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The Cloud Will Amplify Artificial Intelligence (AI)
Peter Cochrane, FIEEE
University of Hertfordshire, www.herts.ac.uk
Tel: (+44) 1394 420711, Mob: (+44) 7747 863013, e-mail: [email protected]
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II. REALITY & WHAT WE KNOW FOR SURE
Abstract:
Memory and processing power are often
perceived to be the key components defining the intelligence
of living organisms and machines. But; ‘brain size’ alone
does not equate proportionately to intelligence. There are
many instances (eg slime moulds and jelly fish) where
intelligence occurs without memory or processing power.
Indeed, most insect life, depends upon sensors and
actuators (feelers/probes) as the primary elements of their
intelligence. It therefore follows that augmenting AI
systems with sensors will add to their abilities and
’awareness’, but we may get the biggest sensory input/gain
for free as sensor nets are being rolled out for every
conceivable purpose: road traffic management; crime
prevention; health and medical monitoring; industrial
control; weather monitoring; energy management;
pollution control; and more. But the biggest contributors
are likely to be mobile devices with their growing array of
built-in sensors, which is now being amplified by 'Device
Clouds' forming a new and ‘distributed’ dimension.
Along with many other words in the English language 'in­
telligence' is in common use everyday as if we knew what it
meant. In reality we have failed to adequately describe it
and totally failed to quantify it by any meaningful measure.
And whilst the IQ (Intelligence Quota) measure suggested
by Alfred Binet in the early 1900s is in common use for hu­
man subjects ,despite being grossly wrong, we do not even
have an equivalent of any kind for AI.
However, a recent quantification of AI systems has been de­
rived for a limited number of configurations and cases. The
analysis [3] proceeds as follows:
- All biological intelligences invoke internal and external
state changes, and taking an 'information theoretic stance'
this dictates all intelligences are inherently entropic and
produce some overall re­ordering of bits and/or atoms.
- Slime moulds and jellyfish (et al) exhibit intelligent
behavior without distinct memory or processing. They only
have ‘directly wired’ input sensors and output actuators. A
necessary proviso here is the exclusion of delay as a
memory function, and sensor­actuators seen as a one­bit­
processor on the basis of being minimal and insignificant.
This also turns out to be a good engineering approximation.
I. THE NATURE OF INTELLIGENCE
I
n 1997 Gary Kasparov and the world of chess were
changed forever when he was defeated by IBM Deep
Blue [1]. In 2011 that defeat was eclipsed by IBM Watson
dethroning Ken Jennings as the grand master of general
knowledge [2]. Perhaps more importantly, 2012 saw IBM
Watson moving into the field of medicine for patient diag­
nostics. Today, that AI machine is available as an App on
The Internet and in The Cloud. Needless to say, all this
changed the perception of AI, the ongoing debate on what
constitutes intelligence, the rate of AI advance; and the po­
tential consequences for industry and society.
- Our machines have memory and processing maintained as
distinctly separate entities, but this is seldom the case in
biological organisms where they share functionality and
interconnection. However the assumption of separation will
suffice for the class of machines being considered.
- Intelligent behavior is possible without memory or
processors as sensors and actuators alone often furnish that
facility. Conversely, systems cannot exhibit intelligence
without an input sensory system or output actuators. This
recently writ large by experiments on comatose human
patients. Where MRI scanners reveal their attempts to
communicate whilst their bodies do not! And this inability
to 'output' has seen them classified as brain dead [The
Lancet Neurology, 5/11,P 906, Nov 2006]).
Unfortunately, AI as a field of study was mired by
controversy from the outset, with unreasonable
expectations, exaggerated claims, broken promises, and
delayed/impossible deliverables. But all of these are a
damaging manifestations of a singular problem; a lack of
understanding of the complexity involved coupled with an
inability to define and quantify intelligence. Also, and to a
large degree, the general expectations were set by the
depictions of AI and robotics in science fiction with
humanoid intelligences comparable or exceeding ours.
Iii. ANALYSIS PATH STARING POINT
Using an entropic measure to account for the reduction or
increase in system information/state change, before and
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after, the application of intelligence, we define a measure of
comparative intelligence as follows:
Change in Entropy = I = MOD{E –E } ...(1)
c
Where:
i
o
E = The input or starting entropy
i
E = The output or completion entropy
o
I = Comparative Intelligence
c
The Modulus is used as the ‘state change’ Entropy measure
E = Information (change) exactly defining the system state
Fig 1: The assumed 'simple' model
IV. ANALYSIS OUTLINE
This (3) confirms/agrees two essential properties seen in
natural systems/consistent with experimental findings:­
For a detailed analysis, formulation, and demonstration, we
assume a (very) simple intelligent entity consisting of a
Sensor, Processor, Memory and Actuator depicted in Fig 1.
