herbert Simon and Allen Newell, had recorded several students “thinking out loud” while solving this and other logic puzzles. Simon and Newell then designed their program to mimic what they believed were the students’ thought processes.
I won’t go into the details of how GPS worked, but its symbolic nature can be seen by the way the program’s instructions were encoded. To set up the problem, a human would write code for GPS that looked something like this:
LEFT-BANK = [3 MISSIONARIES, 3 CANNIBALS, 1 BOAT]
RIGHT-BANK = [EMPTY]
LEFT-BANK = [EMPTY]
RIGHT-BANK = [3 MISSIONARIES, 3 CANNIBALS, 1 BOAT]
In English, these lines represent the fact that initially the left bank of the river “contains” three missionaries, three cannibals, and one boat, whereas the right bank doesn’t contain any of these. The desired state represents the goal of the program—get everyone to the right bank of the river.
Copyright © 2019 by Melanie Mitchell