Las Bases para el estudio de la inteligencia artificial están determinados y sobre ellos se están realizando multitud de proyectos para establecer una forma de pensar que se pueda traducir a código máquina. El que se puede considerar el "padre" de estas bases, el que más conocimiento ostenta sobre este tema en estos momentos, se llama John Mc Carthy, y trabaja en la Universidad de Stanford (US)
Ha publicado diversos trabajos, pero como forma resumido, reproduzco aquí, la carta en la que señala los fundamentos de esta "ciencia" de nuevo acuño. Os traduzco algunas de las cuestiones aquí mencionadas:
Antes de empezar el cuestionario, si os interesa el tema, podeis visitar la pagina web de Marvin Minsky, otro guru del tema:
Q. What is artificial intelligence?
A. It is the science and engineering of making intelligent machines, especially
intelligent computer programs. It is related to the similar task of
using computers to understand human intelligence, but AI does not have to
confine itself to methods that are biologically observable.
Es la ciencia e ingeniería que trata de fabricar máquinas inteligentes, especialmente programas de ordenador. Está relacionada con el uso de ordenadores para entender la inteligencia humana, pero AI no se confina solamente a los métodos que son biológicamente observables.
Q. Yes, but what is intelligence?
A. Intelligence is the computational part of the ability to achieve goals in
the world. Varying kinds and degrees of intelligence occur in people, many
animals and some machines.
Qué es inteligencia? Es la parte computacional de la habilidad de conseguir objetivos en el mundo. Hay diferentes tipos y grados de inteligencia en las personas, animales y algunas máquinas.
Q. Isn’t there a solid definition of intelligence that doesn’t depend on
relating it to human intelligence?
A. Not yet. The problem is that we cannot yet characterize in general
what kinds of computational procedures we want to call intelligent. We
understand some of the mechanisms of intelligence and not others.
Se puede relacionar la inteligencia a algo no humano?
No aún. El problema es que no podemos caracterizar aún en general qué tipos
de procedimientos computaciones queremos que se llamen inteligentes. Compren
demos algunos de los mecanismos inteligencia y no otros.
Q. Is intelligence a single thing so that one can ask a yes or no question
“Is this machine intelligent or not?”?
A. No. Intelligence involves mechanisms, and AI research has discovered
how to make computers carry out some of them and not others. If doing a
task requires only mechanisms that are well understood today, computer programs
can give very impressive performances on these tasks. Such programs
should be considered “somewhat intelligent”.
La inteligencia es algo simple de tal forma que se pueda contestar afirmativa
o negativamente a la pregunta : esta máquina es inteligente?
No. La inteligencia required mecanismos, y la investigación ha descubierto cómo
hacer ordenadores con algunos mecanismos y no otros.
(resto en futuras ediciones de este posteo)
Q. Isn’t AI about simulating human intelligence?
A. Sometimes but not always or even usually. On the one hand, we can
learn something about how to make machines solve problems by observing
other people or just by observing our own methods. On the other hand, most
work in AI involves studying the problems the world presents to intelligence
rather than studying people or animals. AI researchers are free to use methods
that are not observed in people or that involve much more computing
than people can do.
Q. What about IQ? Do computer programs have IQs?
A. No. IQ is based on the rates at which intelligence develops in children.
It is the ratio of the age at which a child normally makes a certain score
to the child’s age. The scale is extended to adults in a suitable way. IQ
correlates well with various measures of success or failure in life, but making
computers that can score high on IQ tests would be weakly correlated with
their usefulness. For example, the ability of a child to repeat back a long
sequence of digits correlates well with other intellectual abilities, perhaps
because it measures how much information the child can compute with at
once. However, “digit span” is trivial for even extremely limited computers.
However, some of the problems on IQ tests are useful challenges for AI.
Q. What about other comparisons between human and computer intelligence?
Arthur R. Jensen [Jen98], a leading researcher in human intelligence,
suggests “as a heuristic hypothesis” that all normal humans have the same
intellectual mechanisms and that differences in intelligence are related to
“quantitative biochemical and physiological conditions”. I see them as speed,
short term memory, and the ability to form accurate and retrievable long term
Whether or not Jensen is right about human intelligence, the situation in
AI today is the reverse.
