Foundations | guIA

What is artificial intelligence?

Sat, 05 Sep 2026 00:00:00 +0000

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 . 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 from v1, available in Spanish.

Continue with Alan Turing

Alan Turing: the machine that imitates any machine

Sat, 05 Sep 2026 00:00:00 +0000

Almost everything we argue about today when we argue about AI —whether a machine can surprise the person who programmed it, whether behaviour is enough as a criterion of intelligence, whether the brain is a computer— is already set out in three pieces Alan Turing wrote between 1936 and 1950. They are worth reading in order, because the sequence shows how a question about the foundations of mathematics turns into a question about the mind.

1936: modelling the human computer

From the seventeenth century, “computer” named a person: someone who did calculations. They were easy calculations but extremely long ones, and they were tied to the production of scientific knowledge —predicting the path of Halley’s comet, compiling the logarithm tables that scientists and engineers used.

Turing’s move was to model that activity. His machine is abstract and mathematical, but what guides the modelling is a person with a pencil:

Computing is normally done by writing certain symbols on paper. We may suppose this paper is divided into squares like a child’s arithmetic book. […] I assume then that the computation is carried out on one-dimensional paper, i.e. on a tape divided into squares. I shall also suppose that the number of symbols which may be printed is finite. […] The behaviour of the computer at any moment is determined by the symbols which he is observing, and his “state of mind” at that moment.

Putting a “state of mind” into a piece of mathematical logic, at a time when the mental was being pushed out of the domain of the scientific, is odd and does not look like an oversight. The paper can be read as an investigation into the nature of mind and its relation to a physical substrate.

The most important thing here is not the negative answer to the decision problem, but a by-product: the universal machine, a machine that takes instructions to behave as if it were any other machine. In 1936, almost in passing, Turing discovers the software industry.

1939-1945: Enigma, or finding patterns in noise

(Yes, the one from the film.) Turing’s contribution was decisive in breaking the Enigma machine the German military used to encipher its communications.

What matters here is the kind of reasoning the task demanded, closer to detective work and statistics than to logic: guessing the configuration of a machine from a string of characters that looks random but hides a message. Finding a regularity that will then work for any message enciphered with that same configuration.

Finding meaningful patterns in what looks like randomness, by trial and error. It is the most economical description there is of what a machine learning system does.

And the Bombe, the electromechanical device that did the work, had one feature the other wartime computers lacked: it did not compute numbers. It simulated being another machine.

1947: the brain as a computer without a clock

After the war, Turing turned officially to building a digital computer for the British government: the ACE (Automatic Computing Engine, a name that betrays the debt to Babbage).

In the lecture he gave that year to the London Mathematical Society, after explaining why the clock is central to the ACE’s design, he drops this:

We might say that the clock enables us to introduce a discreteness into time, so that time for some purposes can be regarded as a succession of instants instead of a continuous flow. A digital machine must essentially deal with discrete objects, and in the case of the ACE this is possible through the use of a clock. All other digital computing machines except for human and other brains that I know of do the same.

The brain joins the list of digital machines, with the peculiarity of computing without a central clock.

The same lecture contains the objection you still hear, and its refutation:

It has been said that computing machines can only carry out the processes that they are instructed to do. This is certainly true in the sense that if they do something other than what they were instructed then they have just made some mistake. It is also true that the intention in constructing these machines in the first instance is to treat them as slaves, giving them only jobs which have been thought out in detail, jobs such that the user of the machine fully understands in principle what is going on all the time. Up till the present machines have only been used in this way. But is it necessary that they should always be used in such a manner?

The “user” Turing means is the person programming, who can in principle know everything the machine does at the level of abstraction they are working in. (The distinction between user and programmer arrives later, when the computer becomes a product for non-experts.)

Universality lets Turing suggest that this servile mode of use, the most natural one at first, need not be the only one. Under a different kind of programming, the program can include instructions that modify its own configuration: not just the state, but the transition rules between states. If such a machine reaches the expected results by a more efficient route than the one foreseen, Turing is willing to call that behaviour intelligent. It is probably the first formulation of what we now call machine learning, and it arrives fifteen years before the phrase exists.

1950: the imitation game

In Computing machinery and intelligence, Turing returns to the problem with a strategy that resembles the Enigma work: putting himself in the position of someone who has to determine whether they are dealing with a machine.

Since defining “intelligence” is a swamp, he proposes an operational definition by way of a test procedure. The game has three parties: a judge, a human being playing a human being, and a computer playing a human being. The judge communicates with both in writing —today it would be a chat window—, knows one of the two is not human, and has to work out which.

Turing predicted:

I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 10^9, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning.

