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Artificial intelligence and the Singularity - History, Trends and Reality Check


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A lecture given at the second LAST festival ( by Piero Scaruffi on Artificial intelligence and the Singularity - History, Trends and Reality Check. This is a very old presentation. See the updated one at

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Artificial intelligence and the Singularity - History, Trends and Reality Check

  1. 1. Artificial Intelligence and the Singularity piero scaruffi October 2014 "The person who says it cannot be done should not interrupt the person doing it" (Chinese proverb)
  2. 2. Table of Contents • The future of mind – Knowledge-based systems – Connectionist systems – ...and their cultural background • The future of body – Robots – Implants – Synthetic Biology 2 • The Singularity? – Reality Check – Accelerating Progress? – Non-human Intelligence – Human Intelligence – A Critique of the Turing Test
  3. 3. A timeline of Artificial Intelligence:
  4. 4. 4 A Brief History of Logic George Boole's "The Laws Of Thought" (1854): the laws of logic “are” the laws of thought Propositional logic and predicate logic: true/false!
  5. 5. 5 A Brief History of Logic Axiomatization of Thought: Gottlob Frege's "Foundations of Arithmetic" (1884) Giuseppe Peano's "Arithmetices Principia Nova Methodo Exposita" (1889) Bertrand Russell's "Principia Mathematica" (1903)
  6. 6. A Brief History of Logic • David Hilbert (1928) – Entscheidungsproblem problem: the mechanical procedure for proving mathematical theorems – An algorithm, not a formula – Mathematics = blind manipulation of symbols – Formal system = a set of axioms and a set of inference rules
  7. 7. 1912 –1954 Alan Turing
  8. 8. 8 The Cultural Context • 1910-1950 Everything changed: – Everyday life – The foundations of science – The concept of art – The geopolitical order
  9. 9. 9 • Electricity • Regriferator • Automobile • Airlane • Telegraph • Telephone • Phonograph • Camera • Cinema • Radio • Typewriter • Calculator • Skyscraper • Plastic The 1910s
  10. 10. 10 You are a formulaEverything is relative You are and you are not You are just a reflex You are a probability Everything is uncertain Everything is moving away from you
  11. 11. 11 The emancipation of the dissonance History is a nightmare from which I am trying to awake.
  12. 12. 12 There will always be something you cannot prove Your mind creates reality Truth is an opinion Life and machines obey the same laws of nature Everything is information Everything comes from just one point Mind is a symbol processor
  13. 13. 13 Cultural Context • Bottom line: – Nonconformism – Anxiety – Noise – Freedom
  14. 14. 14 Cultural Context • World War II (1939-45) • The Holocaust • Hiroshima • Disintegration of the British Empire • Rise of the USA and Soviet Union
  15. 15. 15 Alan Turing • Hilbert’s challenge (1928): an algorithm capable of solving all the mathematical problems • Turing Machine (1936): a machine whose behavior is determined by a sequence of symbols and whose behavior determines the sequence of symbols • A universal Turing machine (UTM) is a Turing machine that can simulate an arbitrary Turing machine
  16. 16. Alan Turing • Alan Turing (1936) – Universal Turing Machine: a Turing machine that is able to simulate any other Turing machine – The universal Turing machine reads the description of the specific Turing machine to be simulated Turing Machine
  17. 17. 17 Alan Turing (BTW, the halting problem is undecidable, i.e. Hilbert’s Entscheidungsproblem is impossible)
  18. 18. Alan Turing • Turing machines in nature: the ribosome, which translates RNA into proteins – Genetic alphabet: nucleotides ("bases"): A, C, G, U – The bases are combined in groups of 3 to form "codons“ – RNA is composed of a string of nucleotides ("bases") according to certain rules – There are special carrier molecules ("tRNA") that are attached to specific aminoacids (proteins) – The start codon encodes the aminoacid Methionine – A codon is matched with a specific tRNA – The new aminoacid is attached to the protein – The tape then advances 3 bases to the next codon, and the process repeats – The protein keeps growing – When the “stop” codon is encountered, the ribosome dissociates from the mRNA
  19. 19. 19 Alan Turing • World War II: – Breaking the Enigma code (Bombe) – Turing worked at Bletchley Park where the Colossus was built but it was not a universal Turing machine (not general purpose) Replica of the Bombe
  20. 20. 20 The Turing Century • Can you name any achievement of the last 50 years (from the Moon landing to animal cloning) that would have happened even without programmable computers?
  21. 21. 2121 Electronic Brains • 1941: Konrad Zuse's Z3 programmable electromechanical computer, the first Turing- complete machine • 1943: Tommy Flowers and others build the Colossus, the world's first programmable digital electronic computer
  22. 22. 2222 Electronic Brains • 1944: Howard Aiken of IBM unveils the first computer programmed by punched paper tape, the electromechanical Harvard Mark I • 1945: John Von Neumann designs a computer that holds its own instructions, the "stored- program architecture"
  23. 23. 23 Electronic Brains 1945: John Von Neumann's computer architecture Control unit: •reads an instruction from memory •interprets/executes the instruction •signals the other components what to do •Separation of instructions and data (although both are sequences of 0s and 1s) •Sequential processing
  24. 24. 2424 Electronic Brains 1946: The first non-military computer, ENIAC, or "Electronic Numerical Integrator and Computer", is unveiled, built by John Mauchly and Presper Eckert at the University of Pennsylvania
  25. 25. 2525 Electronic Brains Computation
  26. 26. 2626 Electronic Brains • Apr 1949: The Manchester Mark 1, the first stored-program electronic computer • May 1949: Cambridge's EDSAC, the second stored-program electronic computer • Aug 1949: Philadelphia's EDVAC, the third stored-program electronic computer • 1950: The Pilot ACE computer
  27. 27. 2727 Electronic Brains • May 1950: The first stored-program electronic computer to be deployed in the USA, the SEAC, and the first to use semiconductors instead of vacuum tubes • Feb 1951: The Ferranti Mark 1, the first commercial computer, an evolution of the EDSAC • 1952: A Univac 1 correctly predicts that Eisenhower would win the elections
  28. 28. 2828 Electronic Brains (Computer History Museum, Mountain View) • Goldstine and Eckert with the electronics needed to store a single decimal digit
  29. 29. 2929 Electronic Brains Computer programmers of 1951: Patsy Simmers (holding an ENIAC board) Gail Taylor (holding an EDVAC board), Milly Beck (holding an ORDVAC board), Norma Stec (holding a BRLESC-I board)
  30. 30. 3030 Electronic Brains 1954: IBM's first “mass-produced” computer, the 650 (1,800 units sold - $200-400,000 each)
  31. 31. 3131 Electronic Brains • USA/ Semiconductors – 1947: AT&T's Bell Labs invent the transistor (William Shockley, John Bardeen, Walter Brattain) – 1949: The USA files an antitrust lawsuit against AT&T – 1952: AT&T's symposium on the transistor, open to everybody – 1954: Texas Instruments introduces the first commercial transistor – 1954: The first transistor radio (“Regency”)