Computer programs have plenty of speed and memory but their abilities
correspond to the intellectual mechanisms that program designers understand
well enough to put in programs. Some abilities that children normally don’t
develop till they are teenagers may be in, and some abilities possessed by
two year olds are still out. The matter is further complicated by the fact
that the cognitive sciences still have not succeeded in determining exactly
what the human abilities are. Very likely the organization of the intellectual
mechanisms for AI can usefully be different from that in people.
Whenever people do better than computers on some task or computers
use a lot of computation to do as well as people, this demonstrates that the
program designers lack understanding of the intellectual mechanisms required
to do the task efficiently.
Q. When did AI research start?
A. After WWII, a number of people independently started to work on
intelligent machines. The English mathematician Alan Turing may have
been the first. He gave a lecture on it in 1947. He also may have been the
first to decide that AI was best researched by programming computers rather
than by building machines. By the late 1950s, there were many researchers
on AI, and most of them were basing their work on programming computers.
Q. Does AI aim to put the human mind into the computer?
A. Some researchers say they have that objective, but maybe they are
using the phrase metaphorically. The human mind has a lot of peculiarities,
and I’m not sure anyone is serious about imitating all of them.
Q. What is the Turing test?
A. Alan Turing’s 1950 article Computing Machinery and Intelligence [Tur50]
discussed conditions for considering a machine to be intelligent. He argued
that if the machine could successfully pretend to be human to a knowledgeable
observer then you certainly should consider it intelligent. This test
would satisfy most people but not all philosophers. The observer could interact
with the machine and a human by teletype (to avoid requiring that
the machine imitate the appearance or voice of the person), and the human
would try to persuade the observer that it was human and the machine would
try to fool the observer.
The Turing test is a one-sided test. A machine that passes the test should
certainly be considered intelligent, but a machine could still be considered
intelligent without knowing enough about humans to imitate a human.
Daniel Dennett’s book Brainchildren [Den98] has an excellent discussion
of the Turing test and the various partial Turing tests that have been implemented,
i.e. with restrictions on the observer’s knowledge of AI and the
subject matter of questioning. It turns out that some people are easily led
into believing that a rather dumb program is intelligent.
Q. Does AI aim at human-level intelligence?
A. Yes. The ultimate effort is to make computer programs that can solve
problems and achieve goals in the world as well as humans. However, many
people involved in particular research areas are much less ambitious.
Q. How far is AI from reaching human-level intelligence? When will it
A. A few people think that human-level intelligence can be achieved by
writing large numbers of programs of the kind people are now writing and
assembling vast knowledge bases of facts in the languages now used for expressing
However, most AI researchers believe that new fundamental ideas are
required, and therefore it cannot be predicted when human level intelligence
will be achieved.
Q. Are computers the right kind of machine to be made intelligent?
A. Computers can be programmed to simulate any kind of machine.
Many researchers invented non-computer machines, hoping that they
would be intelligent in different ways than the computer programs could
be. However, they usually simulate their invented machines on a computer
and come to doubt that the new machine is worth building. Because many
billions of dollars that have been spent in making computers faster and faster,
another kind of machine would have to be very fast to perform better than
a program on a computer simulating the machine.
Q. Are computers fast enough to be intelligent?
A. Some people think much faster computers are required as well as new
ideas. My own opinion is that the computers of 30 years ago were fast
enough if only we knew how to program them. Of course, quite apart from
the ambitions of AI researchers, computers will keep getting faster.
Q. What about parallel machines?
A. Machines with many processors are much faster than single processors
can be. Parallelism itself presents no advantages, and parallel machines
are somewhat awkward to program. When extreme speed is required, it is
necessary to face this awkwardness.
Q. What about making a “child machine” that could improve by reading
and by learning from experience?
A. This idea has been proposed many times, starting in the 1940s. Eventually,
it will be made to work. However, AI programs haven’t yet reached
the level of being able to learn much of what a child learns from physical
experience. Nor do present programs understand language well enough to
learn much by reading.
Q. Might an AI system be able to bootstrap itself to higher and higher
level intelligence by thinking about AI?
A. I think yes, but we aren’t yet at a level of AI at which this process can
Q. What about chess?
A. Alexander Kronrod, a Russian AI researcher, said “Chess is the Drosophila
of AI.” He was making an analogy with geneticists’ use of that fruit fly to
study inheritance. Playing chess requires certain intellectual mechanisms and
not others. Chess programs now play at grandmaster level, but they do it
with limited intellectual mechanisms compared to those used by a human
chess player, substituting large amounts of computation for understanding.
