It Is Artificial Idiocy That Is Alarming, Not Artificial Intelligence
David Sanders and Giles Tewkesbury
School of Engineering, University of Portsmouth, Anglesea Road Building, Portsmouth, U.K.
Keywords: Artificial Intelligence, AI, Computer, System.
Abstract: Lots of people believe the brain can be simulated by machines and because brains are intelligent, simulated
brains must also be intelligent; thus machines can be intelligent. This position Paper discusses whether that
is true and whether we should be worried about it if it is.
1 INTRODUCTION
Artificial Intelligence (AI) is about computer
systems that can simulate intelligent behaviour and
perform tasks normally requiring human
intelligence, such as visual perception, speech
recognition and decision-making (Bergasa-Suso et al
2005; Chester et al 2006; Sanders 1999, 2009a).
Engineers also expect systems to interact with
the real world to do something, such as to get a
mechanical system to move (Sanders, 1995, 2007,
2008b; Sanders and Stott, 1999; Sanders and
Tewkesbury, 2009). That involves a lower form of
intelligence within control loops that interface with
sensors and actuators (Sanders, 2008c), similar to
autonomic nervous systems in animals.
Systems with a lower form of intelligence tend to
act repetitively and unconsciously. In animals they
regulate heart rate etc. These autonomic nervous
systems have two branches: sympathetic and
parasympathetic (Pocock, 2006). A sympathetic
system is quick and mobilizing, and parasympathetic
is a more slowly activated dampening system. In
engineering that can be similar to needing to quickly
control actuators (such as motors) and more slowly
to monitor sensors (Sanders et al, 1996). Autonomic
systems need to be told what to do and for that,
some higher intelligence was required.
As autonomic systems became more reliable,
they could be left unattended, and attention shifted
to more intelligent systems to supervise them.
2 THE BRAINS OF ANIMALS
The more intelligent systems are similar to the
brains of animals (Kandel, 2012). That is, they tend
to be more cognisant and less repeatable.
The brain is the higher control centre for
functions such as walking and it controls our
thinking functions and all our intellectual (cognitive)
activities. It plans and decides how we will do
things, how we understand our world, and it learns
and remembers.
Originally, computer engineering was all about
continuous systems but the development of digital
computers led to discrete computer systems because
communications and action were managed by
clocks. Many engineering systems that are
computer controlled still consist of both digital and
analogue components as the analogue components
interface to the real world.
Some believe the brain can be simulated and
because brains are intelligent, simulated brains must
also be intelligent; thus machines can be intelligent.
And a computer can do many things over and above
managing an autonomic system or two (Chester,
2007; Sanders et al, 2009; Stott,and Sanders, 2000).
It may be technologically feasible to copy the brain
directly into hardware and software, and that such a
simulation will be essentially identical to the original
(Russell and Norvig, 2003; Crevier, 1993).
Computers have heaps of speed and memory but
they can only do what their software designers
understood well enough to allow them to do. Some
skills and talents that children don’t normally
develop until they are teenagers may be there, and
some competences enjoyed by a two year old are
still out (Questions, 2015). The matter is further
complicated by the fact that we still have not
determined exactly what human abilities are.
When someone does better than a computer on
345
Sanders D. and Tewkesbury G..
It Is Artificial Idiocy That Is Alarming, Not Artificial Intelligence.
DOI: 10.5220/0005473103450350
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 345-350
ISBN: 978-989-758-106-9
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
some task or if a computer uses a lot of computation
to do as well as a human, then that demonstrates that
the software engineers lacked a comprehension of
the intelligent processes and structures required
(Sanders, 2009b; Sanders et al, 1999, 2005, 2010;
Stott, et al, 1997).
Novel computer technologies have appeared on
the horizon that may change things (Masi, 2007).
Computing has developed over the decades and
some has begun to be regarded as AI since John
McCarthy coined the term (Skillings, 2006).
3 ARTIFICIAL INTELLIGENCE
AI is the intelligence exhibited by machines or
software. It is often not about just feigning human
intelligence. Expert systems sometimes try to learn
something about how to solve problems and behave
by observing people but most engineering involves
studying real problems rather than studying people
or animals. General intelligence is still amongst the
long term goals (Kurzweil, 2005) and the central
challenges of AI include reasoning, knowledge,
planning, learning, communication and perception.