  32. 32. 3232 Electronic Brains • USA/ Semiconductors – 1961: Texas Instruments introduces the first commercial integrated circuit – Military and space applications use the integrated circuit – 1965: Gordon Moore predicts that the processing power of computers will double every 18 months – 1971: Intel invents the microprocessor – Universities are irrelevant in semiconductor progress because the manufacturing process is too costly – Universities are crucial for progress in computers Jack Kilby’s I.C. Intel 4004
  33. 33. 33 Electronic Brains The future of your brain is coming faster than your brain can think…
  34. 34. 3434 Electronic Brains Software • 1958: Jim Backus (at IBM) invents the FORTRAN programming language, the first machine- independent language • 1964: IBM introduces the first "operating system" for computers (the OS/360) • 1968: The Arpanet is established based on Baran’s idea (four nodes: UCLA, Stanford Research Institute, UCSB, University of Utah) • 1969: the Unix operating system is born
  35. 35. 3535 Electronic Brains Democratizing technology • Antitrust policies contribute to the rapid diffusion of intellectual property throughout the computer and semiconductor industries • 1956: IBM and AT&T settle antitrust suits by licensing their technologies to competitors • 1969: The “unbundling” of software by IBM creates the software industry
  36. 36. Cybernetics The Steam Engine • Biggest impact on daily life since the printing press • Inventors are ordinary people, not academics • The automation of manufacturing begins in Lancashire, not at a university James Watt (1776)
  37. 37. Cybernetics • Mathematician Norbert Wiener, physiologist Arturo Rosenblueth and engineer Julian Bigelow: "Behavior, Purpose and Teleology" (1943)
  38. 38. Cybernetics • Macy Conference on Cybernetics (March 1946, New York) – John von Neumann (computer science) – Rafael Lorente de No (neurophysiology) – Norbert Wiener (mathematics) – Arturo Rosenblueth (physiology) – Warren McCulloch (neuropsychiatry) – Gregory Bateson (anthropology) – Margaret Mead (anthropology) – Walter Pitts (mathematics) – Ralph Gerard (neurophysiology) – Heinrich Kluever (psychology) – Lawrence Frank (sociology) – Molly Harrower (psychology) – Lawrence Kubie (psychoanalysis) – Filmer Northrop (philosophy) – Paul Lazarsfeld (sociology)
  39. 39. Cybernetics Norbert Wiener (1947) • Bridge between machines and nature, between "artificial" systems and natural systems • Feedback, by sending back the output as input, helps control the proper functioning of the machine • A control system is realized by a loop of action and feedback • A control system is capable of achieving a "goal", is capable of "purposeful" behavior • Living organisms are control systems
  40. 40. 40 The Turing Test 1950: Alan Turing's "Computing Machinery and Intelligence" (the "Turing Test") Can machines think?
  41. 41. 41 The Turing Test The Turing Test (1950) • Hide a human in a room and a machine in another room and type them questions: if you cannot find out which one is which based on their answers, then the machine is intelligent
  42. 42. 42 The Turing Test The “Turing point”: a computer can be said to be intelligent if its answers are indistinguishable from the answers of a human being ??
  43. 43. 43 Disembodied Intelligence • What Turing and Wiener did – Removed the body from intelligence – Intelligence has to do with manipulating, transmitting, information – Intelligence is independent of the material substrate – They did not interpret machines as humans, but humans as (information-processing) machines – They moved humans closer to machines, not machines closer to humans
  44. 44. 44 Fear of the Technocracy • Wright Mills: "The Power Elite" (1956) • Jacques Ellul: "The Technological Society" (1964) • Herbert Marcuse: "One-dimensional Man" (1964) • John Kenneth Galbraith: "The New Industrial State" (1967) • Lewis Mumford: "The Myth of the Machine" (1967) • Theodore Roszak: "The Making of a Counterculture" (1969) • Charles Reich: "The Greening of America" (1970)
  45. 45. 45 Artificial Intelligence 1954: Demonstration of a machine- translation system by Leon Dostert's team at Georgetown University and Cuthbert Hurd's team at IBM 1956: Dartmouth conference on Artificial Intelligence Artificial Intelligence (1956): the discipline of building machines that are as intelligent as humans
  46. 46. 46 Artificial Intelligence 1956: Allen Newell and Herbert Simon demonstrate the "Logic Theorist“, the first A.I. program, that uses “heuristics” (rules of thumb) and proves 38 of the 52 theorems in Whitehead’s and Russell’s “Principia Mathematica” 1957: “General Problem Solver” (1957): a generalization of the Logic Theorist but now a model of human cognition
  47. 47. 47 Artificial Intelligence 1957: Noam Chomsky's "Syntactic Structures" S stands for Sentence, NP for Noun Phrase, VP for Verb Phrase, Det for Determiner, Aux for Auxiliary (verb), N for Noun, and V for Verb stem
  48. 48. 48 Artificial Intelligence 1957: Frank Rosenblatt's Perceptron, the first artificial neural network
  49. 49. 49 Artificial Intelligence 1959: John McCarthy's "Programs with Common Sense" focuses on knowledge representation 1959: Arthur Samuel's Checkers, the world's first self-learning program 1960: Hilary Putnam's Computational Functionalism 1962: Joseph Engelberger deploys the industrial robot Unimate at General Motors
  50. 50. 50 Artificial Intelligence 1963 Irving John Good speculates about "ultraintelligent machines" (the "singularity") 1964: IBM's "Shoebox" for speech recognition 1965: Ed Feigenbaum's Dendral expert system: domain-specific knowledge
  51. 51. 51 Artificial Intelligence 1966: Ross Quillian's semantic networks
  52. 52. 52 Artificial Intelligence 1966: Joe Weizenbaum's Eliza 1967: Zuse suggests that the universe is a computation 1968: Peter Toma founds Systran to commercialize machine- translation systems
  53. 53. 53 Artificial Intelligence 1969: Marvin Minsky & Samuel Papert's "Perceptrons" kill neural networks 1969: Stanford Research Institute's Shakey the Robot 1972: Bruce Buchanan's MYCIN •a knowledge base •a patient database •a consultation/explanation program •a knowledge acquisition program Knowledge is organised as a series of IF THEN rules
  54. 54. 54 Artificial Intelligence 1972: Terry Winograd's Shrdlu
  55. 55. 55 Artificial Intelligence 1972: Hubert Dreyfus's "What Computers Can't Do" 1974: Marvin Minsky's Frame (see chapter on “Cognition”) 1975: Roger Schank's Script (see chapter on “Cognition”) 1975: John Holland's Genetic Algorithms 1976: Doug Lenat's AM 1979: Cordell Green's system for automatic programming 1979: Drew McDermott's non-monotonic logic 1979: David Marr's theory of vision
  56. 56. 56 Artificial Intelligence 1980: John Searle’s "Chinese Room" 1980: Intellicorp, the first major start-up for Artificial Intelligence 1982: Japan's Fifth Generation Computer Systems project 1980s: Second A.I. bubble 1988: Hans Moravec in "Mind Children" (1988): "robots will eventually succeed us: humans clearly face extinction".
