1. Cellular Automata

2. John von Neumann 1903 – 1957 “a Hungarian-American mathematician and polymath who made major contributions to a vast number of fields, [1] including mathematics (set theory, functional analysis, ergodic theory, geometry, numerical analysis, and many other mathematical fields), physics (quantum mechanics, hydrodynamics, and fluid dynamics), economics (game theory), computer science (linear programming, computer architecture, self- replicating machines, stochastic computing), and statistics. He is generally regarded as one of the greatest mathematicians in modern history. [2] ”Hungarian-American polymath who utions tfunheory, geoical analysis, and al fields), phechanicsamics, and fluid dmming, computer aeplicating macomputing), anis generad as one ofmathematicians i2] ” From wikipedia: Published report on the EDVAC “a computer architecture in which the data and the program are both stored in the computer's memory in the same address space”computer architecture

3. 3 Self reproducing machines

4. 4 “Quines”

5. 5 Self reproducing machines Quotes “Truth is much too complicated to allow anything but approximations.” “ You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics....In the second place, and more important, no one really knows what entropy really is, so in a debate you will always have the advantage.”entropy ■Suggesting to Claude Shannon a name for his new uncertainty function, as quoted in Scientific AmericanVol. 225 No. 3, (1971), p. 180Shannon a name “If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.” “ I am thinking about something much more important than bombs. I am thinking about computers”.-John von Neumann, 1946

6. What is a cellular automaton? light bulbs pictures relation to Turing machines –“non-von-Neumann-style architecture” invented by von Neumann CAs and universal computation

7. What is a cellular automaton? Circular (“toroidal”) boundary conditions

8. time = 1time = 2 Conway talks about creating LifeConway talks about creating Life: http://www.youtube.com/watch?v=FdMzngWchDkMzngWchDk

9. Example: Game of Life (John Conway, 1970s) Neighborhood: 2 dimensional 3x3 neighborhood: Rules: –A dead cell with exactly three live neighbors becomes a live cell (birth). –A live cell with two or three live neighbors stays alive (survival). –In all other cases, a cell dies or remains dead (overcrowding or loneliness).

10. Demo: http://golly.sourceforge.net A “glider”

11. Netlogo models library: Computer science –> Cellular Automata –> Life Go through code See http://www.bitstorm.org/gameoflife/ee http://www.bitstorm.org/gameofli See http://en.wikipedia.org/wiki/Conway%27s_Game_of_ LifeSee http://en.wikipedia.org/wiki/Conway%27s_Game_of_

12. Is there a general way (a “definite procedure”) to predict the behavior of Life from a given initial configuration?

13. Relation to the Halting Problem.

14. Is there a general way (a “definite procedure”) to predict the behavior of Life from a given initial configuration? Relation to the Halting Problem. Answer: No.

15. Is there a general way (a “definite procedure”) to predict the behavior of Life from a given initial configuration? Relation to the Halting Problem. Answer: No. Reason “Life is Universal.” http://rendell- attic.org/gol/tm.htmhttp://rendell- attic.org/gol/tm.htm

16. Elementary cellular automata One-dimensional, two states (black and white)

17. Rule:

18. Elementary cellular automata One-dimensional, two states (black and white)

19. Rule: Elementary cellular automata One-dimensional, two states (black and white)

20. Rule:

21. Elementary cellular automata One-dimensional, two states (black and white) Rule:

22. Elementary cellular automata One-dimensional, two states (black and white) Rule:

24. http://mathworld.wolfram.com/http://mathworld.wolfram.com/ElementaryCellularAutom aton.htmltml See Netlogo modelsSee Netlogo models library –> Computer Science –> Cellular Automata –> CA 1D Elementaryience –> http://www.youtube.com/watch?v=60P7717-XOQ

25. Wolfram’s Four Classes of CA Behavior Class 1: Almost all initial configurations relax after a transient period to the same fixed configuration (e.g., all black). Class 2: Almost all initial configurations relax after a transient period to some fixed point or some temporally periodic cycle of configurations, but which one depends on the initial configuration Class 3: Almost all initial configurations relax after a transient period to chaotic behavior. (The term ``chaotic'‘ here refers to apparently unpredictable space-time behavior.) Class 4: Some initial configurations result in complex localized structures, sometimes long-lived.

26. Rule: ECA 110 is a universal computer (Matthew Cook, 2002) Wolfram’s numbering of ECA: 0 1 1 0 1 1 1 0 = 110 in binary

27. –Transfer of information: moving particles From http://www.stephenwolfram.com/publications/articles/ca/86-caappendix/16/text.html

28. –Transfer of information: moving particles From http://www.stephenwolfram.com/publications/articles/ca/86-caappendix/16/text.html

29. –Transfer of information: moving particles –Integration of information from different spatial locations: particle collisions From http://www.stephenwolfram.com/publications/articles/ca/86-caappendix/16/text.html

30. –Transfer of information: moving particles –Integration of information from different spatial locations: particle collisions From http://www.stephenwolfram.com/publications/articles/ca/86-caappendix/16/text.html

31. Outline of proof 1. Define “cyclic tag systems” and prove they are universal (they can emulate Turing machines). 2. Show ECA 110 can emulate a cyclic tag system.

32. Wolfram’s hypothesis: –All class 4 CAs can support universal computation

33. Outline of Wolfram’s A New Kind of Science (from MM review, Science, 2002) Simple programs can produce complex, and random- looking behavior –Complex and random-looking behavior in nature comes from simple programs. Natural systems can be modeled using cellular-automata- like architectures Cellular automata are a framework for understanding nature Principle of computational equivalence

34. Principle of Computational Equivalence 1. The ability to support universal computation is very common in nature. 2. Universal computation is an upper limit on the sophistication of computations in nature. 3. Computing processes in nature are almost always equivalent in sophistication.