That 10 followed by nine zeros is barely a gigabyte. Today it sounds like nothing; in 1950 it was an enormous figure. The ACE was to have some 25 kilobytes, and the EDVAC —the machine of the period closest to it in architecture— had around 5.6 kilobytes of fast memory.

Turing adds, in the same prediction, something that gets quoted less and has aged better: that by the end of the century the use of words would have changed enough that one could speak of thinking machines without expecting to be contradicted. He did not predict a technical capability. He predicted a shift in language. He was right.

Lady Lovelace’s objection

The most valuable part of the 1950 paper is the objections Turing takes the trouble to consider, from the theological (“thinking is a function of man’s immortal soul”) to those resting on Gödel’s incompleteness theorems. The one most pertinent to today’s argument is the one he attributes to Ada Lovelace:

In this she states, “The Analytical Engine has no pretensions to originate anything. It can do whatever we know how to order it to perform” (her italics).

That is: the machine can do only what we know how to break into steps and instruct precisely. Does it resemble the 1956 Dartmouth conjecture from the ? It resembles it closely, though one states it as a limit and the other as a research programme.

Turing’s answer is not that the machine is creative. It is more modest and harder to dodge: machines surprised him many times, and not through faults. The error is in how we think about consequences.

The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false.

There is an epistemological thesis there and not just a reply to an objection: deriving consequences from data and general principles is work, and it produces knowledge that was not available before the work was done. That is the subject of , a couple of entries further on.

Recent history: why it worked now and not before

Sat, 05 Sep 2026 00:00:00 +0000

The central ideas of deep learning are old. Artificial neural networks date from the middle of the twentieth century, the algorithm that trains them became popular in the eighties, and the field still spent decades promising things it could not deliver. The interesting question is not where the ideas came from: it is why they only started working now.

The short answer is that the theory did not change, the scale did. v1 of this guide counted three factors, all three turning on the same concept —the datum—: producing data, storing it and processing it. Two more are needed today.

Processing: machines that do more per second

A convenient measure of progress is the number of transistors on a processor. In 1965 Gordon Moore observed that the number had been doubling every year since the transistor was invented, and ventured that it would keep doing so for another decade. The observation became known as “Moore’s law”, though there is little law about it: it is an industrial trend, and one running out against the physical limits of miniaturisation.

What matters is the shape of the curve. If capacity doubles every two years, then in four years there is not twice as much: there is far more than twice as much. The 1971 Intel 4004 had 2,300 transistors on 12 × 12 millimetres; current processors have billions on less area.

With one correction that matters: the hardware that made deep learning possible is not the general-purpose processor but the graphics card, designed for video games and repurposed because rendering polygons and multiplying matrices turn out to be the same problem. Much of today’s semiconductor geopolitics is explained by that accident.

Production: data everywhere

Almost everything we do can be turned into data, because there is a digital mediation that registers the smallest change of state:

Clicks on a page generate data about which content draws attention.
Searches generate data about what people want to know.
Likes, comments and shares generate data about popularity.
Histories and cookies generate data about browsing patterns.
Product reviews generate data about consumer preferences.
The phone’s GPS generates data about people’s movements.
Connected devices generate environmental, fitness, energy-consumption and industrial-production data.

Luciano Floridi called the infosphere the informational environment that digital technologies created around the planet: the totality of what is produced, stored, shared and processed by digital means. His thesis is that just as living organisms interact with the biosphere and constitute it, we interact with the infosphere and constitute it. Hence his proposal that we think of ourselves as informational organisms living an onlife existence, with no sharp border between connected and unconnected.

The metaphor has a limit worth keeping in mind, and it is the same one “the cloud” has: it names as ethereal something that sits in a shed, consumes electricity and occupies a plot with a postal address.

Storage: having somewhere to put it

There is so much data available partly because there is somewhere to keep it, and that is as hard a technological problem as processing. Here the decisive factor is economic: the cost per gigabyte fell so many orders of magnitude that it stopped being a design constraint. In the late eighties, storing a gigabyte cost on the order of a hundred thousand dollars; today it is measured in cents.

Put on Turing’s scale: the gigabyte he estimated the imitation game would need would have cost, on 1950s disks, on the order of nine million dollars, and in fast memory a figure not worth writing down because it was also technically impossible.

Source: .

Notice anything odd about the shape of the chart? Look at the scale on the vertical axis.

Architecture: the transformer

The three factors above explain the possibility, not the result. The missing piece arrived in 2017 with the transformer, the architecture proposed in Attention is all you need, whose decisive advantage is not so much that it understands language better as that it parallelises: it can use thousands of cards at once, where earlier architectures processed in sequence.

That turned the problem of artificial intelligence into a budget problem. If more compute and more data systematically produce better models, the question stops being “what idea are we missing?” and becomes “who can pay for the next training run?”. The public break came in late 2022, when that technical shift became a mass consumer product.