The whole discipline is interdisciplinary in an effort
to cover all of that, and includes philosophers,
linguists, engineers, computer scientists,
psychologists, mathematicians and neuroscientists.
Prevalent approaches to achieve them include
statistical methods, computational intelligence and
traditional symbolic AI. There are a large number of
tools used in AI, including search and mathematical
optimization, logic, methods based on probability,
and many others: knowledge-based systems, fuzzy
logic, automatic knowledge acquisition, neural
networks, genetic algorithms, case-based reasoning
and ambient-intelligence(Sanders and Gegov, 2013).
The appropriate deployment of the new AI tools
will contribute to the creation of more capable
computer systems. Other technological
developments that will impact on AI include data
mining techniques, multi-agent systems and
distributed self-organising systems.
All that still presupposes that at least some of
something like human intelligence can be so
completely and exactly described that a machine can
be built to replicate it. That raises philosophical
issues about ethics and the character of the mind,
issues addressed by fable, literature and philosophy
since time immemorial (McCorduck, 2004). But
how can it be done?
Mechanical or formal reasoning was developed
by philosophers and mathematicians in ancient times
and a study of logic led directly to the programmable
digital electronic computer. Turing's theory of
computation suggested that a machine could
simulate any conceivable act of mathematical
deduction by shuffling symbols such as "0" and "1"
(Berlinski, 2000). That, along with discoveries in
neurology, information theory and cybernetics,
inspired researchers to consider the possibility of
building an electronic brain (McCorduck, 2004).
The brain is an analogue computer and not a
digital computer (Dyson, 2014). Intelligence in the
brain may not be an algorithm. There is little
evidence for a programmable digital computer
evolving abilities to take initiative or make new
choices. Why should we think that a digital
computer is a good model for a brain? Turing
machines are discrete machines and we are
continuous organisms. Advances have been made
with continuous models of neural systems but the
present state of the system tends to determine the
next state of the system, so that next state is
constrained by the rules and formulae in them.
There may be little for awareness and perception to
do in a purely digital system.
In the future, humankind may construct a
formidable AI but it is not here yet, although it
always feels like it is just around the next corner.
4 HOW DID WE GET HERE?
It was probably the idea of making a ``child
machine'' that could improve itself by reading and
learning from experience that began the study of
machine intelligence (Sanders and Gegov, 2015).
That was first proposed in the 1940s and a number
of people independently started to work on
intelligent machines. Zadeh (1950) published a
paper entitled "Thinking Machines” and Turing
(1950) discussed the conditions for considering a
machine to be intelligent. He made his claim that if
a machine could successfully pretend to be human to
a knowledgeable observer then it should be
considered intelligent.
In 1956, some computer scientists gathered at
Dartmouth College to contemplate a new topic; AI.
John McCarthy coined the name "Artificial
Intelligence" just ahead of that conference. A
fundamental notion was that characteristics of
human intelligence could be defined. McCarthy
defined AI as "the science and engineering of
making intelligent machines" (McCarthy, 2008).
By the 1960s, there were many researchers in the
area, and most based their work on programming
WEBIST2015-11thInternationalConferenceonWebInformationSystemsandTechnologies
346
computers. Minsky predicted in 1967 that "within a
generation the problem of creating AI will be
substantially solved" (Dreyfus, 2008). But then, the
discipline ran into unforeseen problems with the
failure of any machine to fathom even the most
elementary children's story. Machine Intelligence
programs lacked intuitive common sense.
Now (nearly sixty years after that first
conference), we have still not managed to create a
``child machine'' (Sanders and Gegov, 2015).
Programs still can’t learn much of what a child
learns naturally.
But, we may be at a time when our biology
seems too fragile, sluggish and complex in many
situations (Sanders, 2008a). We are turning to
powerful new technologies to overcome those
weaknesses, and the longer we use that technology,
the more we are getting out of it. Our machines are
exceeding human performance in more and more
tasks. As they merge with us more intimately and
we combine our brain power with computer capacity
to deliberate, analyse, deduce, communicate, and
invent then many scientists are predicting a period
when the pace of technological change will be so
fast and far-reaching that our lives will be
irreversibly altered.
A difficulty is that we cannot come to agreement
about what kinds of computation we call intelligent.
Some think that human-level intelligence might be
accomplished by writing large numbers of programs
or by assembling enormous knowledge bases in the
computer languages being used now. Though, the
majority of researchers now appear to believe that
new underlying elementary ideas are needed, and so
we cannot predict when human-level intelligence
will be achieved (McCarthy, 2008).
