The Singularity is Far (Singularity U presentation by Bruce Damer Aug 2010)

1. The Singularity is Far
Computing Nature
Artificial Life, Virtual Worlds & Simulation
Bruce Damer
for the Singularity University
August 3, 2010

2. • I. The Birth of Computing
(and the Von Neumann Bottleneck)
II. The Birth of Visual Computing and Virtual
Worlds (still running through the Von Neumann
Bottleneck)
III. State of the Art in Simulating Nature
(Physics) for Space and Chemistry
IV. Computing Nature (?)
Discussion

3. III. Computing Nature (?)
Can Von Neumann Do It?

4. Conventional vs Natural Computation
Systemic Computer model by Peter J.
Bentley, UCL, Digital Biology Group
Conventional Natural
Deterministic Stochastic
Synchronous Asynchronous
Serial Parallel
Heterostatic Homoestatic
Batch Continuous
Brittle Robust
Fault intolerent Fault tolerant
Human-reliant Autonomous
Limited Open-ended
Centralised Distributed
Precise Approximate
Isolated Embodied
Linear causality Circular causality
Table 1 Features of conventional vs Natural
computation

5. Non-living natural world supports a massive number of
parallel interactions but they are finite, bounded

6. Living natural world supports infinitely repeatable
computations in a massively parallel fashion

7. E-coli, a massively parallel computing universe
David S. Goodsell from The Machinery of Life

8. E-coli, a massively parallel computing universe

9. The complexity of Cytoplasm
A cube 100nm on the side
contains roughly:
- 450 proteins
- 30 ribosomes
- 340 tRNA molecules
- several mRNA molecules
- 30,000 small organic
molecules (amino acids,
nucleotides, sugars, ATP etc)
- 50,000 ions
- remaining 70% is water
- all in continuous interaction

10. Nerve cells: two orders of magnitude more complex
than e-coli

11. So can any kind of (Von Neumann) machine
simulate a whole cell?
Definitely not
Low level
approximations
(overhead)
How about a lot of these?
Perhaps… for the equivalent
of a small volume of aqueous
chemicals, Anton: 1
microsecond per month

12. You need this…. to originate and evolve
complex life (and civilization)
Penny Boston, CONTACT Conference 2009, NASA Ames

13. Question: What is the computing
architecture and cost of simulating a single
neuron at the molecular dynamics level?
Answer: This is beyond the current and
probably subsequent two or three
generations of supercomputers, even those
dedicated to MD simulation.

14. Result: Even excluding the “non
informational/maintenance” parts of the simulation
of a neuron, the high fidelity modeling of a single
neuron is still a substantial computing challenge.
Therefore concepts of a Singularity as derived from
science fiction (Vinge) remain wholly in the realm of
science fiction.

15. So how to map this computer onto this one?
Perhaps…
…toil for a number of decades toward a most
minimal type of “Singularity”,
an Artificial Origin of Life

16. The EvoGrid
An “artificial origin of life” in cyberspace in this Century

17. Origins of Life: Archaean to Cambrian
1997: Digital Burgess - quest for life’s algorithmic
origins in the “Cambrian Explosion”, Biota.org

18. “Soft” Artificial Life Through the Ages:
field named in the 1980s, progress through the
1990s, 2000s
Early exemplar: Karl Sims’ Evolving Virtual
Creatures (1991-4)
Evolving Virtual Creatures
by Karl Sims
Inspired a generation of Soft Alife
developers
in the 1990s-2000s

19. Karl Sims: Evolving Virtual Creatures

20. State of the art of “Soft” Artificial Life
Early exemplar: Karl Sims’ Evolving Virtual
Creatures (1991-4)

21. Enter the EvoGrid
Roll tape!
Roll Tape!

22. EvoGrid The Movie

23. The EvoGrid: conceptually a large central artificial
chemistry simulation operated upon by analysis clients

25. What is the
‘Secret Sauce’
of the EvoGrid?
Answer: Stochastic
hill-climbing
algorithm utilizing
analysis, feedback
and temporal
backtracking

26. EvoGrid Engine

28. Odd Future Applications of EvoGrids
Roll tape!
Roll Tape!

29. EvoGrid Asteroid Eaters

30. But how realistic is this?
Enter “wet” artificial life
(not synthetic biology)

31. Creation of life “from scratch”
(ie: not Craig Venter)

32. The Dawn of “Wet” ALife
Protocells (Monnard, Rasmussen, Bedau et al)

33. Model for a minimal cell

34. Protocells must form on their own through
successive “ratchets” of complexity
Ref Pierre-Alain Monnard, FLinT

35. Fundamental Living Technologies Laboratory
Odense, Denmark
University of Southern Denmark, Odense

36. Protocells from Chemical Soups

37. Origins of Life the “hard way””

38. Your chemical origins of life computing equipment

39. Micelle Simulation
Exploring Life’s Origins Project (Harvard)

40. Micelle Division

41. Radically new chemical life cycles

42. addition of
resources
heating
feeding
information
container replication
division
light (hv) metabolic
conversion

43. FLinT Protocell Life Cycle (draft!)
Roll tape!
Roll Tape!

44. Radically new chemical life cycles

45. But what does a Whole Cell look like?
Roll tape!
Harvard’s Inner Life of a Cell
Roll Tape!

46. Harvard’s Inner Life of a Cell

47. Will we create a digital or in vitro primordial soup
any time soon?

48. Closing Thought

49. Resources and Acknowledgements & Discussion
Project EvoGrid at: http://www.evogrid.org
Project Biota & Podcast at: http://www.biota.org
DigitalSpace 3D simulations and all (open) source code at:
http://www.digitalspace.com
We would also like to thank NASA and many others for funding support for
this work. Other acknowledgements include: Dr. Richard Gordon at the
University of Manitoba, Tom Barbalet, DM3D Studios, Peter Newman, Ryan
Norkus, SMARTLab, Peter Bentley, University College London, FLiNT,
Exploring Life’s Origins Project, Scientific American Frontiers, DigiBarn
Computer Museum, The Shelby White and Leon Levy Archives Center,
Institute for Advanced Study, Princeton, NJ, USA, and S. Gross.