PROLOGUE
In 1997 Gary Kasparov and the world of chess was outraged when
he was defeated by Deep Blue (IBM). This was a long awaited epoch;
the time when man would be outclassed by machine in a game seen
as intellectually superior and beyond the reach of a mere 'clockwork'
mind. At the time sound-bites included: 'something strange is
going on; it didn't play a regular game of chess; it didn't play
like a human; it didn't play fair' etc. [1]
Interestingly, no
one asked the most important question; how did Deep Blue win -
what was the magic here? [2]
The key was a new intelligence - a powerful computer that didn't,
or couldn't, think like us. It brought something new - a new dimension,
a new way of thinking and problem solving - and one devoid of
emotion. That was the prime value - a new approach - and it is
also the key contribution on offer from all Artificial
Intelligence(s) (AI).
AI was mired by controversy
from the outset. [3]
Unreasonable expectations, exaggerated claims, broken promises,
and delayed deliverables are but a few damaging manifestations
of a singular problem; a lack of understanding of complexity coupled
with an inability to define and quantify intelligence. [4]
To a large degree expectations were set by the depictions of Hollywood
with humanoid intelligences comparable or exceeding ours. [5]
[6]
Futurists have also been guilty of using questionable assumptions
that led to graphs of the following form:
Fig 1: Typical prediction curve for machine intelligence
REALITY CHECK
Industry and commerce now depend upon AI for the control of engines,
elevators, logistics, finance, banking, production robotics, and
networks. [6]
Surprisingly then, we still lack a complete understanding of what
intelligence is, and how to quantify it, [7]
and that HAL9000 (2001) conversation with a machine still seems
a distant dream. [5]
However we are constantly
surprised by AI systems and the answers they contrive. On many
occasions we lack the facility to fully understand, but that does
not preclude us using the results! [8]
Moreover, we have gradually realised that the solution of industrial,
scientific and governmental problems will continue to defy human
abilities, whilst AI will continue to improve as it evolves to
embrace larger data sets and sensor networks.
On the creativity
front we have seen many of our key electronic and system inventions
enhanced (or bettered) by AI, and in some quarters machine based
contributions outweigh that of humans. [8]
In fact the machine I am typing this article on has chips that
owe more to machine design than any human contributor, and we
might expect this disparity to expand further. [9]
[10]
Our system of mathematics
is a key limiter as it constrains our analysis and design of systems
with large numbers of feedback/forward loops. [11]
Machines know no such constraints and utilise designed and parasitic
loops to advantage in a way we do not fully understand. It should
come as no surprise then that we cannot fully describe and understand
many key electronic elements, or indeed the non-laminar flow of
fluids and gasses have to be modelled by machine.
FACILE DEBATE
So far the march of AI has transited several hotly contested stages,
with a few more to go:
1) Machines can't
think!
2) Machines will never be intelligent and creative!
3) Machines will never be self-aware!
4) Machines will never have imagination!
5) Machines will never hypothesise or conceptualise!
6) Machines will never be more intelligent than us!
Such arguments seem
to be born out of ignorance, fear, religious belief, limited imagination
and vision, but not of scientific thinking. For sure (1 &
2) above have been surpassed, [9]
[10]
whilst (3) is currently being contested and challenged
by combinations of AI and sensors. To some extent (4 - 6) remain
the 'Holy Grail' and seem to be prospects that upset far more
people than (1 - 3). They also remain some way off, but
are more likely to happen than they did 20 years ago! [12]
An obvious question
is; why should intelligence be sacrosanct and exclusive to carbon
based life systems, and why should we and other animals be so
special that only we can develop self-awareness and problem solving
abilities including the creation of tools? A dispassionate analysis
would seem likely to come down on the side of AI!
This situation almost mirrors medieval times when clerics and
scholars (may have) debated [13]
the number of angels on a pinhead! The fundamental problem is
that we cannot describe, define, or quantify any of the fundamental
aspects of the argument. [14]
[15]
In short we have no meaningful measure of intelligence! So, a
more pragmatic approach is to ignore all debate and get on with
developing systems, observing their actions, and trying to understand
the fundamentals - whilst periodically addressing the core question;
what is intelligence, and can it be quantified?
