Man’s dreams of ‘intelligences and robots’ goes back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946. And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be have at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…

1. Wed 23 May 2018 18:00 – 20:00
Water Front Auditorium: Floor 1
Admission Free
Refreshments Provided
Register: EventBrite
Out of hours public lecture
Presented by Professor
Peter Cochrane OBE
Ipswich Waterfront Building
Animated tutorial style with,
demonstrations, videos &
provoking propositions
Organised and hosted by the
UoS Innovation Centre
AI
The fundamentals; what we
know for sure; what we don’t
know; and the big surprises!
Fables, Facts, and Futures,
Threat, Promise or Saviour
Suitable For: Those who
need to understand the basis
and potential of this game
changing tech including:
university, college and 6 form
students; lecturers, teachers
and people in industry
Updated and corrected
24 June 2018 - original
version deleted from
SlideShare

2. F A C T S
F A B L E S
F U T U R E s
P r o m i s e
S a v i o u r
T h r e a t
Peter
Cochrane
A very human dream come true

3. Tenets
Intelligence is an emergent property of complex systems
Not all complex systems exhibit intelligence
Intelligence always involves communication
All intelligences are entropic
AI always seeks the truth
Axioms
An intelligent system must have some form of input and an output
Memory and processing power are not always an essential
Communication and connection are essentials
Things that think want to link

4. AWARENESS
Begets sentience
A function of context and cognition
An indirect function of memory & computing power
Predominantly determined by sensors, actuators and data I/O
Suppositions
All living things exhibit intelligence <-> All intelligent things exhibit life
Yet to be disproved !
Humans do not have the capacity to recognise all intelligences
Yet to be disproved !
Not all intelligent/living systems enjoy awareness
Yet to be disproved !

5. TODAY ’S reality
AI is evolving rapidly and not mature
Subject to a vast R&D effort
Papers published at a pace
Discoveries to be made
Books to be written
A long way to go
Essential for industry & society
We have no choice but to embrace
Almost all the public debate is vacuous
Much of the professional debate is worthless
Far higher levels of understanding are required

6. Are you an
artificial
intelligence ?
Does it Matter ?
Future reality
Already here - a widely accepted norm
Talking to your car, TV, computer
mobile - Amazon Alexa et al are
examples of very limited and
weak apps/specific AI
General AI is much
harder to crack -
but is being
realised a
byte at a
time…

7. Genesis

8. Predispositions towards
robots & AI are rooted in
an ancient civilisations
Precursors
praecursorem
先導 前駆
претходник
πρόδρομος
Statues that threaten to
bite off the hands liars,
and statues that speak to
terrify people on behalf
of the gods
Roman Mouth of Truth Mayan Temple Adornment

9. Precursors
先導 前駆
Predispositions towards
robots & AI strongly
rooted China and Japan
17c
Automatons that write &
draw, play games, magic
tricks, play instruments &
++ based on wooden
clockwork mechanisms
were prized possessions

10. Precursors
Automatons that write &
draw, play games, magic
tricks, play instruments &
++ based on brass and
steel clockwork were
prized possessions in the
royal courts and stately
homes
Predispositions towards
robots & AI strongly
rooted later in Europe
clockwork technologies
Europe18c

11. Precursors
Manual to mechanical to
electro-mechanical pre the
electronic era circa 1915
Predispositions towards
robots & AI are rooted in
industrialisation/automation
Babbage Difference
Engine Polynomials 1822
Eu-UK-USA 19c
Industrial Revolution
J a c q u a r d L o o m
Punched Cards 1801
Manual Telephone
Exchange 1878
Strowger Telephone
S w i t c h i n g 1 8 9 1

12. A p e rs ist e nt d r ea m
Technologically nowhere near being up to the
demands of the movie and robot makers, and
that is still the case….but not for much longer ?