Once we understand these mechanisms better, we can build human-level
chess programs that do far less computation than do present programs.
Unfortunately, the competitive and commercial aspects of making computers
play chess have taken precedence over using chess as a scientific domain.
It is as if the geneticists after 1910 had organized fruit fly races and
concentrated their efforts on breeding fruit flies that could win these races.
Q. What about Go?
A. The Chinese and Japanese game of Go is also a board game in which
the players take turns moving. Go exposes the weakness of our present understanding
of the intellectual mechanisms involved in human game playing. Go
programs are very bad players, in spite of considerable effort (not as much as
for chess). The problem seems to be that a position in Go has to be divided
mentally into a collection of subpositions which are first analyzed separately
followed by an analysis of their interaction. Humans use this in chess also,
but chess programs consider the position as a whole. Chess programs compensate
for the lack of this intellectual mechanism by doing thousands or, in
the case of Deep Blue, many millions of times as much computation.
Sooner or later, AI research will overcome this scandalous weakness.
Q. Don’t some people say that AI is a bad idea?
A. The philosopher John Searle says that the idea of a non-biological machine
being intelligent is incoherent. He proposes the Chinese room argument
www-formal.stanford.edu/jmc/chinese.html The philosopher Hubert Dreyfus
says that AI is impossible. The computer scientist Joseph Weizenbaum says
the idea is obscene, anti-human and immoral. Various people have said that
since artificial intelligence hasn’t reached human level by now, it must be
impossible. Still other people are disappointed that companies they invested
in went bankrupt.
Q. Aren’t computability theory and computational complexity the keys
to AI? [Note to the layman and beginners in computer science: These are
quite technical branches of mathematical logic and computer science, and
the answer to the question has to be somewhat technical.]
A. No. These theories are relevant but don’t address the fundamental
problems of AI.
In the 1930s mathematical logicians, especially Kurt G¨odel and Alan
Turing, established that there did not exist algorithms that were guaranteed
to solve all problems in certain important mathematical domains. Whether
a sentence of first order logic is a theorem is one example, and whether a
polynomial equations in several variables has integer solutions is another.
Humans solve problems in these domains all the time, and this has been
offered as an argument (usually with some decorations) that computers are
intrinsically incapable of doing what people do. Roger Penrose claims this.
However, people can’t guarantee to solve arbitrary problems in these domains
either. See my Review of The Emperor’s New Mind by Roger Penrose. More
essays and reviews defending AI research are in [McC96a].
In the 1960s computer scientists, especially Steve Cook and Richard Karp
developed the theory of NP-complete problem domains. Problems in these
domains are solvable, but seem to take time exponential in the size of the
problem. Which sentences of propositional calculus are satisfiable is a basic
example of an NP-complete problem domain. Humans often solve problems
in NP-complete domains in times much shorter than is guaranteed by the
general algorithms, but can’t solve them quickly in general.
What is important for AI is to have algorithms as capable as people at
solving problems. The identification of subdomains for which good algorithms
exist is important, but a lot of AI problem solvers are not associated
with readily identified subdomains.
The theory of the difficulty of general classes of problems is called com-
putational complexity. So far this theory hasn’t interacted with AI as much
as might have been hoped. Success in problem solving by humans and by
AI programs seems to rely on properties of problems and problem solving
methods that the neither the complexity researchers nor the AI community
have been able to identify precisely.
Algorithmic complexity theory as developed by Solomonoff, Kolmogorov
and Chaitin (independently of one another) is also relevant. It defines the
complexity of a symbolic object as the length of the shortest program that
will generate it. Proving that a candidate program is the shortest or close
to the shortest is an unsolvable problem, but representing objects by short
programs that generate them should sometimes be illuminating even when
you can’t prove that the program is the shortest.