  57. 57. 57 Artificial Intelligence 1980: Kunihiko Fukushima’s “Neocognitron”
  58. 58. 58 Artificial Intelligence 1982: John Hopfield’s simulation of annealing 1983: Geoffrey Hinton's and Terry Sejnowski's Boltzmann machine for unsupervised learning 1985: Judea Pearl's "Bayesian Networks" 1986: Paul Smolensky's Restricted Boltzmann machine 1986: David Rumelhart’s “Parallel Distributed Processing” Rummelhart network Neurons arranged in layers, each neuron linked to neurons of the neighboring layers, but no links within the same layer Requires training with supervision Hopfield networks Multidirectional data flow Total integration between input and output data All neurons are linked between themselves Trained with or without supervision
  59. 59. Artificial Intelligence Deep Learning 1998: Yann LeCun 's second generation Convolutional Neural Networks 2006: Geoffrey Hinton’s Geoffrey Hinton's Deep Belief Networks 2007: Yeshua Bengio's Stacked Auto-Encoders Deep Learning
  60. 60. Artificial Intelligence Deep Learning mimics the workings of the brain: the audiovisual cortex works in multiple hierarchical stages
  61. 61. 61 Artificial Intelligence Genealogy of Intelligent Machines Hydraulic machines Steam engines Cybernetics Neural networks Logic Hilbert Turing Machine Computers Expert Systems
  62. 62. 62 Artificial Intelligence 1997: IBM's "Deep Blue" chess machine beats the world's chess champion, Garry Kasparov 2011: IBM's Watson debuts on a tv show 2014: Vladimir Veselov's and Eugene Demchenko's program Eugene Goostman passes the Turing test
  63. 63. 63 Artificial Intelligence 2014: Li Fei-Fei's computer vision algorithm that can describe photos ("Deep Visual-Semantic Alignments for Generating Image Descriptions", 2014)
  64. 64. 64 Artificial Intelligence 2014: Vladimir Veselov's and Eugene Demchenko's program Eugene Goostman, which simulates a 13-year-old Ukrainian boy, passes the Turing test at the Royal Society in London 2014: Alex Graves, Greg Wayne and Ivo Danihelka publish a paper on "Neural Turing Machines" 2014: Jason Weston, Sumit Chopra and Antoine Bordes publish a paper on "Memory Networks" 2014: Microsoft demonstrates a real-time spoken language translation system
  65. 65. 65 Information-based System Data Base Who is the president of the USA? Where is Rome? OBAMA ITALY
  66. 66. 66 Knowledge-based System Know ledge Base Who will the president of the USA? Where is Atlantis? X Y
  67. 67. 67 Artificial Intelligence Artificial Intelligence A New Class of Applications A New Class of Technologies
  68. 68. 68 Artificial Intelligence A New Class of Applications Expert Tasks Heuristics Uncertainty “Complex” Problem Solving The algorithm does not exist A medical encyclopedia is not equivalent to a physician The algorithm is too complicated Design a cruise ship There is an algorithm but it is “useless” Don’t touch boiling water The algorithm is not possible Italy will win the next world cup
  69. 69. 69 Artificial Intelligence A New Class of Technologies Non-sequential Programming Symbolic Processing Knowledge Engineering Uncertain Reasoning
  70. 70. Common Sense Small minds are concerned with the extraordinary, great minds with the ordinary" (Blaise Pascal)
  71. 71. 71 Common Sense Types of inference Induction Concept formation Probabilistic reasoning Abduction Diagnosis/troubleshooting Scientific theories Analogy Transformation Derivation
  72. 72. 72 Common Sense • Plausible reasoning – Quick, efficient response to problems when an exact solution is not necessary • Non- monotonic Logic – Second thoughts: inferences are made provisionally and can be withdrawn at any time
  73. 73. Common Sense The Frame Problem – Classical logic deducts all that is possible from all that is available – In the real world the amount of information that is available is infinite – It is not possible to represent what does “not” change in the universe as a result of an action ("ramification problem“) – Infinite things change, because one can go into greater and greater detail of description – The number of preconditions to the execution of any action is also infinite ("qualification problem“)
  74. 74. Common Sense Uncertainty “Maybe i will go shopping” “I almost won the game” “This cherry is red” “Bob is an idiot” Probability Probability measures "how often" an event occurs But we interpret probability as “belief” Glenn Shafer’s and Stuart Dempster’s “Theory of Evidence” (1968)
  75. 75. Common Sense Principle of Incompatibility (Pierre Duhem) The certainty that a proposition is true decreases with any increase of its precision The power of a vague assertion rests in its being vague (“I am not tall”) A very precise assertion is almost never certain (“I am 1.71cm tall”)
  76. 76. Common Sense Heuristics • Knowledge that humans tend to share in a natural way: rain is wet, lions are dangerous, most politicians are crooks, carpets get stained… • Rules of thumbs György Polya (1940s): “Heuretics“ - the nature, power and behavior of heuristics: where it comes from, how it becomes so convincing, how it changes
  77. 77. 77 Connectionism A neural network is a set of interconnected neurons (simple processing units) Each neuron receives signals from other neurons and sends an output to other neurons The signals are “amplified” by the “strength” of the connection
  78. 78. Connectionism The strength of the connection changes over time according to a feedback mechanism (output desired minus actual output) The net can be “trained” Output Correction algorithm
  79. 79. Connectionism • Distributed memory • Non-sequential programming • Fault-tolerance • Recognition • Learning
  80. 80. Connectionism Where are we? Biggest neural computer: – Stanford and NVIDIA (2013): 11.2 billion connections (three servers accelerated using 16 GPUs) – Google (2012): 1.7 billion connections (on a 1,000- server network) learn to recognize cats in YouTube videos • Worm’s brain: – 1,000 neurons – But the worm’s brain still outperforms neural nets Human brain: – 100 billion neurons – 200,000 billion connections
  81. 81. Hence… Artificial Intelligence • Knowledge-based approach • Neural-net approach
  82. 82. Meanwhile… 1947: The "transistor" 1949: The first stored-program computers 1951: The first commercial computers 1956: The hard-disk drive 1957: The first serious programming language 1961: The first database management system 1961: The first industrial robot 1963: The first computer graphics program 1964: The first online transaction processing 1965: The first serious mini-computer 1969: The Unix operating system 1969: The Arpanet (Internet) 1970: The first pocket calculators 1970: The first practical optical fiber 1971: The microprocessor 1972: The Global Positioning System (GPS) 1972: Email 1973: The first cellular telephone 1974: The first GUI 1975: The first personal computers 1982: The compact disc 1984: The first personal assistant 1984: The first virtual-reality 1984: Flash memory 1987: JPEG 1990: The first digital camera 1990: The World-Wide Web 1990: The first Internet search engine 1991: The first "browser" 1992: The first text message 1995: The first "wiki" 1996: The first "smartphone" 1997: The first social network 1998: The first MP3 player