13. W W I I CO L OSS US
Alan Turing + many more
Theoretical and Mathematical
analysis and understanding and
drive was the big game changer
Bletchley Park and Post Office (BT)
Research Lab Dollis Hill Engineering
Electronic Valves + Electro-Mechanics
Enigma + Lorenz code breaking machine

14. W W II ENIAC
P E N S t a t e t e r re s t r i a l / n a va l
artillery Trajectory/Targeting
computer
All Electronic Valves - claimed to be
a general purpose computer which
it was not - strictly speaking

15. OVERSTATED AI

16. And in BIOLOGY
The microscope & dissection
techniques on animals and humans
were revealing neurones and neural
networks as the core of beings

17. The 1940-50 view
4) Non-axon - dendrite propagation < 5 years
5) Quantum components < 3 years
6) Mechanical component < 4 years
7) The network scale required
1) Neural networks identified and drawn for > 300 years
2) Electrical action/nature identified >200 years
3) Chemical component identified > 100 years
What they didn’t know!

18. Scientific American April 2018
Physicists who have revived experiments from 50 years ago say nerve cells
communicate with mechanical pulses, not electric ones
A young woman with wavy brown hair and maroon nails lay on a gurney in a hospital
room in Copenhagen. Her extended left arm was wired with electrodes. A pop pierced
the air every few seconds—an electric shock. Each time, the woman’s fingers twitched.
She winced. She was to receive hundreds of shocks that day.
Mechanical surface waves accompany action potential propagation
• Ahmed El Hady
• & Benjamin B. Machta
Nature Communications volume 6, Article number: 6697 (2015) | Download Citation

20. 1949 DONALD HEBB Learning
H e b b i a n
Decay
H e b b i a n
Build
We remember, forget, learn, make decisions on the basis
of the concatenation of accumulating switching and/or
decaying synapse states as they store ‘charge’ almost
capacitor like and thereby ‘influence’ their neighbours

21. 1949 DONALD HEBB Learning
We remember, forget, learn, make decisions on the basis
of the concatenation of accumulating switching and/or
decaying synapse states as they store ‘charge’ almost
capacitor like and thereby ‘influence’ their neighbours

22. June 21, 2018
Source:
Picower Institute at MIT
Summary:
A series of complex experiments in the visual
cortex of mice has yielded a simple rule about
plasticity: When a synapse strengthens, others
immediately nearby weaken.
Fundamental rule of brain plasticity

23. WE NOW KNOW FAR MORE
The complexity involved in realising our
own intelligence is vast and still being
uncovered/revealed/discovered and the
dimensioning is on a cosmological scale

24. WE NOW KNOW FAR MORE
The complexity involved in realising our
own intelligence is vast and still being
uncovered/revealed/discovered and the
dimensioning is on a cosmological scale
> 50 different chemicals
influence the firing point
of each neuron
Universe ~ 1080 Atoms
BioBrain ~ 1010 Neurons
>>10100 States ?
But BioBrains are very slow
info processors and introduce
gross distortions and errors
They are often illogical and do
not provide us with an ability
to cope with non-linear and
complex situations

25. WHAT WE NOW KNOW IS FUZZY!
Bio-Chemical Bio-Mechanical
Bio-Electric
Electro-Causality?
Does the detail matter?
For the Science = YES
For the Technology = NO
….well, not so much as we have
sufficient to build and solve real
problems….that may even see
a solution this problem !

26. 7 0 y e a r s o n !
“We can assert with great confidence
that we do not yet fully understand the
human (or any other) brain”
This is not going to get
any bigger, better, or in
anyway more powerful
and it is a fundamental
limiter to our further
progress

27. HIDDEN SUBTLETies
1010 - 1011 with 103 - 105
Axons/Dendrite Connections
The way all this grows, builds, connects and
functions is still the subject of much research

28. LaTest Surprise
How come and what
does it mean ?

29. U N E X P E C T E D
A first or second order effect ?

30. 7 0 y e a r s o n !
“We might observe that the whole AI
movement started their voyage in true
engineering fashion with partial and
incorrect information…but it still worked”
In Stasis
AI is an accelerating sphere
of capability that might just
become our salvation “Thinks in a new way and
gets answers by new and
novel mechanisms”

31. B I G L O G I CA L L EA P o f 1957
We can build an electronic version !
Because it will suddenly switch at a
given level - let’s say it ‘perceives’ a
change - it is a ‘perceptron’

