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\begin{document}
  \title{Real AI}
   \author{Matja\v{z} Gams \\
      Jo\v{z}ef Stefan Institute, Jamova 39, Ljubljana, Slovenia\\
      Phone: $+$386 61 1259 199, Fax: $+$386 61 1258 058 \\
      E-mail: matjaz.gams@ijs.si }
  \titleodd{Real AI}
  \authoreven{M. Gams}
  \keywords{artificial intelligence, viewpoint}
  \edited{Branko Sou\v{c}ek} 
  \received{December 12, 1992}
  \revised{February 9, 1993}
  \accepted{March 1, 1993}

  \abstract{In the first part, four viewpoints on AI are presented. It is
    proposed that a program exhibiting AI is one that can change as
    a result of interactions with the environment. While no program can
    be proclaimed as intelligent at the moment, intelligence may be
    just an emerging property of successful information machines or
    beings. In the second part, ideas, problems and misconceptions
    about AI are analysed through grouping into three categories:
    (1) facts - opinions that are supported by facts; (2) legends -
    opinions, based on facts but largely exaggerated; (3) myths -
    opinions not based on facts.}

  \maketitle

\section{Introduction}
Discussions about AI have attracted most of the researchers
inside the field as well as many outside (Fox 1990, Hayes-Roth
\& Fikes 1991, Hayes et. al. 1992, Mettrey 1992, Schank 1991,
Searle 1982, Wilkes 1992). In particular, there have been
important shifts and modifications in world-wide opinion
observed in recent relevant publications. These debates have
motivated us to make an attempt to summarise them in a coherent
way. \footnote{A similar but shorter paper (Gams 1992) has been
presented as the first paper in the AI section at the ERK'92
conference - similar parts are reprinted with permission.}

First, let us analyse the notion of artificial intelligence. 

\section{Viewpoints on AI}

Thinking about the definition of AI, one should ask `Where's the
AI?' (Schank 1991). There seem to be at least four prevailing
answers to that question.

The {\bf first view} sees AI as something magic that emerges out of
a computationally effective computer after you put in it enough
things. Indeed, it is still often acclaimed that neural networks 
mimic the behaviour of human brains. That is rather surprising 
since what they do at present is to mimic a numeric filter at best
able to tune predefined parameters. Not surprisingly,
computationally more diverse and structurarily more complex
statistical methods typically achieve better classification
accuracy (Henery \& Taylor 1992).

This approach has already contributed to the first dark
age at the beginning of AI and is still present in many
subfields of AI. On the other hand, one should not
underestimate their advantages such as flexibility and
robustness.

The {\bf second view} sees AI as a superb inference engine and 
therefore resembles knowledge engineering. What one has to do is 
to find an expert, encode his knowledge into lists of rules, add 
an inference engine with appropriate interface and there you are.
This has led many people to think that AI means rule-based expert
systems, and then they thought they understand them as well as 
AI. And since they have also learned the limitations of 
rule-based systems, they also think that is the limitation of AI, 
not just of one component of AI (Hayes-Roth \& Fikes 1991). While 
connectionism as well as knowledge engineering and inference 
engines are important parts of AI research and applications, 
labelling it intelligent or as AI itself is misleading (Schank 
1991).

The {\bf third view} maintains that, if no machine ever did it before, 
it must be AI. For example, years ago research in computer chess
was one of the central themata in AI. People thought that computer
programs playing chess well would certainly have to be
intelligent. Now, nearly everybody agrees that these programs
do not have any deep intelligence at all. 
Luckily, there are several explanations of this contradiction.
For one thing, this viewpoint seems to confuse getting a machine
to do something intelligent with getting it to be a model of
human intelligence. More important, if you define AI in that way
- to be one of frontiers of computer science, once the area that
you are looking at is understood, then it is no longer at
the frontiers of computer science and therefore no longer AI, and so
it is a no-win kind of situation (Schank 1991).

So, where is the intelligence in computer programs? As mentioned
earlier, many difficult problems which had long been thought to
require real intelligence, have been solved by rather 
unintelligent methods. Intensifying this argument, even superb
intelligent behaviour does not guarantee that real intelligence and
understanding have been achieved. For example, Searle (Searle 
1982) has constructed a hypothetic Chinese room in which a group of
workers performs intelligent translation between two natural
languages (English-Chinese) and each of them performs only a
subpart of the whole process on the basis of a predefined procedure.
Although such a Chinese room could pass Turing's test, the
room (and nobody inside it) does not understand the whole process
and there is no real intelligence at all.

