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The Limits of AI – Salvation Or Damnation of Mankind © P Prediction is hard, especially if it is about the future Niels Bohr (1957), Nobel Prize winner Some remarkable bloops ● The Internet is like a Supernova and will completely collapse in 1996 Bob Metcalfe (1995), Ethernet LAN inventor ● 640 KB (RAM) should be sufficient for anyone "Urban Legend“ (1981), wrongly attributed to Bill Gates ● There is no reason why anyone would want a computer at home Ken Olson, DEC CEO (1977) ● Future computers could weigh less than 1.5 Ton Popular Mechanics (1949) ● There could be a global market for five computers T.J.Watson, IBM CEO (1943) ● Atomic energy is “moonshine” Lord Rutherford, Nobel Prize winner (11.09.1933, 24h before Leo Szilard published how to do it) ● Everything that could be invented, has been invented US Patent Office Director (1899) ● Heavier than air flying machines are impossible Lord Kelvin (1895) ● Predicting the future is like scratching yourself before it starts to itch Peter Sellers The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 0 / 50 The Limits of (Artificial) Intelligence AI – Salvation or damnation of mankind Ph. Janson 21 July 2020 The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 1 / 50 R. Kurzweil’s Law of Accelerating Returns captured by K. Kelly’s 12 technological trends (Kevin Kelly – The Inevitable (2016)) 1. Becoming – constant change, development, beta-testing 2. Cognifying – AI everywhere 3. Flowing – information fluidity and streaming 4. Screening – pixels instead of paper 5. Accessing – instead of owning => customer lock-in! Kurzweil’s 6. Sharing – through “dot.communismus” cooperation accelerating 7. Filtering – selecting from massive info wealth returns 8. Remixing – “mashups” … [as am doing in this talk] … 9. Interacting – with “Internet-of-Things” devices 10. Tracking – through “Internet-of-Things” devices => privacy threat 11. Questioning – anybody can publish anything => democracy threat 12. Beginning – “innovation on steroïds” The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 2 / 50 Central question of this talk (R. Kurzweil, H. Moravec, E. Fredkin, M.Tegmark: Life 3.0 (2017), Y.N.Harari: Sapiens (2014), D.Christian: A Big History of Everything (2018)) Time Life 1.0 Biology, genetics : Darwin’s evolution = natural selection Life 2.0 Human Intelligence Sociology: cultural evolution = religions, politics Life 3.0 Artificial Intelligence Technology: AI takeover Evolutionary Force: Intelligence? The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 3 / 50 C. Christensen’s Inventor’s Dilemna applied to human intelligence ? Technology ages, is abandoned, replaced, and disappears Roy Amara: Technology is underestimated in the long term Does that hold for biological intelligence? Roy Amara: Technology is overestimated In the short term The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 4 / 50 Agenda ●What is AI ? ●Computability limits => intelligence limits ? ●The Singularity – if AI > HI then AI >> HI ? ●AI Risks ●Metaphysical ruminations – the future of mankind and intelligence ? ●Outlook The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 5 / 50 The Universal Turing Machine No computer (however fast) will ever be able to compute anything that Turing’s Universal Machine could not compute – albeit much slower • NB: RNA Polymerase has the exact same capabilities as a Turing Machine The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 6 / 50 Artificial Intelligence (John McCarthy (1955)) ●L. Tessler’s Theorem: AI is «whatever computers still cannot compute» ●What should computer intelligence mean ?=> Turing Test •●A computer counts as intelligent when it exhibits human cognitive abilities ●Perception, knowledge representation, argumentation, problem solving, learning, etc. => mimicking a human brain The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 7 / 50 Brain simulation research projects ●EU (EPFL, ETHZ, …): Human Brain Project ●DARPA (US): SyNAPSE Project ●IBM: SyNAPSE Chip: 4’000 processors, 63 mW, 256’000 synapses connecting 1 million neurons Target: SyNAPSE Computer 2 dm³ 4KW, 100T synapses connecting 10 billion neurons 3 15 ●NB: Human brain: 1.5 dm , 20W, 10 synapses connecting 100 billion neurons 1016 computations per second =>• Computers are are only a few orders of magnitude away from that ≈> 10-100 years ? The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 8 / 50 Machine Learning (ML): How do computers learn ? https://www.simplilearn.com/machine-learning-tutorial 1. Supervised Learning Learning by examples (as a child under guidance of parents / teachers) (Computers need million more examples than kids!) e.g. Deep Machine Learning = through deep