THINKING DIFFERENT
There are two 'wisdoms' from my student days I periodically recall.
The first came from an engineering academic with a long and distinguished
industrial career. His words still ring in my ears: "Mr Cochrane,
whilst it is acceptable for the mathematicians, physicists and
theorists to declare that there is no solution, we in engineering
enjoy no such luxury. We always have to find a solution"!
The second came from a mathematician with years of industrial
experience. Prophetically he said: "Before you even start
a problem it is worth thinking what form the answer might be,
and what would be reasonable".
For me the past 40
years has seen these maxims go from strength to strength as 'simple-minded'
linear assumptions have given way to an increasingly complex and
connected world where non-linearity dominates and chaotic behaviours
are the norm. [16]
THE CHALLENGE
It was pure serendipity that a customer problem coincided with
my efforts to establish some means of quantifying intelligence.
The dilemma came in the form of an engineering challenge to compare
AI systems contesting for deployment in an industrial application.
Just how do you judge the efficacy of complex, and very large,
AI systems?
Fortunately, previous
work on a similar problem had prompted the question; what would
be involved in the quantification and comparison of 'intelligent
systems' and what form might the answer take? So, I had also established
that I was alone, without books or published papers offering any
depth or glimmer of a solution. I was in new territory - with
plenty of opinion and few facts or tried and tested methods offering
any real value.
At a modest estimate
there are over 120 published definitions of intelligence penned
by philosophers and theorists. [14]
[15]
Unfortunately, none provide any real understanding or an iota
of quantification. And the long established IQ measure by Alfred
Binet (1904) is both a flawed and a singularly unhelpful idea
in this instance. [17]
The limitations of the approach were detailed by Binet, but ignored
by those eager to apply the IQ concept in its full simplicity
and meaningless authority! [18]
A commonly used engineering
comparison involves counting the number of processors and interconnections,
and using the product as a single figure of merit. This is often
combined with Moore's Law projections to justify claims of exponential
growth. [19]
But this 'product method' seems far too simplistic to be meaningful
and certainly does not reflect any notion of intelligence. In
fact, machine intelligence estimates on this basis would suggest
HAL9000 (2001) should be alive and well, but clearly he is not!
[5]
A SUBSET OF WHAT
DO WE KNOW FOR SURE
Like a lot of simple organisms our machines focus on limited sub-sets
of problems and unlike us are not 'general purpose' survivalists.
We (homo sapiens) appear fairly unique in the combination of our
mental and physical abilities - bipedal with binocular vision,
apposing digits, and very broad mental powers that facilitate
language, communication, conceptualisation and imagination. Whilst
other carbon species have far bigger brains, better sight and
hearing (and other senses), they lack the critical combinations
we posses, and so do all machines to date. [20]
From an engineering
and theoretical basis it seemed reasonable to start at a very
simple level, to try and build a model that would be both applicable,
give useful results, and might then expand to the general case.
Leveraging the thoughts of others on 'thinking, intelligence,
and behaviour' we can glean interesting pointers by considering:
1) Slime moulds
and jellyfish (et al) [21]
[22]
exhibit intelligent behaviour without distinct memory or processor
units. They have 'directly wired' input sensors and output actuators
- ie they chemically sense, physically propagate and devour
food. Of course a proviso is that we neglect the delay between
sensing and reacting as a distinct memory function, and the
sensor-actuator as a one-bit-processor on the basis of being
so minimal as to be insignificant. This turns out to be a good
engineering approximation based on the processing and memory
capabilities of far more complex machines and organic systems.
Reproduced with the kind permission of Mike Johnston and Paul
Morris
Fig 2: Slime mould & jellyfish exhibit intelligence without
a brain
2) In the main
our machines have memory and processing maintained as distinct,
entities - FLASH, RAM, HD, but this is seldom so in organic
systems where there tend to be distributed and share functionality.
[23]
But again this assumption of separation suffices for the class
of machines being considered.