2 Branches of AI
Q. What are the branches of AI?
A. Here’s a list, but some branches are surely missing, because no-one
has identified them yet. Some of these may be regarded as concepts or topics
rather than full branches.
logical AI What a program knows about the world in general the facts
of the specific situation in which it must act, and its goals are all
represented by sentences of some mathematical logical language. The
program decides what to do by inferring that certain actions are appropriate
for achieving its goals. The first article proposing this was
[McC59]. [McC89] is a more recent summary. [McC96b] lists some of
the concepts involved in logical aI. [Sha97] is an important text.
search AI programs often examine large numbers of possibilities, e.g. moves
in a chess game or inferences by a theorem proving program. Discoveries
are continually made about how to do this more efficiently in various
pattern recognition When a program makes observations of some kind,
it is often programmed to compare what it sees with a pattern. For
example, a vision program may try to match a pattern of eyes and a
nose in a scene in order to find a face. More complex patterns, e.g. in
a natural language text, in a chess position, or in the history of some
event are also studied. These more complex patterns require quite
different methods than do the simple patterns that have been studied
representation Facts about the world have to be represented in some way.
Usually languages of mathematical logic are used.
inference From some facts, others can be inferred. Mathematical logical
deduction is adequate for some purposes, but new methods of non-
monotonic inference have been added to logic since the 1970s. The
simplest kind of non-monotonic reasoning is default reasoning in which
a conclusion is to be inferred by default, but the conclusion can be
withdrawn if there is evidence to the contrary. For example, when
we hear of a bird, we man infer that it can fly, but this conclusion
can be reversed when we hear that it is a penguin. It is the possibility
that a conclusion may have to be withdrawn that constitutes the
non-monotonic character of the reasoning. Ordinary logical reasoning
is monotonic in that the set of conclusions that can the drawn from
a set of premises is a monotonic increasing function of the premises.
Circumscription is another form of non-monotonic reasoning.
common sense knowledge and reasoning This is the area in which AI
is farthest from human-level, in spite of the fact that it has been an
active research area since the 1950s. While there has been considerable
progress, e.g. in developing systems of non-monotonic reasoning and
theories of action, yet more new ideas are needed. The Cyc system
contains a large but spotty collection of common sense facts.
learning from experience Programs do that. The approaches to AI based
on connectionism and neural nets specialize in that. There is also learning
of laws expressed in logic. [Mit97] is a comprehensive undergraduate
text on machine learning. Programs can only learn what facts
or behaviors their formalisms can represent, and unfortunately learning
systems are almost all based on very limited abilities to represent
planning Planning programs start with general facts about the world (especially
facts about the effects of actions), facts about the particular
situation and a statement of a goal. From these, they generate a strategy
for achieving the goal. In the most common cases, the strategy is
just a sequence of actions.
epistemology This is a study of the kinds of knowledge that are required
for solving problems in the world.
ontology Ontology is the study of the kinds of things that exist. In AI,
the programs and sentences deal with various kinds of objects, and
we study what these kinds are and what their basic properties are.
Emphasis on ontology begins in the 1990s.
heuristics A heuristic is a way of trying to discover something or an idea
imbedded in a program. The term is used variously in AI. Heuristic
functions are used in some approaches to search to measure how far
a node in a search tree seems to be from a goal. Heuristic predicates
that compare two nodes in a search tree to see if one is better than the
other, i.e. constitutes an advance toward the goal, may be more useful.
genetic programming Genetic programming is a technique for getting programs
to solve a task by mating random Lisp programs and selecting
fittest in millions of generations. It is being developed by John Koza’s
group and here’s a tutorial1.
3 Applications of AI
Q. What are the applications of AI?
A. Here are some.
game playing You can buy machines that can play master level chess for
a few hundred dollars. There is some AI in them, but they play well
against people mainly through brute force computation—looking at
hundreds of thousands of positions. To beat a world champion by
brute force and known reliable heuristics requires being able to look at
200 million positions per second.
speech recognition In the 1990s, computer speech recognition reached a
practical level for limited purposes. Thus United Airlines has replaced
its keyboard tree for flight information by a system using speech recognition
of flight numbers and city names. It is quite convenient. On the
the other hand, while it is possible to instruct some computers using
speech, most users have gone back to the keyboard and the mouse as
still more convenient.
understanding natural language Just getting a sequence of words into a
computer is not enough. Parsing sentences is not enough either. The
computer has to be provided with an understanding of the domain
the text is about, and this is presently possible only for very limited
computer vision The world is composed of three-dimensional objects, but
the inputs to the human eye and computers’ TV cameras are two dimensional.