After a decade of quite intensive debate, there has not been any
definitive answer to this paradox since it is actually a
philosophical question: Is performance, i.e. a mechanicistic 
approach really sufficient? From a practical point of view,
most things in our world work in the mechanicistic mode. Of course, 
there are paradoxes and unsolved questions (e.g. are there other
universes or is there only ours?) but people have successfully
lived with them. Not to mention that other approaches based on 
ideology or spiritism have not yielded similarly good results.

Therefore, it seems rather surprising that criticism is so
strong in the AI area even if the same arguments are being repeated
over and over again. A recent example of this kind could be the
article ``Artificial Intelligence as the Year 2000 Approaches''
(Wilkes 1992). It provoked harsh replies (Hayes et. al. 1992) in
which several errors and misconceptions were exposed.
Nevertheless, in all this argumentation there are at least two
points where the AI community still has to prove itself:
\begin{itemize}
\item if intelligence (in computers) were simple, fast and powerful
computers would have facilitated it a long time ago, and
\item many of the ideas in the AI field have produced much more 
optimism than real improvements.
\end{itemize}
Here we shall devote attention to the first argument, and the 
second argument will be analysed in the second part of this 
paper. 

For example, Wilkes (Wilkes 1992) claims that intelligence may
come from analogue circuitry since, obviously, it has not come from
digital computers so far. Searle (Searle 1982) claims that
digital machines can not be intelligent as biological beings
since they are essentially different. Although Hayes (Hayes et.
al. 1992) claims that no proof is given for such claims, the
same is valid also for the reverse claim.

At this point we can only agree that real intelligence in
machines has not been achieved yet. Furthermore, we still do not
have any good ideas how to make a true intelligent machine.
However, two arguments seem plausible:
\begin{itemize}
  \item Real human intelligence is very complex. If it were simple
    enough for us to understand it, than we would be too simple to
    perceive that (as claimed by several authors).
  \item Intelligence may be just an emerging property of
    successful information machines or beings. There does not have to
    be any deeper motive or principle behind it. This approach is
    very close to the ``artificial life'' where computational models
    share many characteristics with biological computation (Brooks
    1991).
\end{itemize}

Furthermore, in computing there are good foundations and
clear concepts like Turing's machine or Church's thesis. There
is also Turing's test in which real intelligence is achieved
when human judges can not distinguish between the performance of
a computer and human. Since computer programs are far away from
achieving such a level, the contest area is often limited to a
domain which still requires intelligence by human counterpart.
However, it is important to notice that the communication
between the judge and the contenders is an open one. Therefore,
a computer program playing superb chess but unable to explain
the motives of its moves certainly would not pass the test while even
a novice player with normal explanation and reasoning capabilities
certainly would. In recent years slightly modified tests, or
competitions, are becoming annual events with rewards
up to \$100,000 (Epstein 1992).

This leads us to the {\bf fourth view} on AI. True intelligence,
exhibited by computer programs, would have to have many or even
most properties of human intelligent behaviour regardless of how
narrow the application area was. One of the main such properties is
{\em learning} since intelligence first of all means getting
better over time. In relation to Turing's test, a computer
program unable to learn from its mistake would certainly be
exposed. Today, hardly any AI programs learn from their mistakes,
although - with very good reason, learning is the central area
of AI at least in the last decade.

There is some additional reasoning about intelligence:
\begin{itemize}
  \item Intelligence is in size. It is
    hard to expect a small program to display intelligence.
    Intelligence is neither simple nor easy understandable.
  \item Intelligence is
    in complexity and heterogeneity. This area is sometimes related
    to multi-strategy learning (Michalski \& Tecuci 1992), a multiple-knowledge
    approach (Gams et. al. 1991) and multi-agents (Minsky 1987).
  \item Intelligence is in the ability to perform well real-life tasks which
    require the use of knowledge. For example, Mathematica, a program
    for symbolic computing is regarded as approximately as
    intelligent as a numeric library. Contrary to recent interest in
    logic programming, it is quite probable that intelligence there
    will be at a similar level to Mathematica until real-life knowledge
    is incorporated into programs.
\end{itemize}

Furthermore, there are several aspects of intelligence each of
which can be compared if not measured on a scale. For example,
motional intelligence can be quite high in many animals.
In another aspect, AI research can well be at the frontiers of
computer science while AI applications fell into
an application area years away from scientific achievements.
AI applications do not have to be intelligent, they have to be 
related to AI research similar to other science/application 
relations.

In short, while agitated debates about AI raise interest and
in both ways affect funds, what really matters is what works and
which new discoveries are produced. {\em It is not that AI needs
definitions; it is more that AI needs substance} (Schank 1991). Although
general artificial intelligence has not yet been achieved, we
know more and more about it. Some basic facts, legends and myths 
about AI will be represented in the following sections.