Reproduced with the kind permission of Richard Greenhill and
Hugo Elias
Fig 3: A modern robotic hand with separate sensors and actuator
functions
3) It turns out
that whilst intelligent behaviour is possible without memory
or processor, this is not true of simple sensors and actuators
combined. This was recently writ large by a series of experiments
on human subjects. Place a fully able human in an MRI scanner,
ask then to close their eyes and imagine they are playing tennis,
and their brain lights up. Now repeat that experiment with comatosed
patients of many years and the same result is often evident!
[24]
[25]
So these poor victims
have an input mechanism that is functional, but no means of
communicating with the outside world. To the casual observer,
and until very recently to the medical profession, they have
appeared brain dead, mere inanimate entities, living and breathing,
but non-functional!
4) Colonies of
relatively incapable entities such as ants, termites, bees and
wasps poses a 'hive/swarm intelligence' that is extremely adaptive,
and capable of complex behaviours. [25]
Moreover, whilst the 'rules of engagement' of the individuals
might be easy to define, the collective outcome is not! Another
key feature is the part played by evolution over millennia,
and the honing to become fit for purpose. Unlike us, Mother
Nature optimises nothing and concentrates on fit for purpose
solutions. This makes her systems extremely resilient with a
high percentage of survivability overall as she is also impartial
to the loss of a colony or indeed a complete species no longer
suited to a changing environment. [26]
Reproduced with the kind permission of Humanrobo
Fig 4: Designed
and optimised for a single purpose
5) Our final observation
is that all forms of intelligence encountered to date invoke
state changes in their environment. A comparison of such change
can be an expansion or compression of the quantity of the information
or state. For example; the answer to the question 'why is the
sky blue?' would contain a far more words and perhaps some diagrams,
whilst the reply to 'do we know why the sky is blue' would be
a simple yes!
KEY ASSUMPTION
& DEFINITIONS
Based upon we actually know it seems entirely reasonable to assume
an entropic measure to account for the reduction or increase in
the system state, before and after, the application of intelligence.
[27]
We therefore define a measure of comparative intelligence as:
(1)
Applied Intelligence = The Change in Entropy =
Ia = MOD{Ei -Eo}
Where
Ei = The input or starting entropy
Eo = The output or completion entropy
We take the Modulus value here as we are using the 'state change'
as our measure
Entropy = E = The amount of information to exactly
define systems state
And for the purpose
of an efficacy measure we include the 'time to complete' component
in the form of machine cycles (N) or FLOPs (Floating Operations):
(2)
For the purpose
of modelling we adopt a simple system representative of engineering
reality, and Fig 5 shows the relationship between
Sensor (S ), Actuator (A ), Processor (P )
& Memory (M).
Here it is assumed
that sound, light, vibration/movement or chemicals activate the
sensor and the signal si
is fed to the actuator, processor and memory. The processed output
of each is then fed to the actuator. We note that in many biological
systems other loops feed signals back to the sensor - typically
to adjust the sensitivity, or in anticipation of the actuator
response, or indeed a memorized event sequence - in our case the
eyes are a prime example where we continually adjust their sensitivity.
However, for our immediate purpose, and the sake of clarity, it
is easier to leave this aspect out of the analysis.
The sum of all the
processed signals results in an output from the actuator (sound,
light, movement or chemicals) that influences/changes the environment.
And so the 'looped process' continues. To help visualize this
consider a robot picking up and disposing of a plastic cup or
playing a game of chess by physically moving the pieces. All movement
would be iterative and determined by the perceived incremental
scene - a moment and movement at a time.
At this point it
is worth noting than numerous configurations of simpler and more
complex kinds are possible. Many of these have been modelled,
including multiple sensors and actuators, distributed processing
and memory, with far more feedback and feed-forward loops. All
have resulted in very similar outcomes.
Fig 5: Assumed System Configuration
(3)
We can now derive a transfer function of the form:
h = a(1 +
m + p1 p2 + p2 m p1 ) s
(4)
By consolidating the weighted memory and processing elements as
opposed to their complex operators, this further reduces to:
h = SA(1
+ M[ 1+ P ] + P )
(5)
Now, taking (by orders
of magnitude) the dominant terms:
ENGINEERING LICENCE
In the general case it is impossible to define the complex nature
of the operations performed by S, A, P, M.