  83. 83. 83 The Decline of Knowledge-based Systems • 1. Google it… – Artificial Intelligence was trying to develop “expert systems” capable of finding a solution to every problem in a given domain, just like a human expert in that domain – Overt assumption: Domain knowledge is the key to finding solutions – Hidden assumption: Logical inference is the key to finding the solution
  84. 84. 84 The Decline of Knowledge-based Systems • Google it… – Artificial Intelligence never delivered on the promises of “expert systems”… – …but search engines did: there is at least one webpage somewhere that has the solution to a given problem, and it’s just a matter of finding it – Crwdsourcing did it
  85. 85. 85 The Decline of Knowledge-based Systems • Google it… – Logical inference (intelligence) is irrelevant. – It’s the quantity of information (not the quality of inference) that matters – All we needed is a (digital) library big enough and computers powerful enough to search it – What those computers don’t need is: intelligence
  86. 86. 86 The Decline of Knowledge-based Systems • Google it… – A person can solve any problem as long as she is capable of searching the Web for the solution – No other skills required beyond reading skills – No large, expensive supercomputer required: just a (relatively dumb) smartphone – The Web plus the search engine does what AI wanted to do: it gives an answer to every possible question that a human can answer (in fact, many more than any one person can answer)
  87. 87. 87 The Decline of Knowledge-based Systems • 2. Big Data – Very soon Homo Sapiens will be producing more data every year than in the previous 200,000 years – Big Data shift the disadvantage to the knowledge-based approach: too much knowledge makes it unfeasible 87
  88. 88. 88 The Decline of Knowledge-based Systems • 3. Cheap computing – Computational power per $ increased dramatically – Neural nets and Bayesian nets became feasible (“deep learning”) – A.I. based on statistical analysis – “Best Guess AI” • Translation • Search • Voice recognition
  89. 89. 89 2010s • Machine learning – Statistical method yields a plausible result but it has not learned why – The learned skills cannot be applied to other fields
  90. 90. 90 2010s • Machine learning – Jürgen Schmidhuber (1990-…): active, unsupervised, curious, creative systems that create their own self-generated tasks to improve their understanding of the world • an adaptive predictor or compressor or model of the growing data history as the agent is interacting with its environment • a reinforcement learner (motivated to invent skills leading to interesting or surprising novel patterns that the predictor/compressor can learn)
  91. 91. 91 2010s • The personal assistant – Siri (2011) – GoogleNow (2012) – Tom Mitchell’s “learning personal assistant” at CMU (1994-…) Apple 2011 Stanley Kubrick (1968) “2001: A Space Odyssey”
  92. 92. 92 2010s • Multi-billion dollar investments in artificial intelligence and robotics in the 2010s – Amazon (Kiva, 2012), – Google (Industrial Robotics, Meka, Holomni, Bot & Dolly, DNNresearch, Schaft, Bost, DeepMind, Redwood Robotics, 2013-14), – IBM (Watson project), – Microsoft (Project Adam, 2014), – Apple (Siri, 2011), – Facebook (DeepFace, 2013), – Yahoo (LookFlow, 2013), – Baidu (Deep Learning Institute in Cupertino, 2013; hired Andrew Ng, 2014) 92
  93. 93. 93 2010s • DeepMinds machine learning • Facebook image recognition • • Saffron • Narrative Science • Vicarious • … 93
  94. 94. 94 The Future of Mind Computers Humans Decision Making Data Processing 1960 2014 Algorithmic Programming Artificial Intelligence Singularity?
  95. 95. 95 The Future of Body 95
  96. 96. 96 Robots
  97. 97. 97 Robots • Stats
  98. 98. 98 Robots 1962: Joseph Engelberger deploys the industrial robot Unimate at General Motors 1969: Stanford Research Institute's Shakey the Robot
  99. 99. 99 Robots • Valentino Breitenberg’s “vehicles” (1984) – Vehicle 1: a motor and a sensor – Vehicle 2: two motors and two sensors – Increase little by little the circuitry, and these vehicles seem to acquire not only new skills, but also a personality.
  100. 100. 100 Robots 2000: Cynthia Breazeal's emotional robot, "Kismet" 2003: Hiroshi Ishiguro's Actroid, a young woman Which is the robot?
  101. 101. 101 Robots 2004: Mark Tilden's biomorphic robot Robosapien 2005: Honda's humanoid robot "Asimo" Asimo over the years
  102. 102. Robots Special purpose robots: 2001: NEC PaPeRo (a social robot targeting children) 2005: Toyota's Partner (designed for assistance and elderly care applications) 2007: RobotCub Consortium aggreement, the iCub (for research in embodied cognition) 2008: Aldebaran Robotics' Nao (for research and education) 2010: NASA's Robonaut-2 (for exploration)
  103. 103. 103 Robots 2005: Boston Dynamics' quadruped robot "BigDog“ 2008: Nexi (MIT Media Lab), a mobile-dexterous-social robot 2010: Lola Canamero's Nao, a robot that can show its emotions 2011: Osamu Hasegawa's SOINN-based robot that learns functions it was not programmed to do 2012: Rodney Brooks' hand programmable robot "Baxter"
  104. 104. 104 Robots • Stats
  105. 105. 105 Domestic Robots • Luna (2011) • Jibo (2014) • Pepper (2014)
  106. 106. Robots 2015: Selma Bringsjord’s robot solves the “wise men” riddle
  107. 107. 107 Case study: Japan Takayama Festival of Mechanical Puppets
  108. 108. 108 Case study: Japan • Joruri/ puppet theater (~1650) • “Automated mechanisms, or karakuri, were originally separate from the puppets, used only in stage machinery or in robot dolls that performed between acts. But the machinery eventually found its way into the bodies of the puppets” (Chris Bolton)
  109. 109. 109 Case study: Japan • Oriza Hirata’s robot theater “I, Worker” (2008) “Sayonara” (2010)
  110. 110. 110 Case study: Japan • The uncanny valley – Ernst Jentsch: “On the Psychology of the Uncanny” (1906) – Masahiro Mori: “The Uncanny Valley” (1970)
  111. 111. 111 Case study: Japan • The uncanny valley – Japanese robots tend to be female because they look less threatening
  112. 112. 112 Artificial Intelligence • McKensey on A.I. 112
  113. 113. 113 A Brief History of Bionics Jose Delgado
  114. 114. 114 A Brief History of Bionic Beings 1957: The first electrical implant in an ear (André Djourno and Charles Eyriès) 1961: William House invents the "cochlear implant", an electronic implant that sends signals from the ear directly to the auditory nerve (as opposed to hearing aids that simply amplify the sound in the ear) 1952: Jose Delgado publishes the first paper on implanting electrodes into human brains: "Permanent Implantation of Multi-lead Electrodes in the Brain" 1965 : Jose Delgado controls a bull via a remote device, injecting fear at will into the beast's brain 1969: Jose Delgado’s book "Physical Control of the Mind - Toward a Psychocivilized Society" 1969: Jose Delgado implants devices in the brain of a monkey and then sends signals in response to the brain's activity, thus creating the first bidirectional brain-machine-brain interface.