We also skirt the complex and precise functional operations
of the Sensor, Actuator, Processing and Memory functions
as in the general case we have no method or indeed mathe­
matical framework that allows us to adequately describe
these. Fortunately, it turns out to be sufficient to apply
‘weighting values’ denoted as:
-With zero processor and/or memory power intelligence is
still possible
-With zero sensor and/or actuator capability intelligence is
impossible
We also see that (3) flies in the face of the established
wisdom as intelligence growth being proportional (in some
way) to the product of processing power and memory (P.M).
S = Sensor, A = Actuator, P = Processor, M = Memory
Our analysis demonstrates that 'Intelligence growth' is
logarithmic and not exponential with memory and
processing power. So, a 1,000 fold increase in the product
of Processing and Memory (P.M) power, intelligence only
increases by some 10 fold. Hence a 1,000,000 increase in
(P.M) sees intelligence grow by a factor of 20, which is far
slower than previously assumed and explains the widening
gap between prediction, expectation and reality.
What follows is a shadow of the complexity we really en­
counter in nature and many man made systems where multi­
ple (1000s) of inputs and outputs, plus Hebbian storage, is
compounded by 1000s of feedback and feedforward loops.
A primary limiter here is our mathematical framework not
extending beyond Order 5, and even at this level we can
only deal with a very narrow choice of options. However,
testing the Fig 1 model by the inclusion of many more
loops and elements up to and including Order 5, followed
by practical system experimentation, and tests, has justified
(to a high degree) the approach and general approximations
employed. Further, every system and model tried, tested and
analyzed reduced to the general form depicted in (2) below.
NOTE: An key observation is the previously neglected
sensors & actuators are vital elements in any intelligent
system - (3) shows them to be fundamental! Interestingly,
this fact has been observed in other disciplines but not
directly linked to the whole including AI.
V. EXTENDING THE ANALYSIS
We therefore proceed with caution and keep in mind the
initial conditions, compromises, crudities and approxima­
tions in terms of 'real world' application. Simple ‘transfer
function’ analysis leads to a (reasonably) general formula
for a machines comparative as follows:
Suppose we now include the growth of memory and
processing power as exponential components as per Gordon
Moore’s Law – so that:­
bt
Ic = K log [1 + A.S( 1 + PM )]
2
with P.M >> 1 & A.S >> 0
AND suppose further, that our actuator and sensor
technology is improving at an exponential rate, then, we can
show that:
Whilst the relative intelligence is given as:
Ir = K log [1 + A.S( 1 + PM )]
N 2
dt
P = pe and M = me
...(2)
…(3)
Ic ~ K1(a + s)t + K2(b + d)t
Where N = The number of computational cycles or FLOPs
2/2
…(4)
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SO: The growth of intelligence with time is at best linear
and not exponential!
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VI. INTERNET GROWTH LIMITATIONS
Almost by a process of ‘osmosis and dawning necessity’ it
has been realized that; ‘The Internet’ cannot scale and
‘Clouds’ are our only viable option if we are going to meet
the demands of >50Bn ‘Things’ on­line. One simple way of
demonstrating the limitation of scalability is to consider:
Telephone system growth is ~ N2
Where N = The number of nodes ~ 6Bn today
The Internet growth ~ 2n
Where n = The number of nodes ~ 4Bn today
At this point it is worth noting that the energy consumption
of the Internet today is around 3 – 5% of the global total
production.
And so, if we increase 'n' according to the latest future
projections by industry ~50 – 250Bn things online ­ it is
clear that there is not enough energy capacity on the planet
to power the telephone network or Internet on such scale.
The Internet will not scale economically, ecologically, or
indeed functionally, without severe latency and reliability
problems. The Cloud, or Clouds on the other hand will!
Fig 2: A comparison of Entropic and Simple Linear
Projections of AI
VI. CLOUD GROWTH
V. BIOLOGICAL ANALOGIES
As of today we can map out a considerable future for
Clouds. They already appear on chip, card, shelf, rack,
floor, building, campus, village, town, city, region, country,
continent, on and off planet. Also, we see personal, private,
public, open, closed, secure, insecure, company,
commercial, institutional, educational, governmental,
military, medical and more. Then of course there are fixed,
mobile, permanent, transient, opportunistic, singular and
plural. Furthermore the technologies used are equally varied
spanning: ZigBee, Bluetooth, WiFi, WiMax, 3G, 4G,
InfraRed. We might confidently expect that new additions
to this tentative listing will arrive over the next decades
including 60, 90, 120,180GHz solutions furnishing massive
data transfer capacities over short distances.
The smartest intelligences we know do not cluster all the
processing and memory in one place ‘the head’. It is
distributed, singularly or small clusters, around the body
with a high degree of autonomous functionality. This
overcomes the ‘delay of transmission’ from sensor to brain
and brain to (actuator) muscle, and it is a vital element in
the equation of survival and damage control.
Other elements such as eyes and ears provide vast amounts
of pre­processing of visual and acoustic input, whilst the
brain stem supplies the essential ‘clock functions’ in all
vertebrates.