Machine Intelligence combines several cutting-
edge technologies to give computers an ability to
learn, adapt, make decisions and show new
behaviours. There are some technologies that might
appreciably boost the ability of computers
(Brackenbury, 2002; sanders, 2008):
• Natural language understanding.
• Machine reasoning to provide inference,
theorem-proving, and relevant solutions.
• Knowledge representation for perception and
problem solving.
• Knowledge acquisition using sensors to learn
automatically for problem solving.
At one end of the spectrum of research there are
handy robotic devices such as vacuum cleaners and
more personal robots. These could be the beginning
of a new generation of inexpensive robots with new
abilities. At another end of the spectrum, direct
brain-computer interfaces and brain augmentation
are being considered (together with ultra-high-
resolution scans of the brain charted by computer
imitation). Some of these are implying the prospect
of smarter-than-human intelligence. But what does
“smarter-than-human” mean?
There are negative opinions. John Searle says
the idea of a non-biological machine being
intelligent is incoherent; Hubert Dreyfus says that it
is impossible. Joseph Weizenbaum says the idea is
obscene, anti-human and immoral. Some people are
disillusioned because they invested in AI and
computing companies that went bankrupt
(Questions, 2015) and others are concerned that AI
systems may do us harm (either intentionally or by
mistake).
5 CAN THEY TURN HOSTILE?
Is AI going to terminate us all (Lanier, 2014)? We
have not created a formidable AI yet but only last
December an open letter was signed by a large (and
growing) number of people that called for
cautiousness to make sure intelligent machines do
not run ahead of our control.
A recent letter from Stephen Hawkin notes
"There is now a broad consensus that AI research is
progressing steadily, and that its impact on society
is likely to increase. The potential benefits are huge,
since everything that civilization has to offer is a
product of human intelligence; we cannot predict
what we might achieve when this intelligence is
magnified by the tools AI may provide, but the
eradication of disease and poverty are not
unfathomable. Because of the great potential of AI,
it is important to research how to reap its benefits
while avoiding potential pitfalls."
Other essayists added that “our AI systems must
do what we want them to do,” and they listed some
research priorities they think may “maximize the
societal benefit.” The chief worry is not eerie
growing consciousness but merely the ability to
make high-quality decisions that are aligned with
our values (Lanier, 2014), something else that is
tricky to identify.
A system that optimises a function of variables,
where the objective depends on a subset of them,
will often set remaining unconstrained variables to
extremes, for example 0 (Russel, 2014). But if one
of those unconstrained variables is actually
something we care about, any solution may be
highly undesirable. This is the old legend of the
ItIsArtificialIdiocyThatIsAlarming,NotArtificialIntelligence
347
genie in the lamp; you get precisely what you ask
for, not what you want. As systems become more
accomplished decision makers and are connected
through the Internet then they could have an
unanticipated and unpredictable impact.
Improving decision quality has not proven to be
easy though. Research has been gathering pace as
chunks of the abstract structure come together and
the sub systems increase in size, quantity and
potency. Researchers are conspicuously more
hopeful and confident than they were a few years
ago but there is a respectively bigger unease about
hypothetical but possibly conceivable menaces.
Instead of just crafting pure intelligence though,
we need to be making more useful intelligence. AI
is a tool not a threat (Brooks, 2014). He says “relax;
chill” … because it all comes from basic
misinterpretations about the kind of advancement
being made, and from a misunderstanding of how far
we really are from having artificially intelligent
beings. It is a mistake to worry about us creating
malign AI anytime soon and worry stems from not
differentiating between the real recent advances, and
the massive difficulty of creating perceptive AI.
Machine learning allows us to teach things like how
to differentiate between categories of responses and
to fit curves to data. But that is only a tiny portion of
the puzzle. The learning does not help a computer to
understand anything about the human users or their
intentions or desires. Any spiteful AI would require
those abilities.
The intelligent systems we are creating are not
able to relate to the real world with any
understanding. They do not know that humans exist
in any meaningful way (Brooks, 2014). The systems
don’t even know that computers exist. But they do
know about a tiny portion about the world and they
might have a little common sense.
There is attention-grabbing research in cloud
computing and big data. Connecting the semantic
knowledge learned by many computers into a
collective public depiction can mean that anything
learned by one can be speedily distributed to all, but
that can just make the challenges greater. But it is
not just a matter of throwing more computation at
challenges. What we need is more superior, more
convenient and more suitable AI.