  115. 115. 115 A Brief History of Bionics 1997: Remotely controlled cockroaches at Univ of Tokyo 1998: Philip Kennedy develops a brain implant that can capture the "will" of a paralyzed man to move an arm (output neuroprosthetics: getting data out of the brain into a machine)
  116. 116. 116 A Brief History of Bionics 2000: William Dobelle develops an implanted vision system that allows blind people to see outlines of the scene. His patients Jens Naumann and Cheri Robertson become "bionic" celebrities. 2002: John Chapin debuts the "roborats", rats whose brains are fed electrical signals via a remote computer to guide their movements
  117. 117. 117 A Brief History of Bionics 2002: Miguel Nicolelis makes a monkey's brain control a robot's arm via an implanted microchip 2005: Cathy Hutchinson, a paralyzed woman, receives a brain implant from John Donoghue's team that allows her to operate a robotic arm (output neuroprosthetics) 2004: Theodore Berger demonstrates a hippocampal prosthesis that can provide the long-term-memory function lost by a damaged hippocampus
  118. 118. 118 A Brief History of Bionics 2004: Color-blind artist Neil Harbisson becomes the first person in the world to have an antenna implanted in his skull, a device that turns color into sound
  119. 119. A Brief History of Bionics The age of two-way neural transmission… 2006: The Defense Advanced Research Projects Agency (Darpa) asks scientists to submit "innovative proposals to develop technology to create insect-cyborgs 2011: Matti Mintz replace a rat’s cerebellum with a computerized cerebellum 2013: Miguel Nicolelis makes two rats communicate by capturing the "thoughts" of one rat's brain and sending them to the other rat's brain over the Internet
  120. 120. 120 A Brief History of Bionics The age of two-way neural transmission… 2013: Rajesh Rao and Andrea Stocco devise a way to send a brain signal from Rao's brain to Stocco's hand over the Internet, i.e. Rao makes Stocco's hand move, the first time that a human controls the body part of another human 2014: An amputee, Dennis Aabo, receives an artificial hand from Silvestro Micera's team capable of sending electrical signals to the nervous system so as to create the touch sensation
  121. 121. 121 A Brief History of Bionics 2015: Nicolelis connects brains of monkeys so that they collaborate to perform a task
  122. 122. 122 A Brief History of Bionics Stimulating the vagus nerve
  123. 123. 123 A Brief History of Bionics Neuro-engineering? ( ( The future of the human brain? neural implants that offer you pleasant experiences for free in exchange for sending neural impulses to your brain that will make you desire their products
  124. 124. A Brief History of Bionics • Genetic engineering of mental and cognitive skills? – 1999: Joseph Tsien adds NR2B genes to mice to improve their ability in learning and memory
  125. 125. 125 A Brief History of Bionics Microchip implants 1998: Kevin Warwick at the University of Reading implants an RFID tag above his left elbow 2004: VeriChip starts selling an RFID chip implant for humans 2006: Amal Graafstra has a microchip in each hand, one for storing data (that can be uploaded and downloaded from/to a smartphone) and and one for a code that unlocks his front door and logd into his computer
  126. 126. 126 A Brief History of Bionics Microchip implants 2012: Amal Graafstra implants chips on attendees of the Toorcamp for $50 each 2013: Rich Lee implants headphones into his ears 2014: Ada Poon at Stanford invents a safe way to transfer energy to chip implants ("electroceutical devices")
  127. 127. 127127 Biotech Genetics 1944: Oswald Avery discovers that genes are made of DNA 1953: Francis Crick and James Watson discover the double helix of the DNA 1961: Jacob and Monod discover gene regulation 1961: Jacob and Brenner discover messenger RNA 1961: Marshall Nirenberg cracks the genetic code (translation of four-letter genetic code into twenty-letter language of proteins)
  128. 128. 128128 Biotech Genetics 1973: Stanley Cohen and Herbert Boyer create the first recombinant DNA organism 1976: Genentech, the first major biotech company 1977: Frederick Sanger publishes the first full DNA genome of a living being 1990: William French Anderson performs the first procedure of gene therapy 1992: Calgene creates the "Flavr Savr" tomato, the first genetically- engineered food to be sold in stores
  129. 129. 129 Biotech 1997: Ian Wilmut clones the first mammal, the sheep Dolly 2003: The Human Genome Project is completed 2006: Personal genomics (23andMe, Syapse, Genophen) 2010: Craig Venter and Hamilton Smith reprogram a bacterium's DNA 2010: Cheap printers for living beings (OpenPCR, Cambrian Genomics) 2012: Markus Covert simulates an entire living organism in software (Mycoplasma Genitalium) 2012: Crisp/Cas9 technique for genome editing
  130. 130. Biotech The price of DNA sequencing your genome has dropped 99% since 2003 ($3,000 in 2013) 130 Moore's law vs Cost per genome
  131. 131. Biotech 131
  132. 132. Biohacking • “Biology is technology” (Rob Carlson) • A community of worldwide hobbyists • Public-domain databases of genetic parts • MIT Registry of Standard Biological Parts (2003) • iGEM Jamboree and BioBricks (2004) • BioCurious (2010) 132
  133. 133. Biohacking • Biocurious (Sunnyvale) - -20C Freezer – PCR Machines – qPCR – Balance – Autoclave – Micropipettes, single and multi-channel – Fluorescent Microscope – Microcentrifuges – Protein Purification System – Vortexers – Ultrasonic Bath – CO2 Incubator 133
  134. 134. Biohacking • iGEM = International Genetically Engineered Machine • “Open source” biotech • Global grassroots synthetic-biology revolution • Student bioengineers from all over the world create new life forms and race them every year at the iGEM Jamboree in Boston (since 2004) • 2014: 2,500 competitors from 32 countries • Repository of 20,000 biological parts (biobricks) • They create mostly microbes (e.g., organisms detecting and eliminating water pollutants) 134 Drew Endy (Stanford), iGEM and BioBricks Foundation
  135. 135. Biohacking • PCR printers (identify a piece of DNA and make copies of it) • OpenPCR (cheap Polymerase Chain Reaction printer) • Cambrian Genomics: a laser printer for living beings 135
  136. 136. Biohacking • Autodesk’s Project Cyborg: design tools for biohackers (quote: “Project Cyborg is a cloud- based meta-platform of design tools for programming matter across domains and scales”) 136
  137. 137. 137 The Future of Body Machines Humans Mind Uploading Flesh and Bones 2014 Singularity? Bio re-engineering Neural Implants
  138. 138. 138 The Future of Body • Meditation: – The longest living bodies on the planet have no brain: bacteria and trees.