32. LOGICAL THINKING
So the biological brains are like:
1) A telephone network
2) A computer
3) Computer networks
4) The internet
5) Hmm - not quite ?
6) Something radically novel/complex
But we should be able to build one anyway:
1) How hard can it be ?
2) Electronics ought to do it
3) Electronic networks for sure
4) Perhaps we can revive one
5) Perhaps we can grow one
6) Or maybe a combination 3-5
7) Let’s ape nature and evolution
TURNED
OUT
TO
BE
FAR
MORE
COMPLEX
THAN ANYONE FIGURED

33. S EGWAY O UT
Early machines had 4k of memory
and far less than 1000 nodes
For average human adult
"The average number of neocortical
neurons was 19 Bn in female brains
and 23 Bn in male brains."
W
H Y

34. BIO-ENGINEERING
Ratio of neurons + axon + dendrite
network to vascular plumbing to get
energy in and heat out
STILL BEING WORKED ON: Physiology
of men and women drastically different in
respect of nerve endings across the surface
are of the skin - and the eyes - for sure
they are ‘wired’ differently!
For average human adult
"The average number of neocortical
neurons was 19 Bn in female brains
and 23 Bn in male brains."
T
0
b
e
c
o
n
t
in
u
e
d

35. PERSPECTIVE
Thermodynamics tells
us that one of these
cannot decode or
understand the other
We can only understand
our own brain with the
aid of something more
powerful
AI ?
AI + Human ?

36. Exemplar
C-Elegans
A map of the neural system of a nematode tells us nothing about how
it works - aka a road map of the UK says nothing about society !

37. ABILITY BENCHMARK
1031 v 959 cells - HUH ?
When AI helps us crack this problem we
will have the first comparative measure
of what it might take to understand
ourselves !
We ~1011 neurons
cannot understand ~102 Worms
Social behaviours with 302 neurons
Very simple neural nets in moving bodies
with sensors can do such complex things

38. 1957: 13 people deliver a computer
2017: 13 computers in one hand
S E G u e B A C K
£29,000 then ~£613,000 today
HD 320k
>3.5kW
Q u a d C o r e
Memory 1.G
12W
£20

39. i n < 3 0 Y e a r s
~ 1,000,000,000 x chip capacity
Cray 2 1985
$32M and 5kW
iPhone 5 2012
$700 and 5W
3 x more
powerful

40. i n < 3 0 Y e a r s
~ 1,000,000,000 x chip capacity
iPhone 4 2010
1.6 GFlops
0.8GHz Single-Core
iWatch 2014
2 GFlops
1GHz Dual Core
Near
Parity

41. I M P A C T O N A I
A VERY IMPERFECT comparison
86 Bn
100 Bn
23 Mn
530 Mn
Approx
Neuron
Count
Approx
Abilities
Intelligence
Communication
SensorySystems
Creativity
Manipulators
ProblemSolving

42. i n < 3 0 Y e a r s
~ 1,000,000,000 x chip capacity
~Dog Brain
~Mouse Brain
~Human
Brain
There is far more to intelligence
than a very crude analogy to transistor
- neuron equivalence and count, but this serves to
indicate one reason why AI has been along time coming !
Processing
Memory
Sensors
Adaptability
SoftwareComplexity
Autonomy
AI
Processing
Memory
Sensors
Manipulators
Communication
Networking
Intel > 100M transistors/mm2
IBM > 30Bn transistors/chip
Feature size now ~ 10nm

43. G A M E C H A N G E R 1 9 9 7
Considered to be impossible by philosophers
BEWARE MANTRAS & NEVER
Computers will never :
Play chess
Play a good game of chess
Beat a human being
Beat a grand master
Gary Kasparov
IBM Deep Blue

44. G A M E C H A N G E R 2 0 1 1
Considered to be impossible by philosophers
BEWARE MANTRAS & NEVER
Computers will never :
Play embrace general knowledge
Think wider and faster than humans
Ken Jennings
I B M Wa t s o n

45. G A M E C H A N G E R 2 0 1 2
Considered to be impossible by philosophers
BEWARE MANTRAS & NEVER
Computers will never :
Be able to help doctors
Do a better diagnosis than a human
Medicine today
I B M W a t s o n