\section{Facts}

{\bf The first AI concept is search.} Most difficult problems
involve choosing between alternative solutions and evaluation
processes in which the best solution is found. This basic search
schema may not be immediately observed in diverse subareas of
AI such as scheduling, games, learning or expert systems. Novice
readers in AI might get distressed by different terminology and
diverse techniques. However, even one of the oldest definitions
of AI promotes it as a fight against combinatorial explosion.

While faster computers certainly help, simple search techniques 
can not ever deal with the exponential growth rate of the number 
of possibilities in a search tree. For example, in a single 
factory having 85 orders, 10 operations, and only one 
substitutional machine, one could create over $10^{880}$ 
alternative schedules (Fox 1990) while the number of all atomic 
particles in our universe is estimated at $10^{80}$. Obviously, 
the key question is how to reduce search space.

{\bf The second AI concept is knowledge representation.} It is not
that the knowledge representation concept is second to search; it is
one of the two. Knowledge is probably even more important than
search in biological systems. In real life, response to a
specific pattern is usually prestored - learned through
experience. This resembles the fourth viewpoint on AI presented
earlier. But from a practical point of view, computers as well as
AI were more successful in search than in knowledge
representation.

Although there are many techniques from semantic nets to frames,
the most successful AI applications so far are expert systems.
At times, it was thought that the expert-systems approach enables a
uniform solution to knowledge representation problems. It has
led to overenthusiasm and overselling the technological
possibilities. Now we know that expert systems are appropriate
only when problems are relatively small and stable or can be
decomposed into such subproblems, meaning that experts agree
with each other upon a proposed knowledge base.

The main problem, how to represent different kinds of knowledge, 
complex and heterogeneous knowledge, and combine them into one 
system has not been solved yet. As a consequence, successful
learning from interactions with the environment has not been, and quite
probably can not be achieved without it.

{\bf AI copes with the search combinatoric explosion by using 
knowledge.} The use of knowledge enables successful pruning of a
search tree. For example, in an expert system OPEX (Gams et. al. 
1991) for generating appropriate machining operation sequences,
designed in cooperation with researchers from the Faculty of
Mechanical Engineering and Jo\v{z}ef Stefan Institute, there are
three levels of rules: \\
(a) Rules for applying basic machining operation. Example: \\
operation drilling : if \\
\hspace*{0.3cm} gdb:fc is-a-cylinder-in and \\
\hspace*{0.3cm} gdb:dc included interval(3,40) and \\
\hspace*{0.3cm} gdb:lc/max(gdb:dc) = 10 and \\
\hspace*{0.3cm} gdb:nc subset interval(11,12) \\
then \\
\hspace*{0.3cm} fc := is-a blank and dc := 0 and nc := undefined  \\
(b) Rules that define various possibilities of linking basic 
machining operations within an individual feature. Example: \\
from boring to drilling if true end.  \\
(c) Rules for combining operation sequences that define which 
operation sequences should be adopted for a combination of 
features. Example: \\
combination drilling and drilling if true end. 

The task of OPEX is to design operation sequences for a 
machine and a specified part, and sort them according to
predefined criteria. Naive combining of operations quickly leads
to combinatorial explosion, but through smarter selection of
possibilities, i.e. by utilising domain knowledge, the
combinatorics is reduced to a feasible level.

As indicated by previous example, AI enhances search by
reformulating problems, through the use of opportunism,
heuristics, and by abstraction and differentiation of
quantitative models. These are techniques behind the general
principle of using knowledge to control search. In essence they
perform similar improvements of search as hierarchical search or
dynamic programming, however, the use of knowledge can greatly 
improve performance.

{\bf AI systems can increase productivity.} Various reports
estimate the number of AI systems regularly in use to around
3000 with some of them being in use for more than 10 years. The
main problem with such estimates is where to put borders between
AI and non-AI applications. For example, is Prolog interpreter
an AI application or not? Clearly, there is no intelligence in
it. On the other hand, Prolog as well as Lisp and many other
products were designed as a by-product in AI research. In our
opinion, they should be included as AI applications as well as
neural networks. Actually, marketplace AI-software packages fall
into at least four major categories: programming languages,
programming environments, problem-solving shells (for a class of
problems), and application shells (specialised for a given
domain).

For example, in our rather small country of Slovenia, in two AI
laboratories at Jo\v{z}ef Stefan Institute and the Faculty for
Electrical Engineering and Computing, around 60 applications were
successfully performed in recent years and 5 original
programming systems with several thousands of lines are
in regular use (Urban\v{c}i\v{c} \& Kri\v{z}man 1991).

\section{Legends}

{\bf AI systems are easy to build.} Indeed, under specific
conditions, improvements in speed and productivity are enormous
when using AI systems. For example, having stored a history of
events, it is possible to design an expert system with the use
of inductive learning tools in a couple of days. On the other
hand, there are problems which take more or even much more time
than by classical methods. 