  139. 139. 139 The Singularity? 139 Ray Kurzweil at the Singularity University Wells Cathedral Clock of the 14th century, a machine that can do something that no human can do: keeping time Arakawa’s 1966 solution to the weather forecast, the "mission impossible" of the early computers
  140. 140. 140 The Singularity? Ray Kurzweil’s predictions • Infinite life extension: “Medical technology will be more than a thousand times more advanced than it is today… every new year of research guaranteeing at least one more year of life expectancy”* (2022) • Precise computer simulations of all regions of the human brain (2027) • Small computers will have the same processing power as human brains (2029) • 2030s: Mind uploading - humans become software-based • 2045: The Singularity 140* recently postponed to 2040
  141. 141. 141 The Singularity? 2014: Deep Knowledge Ventures (Hong Kong) appoints an algorithm to its board of directors 141
  142. 142. 142 The Singularity? • The Apocalypse has happened many times – Book of Revelation (1st c AD) – … – Year 1,000 – … – Nostradamus (16th century) – … – Pierre Teilhard de Chardin’s Omega Point (1950) – Dorothy Martin/Marion Keech’s planet Clarion (1954) – Nuclear holocaust (1950s-80s) – Heinz von Foerster (1960): "Doomsday: Friday, November 13, AD 2026," – Majestic 12 conspiracy theory (1980s) – Year 2,000 – Harold Camping’s Biblical calculations (2011) – End of the Mayan calendar (2012) 142 Albrecht Dürer: The Four Horsemen of the Apocalypse (1498)
  143. 143. 143 The Singularity? The five dogmas of the Singularity movement 1. Artificial Intelligence systems are producing mindboggling results 2. Progress is accelerating like never before 3. Technology is creating super-human intelligence 4. Humans benefit from smarter machines 5. Machines that pass the Turing Test are at least as intelligent as humans 143
  144. 144. 144 The Singularity? Five arguments against the Singularity 1. Reality Check 2. Accelerating Progress? 3. Non-human Intelligence 4. Human Intelligence 5. A Critique of the Turing Test 144
  145. 145. 145 Reality Check Why the Singularity is not Coming any Time Soon & other Meditations on the Post-Human Condition and the Future of Intelligence
  146. 146. 146 Reality Check • The curse of Moore’s law – The motivation to come up with creative ideas in A.I. was due to slow, big and expensive machines. – Brute force (100s of supercomputers running in parallel) can find solutions using fairly dumb techniques – Actually, you can find the answer to most questions by simply using a search engine: no need to think, no need for intelligence
  147. 147. 147 Reality Check • Recognizing a cat is something that any mouse can do (it took 16,000 computers working in parallel) • It took 1.2 million human-tagged images for Deep Learning to lower the error rate in image recognition • Voice recognition and handwriting recognition still fail most of the time, especially in everyday interactions
  148. 148. 148 Reality Check • Examples of searches in 2015
  149. 149. 149 Reality Check • IBM's Watson does not understand the question (it is fed in digital format) • IBM’s "Deep Blue" beat a chess master but was given unfair advantages • “What Curiosity (robot) has done in 200 days a human field researcher could do in an easy afternoon" (NASA planetary scientist Chris McKay, 2013)
  150. 150. 150 Reality Check • Machine translation in 2013 (random sentences from my website translated by Google): – "Graham Nash the content of which led nasal harmony“ – "On that album historian who gave the blues revival“ – "Started with a pompous hype on wave of hippie phenomenon"
  151. 151. 151 Reality Check • A remote-controlled toy is NOT a step toward superhuman intelligence • Human-looking automata that mimic human behavior have been built since ancient times • A human being is NOT a toy (yet)
  152. 152. 152 Reality Check • The brain of the roundworm (a few hundred neurons connected by a few thousand synapses) is still smarter than the smartest neural network ever built.
  153. 153. 153 Reality Check • An easy science – Artificial Intelligence is not subjected to the same scrutiny as other sciences – Its success stories are largely unproven
  154. 154. 154 Reality Check • 60 years later it is not machines that learned to understand human language but humans who got used to speak like machines in order to be understood by automated customer support (and mostly not even speak it but simply press keys)
  155. 155. 155 Reality Check • What “automation” really means… – The jobs that have been automated are repetitive and trivial. – And in most cases the automation of those jobs has required the user/customer to accept a lower (not higher) quality of service. – The more automation around you, the more you (you) are forced to behave like a machine to interact with machines
  156. 156. 156 Reality Check • Intelligent Behavior from Structured Environments
  157. 157. 157 Reality Check • Structuring the Environment – We structure the chaos of nature because it makes it easier to survive and thrive in it – The more we structure the environment, the easier for extremely dumb people and machines to survive and thrive in it. – It is easy to build a machine that has to operate in a highly structured environment – What really "does it" is not the machine: it's the structured environment
  158. 158. 158 Reality Check • Semantics – It is not intelligent to talk about intelligent machines: whatever they do is not what we do, and, therefore, is neither "intelligent" nor "stupid" (attributes invented to define human behavior) – We apply to machines many words invented for humans simply because we don't have a vocabulary for the states of machines
  159. 159. 159 Reality Check • Semantics – Memory is reconstructive – Data storage is not “memory” – Exponentially increasing data storage does not mean better memory – What is “computer speed”? – Who is faster at picking a cherry from a tree, the fastest computer in the world or you?
  160. 160. 160 Reality Check • Where A.I. is truly successful… – Most machine intelligence is being employed to couple real-time customization and machine learning in order to understand who you are and tailor situations in real time that will prompt you to buy some products (custom advertising) – "The best minds of my generation are thinking about how to make people click ads" (former Facebook research scientist Jeff Hammerbacher in 2012) – So far A.I. has not created better doctors or engineers, but better traveling salesmen
  161. 161. 161 Artificial General Intelligence • Task-specific vs General-purpose Intelligence • Originally, A.I. was looking for general- purpose intelligence • Today’s A.I. is looking for task-specific intelligence (recognizing a cat, driving a car)
  162. 162. 162 Artificial General Intelligence • How to simulate an average human (not just one human task) - the “logic theorist” solution (1960s): create a system that can perform reasoning on knowledge and infer the correct behavior for any situation • How to simulate an average human (not just one human task) - the brute force solution (2000s): create one specific program/robot for each of the millions of possible situations, and then millions of their variants
  163. 163. 163 Artificial General Intelligence • The Multiplication of Appliances and Artificial Intelligence by Enumeration – We have machines that dispense money (ATMs), machines that wash clothes (washing machines), machines that control the temperature of a room (thermostats), and machines that control the speed of a car (cruise controls). – We can build machines for all the other tasks and then collectively call them “equal” to humans
  164. 164. 164 Artificial General Intelligence • The enumeration problem: which human functions qualify as "intelligent"? – There are very human functions that people don't normally associate with "intelligence". They just happen to be things that human bodies do. – Do we really want machines that fall asleep or urinate? – We swing arms when we walk, but we don't consider "swinging arms while walking" a necessary feature of intelligent beings.