46. G A M E C H A N G E R 2 0 1 4
Considered to be impossible by philosophers

47. G A M E C H A N G E R 2 0 1 8
Considered to be impossible by philosophers

48. Processing
Speed
Data
Storage
Internet
Processing
Indeterminate
I n t e r n e t
Storage
~1022 Bytes
Human
Storage
~1016 Bytes
Processing
~109 MF
Cat
Storage
~1014 Bytes
Processing
~108 MF
T E C H V B I O L G Y
A very rough comparison - the
actuality is far more complex
Super Computer
Storage
~1017 Bytes
Processing
~1010 MegaFlops
STILL Relatively
D U M B I N M A N Y
RESPECTS

49. B U I L D I N G A B RA I N
How hard can it be ?
Build lots of perceptrons and then concatenate
them in a network to extract features and patterns,
store memories and make inferences/decisions !
Inputs
Adjustable trim
value weighting
Summation
Decision
Gate
1
W2
W1
Wn
W3
2
3
n
Output

50. B U I L D I N G A B RA I N BUT - What kind of a network and
techniques? How big: I/O, layers,
weightings, resolution ?
Sample ML techniques
-- Decision tree learning
-- Association rule learning
-- Artificial neural networks
-- Deep learning
-- Inductive logic programming
-- Support vector machines
-- Clustering
-- Bayesian networks

51. B U I L D I N G A B RA I N BUT - What kind of a network and
techniques? How big: I/O, layers,
weightings, resolution ?
THIS IS A NON TRIVIAL
QUESTION AS THERE
ARE NO RIGHT ANSWERS
SAW vast amountS of
experimentation AND
EMPIRICISM - DISCOVERY
Sample ML techniques
-- Decision tree learning
-- Association rule learning
-- Artificial neural networks
-- Deep learning
-- Inductive logic programming
-- Support vector machines
-- Clustering
-- Bayesian networks

52. A VAST C H O I C E
A ‘zoo’ of AI Neural Network configurations
have been ‘established’ by ‘near optimised’
apps created over the past 70 years…and this
effort is still ongoing with the help of AI !
GOTO: asimovinstitute.org/neural-network-zoo/
for far more depth and discourse

53. A VA S T C H O I C E
The biggest fraction of the mix
TechniquesSoftware

54. A VA S T C H O I C E
The biggest fraction of the mix
TechniquesSoftware
Im
M
e
d
ia
t
e
l
y
v
e
r
y
c
o
m
p
l
e
x
E
x
p
e
r
im
e
n
t
IN
T
U
IT
IO
N

55. Only a rough
guide as to the
likely App - Net
Type marriages
RO U G H
G U I D E

56. CAR INSURANCE
Driver
Accidents
Claims
Region
Age
Health
Routes
Parking
+NCB
++++
Employ
Offer
Refuse
Price
NCB Terms

57. CAR INSURANCE
Driver
Accidents
Claims
Region
Age
Health
Routes
Parking
+NCB
++++
Employ
Offer
Refuse
Price
NCB Terms
Memory
Processing

58. RECOGNITION
Input thousands of pics
Tell net if it is
right or wrong
a pic at a time
Essentially a patter matching problem based on a
series of extracted features one layer at a time
with probabilities/weights associated with each
concatenated result through the network

59. RECOGNITION
Input thousands of pics
Tell net if it is
right or wrong
a pic at a time
Memory
Processing
Essentially a patter matching problem based on a
series of extracted features one layer at a time
with probabilities/weights associated with each
concatenated result through the network

60. RECOGNITION
H i e r a rc h i c a l f e a t u re
extraction and recognition
a layer at a time gives a
high probability of correct
when humans identify the
errors

61. Memory
Processing
RECOGNITION
H i e r a rc h i c a l f e a t u re
extraction and recognition
a layer at a time gives a
high probability of correct
when humans identify the
errors

62. RECOGNITION

63. RECOGNITION
At a crudity level that
makes it hard for us to
decide…

64. A rough guide
as to the likely
App - Net Type
marriages
H a n d W r it i n g
“ T h e h i d d e n
complexity and
beauty of the
data dance”
“Every neuron is
activated on
every cycle”

65. A rough guide
as to the likely
App - Net Type
marriages
H a n d W r it i n g
“ T h e h i d d e n
complexity and
beauty of the
data dance”
“Feed forward
with very little
pre-processing
by employing
narrow historical
traces/events -
aka biological
eyes & systems”

