I propose that we define "Artificial Intelligence" in the obvious way. An entity is an AI if it is both artificial (in the relevant sense) and intelligent (in the relevant sense).
Despite the apparent attractiveness of this simple analytic definition of AI, standard definitions of AI are more complex. In their influential textbook Artificial Intelligence, for example, Stuart Russell and Peter Norvig define artificial intelligence as "The designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment"[1]. John McCarthy, one of the founding fathers of AI, defines it as "The science and engineering of making intelligent machines, especially intelligent computer programs". In his influential 1985 book Artificial Intelligence: The Very Idea, philosopher John Haugeland defines it as "the exciting new effort to make computers think... machines with minds, in the full and literal sense" (p. 2).
If we define AI as intelligent machines, we risk too broad a definition. For in one standard sense, the human body is also a machine -- and of course we are, in the relevant sense, "intelligent". "Machine" is either an excessively broad or a poorly defined category.
If instead we treat only intelligent computers as AI, we risk either excessive breadth or excessive narrowness, depending on what counts as a computer. If a "computer" is just something that behaves according to the patterns described by Alan Turing in his standard definition of computation, then humans are computers, since they too sometimes follow such patterns. Indeed, originally the word "computer" referred to a person who performs arithmetic tasks. Cognitive scientists sometimes describe the human brain as literally a type of computer. This is contentious but not obviously wrong, on liberal definitions of what constitutes a computer.
However, if we restrict the term "computer" to the types of digital programmable devices with which we are currently familiar, the definition risks being too narrow, since not all systems worth calling AI need be instantiated in such devices. For example, non-digital analog computers are sometimes conceived and built. Also, many artificial systems are non-programmable, and it's not inconceivable that some subset of these systems could be intelligent. If humans are intelligent non-computers, then presumably in principle some biologically inspired but artificially constructed systems could also be intelligent non-computers.
Russell and Norvig's definition avoids both "machine" and "computer", at the cost of making AI a practice rather than an ontological category: It concerns "designing and building". Maybe this is helpful, if we regard the "artificial" as coextensive with the designed or built. But this definition appears to rule out evolutionary AI systems, which arise through reproduction and selection (for example, in artificial life), and are arguably neither designed nor built, except in liberal sense that every evolved entity is.
"Intelligence" is of course also a tricky concept. If we define intelligence too liberally, even a flywheel is intelligent, since it responds to its environment by storing and delivering energy as needed to smooth out variations in angular velocity in the device to which it is attached. If we define intelligence too narrowly, then classic computer programs of the 1960s to 1980s -- arguably, central examples of "AI" as the term is standardly used -- no longer count as intelligent, due to the simplicity and rigidity of the if-then rules governing them.
Russell and Norvig require that AI systems receive percepts from the environment -- but what is a "percept", and why couldn't an intelligence think only about its own internal states or be governed wholly by non-perceptual inputs? They also require that the AI "take action" -- but what counts as an action? And couldn't some AI, at least in principle, be wholly reflective while executing no outward behavior?
Can we fall back on definition by example? Here are some examples: classic 20th-century "good-old-fashioned-AI" systems like SHRDLU, ELIZA, and Cyc; early connectionist and neural net systems like Rosenblatt’s Perceptron and Rumelhart’s backpropagation networks; famous game-playing machines like DeepBlue and AlphaGo; transformer-based architectures like ChatGPT, Grok, Claude, Gemini, Dall-E, and Midjourney; Boston Dynamics robots and autonomous delivery robots; quantum computers.
Extending forward from these examples, we might also imagine future computational systems built along very different lines, for example, partly analog computational systems, or more sophisticated quantum or partly quantum computational systems. We might imagine systems that operate by interference patterns in reflected light, or "organic computing" via DNA. We might imagine biological or partly-biological systems which might not be best thought of as computers (unless everything is a "computer"), including frog-cell based xenobots and systems containing neural tissue. We might imagine systems that look less and less like they are programmed and more and more like they are evolved, selected, and trained. At some point it becomes unclear whether such systems are best regarded as "artificial".
As a community, we actually don’t have a very good sense of what "AI" means. We can easily classify currently existing systems as either AI or not-AI based on similarity to canonical examples and some mushy general principles, but we have only a poor grasp of how to differentiate AI from non-AI systems in a wide range of hypothetical future cases.
The simple, analytic definition I suggested at the beginning of this post is, I think, the best we can do. Something is an Artificial Intelligence if and only if it is both artificial and intelligent, on some vague-boundaried, moderate-strength understanding of both "artificial" and "intelligent" that encompasses the canonical examples while excluding entities that we ordinarily regard as either non-artificial or non-intelligent.
I draw the following lesson from these facts about the difficulty of definition:
General claims about the limitations of AI are almost always grounded in specific assumptions about the nature of AI, such as its digitality or its implementation on "computers". Future AI, on moderately broad understandings of what counts as AI, might not be subject to those same limitations. Notably, two of the most prominent deniers of AI consciousness -- John Searle and Roger Penrose -- both explicitly limit their skepticism to systems designed according to principles familiar in the late 20th century, while expressing openness to conscious AI designed along different lines. No well-known argument aims to establish the in-principle impossibility of consciousness in all future AI systems on a moderately broad definition of what counts as "AI". Of course, the greater the difference from currently familiar architectures, the farther in the future that architecture is likely to be.
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[1] Russell and Norvig are widely cited for this definition, but I don't see this exact quote in my third edition copy. While I await UCR's interlibrary loan department to deliver my 4th ed. version, I'll assume this quote is accurate.