  165. 165. 165 Tips for better A.I. 1. IBM's Watson of 2013 consumes 85,000 Watts compared with the human brain's 20 Watts. 2. The brain is an analog device, not digital 3. What we need: a machine that has only a limited knowledge of all the chess games ever played and is allowed to run only so many logical steps before making a move and can still consistently beat the world champion of chess. 4. Memory is not storage
  166. 166. 166 Tips for better A.I. • What conditions may foster a breakthrough: it is not the abundance of a resource (such as computing power or information) that triggers a major paradigm shift but the scarcity of a resource.
  167. 167. 167167 Accelerating progress? • One century ago, within a relatively short period of time, the world adopted: – the car, – the airplane, – the telephone, – the radio – the record – cinema • while at the same time the visual arts went through – Impressionism, – Cubism – Expressionism
  168. 168. 168168 Accelerating progress? • while at the same time science came up with – Quantum Mechanics – Relativity • while at the same time the office was revolutionized by – cash registers, – adding machines, – typewriters • while at the same time the home was revolutionized by – dishwasher, – refrigerator, – air conditioning
  169. 169. 169169 Accelerating progress? • while at the same time cities adopted high-rise buildings
  170. 170. 170170 Accelerating progress? • There were only 5 radio stations in 1921 but already 525 in 1923 • The USA produced 11,200 cars in 1903, but already 1.5 million in 1916 • By 1917 a whopping 40% of households had a telephone in the USA up from 5% in 1900. • The Wright brothers flew the first plane in 1903: during World War I (1915-18) more than 200,000 planes were built
  171. 171. 171171 Accelerating progress? • On the other hand today: – 44 years after the Moon landing we still haven't sent a human being to any planet – The only supersonic plane (the Concorde) has been retired – We still drive cars, fly on planes, talk in phones, use the same kitchen appliances
  172. 172. 172172 Accelerating progress? • We chronically underestimate progress in previous centuries because most of us are ignorant about those eras.
  173. 173. 173 A Comparative History of Accelerating Progress • On April 3, 1988 the Los Angeles Times Magazine ran a piece titled "L.A. 2013“ – two robots per family (including cooking and washing) – Intelligent kitchen appliances widespread – self-driving cars widespread
  174. 174. 174 A Comparative History of Accelerating Progress • Today there is a lot of change • But change is not necessarily progress • It is mostly fashion created by marketing and/or planned obsolescence • Progress for whom?
  175. 175. 175 A Comparative History of Accelerating Progress • Taking a step forward is easy… just make sure what you are stepping into
  176. 176. 176 What would Turing say today? What took you guys so long???
  177. 177. 177 What would Turing say today? • Why did it take you so long? – The Hubble telescope transmits 0.1 terabytes of data a week, about one million times more data than the Palomar telescope of 1936 – In 1940 the highest point ever reached by an aviator was 10 kms. In 1969 Neil Armstrong traveled 380 million kms up in the sky, i.e. 38 million times higher. – In 60 years the speed of computers has increased “only” ten thousand times
  178. 178. 178 What would Turing say today? • Hardware: other than miniaturization, what has really changed? – It still runs on electricity – It still uses binary logic – It is still a Turing machine (e.g., wildly different in nature and structure from a human brain)
  179. 179. 179 What would Turing say today? • Software: other than having 12 million programmers work on thousands of programs (instead of the six who programmed the ENIAC), what has really changed? – It is still written in an artificial language that is difficult to understand – It is still full of bugs – It still changes all the time – It is still sequential processing (e.g., wildly different in nature and structure from a human brain)
  180. 180. 180 What would Turing say today? And I’m supposed to be impressed?
  181. 181. 181 Non-human Intelligence • Super-human intelligence has been around for a long time: many animals have powers we don't have
  182. 182. 182 Non-human Intelligence • Bats can avoid objects in absolute darkness at impressive speeds • Migratory animals can navigate vast territories • Birds are equipped with a sixth sense for the Earth's magnetic field • Some animals have the ability to camouflage • The best color vision is in birds, fish and insects • Many animals have night vision • Animals can see, sniff and hear things that we cannot
  183. 183. 183 Non-human Intelligence • And don't underestimate the brain of an insect either: how many people can fly and land upside down on a ceiling?
  184. 184. 184 Non-human Intelligence • We already built machines that can do things that are impossible for humans: – Telescopes and microscopes can see things that humans cannot see – We cannot do what light bulbs do – We cannot touch the groove of a rotating vinyl record and produce the sound of an entire philharmonic orchestra
  185. 185. 185 Super-human Machine Intelligence • The medieval clock could already do something that no human can possibly do: keeping time • That’s why we have to ask “What time is it?”
  186. 186. 186 Non-human Intelligence • What is the difference between non- human intelligence (which is already here and has always existed) and super-human intelligence?
  187. 187. 187 Super-human intelligence • Possible: Colin McGinn’s cognitive closure (there are things we will never understand) • Impossible: David Deutsch’s endless explanation (we are as intelligent as it gets)
  188. 188. 188 Dangers of machine intelligence • Who's Responsible for a Machine's Action? • We believe machines more than we believe humans • Should there be speed limits for machines? • We are criminalizing Common Sense • You Are a Budget • The dangers of clouding - Wikipedia as a force for evil
  189. 189. 189 Dangers of machine intelligence • The biggest danger of all: decelerating human intelligence
  190. 190. 190 The Turing Point • The Turing Test was asking “when can machines be said to be as intelligent as humans?” • This “Turing point” can be achieved by 1. Making machines smarter, or 2. Making humans dumber HOMO MACHINE IQ HOMO MACHINE IQ 1. 2.
  191. 191. 191 What can machines do now that they could not do 50 years ago? • They are faster, cheaper, can store larger amounts of information and can use telecommunication lines
  192. 192. 192 What can humans do now that they could not do 50 years ago? • Use the new machines • On the other hand, they are not capable of doing a lot of things that they were capable of doing 50 years ago from arithmetic to finding a place not to mention attention span and social skills (and some of these skills may be vital for survival) • Survival skills are higher in low-tech societies (this has been true for a while) • General knowledge (history, geography, math) is higher in low-tech societies (coming soon)
  193. 193. 193 The Post-Turing Thesis • If machines are not getting much smarter while humans are getting dumber… • … then eventually we will have machines that are smarter than humans • The Turing Point (the Singularity?) is coming HOMO MACHINE IQ
  194. 194. 194 A Tool is not a Skill • In a sense, technology is about giving people the tools to become dumber and still continue to perform • People make tools that make people obsolete, redundant and dumb
  195. 195. 195 Decelerating Human Intelligence • The success of many high-tech projects depends not on making smarter technology but on making dumber users • Users must change behavior in order to make a new device or application appear more useful than it is.
