Tölvumál - 01.11.2011, Side 39

T Ö LV U M Á L | 3 9
Alan Turing is a monumental figure in the history of the field that we know
“computer science”. Turing characterized computation in a way that laid a
cornerstone for the field -- as the sequential reading and writing of symbols
-- embodied by a simple device we now call the “Turing Machine”. He also
developed one of the first electronic computing machines, the Colossus,
near Bletchley Park in England, which helped the Allies win the Second
World War. Turing was also keenly interested in intelligence, and took one
of the most influential steps towards considering intelligence in light of
computational systems. He introduced a test for machine intelligence, a
test now commonly referred to as the “Turing Test”. It is based on a simple
idea: Imagine a machine that interacts with a human via instant messaging
(he didn’t call it “instant messaging” back in 1950, of course). Imagine that
this machine could converse with the human with sufficient sophistication
so as to fool the person with which it is interacting into thinking there is
a fellow human being on the other end. We would then have to consider
such a machine intelligent. And so it happened that the phenomenon of
computation and the phenomenon of intelligence crossed paths, coming
to a singular point in this historically influential character that Alan Turing
turned out to be.
The foundation of what came to be called “artificial intelligence” (AI)
-- the quest for making machines capable of what we commonly refer
to as “thought” and “intelligent action” -- was laid some years later. The
original idea was to create generally intelligent machines that could be
assigned any task, from washing your dishes and cleaning the windows of
skyscrapers to writing research reports and discovering new principles of
the world around us. Today the field has for the most part lost its ambition
towards the *general* part of its original goals and reduced the field to
the making of specialized machines that only slightly push the boundaries
of what traditional computer science tackles every day. This problem is
most clearly exemplified in the fact that a small part of the AI research
community recently branched off to define a track wholly separate from
the mainstream AI research, emphasizing the *general* part by calling
itself the Artificial General Intelligence community, which already has its
own conference series and poised to become a scientific society in its
own right, alongside AAAI -- the largest AI society in the world. While those
studying computer science and software engineering today, and those
familiar with these fields, may think it obvious that AI should sit comfortably
within the bounds of computer science, which gives it a perfectly fitting
context to grow and evolve -- and while that would certainly be great
-- this is unfortunately not the case. Some fundamental deal breakers
prevent computer science as practiced today from being the fertile ground
necessary for the two to have the happy marriage that everybody originally
envisioned. Let me tell you about two of these.
The Turing machine captures a fundamental tenet of computer science,
namely, that computation is the manipulation of symbols: a symbol, or a
series of them, can hold the key to what should be done next in a sequence
of actions; each action can then produce a new symbol or action that is
next in the sequence. The concept is powerful in its simplicity: It seems
like a complete description of the essential elements of computation. But in
fact it isn’t -- it ignores a key real-world ingredient: Time. Everything in the
real world takes time; time is an integral part of reality. Nothing, not even
computation, can be done without being subject to the laws of physics.
Perhaps as a result of tracing its beginnings so strongly to Turing’s work, a
surprisingly large amount of computer science work fails to address time
as a fundamental issue. To take an example, the field today offers very
few if any programming languages where time is a first-class citizen. To
make matters worse, there is also a sore lack of mathematics to deal with
real time performance; good practical support for the design of temporally-
dependent systems largely belongs to the field of embedded systems --
a field that limits its focus to systems vastly simpler than any intelligent
Kristinn R. Þórisson, dósent við
Háskólann í Reykjavík
Artificial General Intelligence and
Computer Science in the Big Picture:
The Removal of Time and the Nature of
Natural Phenomena
Has computer science rendered a majority of
its findings irrelevant to progress in artificial
intelligence by de-emphasizing time in the theory
of computation and software development
practices?