Some useful programs can work solely in two dimensions,
but full computer vision requires partial three-dimensional information
that is not just a set of two-dimensional views. At present there
are only limited ways of representing three-dimensional information directly,
and they are not as good as what humans evidently use.
expert systems A “knowledge engineer” interviews experts in a certain domain
and tries to embody their knowledge in a computer program for
carrying out some task. How well this works depends on whether the
intellectual mechanisms required for the task are within the present
state of AI. When this turned out not to be so, there were many disappointing
results. One of the first expert systems was MYCIN in
1974, which diagnosed bacterial infections of the blood and suggested
treatments. It did better than medical students or practicing doctors,
provided its limitations were observed. Namely, its ontology included
bacteria, symptoms, and treatments and did not include patients, doctors,
hospitals, death, recovery, and events occurring in time. Its interactions
depended on a single patient being considered. Since the
experts consulted by the knowledge engineers knew about patients,
doctors, death, recovery, etc., it is clear that the knowledge engineers
forced what the experts told them into a predetermined framework. In
the present state of AI, this has to be true. The usefulness of current
expert systems depends on their users having common sense.
heuristic classification One of the most feasible kinds of expert system
given the present knowledge of AI is to put some information in one
of a fixed set of categories using several sources of information. An
example is advising whether to accept a proposed credit card purchase.
Information is available about the owner of the credit card, his record
of payment and also about the item he is buying and about the establishment
from which he is buying it (e.g., about whether there have
been previous credit card frauds at this establishment).
4 More questions
Q. How is AI research done?
A. AI research has both theoretical and experimental sides. The experimental
side has both basic and applied aspects.
There are two main lines of research. One is biological, based on the
idea that since humans are intelligent, AI should study humans and imitate
their psychology or physiology. The other is phenomenal, based on studying
and formalizing common sense facts about the world and the problems that
the world presents to the achievement of goals. The two approaches interact
to some extent, and both should eventually succeed. It is a race, but both
racers seem to be walking.
Q. What are the relations between AI and philosophy?
A. AI has many relations with philosophy, especially modern analytic
philosophy. Both study mind, and both study common sense. The best best
reference is [Tho03].
Q. What should I study before or while learning AI?
A. Study mathematics, especially mathematical logic. The more you
learn about science in general the better. For the biological approaches to
AI, study psychology and the physiology of the nervous system. Learn some
programming languages—at least C, Lisp and Prolog. It is also a good idea
to learn one basic machine language. Jobs are likely to depend on knowing
the languages currently in fashion. In the late 1990s, these include C++ and
Q. What is a good textbook on AI?
A. Artificial Intelligence by Stuart Russell and Peter Norvig, Prentice Hall
is the most commonly used textbbook in 1997. The general views expressed
there do not exactly correspond to those of this essay. Artificial Intelligence:
A New Synthesis by Nils Nilsson, Morgan Kaufman, may be easier to read.
Some people prefer Computational Intelligence by David Poole, Alan Mackworth
and Randy Goebel, Oxford, 1998.
Q. What organizations and publications are concerned with AI?
A. The American Association for Artificial Intelligence (AAAI)2, the Eu-
ropean Coordinating Committee for Artificial Intelligence (ECCAI)3 and the
Society for Artificial Intelligence and Simulation of Behavior (AISB)4 are
scientific societies concerned with AI research. The Association for Computing
Machinery (ACM) has a special interest group on artificial intelligence
The International Joint Conference on AI (IJCAI)6 is the main international
conference. The AAAI7 runs a US National Conference on AI.
Electronic Transactions on Artificial Intelligence8, Artificial Intelligence9,
and Journal of Artificial Intelligence Research10, and IEEE Transactions on
Pattern Analysis and Machine Intelligence11 are four of the main journals
publishing AI research papers. I have not yet found everything that should
be in this paragraph.
Page of Positive Reviews12 lists papers that experts have found important.
Funding a Revolution: Government Support for Computing Research by a
committee of the National Research covers support for AI research in Chapter
[Den98] Daniel Dennett. Brainchildren: Essays on Designing Minds. MIT
[Jen98] Arthur R. Jensen. Does IQ matter? Commentary, pages 20–21,
November 1998. The reference is just to Jensen’s comment—one