neural networks Questions Learning AI Trained AI Answers 2. Reinforcement Learning Learning through positive feedback (as a child without guidance but with external feedback) Evolutionary Learning = through trial and error (Good for problems with clear and concise goals) Data (Autonomous) Trial Trained AI Goal Learning AI Feedback 3. Unsupervised Learning Learning through autonomous discovery (as a child exploring with neither guidance nor feedback) “Intelligent” learning = through observation and imitation Data Continuously Improving (Goal) Learning AI results The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 9 / 50 Supervised Learning: Deep Machine Learning (Geoff Hinton, Google & U. of Toronto) (Tensor) / Graphics Processing Unit (T/GPU) have the same capabilities as Universal Turing Machines a a a a Information: a b b a Every node x,y computes b b 푦=푧 ●Data wx,y = σ푦=푎 px,y wx−1,y b c c b ●Text c c ●Graphics c d d c Decision <= output node with highest vote ●Pictures d d d e e d ●Videos e e «Training» the many px,y parameters ●Audio e f f e is extremely intensive, expensive f f and training-data biased! g g Level: Inputs 1 2 3 4 Outputs Remarkable results in translation, medecine, etc. The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 10 / 50 Reinforcement learning: GANs (Generative Adversarial Networks) ●Two GPUs are learing from one another by trying to outsmart one another a a a a a a a a a b b a a b b a b b b b b c c b b c c b c c c c c d d c c d d c d d d d d e e d d e e d e e e e e f f e e f f e f f f f g g g g Both AIs are learning through a sort of Darwinian natural selection Learning does however stop in production, contrary to unsupervised – or human – learning The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 11 / 50 Reinforcement Learning example: walking robot The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 12 / 50 AI performance and potential AI already can & increasingly may solve problems that humans cannot solve ●Climate & environmental crises ●Geology, seismics, archeologie, weather ●Materials science ●Transport, logistics, autonomous vehicles … meat without animals & methane emissions ●Advertising, art, video games … construction without steel and concrete ● Language translation ●Energy production ●Finance, economy … planes without fossil fuel ●Health, drug development ●Resource recycling instead of waste disposal ●Administration, audit ●Economy without growth ● ●(Cyber)security, justice Fair financial world ●Medicine & education ●Politics & peace e.g. UN Policy Priority Inference (PPI) AI ●Space colonisation ●etc. The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 13 / 50 Agenda ●What is AI ? ●Computability limits => intelligence limits ? ●The Singularity – if AI > HI then AI >> HI ? ●AI Risks ●Metaphysical ruminations – the future of mankind and intelligence ? ●Outlook The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 14 / 50 Computation has limits: many things are not computable ●All possible programs are countable, just like integers ●All possible math functions are NOT countable, just like real numbers => Set of all functions >>> set of all programs => Only a small minority of all functions are programmable => An infinite majority of phenomena are not computable => Example: a true random number generator •●Quantum physics uncertainty principle puts limits to measurability => computability The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 15 / 50 Intelligence has limits: Self-references make statements undecidable / meaningless Examples: ●This paradoxical drawing by the Belgian cartoonist Ph. Geluck It is forbidden to read this writing ●The paradoxical statement by the Creatan philosopher Epimenides «All Creatans are liars» (or «I am a liar») •●The paradox of Berry / Russel «N is the smallest number than cannot be defined in less than 20 words» (only 14 words!) The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020 16 / 50 Intelligence has limits: Self-references make questions undecidable / meaningless Further examples: ●No program can ever decide, whether another program always terminates (or not) ●A formal system can never prove itself consistent & complete at the same time (e.g. mathematics, logic, any formal (e.g. programming) language, etc.) (K. Gödel’s Theorems, 1933) ●Reproduction is ‘self-referencing’ (D. Hofstadter’s strange loops, 1979) HI Data in Program Data out Data in AI Data out => Resulting question: can AI, given Gödel’s theorems, be consistent and complete ? •●Are genes meant to reproduce phenotypes OR are phenotypes meant to reproduce genes ?? (R. Dawkins, 1976) The Limits of AI – Salvation or Damnation of Mankind © P. Janson 2020
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