“You wouldn’t put all the brains in the head of a dog”
A key to the viability of the 'Cloudy Future' is the creation
and use of many more short hop wireless links that will
consume considerably less energy.
In addition, the
clustering of devices in nodes will see far more 'short hop
routings' for connection and data transfers, which
overcomes the limitations and waste involved in the highly
variable IP routings spanning countries and continents
today.
In fact, at the time of writing, a new
conceptualization of 'networks without infrastructure' is
being voiced that may well eclipse all previous networks in
terms of the total amount of bytes transported. And this
stems from the likelihood that 'device to device' working
will dominate 'Clouds of Things' as well as clusterd people
working and sharing in close proximity.
We are currently in the process of ‘mirroring’ all of the
above biology in our sensor, mobile, and cloud networks
linked to AI engines via fixed and mobile Apps. Distributed
sensors and processing clusters complete with memory are
increasingly the norm, and Clouds are increasing the scale
of ‘clustering and connectivity’ with the potential of
amplifying AI systems as they too become a part of these
networks.
“Even If we do nothing intelligences are likely emerge
spontaneously on the net…
…but will we be smart enough to recognize them”
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With a projected Cloud population spanning 50 – 250Bn
things, and a likelihood of commonly similar entities likely
to be ~ 104 to 106 we might postulate magnification factors
spanning 100 – 1000 fold. This is about as far as we can go
at this time as we need very definite numbers that are
unlikely to be available for the next 5 – 10 years.
VIII. FINAL COMMENTS
We no longer enjoy the luxury of a simple, slow moving
and disconnected world where we could consider almost
everything (including AI and networking) in isolation. Our
world is now complex, fast, speeding up, and increasingly
connected! And when designing systems we have to take a
much broader view of what is happening and why; what is
available and how it might be exploited; and where
unexpected contributions and effects might occur. From the
above analysis and associated observations it looks as
though the impact of The Cloud, Clouds, Cloud Computing,
Mobility, Things On­Line and Clustering, will impact
greatly on future AI systems. With Intelligence linked
directly with sensors, and the clustering of devices in
Clouds creating magnification factors well in excess of 100
­ 1000 fold, the impact could be could be profound.
Fig3: Consolidation of industrial & academic projections
for ‘Things On Line’
VII. CLOUDS AS AI AMPLIFIERS
Unlike biology that provides intelligence with a body and
physical transport, our initial AI systems are locked down
and the sensors travel. And that is only going to increase
with more mobile devices and Things on line. Later of
course we will most likely see autonomous robots of
‘considerable intelligence’ on the move, but for now mobile
sensors are key! And here Clouds will not only provide
clustering, but the amplification of shared data, memory
capacity and processing power not seen in biological
systems. The how and why is reasonably clear, but the
outcome is far less so!
Hopefully, we will be able to fully understand all this with the help of AI!
IX.
CONTEXT
For related papers, slide sets and videos on and around this
topic by the author GOTO: www.cochrane.org.uk
X.
At the kernel of this prospect is the aggregation of many
(vastly differing) intelligent systems in close proximity
(Cloud wise that is) for an indeterminate time with an
unknown amount of inter­communication and sharing of
data plus pre­processing and filtering of data. This of
course will undoubtedly be influenced by another
intelligence on the periphery ­ us!
REFERENCES
[1] www.research.ibm.com/deepblue/
[2] www.research.ibm.com/deepqa/team.shtml
[3] P. Cochrane, A Measure of Machine Intelligence, Beyond
seeing is believing www.ieeexplore.ieee.org ­ 10 Sept 2010
[4] P. Cochrane, A Measure of Machine Intelligence, The ITP
Journal, Vol 5/1, Jan ­ March 2011, pp26 – 32
XI. SUPPORTING BIBLIOGRAPHY
To date there are no published estimates, or indeed methods
of calculation, that might give a sensible estimate of the
amplification effect of such an aggregation process. This is
virgin territory! BUT if we make the bold (but to some
unknown degree - wrong ?) assumption that all the mobile
units are similar, and so is the cross section of their data,
and ‘experiences’, we can derive a ‘convolved intelligence’
1/2
amplification factor that is approximately ∝ N where N
is the total number of mobile elements in the cluster
population, plus the globally connected elements of that
family, in question.
[5]www­formal.stanford.edu/jmc/whatisai/
[6]www­formal.stanford.edu/jmc/whatisai/node1.html
[7]www.scientificamerican.com/article.cfm?id=evolving­inventions
[8]www.informatik.unitrier.de/~ley/db/conf/icai/icai2008.htm
[9] King R D; Rise of Robo Scientists, Sci American Jan 2011, p59
[10] www.maa.org/mathland/mathtrek_2_23_98.html
[11] www.evolution.berkeley.edu/evosite/evo101/index.shtml
[12] www.wfs.org/content/growth­nonlinear­life­trajectory
[13] www.informatik.uni­trier.de/~ley/db/conf/icai/icai2008.html
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