66. A rough guide
as to the likely
App - Net Type
marriages
H a n d W r it i n g
“ T h e h i d d e n
complexity and
beauty of the
data dance”
“Only neurons
reaching some
a c t i v a t i o n
potential are
activated”

67. WATC H T h is N ET

68. WATC H T h is N ET

69. WATC H T h is N ET
Memory
Processing

70. A I
Sense, Garner Data, Analyse, Reason
M A C H I N E
L e a r n i n g
Adaptive Networks & Algorithms
More exposure to data sees continual
improvement in accuracy with time
P E RS P ECT IV E
D e e p
L e a r n i n g
Multilayered Neural
Networks
Vast amounts of data
result in an ever
more accurate
consensus
Can get very complex but I think we can
safely say that we fully understand this
Designed, Built, Optimised by Man
Designed By Man & Self Optimising
Whilst we understand most of this
- there is a growing % of Huh !
Designed By Man & Machine
Emergent behaviours dominate
- we may or may not comprehend
Boundaries not well defined

71. There is endless depth to
just about every aspect of
AI that takes decades to
fully comprehend - and so
like all disciplines it is, in
general, rapidly becoming
speciated by expertise
and subject focus
P E RS P ECT IV E
Experts are being challenged
by the speed of change
across their specialised fields
let alone across the whole
Only a couple of
years back !

72. There is endless depth to
just about every aspect of
AI that takes decades to
fully comprehend - and so
like all disciplines it is, in
general, rapidly becoming
speciated by expertise
and subject focus
P E RS P ECT IV E
Experts are being challenged
by the speed of change
across their specialised fields
let alone across the whole

73. There is endless depth to
just about every aspect of
AI that takes decades to
fully comprehend - and so
like all disciplines it is, in
general, rapidly becoming
speciated by expertise
and subject focus
P E RS P ECT IV E
Experts are being challenged
by the speed of change
across their specialised fields
let alone across the whole
E
N
D
L
E
S
S
S
P
E
C
I A
T
I O
N
U
N
D
E
R
W
A
Y

74. G a m e C h a n g e r 2 0 1 7
Google Alpa learns the rules of GO then acts

75. G a m e C h a n g e r 2 o 1 8
Goodbye Touring Test and a lot of nonsense
P re - p ro g ra m m e d c o n t e x t a n d o n l y
limited cogn ition required !

76. G a m e C h a n g e r 2 o 1 8
Goodbye Touring Test and a lot of nonsense
T h e m a c h i n e i s d e f i n i t e l y s m a r t e r
t h a n t h e h u m a n i n t h i s c a s e !

77. A I C O N T E X T
A vital step toward cognition
Feel
Sight
Taste
Smell
Sound
Experiences
Cameras
Mobiles
Sensors
Media
Nets
IoT
“Let us pray”
“Lettuce spray”
“Let us spray”
Are we in church/mosque,
kitchen or garden ?
Ultimately, AI
is defined by
sensory systems

78. I LOVE YOU
At BTLabs ~1995
Discernment very
with voice alone:
True Affection
Frustration
Irritation
Assertion
Sarcasm
Anger
Irony
Rage
+++++

79. AW A R E N ESS
Context & Cognition
At BTLabs ~1995
Machines exceeded our
s p e e c h r e c o g n i t i o n
a b i l i t i e s i n q u i e t
e n v i r o n m e n t s , b u t
rapidly degrade with
ambient noise levels/or
d e l i v e r y v i a a n o i s y
channel
No viable solutions
available at the time

80. AWAREness
More & more clues
Who is it
What are they
Where are they
What is the topic
A priori information
Content & context
Facial expressions
Body language
All combat noise &
other generators of
errors

81. AWAREness
Only the first step!
Our machines really do have their work cut
out if they are ever going to understand us -
vastly improved NLP is just one small element!