{\bf Specifications and prototyping largely enhance 
productivity.} This is partially true. If the problem fits an
application shell, knowledge gathered from experts can be put
into a system quickly and then tested. Rapid prototyping elicits
the requirements and specifications of software for ill-defined
problems; in recent years it has been included in software
development approaches as another example of AI product
finishing in classical computer science and applications. But
the limitations of the methodology and conditions for
successfulness have also become known.

{\bf AI systems are easily verified and maintained.} Since AI
systems rely on knowledge instead of formulae, e.g. expert
knowledge in expert systems, it is often propagated that these
systems are highly understandable and, therefore, easy to be
verified and maintained. For example, expert systems provide
explanation possibilities as a sort of rule tracker instead of
`trace' in conventional programming languages. Practical
experience has shown that while it is an important improvement
over classical methods, verification and maintenance remain 
time consuming phases.

\section{Myths}

{\bf Artificial intelligence approach does not need conventional
program-engineering and management techniques.} This incorrect
belief is still quite common due in part to academic ignorance
of the requirements for building production-level systems.

{\bf Systems working on simple examples can easily be upgraded to 
full-scale real-life systems.} Performing speech understanding
for a small vocabulary of, say 50 words, differs greatly from
the same task but with thousands of words. Similarly, many problems
are difficult only because of their size. The myth of simple 
scaling is still very alive mainly due to an academic approach
where it is most important that idea is fresh and attractive
(working on a simple, carefully designed problem). Literature
reviews in AI show that about half of all publications belong
to this category and only half of the systems actually work on
non-toy problems. In the worst scenario, some subareas of AI
have for years attracted interest and funds without actually
producing a program working on a realistic problem. There seem to
be certain similarities to fashion movements in which a new 
direction promoted by famous people attracts global interest.
After a critical mass is obtained, the movement can sustain for
several years without any realistic verification. The problem is
similar in several other sciences. The ``publish or perish''
science tends to produce famous writers instead of famous
scientists, researchers, engineers or inventors. However slowly,
in AI it is changing in favour of more strict verification of
results. For example, there are several projects which for years
have evaluated different methods (Henery \& Taylor 1992). Even at our
laboratories we have been testing all available inductive
learning systems for 5 years and making the results public.

{\bf Small systems can exhibit full-scale human 
intelligence.} In serious AI circles it is known that it is not
possible to simulate full-scale human intelligence without huge 
and complex systems and that searching for a genuine simple
algorithm is similar to searching for perpetuum mobile.

{\bf If we have an expert, then we can create an expert 
system.} Obviously, a lot more is needed; first of all a feasibility 
study.

{\bf AI does not need business motivation to produce valuable 
results.} Several studies have shown that those initiated by management 
have a better chance of returning profit.

{\bf AI tools can enable novices to develop expert systems.}
Inexperience and lack of skill can not be compensated in any
field.

{\bf Expert systems consist of expert systems.} Typically, in 
expert systems there is much more than that, including lots of 
classical programming.

{\bf Expert systems perform as specialised, stand-alone 
programs.} Actually, they access databases, conventional
programming languages, operating systems etc.

{\bf All AI tools are the same.} There are different 
categories.

{\bf All expert systems are rule-based.} Many, but there is much
more.

{\bf Expert systems do not make mistakes.} In real life there is 
no such thing.

{\bf AI replaces conventional approaches.}  Rather, they can 
both be useful depending on conditions, and are often combined together.

{\bf AI knowledge engineering is all we need to know about AI.}
The more you know the better. Again, AI consists of many diverse
subareas.

{\bf AI tools are good only for AI applications.} AI software 
supports qualitative and quantitative reasoning equally well.

{\bf In simple expert systems an exhaustive search can provide 
solutions.} Yes, for toy problems.

{\bf Tools equally support both forward and backward 
chaining.} At the expense of the other.

{\bf The more general the tool the better.} Task specific 
tools are actually more productive but on a more narrow area.

{\bf There exist universal algorithms for specific subareas such
as learning.} In theory, not working in practise.

{\bf Several subareas of AI have good theoretical foundations.}
No true intelligence has it so far.

\section{Conclusion}

AI systems can work well under favourable conditions, and
are neither panaceas nor research curiosities.
AI is not (just) art or a fashion, it is first of all a
scientific discipline. At present, AI can importantly improve
productivity and enhance the application areas of computers. As all
other technologies, it must be used with a certain precaution and
especially when circumstances are favourable. Therefore, more
knowledge about AI in general as well as knowing about common
legends and myths about AI may improve the success rate and
extend the number of AI applications.

\frenchspacing
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\end{document}