And what the scale costs

This is where v1 of this guide stopped, and today it cannot. Scale is not free: training a large model consumes electricity and water, happens in data centres sited where energy and land are cheap —which rarely coincides with where the people using the model live— and depends on badly paid human labour to label data and correct outputs.

This is not an ethical appendix to the technical matter: it is part of the explanation of why this worked now. A model that is only viable because cooling water and labelling work are cheap is an artefact whose condition of possibility is an unequal distribution. That argument, with its sources, is in .

So what is a neural network

Let us join the dots. When people talk about “the algorithm” of Netflix, Spotify or a social network, strictly speaking they are not talking about an algorithm: an algorithm guarantees a result, and there is no such guarantee here. What there is, is a set of computational processes —those are algorithms, and fairly simple ones to program— that configure an artificial neural network able to perform a task from the state it reached by exploring data.

A neural network is a layered logical structure, with nodes that receive signals, process them and pass the result to other nodes. What is distinctive are the weighted connections between nodes, the weights. Training consists of iteratively adjusting those weights according to feedback from the data, until the network models the relation between what goes in and what we want to come out. For a great many tasks you do not need precise instructions on how to tell a dog from a cat: plenty of examples and plenty of compute will do.

And that is where the problem that opens the next entry appears. We solved one thing and created another: if I do not know exactly what the network is doing to achieve what I asked of it, how do I know it is doing it well, and that it will keep doing it well faced with something it has never seen? Put differently: how do I know that it knows? And, before that, how do I know that I know anything?

Going deeper

Floridi, L. (2014). The fourth revolution: How the infosphere is reshaping human reality. Oxford University Press.

Ilcic, A. A. Manuscript, in Spanish.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

If you are not afraid of the mathematics, the full technical explanation is in the route laid out in , and in particular in the 3Blue1Brown series and Karpathy’s Zero to Hero.

Videos on the history of AI

The machine that changed the world

The Thinking Machine (MIT, 1961), included in this compilation of documentaries on the history of AI:

This entry revises and updates from v1, available in Spanish.

Continue with Epistemology?

Epistemology? How we know that a machine knows

Sat, 05 Sep 2026 00:00:00 +0000

This is the entry that looks furthest from the subject and is closest to it. Artificial intelligence is, strictly speaking, a knowledge technology: it produces claims about the world from evidence, and we decide when to accept them. Epistemology is the discipline that has been dealing with that decision for twenty-five centuries, and it has not solved the problem. Worth knowing before delegating it.

Notice what just happened in the previous paragraph. I asked you to accept that the topic matters on the strength of nothing but my say-so. If I now ask how you know what you know, several possible answers are “I heard it”, “I trust the source”, “I looked it up”. That is the whole problem.

Observation

We tend to think the best source of knowledge is direct experience, the kind the senses give, and to treat it as the most objective thing we can aspire to. That idea took work to establish: for centuries the opposite view prevailed, that the senses deceive and it is better to reason than to go and look.

The conventional history places the change in the seventeenth century, when observational facts become the basis of science and the authority of Aristotle and the Bible stops being enough. The emblem is Galileo dropping objects from the tower of Pisa. (That experiment probably never happened —the source is a biography written decades later by a disciple— and it still does the pedagogical work, which is itself an interesting epistemological fact about how knowledge circulates.)

This matters for thinking about AI because we face a new form of observation: the kind that starts with the generation of a datum. And there are so many data available that it is easy to come to believe the data are the world.

With sensor-mediated observation the same thing happens as with observation plain, only more so: we see what we see according to what we are looking to see. Observing is not receiving stimuli. Formulating and accepting an observational statement requires a conceptual framework and prior knowledge. The classic example is a child learning to recognise apples: without corrections from someone who already knows what makes something an apple, they will call apples things that are not.

Exactly that happens to a machine learning system, with two differences. The corrections come from a dataset labelled by people whose conceptual framework gets built in without being declared. And there is nobody there to ask why.

Induction

Deriving a general regularity from observational statements is called induction. You observe that metals A, B and C expand when heated, and induce that all metals do.

Inductivism holds that making good inductions is all it takes to have good knowledge. The trouble is that, unlike deduction, induction does not guarantee the truth of the conclusion even when the premises are true. The requirements proposed for a good inductive inference come apart on close inspection:

Many observations supporting the generalisation. It is not clear how many.
Great variety of conditions. There is no criterion for deciding which variations are relevant.
No known exceptions. Most scientific laws have exceptions.

There is no satisfactory characterisation of induction, and it cannot be justified by appeal to deductive logic or to more induction without circularity. There are more sophisticated versions based on probability and statistics, but they inherit the same problems, with the aggravation that these end up concealed among the assumptions made to get the calculation working.