  196. 196. 196 Turning People into Machines • “They” increasingly expect us to behave like machines in order to interact efficiently with machines: we have to speak a “machine language” to phone customer support, automatic teller machines, gas pumps, etc. • In most phone and web transactions the first question you are asked is a number (account #, frequent flyer#…) and you are talking to a machine • Rules and regulations (driving a car, eating at restaurants, crossing a street) increasingly turn us into machines that must follow simple sequential steps in order to get what we need
  197. 197. 197 Turning People into Machines • Rules to hike in the *wilderness* (there is even a rule for peeing)
  198. 198. 198 Decelerating Human Intelligence • Is it possible that humans have moved a lot closer towards machines than machines have moved towards humans?
  199. 199. 199 The Silicon Valley Paradigm • “They” increasingly expect us to study lengthy manuals and to guess how a machine works rather than design machines that do what we want the way we like it • A study by the Technical University of Eindhoven found that half of the returned electronic devices are not malfunctioning: the consumer just couldn't figure out how to use them
  200. 200. 200 The Singularity • The Turing Test may become a self- fulfilling prophecy: as we (claim to) build “smarter” machines, we may make dumber people. • Eventually there will be an army of greater- than-human intelligence
  201. 201. 201 The Singularity
  202. 202. 202 The Future is not You • The combination of smartphones and websites offers a glimpse of a day when one will not need to know anything because it will be possible to find everything in a second anywhere at any time by using just one omnipowerful tool. • An individual will only need to be good at operating that one tool. That tool will be able to access an almost infinite library of knowledge and… intelligence.
  203. 203. 203 The Difference: You vs It • Human minds are better than machines at – Improvisation – Imagination – (in a word: "creative improvisation") • Human minds can manage dangerous and unpredictable situations • Human minds can be “irrational”
  204. 204. 204 The Difference: You vs It • Modern society organizes our lives to remove danger and unpredictability. • Modern society empowers us with tools that eliminate the need for improvisation and imagination • Modern society dislikes (and sometimes outlaws) irrationality
  205. 205. 205 The Difference: You vs It • We build – Redundancy – Backups – Distributed systems • to make sure that machines can do their job 24/7 in any conditions. • We do not build anything to make sure that minds can still do their job of creative improvisation
  206. 206. 206 A Critique of the Turing Test (while we’re still intelligent)
  207. 207. 207 The Turing Test The “Turing point”: a computer can be said to be intelligent if its answers are indistinguishable from the answers of a human being ??
  208. 208. 208 The Turing Test The fundamental critique to the Turing Test • The computer (a Turing machine) cannot (qualitatively) do what the human brain does because the brain – does parallel processing rather than sequential processing – uses pattern matching rather than binary logic – is a connectionist network rather than a Turing machine
  209. 209. 209 The Turing Test The Turing Test • John Searle’s Chinese room (1980) – Whatever a computer is computing, the computer does not "know" that it is computing it – A computer does not know what it is doing, therefore “that” is not what it is doing – Objection: The room + the machine “knows”
  210. 210. 210 The Turing Test The Turing Test • Hubert Dreyfus (1972): – Experience vs knowledge – Meaning is contextual – Novice to expert – Minds do not use a theory about the everyday world – Know-how vs know that • Terry Winograd – Intelligent systems act, don't think. – People are “thrown” in the real world
  211. 211. 211 The Turing Test The Turing Test • Rodney Brooks (1986) – Situated reasoning – Intelligence cannot be separated from the body. – Intelligence is not only a process of the brain, it is embodied in the physical world – Cognition is grounded in the physical interactions with the world – There is no need for a central representation of the world – Objection: Brooks’ robots can’t do math
  212. 212. 212 The Turing Test The Turing Test • John Randolph Lucas (1961) & Roger Penrose – Goedel’s limit: Every formal system (>Arithmetic) contains a statement that cannot be proved – Some logical operations are not computable, nonetheless the human mind can treat them (at least to prove that they are not computable) – The human mind is superior to a computing machine
  213. 213. 213 The Turing Test The Turing Test • John Randolph Lucas (1961) & Roger Penrose – Objection: a computer can observe the failure of “another” computer’s formal system – Goedel’s theorem is about the limitation of the human mind: a machine that escapes Goedel’s theorem can exist and can be discovered by humans, but not built by humans
  214. 214. 214 The Turing Test • What is measured: intelligence, cognition, brain, mind, or consciousness? • What is measured: one machine, ..., all machines? • What is intelligence? What is a brain? What is a mind? What is life? • Who is the observer? Who is the judge? • What is the instrument (instrument = observer)? • What if a human fails the Turing test? The Turing Test
  215. 215. 215 The Turing Test • Someone has hidden a person in a room and a computer in the other room. • We are allowed to ask any questions. • The person and the computer reply in their own way. • If we cannot tell which one is the person and which one is the computer, then the computer has become intelligent.
  216. 216. 216 Who is Testing • Someone has to determine whether the answers to her questions come from a human or a machine • Who is the judge who decides if the Turing Test succeeds? What instrument does this test use? • A human? A machine? • How “intelligent” is the judge?
  217. 217. 217 Who is Testing • Can a mentally retarded person judge the test? • Can somebody under the influence of drugs perform it? • …a priest, an attorney, an Australian aborigine, a farmer, a librarian, a physician, an economist...? • …the most intelligent human? • The result of the test can vary wildly depending on who is the judge
  218. 218. 218 Who are we Testing? • If a machine fails the test (i.e. the judge thinks the machine is a machine), then Turing concludes that the machine is not intelligent • What does Turing conclude if a human fails the test (if the judge thinks that the human is a machine)? That humans are not intelligent?
  219. 219. 219 What are we Testing? • The Turing Test is about behavior • The Turing test measures how good a machine is at answering questions, nothing more. • “Can a machine be built that will fool a human being into believing it is another human being?” is not identical to “Can a machine think?” • If we answer “yes” to the first question, we don’t necessarily answer “yes” to the second.
  220. 220. 220 The Turing Point • The Turing Test asks when can we say that a machine has become as intelligent as humans. • The Turing Test is about humans as much as it is about the machine because it can be equivalently be formulated as: when can we say that humans have become less intelligent than a machine? • The Turing Test cannot be abstracted from a sociological context. Whenever one separates sociology and technology, one misses the point.
  221. 221. 221 The ultimate Turing Test • Build a machine that reproduces my brain, neuron by neuron, synapses by synapses • Will that machine behave exactly like me? • If yes, is that machine “me”? The Turing Test
  222. 222. 222 See A Timeline of A.I.
  223. 223. 223 The End (for now) “A man provided with paper, pencil, and rubber (and subject to strict discipline) is in effect a universal machine” (Alan Turing, 1948)