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Taking an ancient path towards artificial intelligence 10-12-16

Arend Hintze, Department of Integrative Biology, Department of Computer Science and Engineering, and BEACON Center for the Study of Evolution in Action at Michigan State University, presents his computational analysis of evolutionary processes at the Michigan State University Bioeconomy Institute on 10-12-16.

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Taking an ancient path towards artificial intelligence 10-12-16

  1. 1. Taking an ancient path toward Artificial Intelligence Arend Hintze Department of Integrative Biology Department of Computer Science and Engineering BEACON Center for studying evolution in action Michigan State University
  2. 2. The idea of computation Whitehead & Russell ~1910 2a = b true true “rule” a = b/2 divide by 2 true true true axiom formal process of proof:
  3. 3. Principia Mathematica axiom true true true true true true true true true Kurt Gödel: I am true, but not provable
  4. 4. Turing and the Enigma “XTYENSODNEADF…“Attack at dawn…” “Need refueling soon” “TEHDIENENEEE” possible mappings:
  5. 5. universal computation every possible mapping all possible mappings: “AAAAAAA” “AAAAAAB” … “ZZZZZZZY” “ZZZZZZZZ” Any: “AAAAAAA” “AAAAAAB” … “ZZZZZZZY” “ZZZZZZZZ” Any: Konrad Zuse
  6. 6. your brain is just a process … find:
  7. 7. the 60s, 70s, and 80s (and maybe the 90s) machine intelligence
  8. 8. AI after 2001…
  9. 9. software engineering how to find: reverse engineering!
  10. 10. expert system vs. artificial intelligence (that I want) • specific goal • well defined boundary cases • single purpose • very vague goal • no boundary case • multi purpose • does change during its lifetime vs
  11. 11. engineering vs. evolution
  12. 12. What to evolve?
  13. 13. What to evolve? grammar L system code ANN genetic programming Markov Model -unpredictable mutational effect range -almost all mutations deleterious -exploding number of weights to optimize -fixed topology -over growth -hard to train -no bound on hidden states -Baum Welch Fix: evolvable code Fix: NEAT Fix: pruning
  14. 14. Probabilistic Logic Gates 1 1 1 1 1 90% 1 10% 0 FUZZY MORE MODULAR
  15. 15. evolvable encoding
  16. 16. does it scale? 282 (302) ~100,000 ~250,000 ~1,000,000 ~71,000,000 ~86,000,000,000
  17. 17. one Markov Gate is a group of neurons ≠= MARKOV GATE
  18. 18. Darwin vs. DARPA Synapse ~97% accuracy 250.000 neurons ~1.000.000.000 transistors dedicated neuromorphic chip Markov Brains ~93%-96% accuracy ~1000 deterministic gates hardware from late 70s ~4500 transistors intel 8088 Dave Knoester Samuel Chapman
  19. 19. neuron vs. Markov Gate neuromorphic chip Markov Brains bio neuron 1:1 1:100 neuron 250.000 gates 1000 transistors 1.000.000.000 4000 n:t 1:4000 1:40 goal 344.000.000.000.000 344.000.000.000
  20. 20. 10,000,000,000 100,000,000,000 1.000,000,000,000 10.000,000,000,000 100.000,000,000,000 2016 2024 2032 2040 2048 ~350.000.000.000 ~350.000.000.000.000 US debt $17,872,947,693,177 US debt $19,688,773,606,117
  21. 21. one question answered: What to evolve? one to go: How to evolve it?
  22. 22. How to evolve? How did it evolve? Evolutionary Biology Cognitive Science Psychology Behavior Biology Evolutionary Neuroscience
  23. 23. How to evolve something? R
  24. 24. How to evolve something?
  25. 25. population n=100 evaluate performance best performance worst performance highest chance to make offspring lowest chance to make offspring next generation mutation
  26. 26. after 2000 generations
  27. 27. after 14.000 generations
  28. 28. finally after 49.000 generations
  29. 29. How to do Biology with this?
  30. 30. an evolutionary experiment predation response, foraging, mating ….
  31. 31. 2d swarming agent and prey agents are log layer includes 12 sensors of 158 and a range of 100 dators). Moreover, each la agent. Specifically, the p that is only capable of s have a dual-layer retina, fics and the other senses single predator active du a predator-sensing retina Regardless of the nu slice, the agents only kn that slice, but not how coarse-grain visual syste starlings [42]. For exam slice has two prey in it for that slice activates. S left has both a predator grey triangle) agent in it, s retina Markov network L R 01 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 1 0 0 Figure 1. An illustration of the predator and prey agents in the model. Light grey triangles are prey agents and the dark grey triangle is a predator agent. on October 4, 2rsif.royalsocietypublishing.orgDownloaded from Randal Olson
  32. 32. normal predator confusable predator R. S. Olson, A. Hintze, F. C. Dyer, D. B. Knoester, and C. Adami, Predator confusion is sufficient to evolve swarming behavior (2013) Journal of the Royal Society Interface 10: 20130305. plus many more …. evolution of swarming as a predation response requires a confusable predator
  33. 33. where to go from here? memory entrainment learning neuro correlates: IIT, phi, Representations AI in computer games complex environmentshuman decision making
  34. 34. Memory, learning, entrainment L R F N A x B x C x D x L R F N A x B x C x D x
  35. 35. Hierarchies and organized groups division of labor dominance hierarchy Evolution of autonomous hierarchy formation and maintenance A. Hintze, M. Miromeni, ALIFE 14: Conference Proceedings, 2014
  36. 36. multi agent environments
  37. 37. psychology rational decision maker evolved decision makervs. A. Hintze, R. Olson, C. Adami, R. Hertwig, Risk sensitivity as an evolutionary adaptation. Scientific reports 2014 P. Kvam, J. Cesario, J. Schossau, H. Eisthen, A. Hintze, Computational evolution of decision-making strategies Cognitive Science Conference Proceedings 2015 A. Hintze, N. Phillips, R. Hertwig, The Janus face of Darwinian competition, Scientific reports 2015 A. Hintze, R. Hertwig, The Evolution of Generosity in the Ultimatum Game , Scientific Reports 2016
  38. 38. value judgement task: Heuristic vs. Optimal Strategy generations performance no strategy heuristic optimal strategy
  39. 39. more virtual environments
  40. 40. AI in computer games
  41. 41. the future?
  42. 42. Thank you! Laura Smale Barbara Lundrigan Fred Dyer Tim Pleskac Ralph Hertwig Kay Holekamp Heather Eisthen Chris Adami
  43. 43. Modular Agent Based Evolution Framework https://github.com/ahnt/MABE

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