AI may just be a fake thing (Myhrvold, 2014). It
may just add an unnecessary philosophical layer to
what otherwise should be a technical field. If we
think about specific practical problems confronting
researchers, we actually end up with something
more boring but that makes more sense. For
example, fuzzy logic can decide between
classifications and that is useful (Gegov et al, 2014a
and 2014b). It may not matter so much that they
cannot discuss politics with us. That sort of sensible
puzzle solving is not leading to the creation of life,
and definitely not life that would be superior to us.
If we think about AI as a bundle of methods or as a
mathematics subject then it brings tangible
improvements and benefits. If we think about it as a
mythology then we waste time and effort.
It would be thrilling if AI was functioning so
well that it was about to get frightening (Wastler,
2014) but the parts that may cause real difficulties to
us are not computers but actuators, as they interface
to the real world, and that is where bad things
happen. A one or two year old infant version of AI
may be more frightening. One and two year-olds
don’t realise when they are being damaging.
6 WHERE ARE WE GOING?
Some developments might advance progress: Cheap
parallel computation might deliver the equivalent of
billions of neurons; Big Data might help with
classification; and superior algorithms may allow
high speed learning. Increasing availability of
relatively cheap massive computing power and
improvements in science are allowing recursive
algorithmic solutions to problems as opposed to
searching for closed-form solutions (Kucera, 1997).
But as computers are getting cleverer, what
should they be allowed to? Should they decide who
to kill on the battlefield? The Association for the
Advancement of AI has formally addressed these
ethical issues with a series of panels (Muehlhauser,
2014).
Decision-making systems need interdisciplinary
research and cross-fertilization. Emerging areas
include hybrid systems, fuzzy logic control, parallel
processing, neural networks and learning.
The idea that a machine can ultimately think as
well or better than a human is a welcome one
(Myhrvold, 2014) but our brain is an analogue
device and if we are going to worry about AI we
may need analogue computers and not digital ones.
And analogue computing is making a comeback.
But, the map is not the territory and a model is not
the reality. If our replicas ever surpass the
phenomena they're modelling, it would be a once-in-
a-lifetime event.
People seem to be troubled by the thought that
AI may take over choices that they think should be
made by human beings, for example driving cars or
aiming and firing missiles. These can be life and
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death decisions as well as ethical problems. If an
AI system makes a decision that we regret, then we
change their algorithms. If AI systems make
decisions that our society or our laws do not approve
of then we will modify the principles that govern
them or create better ones. Of course human beings
make mistakes and intelligent machines will make
mistakes too, even big mistakes. Like humans, we
need to keep watching over them, coaching and
improving them but a problem is that we don’t have
a agreement on what is acceptable.
There is a difference between intelligence and
decision-making. Intelligent machines can be very
useful but stupid machines can be scary. As for
human beings, Bishop has said that it is machine
stupidity that is dangerous and not machine
intelligence. A problem is that intelligent algorithms
can make many appropriate decisions and then
suddenly make a crazy one and flunk dramatically
because of an occurrence that did not appear in
training data. That is a problem with bounded
intelligence. But we should fear our own stupidity
more than the theoretical wisdom or foolishness of
algorithms yet to come. Ingham and Mollard have
said that AI machines have no emotions and never
will because they are not subject to the forces of
natural selection.
Kelly has said that there is no metric for
intelligence or benchmark for particular kinds of
learning and smartness and so it is difficult to know
if we are improving.
As AI systems make blunders then we can make
a decision about what is tolerable. Since AI is
taking on some tasks that humans do, we have a lot
to teach to them.
As humans, we only discern the real world
through a virtual model that we think of as reality.
Our memory is a neurological fabrication. Our
brains produce our stories and although they are
inaccurate, they are sufficient for us to stumble
along. We may be beaten on specific tasks but
overall, we tend to do admirably against machines.
Brockman has said that they are a long way from
replicating our flexibility, anger, fear, aggression,
and teamwork. While appreciating the limited,
chess playing talent of powerful computers, we
should not be unsettled by it. Intelligent machines
have helped us to become more skilful chess players.
As AI develops, we might have to engineer ways to
prevent consciousness in them just as we engineer
other systems to be safe. After all, even with Deep
Blue, anyone can pull its plug and beat it into rubble
with a sledgehammer (Provine, 2014).
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