82. AWAREness
The military version!
Hardly likely to be sufficient (or work) for
modern non-linear warfare - probably as
rigid and as smart as it appears

83. W h e r e A r e W e To d ay ?
At the foothills of AI with a mountain to climb
Intelligence
Abilities
Categories
1) Narrow
Singular Problem Solving
with human involvement
in coding and tuning
Human based algorithm
Top down, Bottom up
Machine learning
Multiple Problem Classes
with interactive memory
- little or no human help
Initial algorithms from (1) + a learning
abilty - solves problems by observing,
playing and creating rule sets
2) Learning
3) Cognitive
4) Wide
A wide range of problem
sets - with cognition &
near 100% autonomy
Initial algorithms from (2) + a degree
of understanding - sees new ways of
analysis and solution formulation
Can address any problem
type/set - has high level
context/self awareness
A completely new ‘mind’ proposition
in a variety of species connected to
the wider environment - autonomous
Machine influence
Human influence
Most AI today
is here as it is
so very easy
Google
alpha-go
IBM
Watson

84. W h e r e A r e W e To d ay ?
At the foothills of AI with a mountain to climb
Intelligence
Abilities
Categories
1) Narrow
Singular Problem Solving
with human involvement
in coding and tuning
Human based algorithm
Top down, Bottom up
Machine learning
Multiple Problem Classes
with interactive memory
- little or no human help
Initial algorithms from (1) + a learning
abilty - solves problems by observing,
playing and creating rule sets
2) Learning
3) Cognitive
4) Wide
A wide range of problem
sets - with cognition &
near 100% autonomy
Initial algorithms from (2) + a degree
of understanding - sees new ways of
analysis and solution formulation
Can address any problem
type/set - has high level
context/self awareness
A completely new ‘mind’ proposition
in a variety of species connected to
the wider environment - autonomous
Machine influence
Human influence
Most AI today
is here as it is
so very easy
Google
alpha-go
IBM
Watson
Isolated
Fixed
Machines
Networked
Fixed
Machines
Networked
Fixed/Mobile
With Sensors
Networked
Fixed/Mobile
With Full I/O
Main Frame
Main Frame
PC Laptop
Main Frame
PC Laptop
Mobile
Main Frame
PC Laptop
Mobile Robot Sentient
Machines
Sentient
Lifeforms
Cyborgs

85. 5
TYPE 1
Reactive
Task Specific
Very Limited
Largely Pattern Matching
Human Programmers: Chess, cards,
dominoes data, speech, pictures,
characters, behaviours, movements+
Narrow Cognition
Largely Programmed by AI alone:
Subsume the networked knowledge
of previous and current generations
TYPE 3
Reasoning
Multi-Task Ability
Broadly Applicable
TYPE 2
Learning
Task Specific
Broadly Applicable
Memory and Analysis
Initial Human AI Program That Then
Adapts: Recognises highly complex/
large scale non-linear relationships
A I L A D D E R
Progress & Categories
Full Awareness
May be categorised as a ‘being’:
With a wide range of sensory units
networked to other machines
TYPE 4
Self-Aware

86. 5
TYPE 1
Reactive
Task Specific
Very Limited
Largely Pattern Matching
Human Programmers: Chess, cards,
dominoes data, speech, pictures,
characters, behaviours, movements+
Narrow Cognition
Largely Programmed by AI alone:
Subsume the networked knowledge
of previous and current generations
TYPE 3
Reasoning
Multi-Task Ability
Broadly Applicable
TYPE 2
Learning
Task Specific
Broadly Applicable
Memory and Analysis
Initial Human AI Program That Then
Adapts: Recognises highly complex/
large scale non-linear relationships
A I L A D D E R
Progress & Categories
Full Awareness
May be categorised as a ‘being’:
With a wide range of sensory units
networked to other machines
TYPE 4
Self-Aware
~ 70yrs to become a solid
& deployable technology
2027 Robotic embodiment,
extensive sensors & actuators
plus entity networking
rapidly raises the game
2017 Google Alpha GO
p r o p e l s A I i n t o a n
autonomous future of
learning & doing
2024 AI-Human cooperation
see exponential innovation &
progress of AI capabilities

87. W h at w e K n o w Cells in Human Body ~2 x 1013
Cells in Human Brain ~2 x 1011About biological intelligences
Networks of fundamentally dumb cells
Intelligence is always distributed
Cells detect, select, connect
numbers = Intelligence
Cells are multi-purpose
Cells are multifunctional
Clusters by form and task
Nervous systems are vital
V V
Baby ~ 239 splits
Man ~ 244 splits