The conclusion is not sceptical: it is that facts and theories support each other. You need the existing theory about what you want to know in order to judge which inductive generalisations are acceptable. That only looks like a problem if you were expecting an ultimate truth.

And it describes with uncomfortable precision what a trained model does: generalise from cases, with no theory of the domain, on sufficiency criteria set by whoever assembled the dataset.

Deduction and falsification

Falsificationism starts from the fact that knowledge changes with time and with available resources, and so puts the problem of progress at the centre: explaining why one theory is better than another without any of them being true in an ultimate sense, because a better one can always come along.

The canonical example is physics from Aristotle to Newton to Einstein. Aristotelian physics explained a great many phenomena and was still falsified in several respects. Newton’s was such a large advance that for two centuries it looked definitive. Relativity turned out better still.

What matters here is the displacement: what is at stake is not truth but the process —formulating hypotheses, testing them, falsifying them. Observation and experience are fundamental, but so are imagination and creativity in producing the hypotheses to be tested, and that remains the open challenge for any artificial system. There is a good deal more imagination in human intelligence than the theories centred on full rationality wanted to admit.

Technological knowledge

Worth dismantling in passing a very entrenched idea: that technology is applied science. The linear model running from scientific discovery to practical application became associated with Vannevar Bush’s 1945 report Science, the endless frontier, and with the science policy it organised for decades.

It was criticised on two counts. First, because it ignores the social, political and economic factors that shape development. Second, because the process is neither linear nor one-directional: technology has a logic of its own, responds to practical needs and market forces, and frequently runs ahead of the scientific explanation of why it works.

Deep learning is a textbook case. It works far better than the available theory can explain, and a good part of the research consists of working out afterwards why it worked.

Epistemology is the study of how we know what we know. Social epistemology deals with how we know it through others, which is by far the most common way of knowing, science included.

A situation: someone tells you a rumour. Do you believe it? You can weigh how reliable that person is, or you can look for more information. Either move takes a position on the social nature of knowledge.

Defending a belief by what we know of its source’s reliability is a position called reliabilism. Trust works as a shortcut: instead of verifying every detail, you rely on sources that have proved dependable. It is efficient and it is dangerous for the same reason.

Two things complicate the picture:

The social mechanisms of verification were built when publishing was expensive. Printing a newspaper or running a television channel implied an organisation, and that organisation was what you were assessing when you trusted. Digital technologies took that cost to zero without replacing the mechanism.
Cognitive biases are shortcuts. If a friend usually gives me accurate information, she has earned my trust. But what makes her reliable? Where does she get what she says? Could she be saying what she reckons and, since nobody challenges her, appear to be right?

With generative AI this gets acute. When we interact with a language model, the output can be as polished as an expert’s, and it comes without any of the signals we use to calibrate trust: no source, no track record, nobody to answer for it. If you are used to checking something with a search, it is worth asking what or whom you are trusting exactly.

And it is not only an individual problem. When generated text starts circulating as a source for other generated text, what degrades is not a particular belief but the system by which a community decides what it takes as established. That is the underlying subject of the epistemic commons, one of the axes running through the and much of .

That closes the conceptual floor of the chapter. What follows is the practical part —where to learn all this, in what order and by what criteria— and it is worth walking in with the question in hand. None of the courses ahead asks it, and it is the one that makes all the others useful.

Further thinking

Chalmers, A. F. (2013). What is this thing called science? (4th ed.). Hackett.

Cutcliffe, S. H. (2000). Ideas, machines, and values: An introduction to science, technology, and society studies. Rowman & Littlefield.

Lawler, D. (2020). Los estándares como artefactos. Filosofia Unisinos, 21(1), 24-35.

Parente, D., Berti, A. and Celis Bueno, C. (Eds.). (2022). Glosario de filosofía de la técnica. La Cebra.

This entry revises and updates from v1, available in Spanish.

Continue with Getting started: a map

Foundations | guIA

What is artificial intelligence?

The first black box

From rational agents to uncertainty about ends

The “original” AI: the summer of 1956

AI today: teaching rather than instructing

Reading suggestions

Alan Turing: the machine that imitates any machine

1936: modelling the human computer

1939-1945: Enigma, or finding patterns in noise

1947: the brain as a computer without a clock

1950: the imitation game

Lady Lovelace’s objection

Further reading

Recent history: why it worked now and not before

Processing: machines that do more per second

Production: data everywhere

Storage: having somewhere to put it

Architecture: the transformer

And what the scale costs

So what is a neural network

Going deeper

Videos on the history of AI

Epistemology? How we know that a machine knows

Observation

Induction

Deduction and falsification

Technological knowledge

Social epistemology: whom we trust

Further thinking