All we can say is that they change the state of information and
action according to the complex operators s(t), a(t),
p(t), m(t) in sympathy with the clock cycle of the
machine. In very specific situations these states can be described,
but in general they cannot.
For the purpose of
creating a comparative intelligence measure we thus skirt this
limitation by applying 'weighting values' denoted as: S
= Sensor, A = Actuator, P = Processor, M
= Memory.
ENTROPY RULES!
Using entropic change (1-2) as the defining property of
intelligence, and the dominant terms, a reasonably general formula
results from our analysis:
(6)
(7)
Whilst the
relative intelligence is given as:
From (7) we
can now confirm two essential properties by inspection:
6) With zero processor
and/or memory power intelligence is still possible
7) With zero sensor and/or actuator power intelligence is impossible
This is entirely
consistent with our (organic) experience and experimental findings
[24]
[25].
And further, it flies in the face of the conventional wisdom of
those that worry about 'The Singularity' - the point at which
machines take over because they outsmart us. [19]
[28]
They assume that intelligence is growing exponentially by way
of the product PM and Moore's Law, [29]
[30]
whilst (7) shows it is logarithmic.
Fig 6: A spread of published (P.M ) based predictions v our logarithmic
model
So, if we see 1,000-fold
increase in the product of Processing and Memory (P.M product)
intelligence increases by a factor of only 10. Hence a full 1,000,000
increase sees intelligence grow by a just 20-fold. This is far
slower than previously assumed and goes some way to explain the
widening gap between prediction and reality!
A further important
observation is that sensors and actuators have largely been neglected
as components of intelligence to date, but it is seems (7) they
play a key part in the fundamental intelligence of anything! Without
them there can be no 'evident' intelligence.
MORE THAN A LEAP
OF FAITH
If we make a couple of 'big' assumptions to further approximate
the intelligence formula we can make some further interesting
observations. We start by assuming that:
PM >> 1 and
AS PM >> 1
(8)
Equation (7) then becomes:
If we now observe
that the progress of Actuators, Sensors, Processing Power and
Memory technology is exponential with time ~ eat, est, ept and
emt, then the growth in intelligence derived from equation (8)
looks like this:
(9)
Intelligence
Rate of Growth ~ k.a.s.p.t
This (9) implies
that machine intelligence is growing linearly with time. So the
obvious question is; what happens when a large number of intelligent
machines are networked? If there are sufficient, and their numbers
grow exponentially, then, and only then, will we
see an exponential growth in intelligence.
FINAL THOUGHTS
What does all this mean? With the arrival of low cost sensors
and their rapid deployment on the periphery of networks, and robotics,
we are really much closer to achieving truly intelligent entities
than ever before. Couple this with the creation of addressable
databases and learning systems, then the opportunity for 'intelligent
outcomes' is racing ahead. But for singular machines, it is a
'logarithmic or linear race' and not exponential! Only if we network
vast and exponentially growing numbers of machines will we see
the previously assumed (and feared) exponential intelligence outcome.
Biological hardware
and software is adaptable and evolves by mutation, and our machines
can now do that too! But, biological systems are 'born' into a
supporting ecology and the process has very definitely been from
the simplest to the most complex over millennia. Our machines
on the other hand are being born into an ecology that is being
built top down in a few decades! Will this work as a complete
support system? We don't know - yet!
We see 'life'
exhibiting emergent and adaptable behaviours 'fitting into a mature
world' and competing for survival. Our systems are mostly designed
to be task specific with an assured place in the pecking order.
At this time we do not fully understand the implications for machine
intelligence, but it is clear that it is important, and we are
beyond the 'genesis point' with machines designing (in part and
in full) other machines. In the next phase they will also be interacting
with their biological counterparts and learning from them.
So far professionals
have argued about what is and is not intelligent, and the analysis
presented goes some way to provide a reasonably quantified judgement.
Leaving aside all other issues and arguments it would appear that
the arrival of a more general purpose intelligence is only a matter
of when, and not what if. And there is only one questions left
to ask; will we be smart enough to recognise a new intelligence
when it spontaneously erupts on the internet or within some complex
system?