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AI Fables, Facts and Futures: Threat, Promise or Saviour

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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…

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AI Fables, Facts and Futures: Threat, Promise or Saviour

  1. 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. 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. 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. 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. 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. 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. 7. Genesis
  8. 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. 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. 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. 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. 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. 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. 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. 15. OVERSTATED AI
  16. 16. And in BIOLOGY The microscope & dissection techniques on animals and humans were revealing neurones and neural networks as the core of beings
  17. 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. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 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 > 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
  24. 24. 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 !
  25. 25. 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
  26. 26. 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
  27. 27. LaTest Surprise How come and what does it mean ?
  28. 28. U N E X P E C T E D A first or second order effect ?
  29. 29. 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”
  30. 30. 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’
  31. 31. 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
  32. 32. 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
  33. 33. 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
  34. 34. 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 ?
  35. 35. 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 !
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. 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
  41. 41. 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
  42. 42. 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
  43. 43. 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
  44. 44. 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
  45. 45. G A M E C H A N G E R 2 0 1 4 Considered to be impossible by philosophers
  46. 46. G A M E C H A N G E R 2 0 1 8 Considered to be impossible by philosophers
  47. 47. 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
  48. 48. 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
  49. 49. 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
  50. 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 ? 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
  51. 51. 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
  52. 52. A VA S T C H O I C E The biggest fraction of the mix TechniquesSoftware
  53. 53. 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
  54. 54. Only a rough guide as to the likely App - Net Type marriages RO U G H G U I D E
  55. 55. CAR INSURANCE Driver Accidents Claims Region Age Health Routes Parking +NCB ++++ Employ Offer Refuse Price NCB Terms
  56. 56. CAR INSURANCE Driver Accidents Claims Region Age Health Routes Parking +NCB ++++ Employ Offer Refuse Price NCB Terms Memory Processing
  57. 57. 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
  58. 58. 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
  59. 59. 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
  60. 60. 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
  61. 61. RECOGNITION
  62. 62. RECOGNITION At a crudity level that makes it hard for us to decide…
  63. 63. 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”
  64. 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” “Feed forward with very little pre-processing by employing narrow historical traces/events - aka biological eyes & systems”
  65. 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” “Only neurons reaching some a c t i v a t i o n potential are activated”
  66. 66. WATC H T h is N ET
  67. 67. WATC H T h is N ET
  68. 68. WATC H T h is N ET Memory Processing
  69. 69. 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
  70. 70. 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 !
  71. 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
  72. 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 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
  73. 73. G a m e C h a n g e r 2 0 1 7 Google Alpa learns the rules of GO then acts
  74. 74. 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 !
  75. 75. 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 !
  76. 76. 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
  77. 77. I LOVE YOU At BTLabs ~1995 Discernment very with voice alone: True Affection Frustration Irritation Assertion Sarcasm Anger Irony Rage +++++
  78. 78. 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
  79. 79. 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
  80. 80. 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!
  81. 81. 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
  82. 82. 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
  83. 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 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
  84. 84. 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
  85. 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 ~ 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
  86. 86. 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
  87. 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 INTELLIGENCE is AN EMERGENT PROPERTY OF COMPLEXITY AND IN BIOLOGY THAT IS NETWORKED COMPLEXITY networking simple entities can creatE INTELLIGENCES
  88. 88. 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
  89. 89. N o B r a i n Only sensors and actuators A colony of eukaryotic organisms capable of living alone
  90. 90. C o l l e c t i v e 250k neurons + sensory system - avid networkers
  91. 91. 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
  92. 92. 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
  93. 93. 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...
  94. 94. 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:-
  95. 95. 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
  96. 96. 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
  97. 97. 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!
  98. 98. 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
  99. 99. Ex e m p l a r N e w C r e a t i v i t y
  100. 100. 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
  101. 101. 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 !
  102. 102. 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
  103. 103. 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
  104. 104. 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
  105. 105. 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
  106. 106. 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.
  107. 107. P O R T E N T ? AI-Robots learn fast….
  108. 108. P O R T E N T ? AI-Robots learn fast….
  109. 109. 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
  110. 110. 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…
  111. 111. WOOOO to come ! We should anthropomorphise machines with due care and attention - function over fright !
  112. 112. WOOOO to come ! We should anthropomorphise machines with due care and attention - function over fright !
  113. 113. 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 !
  114. 114. WOOOO to come ! F F F F F F F F F F F We connect to AI and then to the AI networks !
  115. 115. 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.
  116. 116. 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
  117. 117. 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
  118. 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 NEVER APPLIED N o C h a n c e
  119. 119. 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
  120. 120. Thank You www.cochrane.org.uk What are these crazy humans planning to do next ?

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