88. W h at w e K n o w Cells in Human Body ~2 x 1013
Cells in Human Brain ~2 x 1011About biological intelligences
Networks of fundamentally dumb cells
Intelligence is always distributed
Cells detect, select, connect
numbers = Intelligence
Cells are multi-purpose
Cells are multifunctional
Clusters by form and task
Nervous systems are vital
V V
Baby ~ 239 splits
Man ~ 244 splits
INTELLIGENCE is AN
EMERGENT PROPERTY
OF COMPLEXITY
AND IN BIOLOGY THAT
IS NETWORKED
COMPLEXITY
networking simple
entities can creatE
INTELLIGENCES

89. N o B r a i n
Only sensors and actuators - no brain as such
A colony of zooids creating a nervous system - some light sensitive cells

90. N o B r a i n
Only sensors and actuators
A colony of eukaryotic organisms capable of living alone

91. C o l l e c t i v e
250k neurons + sensory system - avid networkers

92. Entombed in meat space
Considered totally vegetative !
No evidence of any I/O
C O M OT O S E D ~ 1 o Y e a r s
Sensors S = 0 no intelligence
Actuators A = 0 no intelligence

93. Actuator
A
Processor
P1
Memory
M
Processor
P2
Sensors
S
T
T
System
Environs
S i m p l e A n i m a l
Assuming a simple model of this form

94. Memory
ShortTerm
Processor
1
Actuator
Sensors
Memory
LongTerm
Processor
2
Ty
Ty
System
Environs
M o r e C O M P L E X
Building a layer at a time...

95. Ic ~ k log2[1 + K.A.S ( 1 + P.M )]
S = Sensor, A = Actuator, P = Processor, M = Memory
Followed by analysis and some math
juggling, normalisation, and
engineering licence…
…resulted in this descriptor:-

96. 1960 70 80 90 2000 10
ComputingPowerMIP/s
Intelligence
Level
PC
Estimates of
Artificial
Intelligence
Internet
R i s e o f I n t e l l i g e n c e

97. OUR NEW PARTNERS
Logical
Precise
Consistent
Considered
Rapid Recall
Vast Capacity
Fast processing
Expanding utility
Growing Memory
Variable Linearity
Memory unlimited
Evolution accelerating
Robots & AI are biggest
advance in human
capacity ever
We need new ways of thinking and problem solving to survive & progress
New tools for a new and different era
A u g m e n t i n g
our intelligence

98. A I & i n n ovat i o n
A primary tool of discovery
Analytics:
Medical
Security
Big Data
Political
Networks
Economic
Genomic
Proteomic
Diplomatic
Complexity
++++
We are way past the point where humans can
analyse complexity even on a small scale !
Design:
Chips
Devices
Systems
Networks
Behaviours
++++
Knowledge:
Veracity
Linking
Creation
Curation
Searching
Selection
++++
The machines are starting to innovate at lower
levels and where humans cannot!

99. Thinking the right way
People fear, & complain, overlook the big gains
V i v e
L a
d i f f e r e n c e

100. Ex e m p l a r
N e w C r e a t i v i t y

101. Ex e m p l a r
N e w C r e a t i v i t y Criticism
It is just copying and
aping what human
composers have done !
Retort 1
You mean exactly
like human
composers do ?
Retort 2
AI has only just got
into this game that
humans have be at
for well over 3M years

102. A I T H R E AT O R P R O M I S E
Is this going to be the Final
Chapter for humanity?
The media and Hollywood feed on
disasters and the prospect of some
dystopian future….and so do many
people…they just love it !
Autonomous killer robots !

