What is artificial intelligence?

There are as many definitions of AI as there are people thinking about AI, and that is not a defect of the field: it is what happens when two vague words come together to name something we have not finished understanding. “Intelligence” and “artificial” are the sort of concepts scientists and philosophers have spent centuries trying to pin down, and the only precision available about them is that they work as a context-dependent cloud of ideas, not as a mathematical definition.

It is worth starting there, because almost every public argument about AI is really an argument about what does and does not fit inside the word.

The first black box

Four classic definitions, collected in Russell and Norvig’s textbook:

“The exciting new effort to make computers think… machines with minds, in the full and literal sense.” (Haugeland, 1985)

“The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight, 1991)

“The study of mental faculties through the use of computational models.” (Charniak and McDermott, 1985)

“AI … is concerned with intelligent behaviour in artefacts.” (Nilsson, 1998)

Notice the tension: some speak of thinking and others of behaving. Anyone out to replicate human intelligence artificially can try to build something that thinks the way we believe we think, or can build an artefact that, for all practical purposes, behaves as if it thought, without much concern for whether anything similar is going on inside.

The trouble with the first route becomes obvious the moment you say it out loud: we do not really know how we think. The first black box is us. And this is not a philosophical aside but an engineering constraint: you cannot faithfully replicate a process you have no model of.

From rational agents to uncertainty about ends

Through its third edition (2009), Russell and Norvig organised the whole textbook around one idea:

We define AI as the study of agents that receive percepts from the environment and perform actions.

A rational agent, then, was one that acts to obtain the best outcome or, under uncertainty, the best expected outcome. And “acting well” was measured by consequences: if the sequence of states the agent produces in its environment is desirable, it acted well. Desirability is fixed by a performance measure defined in advance.

It is a clean, operational definition and a fairly hard one to sustain in everyday life. Who writes the performance measure? Do we act like that?

The fourth edition (2020) acknowledges the problem and changes the frame. AI is no longer defined as building agents that maximise a given objective, but as building beneficial agents that operate knowing they do not know for certain which human objectives they ought to pursue. The turn is worth registering, because it displaces the technical problem: it is no longer only about optimising better, but about what to do when the function being optimised is a hypothesis about what someone wants.

Much of the current argument about alignment, evaluation and usage policy lives in that displacement.

The “original” AI: the summer of 1956

The field’s founding statement is in the Dartmouth workshop proposal:

We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire.

The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.

The emphasis marks the whole bet, and also its weak point. The entire programme rests on intelligence admitting a sufficiently precise formal description. Two months, ten people. It proved to be a considerably harder job.

AI today: teaching rather than instructing

The most useful way to delimit what is called AI today is as a new way of programming. In the old one, somebody had to specify every instruction: what to look at, in what order, by what criterion to decide. In the new one you do not give instructions but examples, and sometimes not even that: you correct the output and the system adjusts.

That displaces the work; it does not remove it. Where there used to be a programmer writing rules, there are now decisions about which data get gathered, where they come from, what gets labelled as correct and who does the labelling. These decisions are every bit as determining as the rules they replace, with the difference that they are far less visible.

Since 2017 the field has been organised around a specific architecture, the transformer, and since late 2022 around a specific product, the general-purpose chatbot. The three levels —a way of programming, an architecture, a product— are worth keeping apart, because the word “AI” names all of them and the slippage between them is where most unfounded promises get in.

How we got here is the subject of Recent history. But first it is worth passing through the person who posed nearly all of these questions when there was not yet an electronic computer to ask them of.

Reading suggestions

Boden, M. A. (2016). AI: Its nature and future. Oxford University Press.

Copeland, J. (1993). Artificial intelligence: A philosophical introduction. Blackwell.

Russell, S. and Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.

This entry revises and updates What is AI? from v1, available in Spanish.