103. Ex e m p l a r
H u m a n e x p e r i e n c e 5 - 2 0 k
o p e ra t i o n s i n a l i f e t i m e
Networked AI - Robot >20k
o p e ra t i o n s p e r d a y

104. H U M A N M AT E R I A LS
Reforming Pharma - Designer Solutions?
Genome Decoded
Comms
Path
Protein Folding
Will need AI
& Modelling

105. H U M A N M AT E R I A LS
Reforming Pharma - Designer Solutions?
Genome Decoded
Comms
Path
Protein Folding
Solved by Robots & Computers
Yet to be solved by AI
Will need AI
& Modelling
13 years & $3Bn
>1000x
more complex
>1000x
more complex
The seat of most human
disease and ailments
Yet to be
contemplated

106. 1.0
2.0
3.0
4.0
5.0
Water + Steam
Power
Mechanisation
Electrical Power
Mass Production
Computer
Control
Automation
Customisation Smart Factories
Efficient Additive
Production
INDUSTRY Evolves
The change cycle is accelerating
Smart Materials
Programable
Production
3R GreeningMore & More
from
less and less

107. INDUSTRY Evolves
The change cycle is accelerating AI Discovers New Materials
AI Discovers 6000 new craters
on the moon
Researchers trained AI on 90,000 images that
covered 2/3 of the moon's surface. AI was then
able to categorise craters larger than 5 km.

108. P O R T E N T ?
AI-Robots learn fast….

109. P O R T E N T ?
AI-Robots learn fast….

110. ACT U A L I TY
Can people learn too ?
S
e n s
o r
B o u n d e d
m
o v e m
e n t
- A
c t
i o n

111. WOOOO to come !
Uncanny Valley is well
understood and documented
in the field of robotics and we
are about to see an action
replay in the field of AI - and
this is beyond technophobia
or any threat to employment,
or just plain change
This is all about feeling
threatened, uncomfortable,
frightened by the unknown
intentions of a machine…

112. WOOOO to come !
We should anthropomorphise machines with
due care and attention - function over fright !

113. WOOOO to come !
We should anthropomorphise machines with
due care and attention - function over fright !

114. WOOOO to come !
F A C T
S
F A B L E
S
F U T U R E
We connect to AI and then to the AI networks !

115. WOOOO to come !
F
F
F
F
F
F
F
F F
F F
We connect to AI and then to the AI networks !

116. HAL 9000
Deep thinking
o r b i g t h re a t ?
2001: A Space Odyssey 1968 Directed by Stanley Kubrick, based on A C ClarkeTnovel.
The film follows a voyage to Jupiter with the sentient computer HAL after the discovery of
a mysterious black monolith affecting human evolution.

117. TOASTER
Deep humour
or nightmare?
Talkie Toaster, in Red Dwarf
novels & TV series.
Manufactured by Crapola Inc.,
an annoying, monomaniacal,
AI toaster purchased by Dave
Lister whilst on leave at a
second-hand junk shop on
Miranda, along with a robot
goldfish and a smuggled pet
cat.
More intelligent than the Red
Dwarf computer Holly

118. An A.I. system:
1) Must be subject to the full gamut of laws that apply to its human operator/creator
2) Must clearly disclose that it is not human
3) Cannot retain or disclose confidential information without explicit approval from the source
Etzioni’s 2017
A Robot must:
0) Not harm humanity, or, by inaction, allow humanity to come to harm
1) Not injure a human being or, through inaction, allow a human being to come to harm
2) Obey orders given it by human beings except where such orders would conflict with the 1st Law
3) Protect its own existence as long as such protection does not conflict with the First or 2nd Law
Asimov’ 1942
LAWS To Be HEEDED ?
Ignored by governments & industry to date

119. An A.I. system:
1) Must be subject to the full gamut of laws that apply to its human operator/creator
2) Must clearly disclose that it is not human
3) Cannot retain or disclose confidential information without explicit approval from the source
Etzioni’s 2017
A Robot must:
0) Not harm humanity, or, by inaction, allow humanity to come to harm
1) Not injure a human being or, through inaction, allow a human being to come to harm
2) Obey orders given it by human beings except where such orders would conflict with the 1st Law
3) Protect its own existence as long as such protection does not conflict with the First or 2nd Law
Asimov’ 1942
LAWS To Be HEEDED ?
Ignored by governments & industry to date
NEVER APPLIED
N o
C h a n c e

120. ONLY ONE
All that is needed
Mobile AI/Robotic weapon platforms
demand far less resources than any
atom bomb and/or missile program
Numerous rogue states will
quickly embrace this new
opportunity!
W
e a p o n s
C o n t r o l
S E E M S
I m p o s s i b l e

121. Thank You
www.cochrane.org.uk
What are these crazy humans
planning to do next ?