Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Patterns In The Chaos

13.498 visualizaciones

Publicado el

Humans have a tendency to invent new problems rather than solve old ones. As we build larger, more complex systems, we unearth global challenges around networks, compute resources and data. Have we neglected to see more elegant examples which existed all along?

It is possible for even the most complex systems to be organized and simplified in ways that may not occur to us. In situations where we still search for the right algorithms, by turning to complex natural systems around us we can find the problem was solved long ago. What we think is a new protocol may in fact be one that has been tested and evolving over hundreds or millions of years. One invented for the early internet is incredibly similar to a strategy evolved by desert ants millions of years ago. And this is why it works.

This talk will address these questions with examples of self-organization, decentralization and diversification from emergent phenomena found in nature.

Publicado en: Tecnología
  • Inicia sesión para ver los comentarios

Patterns In The Chaos

  1. 1. Patterns In The Chaos Scala Days 2018 @helenaedelson
  2. 2. @helenaedelson – Paul Cézanne, French Post-Impressionist painter “We live in a rainbow of chaos.” ORION NEBULA
  3. 3. @helenaedelson Trapezium Cluster, Orion Nebula
  4. 4. @helenaedelson NAUTILUS SHELL ORION NEBULA
  5. 5. @helenaedelson Chaos order within a system that exhibits apparent randomness
  6. 6. @helenaedelson Chaotic Systems unpredictable behavior, despite being fundamentally deterministic
  7. 7. @helenaedelson Patterns On the surface the world seems random and chaotic. If we look closer we see an undercurrent of patterns.
  8. 8. @helenaedelson Pattern Formation emergent structures that are conduits for energy
  9. 9. @helenaedelson Self-Organization Theory Emergence Synchronization Amplification Distributed Networks cellular automata Feedback Loops Systems Evolution Swarming local Emergence of global patterns from chaos
  10. 10. @helenaedelson “Any living cell carries with it the experience of a billion years of experimentation. ” - Max Delbrück Protein molecules within a cell (in green) self-organize in response to stress: Data Lineage patterns evolved to solve problems
  11. 11. @helenaedelson – Margaret Wheatley “Three Images” Noetic Science Review (Spring 96) “We live in a world which in constantly exploring what’s possible, finding new combinations – not struggling to survive, but playing, tinkering, to find what’s possible.”
  12. 12. @helenaedelson Self-Organizing when a system all by itself becomes ordered in space and time
  13. 13. @helenaedelson Order from Disorder chaotic patterns resolving in ordered, even self organizing outcomes
  14. 14. @helenaedelson Fluid Roles for Robustness Migrating Birds in the V Formation
  15. 15. @helenaedelson Foraging over 130 million years of evolution-tuned optimization algorithms across hundreds of thousands of species
  16. 16. @helenaedelson Schooling and Synchrony individual feedback machines receive sensory input and instantaneously react
  17. 17. @helenaedelson Swarming thousands of locally-interacting, noisy information processors dealing with noisy signals, collectively making decisions
  18. 18. @helenaedelson Super-Organisms when entities assemble something extraordinary happens, they behave like a super-organism, with a single purpose
  19. 19. @helenaedelson – Walter J. Freeman III “Perception requires the ‘mass action’ of thousands to millions of neurons.”
  20. 20. @helenaedelson The human brain contains roughly 100 billion neurons each neuron connects to thousands of other neurons
  21. 21. @helenaedelson Our Brains Are The Ultimate Collective • Every decision is the outcome of a neural collective computation • Nothing in the brain tells the rest of it to think or remember Neurons fire signals that only collectively create intelligence
  22. 22. @helenaedelson Patterns In Collective Computation This pattern of information accumulation and consensus is seen in neurons, ants and bees, monkey societies, and many other systems. • Neurons go out and semi-independently collect information about the noisy input, like neural crowdsourcing • Then come together and reach a consensus on what the decision should be
  23. 23. @helenaedelson Swarm Intelligence Computational systems inspired by emergent amplification of collective intelligence, through the cooperation of hundreds to millions of homogeneous agents in a system. Applicable anywhere there is collective decision-making, e.g. search optimization, network routing, image analysis, data mining, training neural networks, democratic elections and fluctuating markets.
  24. 24. @helenaedelson Swarm Intelligence • Autonomy: many agents networked together, interacting locally • Decentralization: no leader, supervisor or global coordination • Order: spontaneous self-assembly into emergent patterns • System-level patterns are unpredictable from behavior of its members • Group intelligence and capabilities far exceed the individual complex adaptive systems that behave in unpredictable ways, wholly different than the behavior of its parts
  25. 25. @helenaedelson Decentralization millions of years of hive mind @mcptato
  26. 26. @helenaedelson No One In Charge • Autonomy: many agents networked together, interacting locally • No leader, supervisor or global coordination • No leader election or follower • No single unit in the network knows what’s going on overall • Nothing tracks or knows all events and change convergence between neurons, bees and ants
  27. 27. @helenaedelson Autonomous Agent • Simple instructions and feedback loops • Subject to common laws (gravity, aerodynamics) • Common processing environment and perception systems • Common goals • Influence and limit each other's actions autonomous agents don’t exist in pure chaos, shared principles bind them together
  28. 28. @helenaedelson Network Diffusion and Contagion How something spreads over hundreds to thousands of unique nodes (not clones) • Regular continual interactions and computation • Amplification: through the node to node feedback loop • Eventual synchronization amplification across the noisy collective
  29. 29. @helenaedelson Swarm Algorithms Swarm intelligence algorithms and strategies are distributed, decentralized, adaptive, scalable and incorporate randomness for performance. • Particle Swarm Optimization (PSO) • Ant Colony Optimization (ACO) • Artificial Bee Colony (ABC) • Stochastic Vehicle Routing problem (VRP) • Traveling Salesman Problem (TSP) • Bee Nest-Site Selection Scheme (BNSSS)
  30. 30. @helenaedelson Computational Agent • Limited capabilities and intelligence • Governed by a set of very simple rules • Rules provide criteria to make decisions • Shares information with proximal peers • Communicates often through brief interactions individuals behave like neurons in a human brain
  31. 31. @helenaedelson Computers Embedded In Nature bee swarms operate like neural nets
  32. 32. @helenaedelson – Adrian Dyer, Researcher, Royal Melbourne Institute of Technology “Our computers are electricity-guzzling machines.” The bee, however, “is doing fairly high-level cognitive tasks with a tiny drop of nectar. Their brains are probably processing information in a very clever way.”
  33. 33. @helenaedelson Bees The colony’s collective behavior enables solving complex tasks like: • Maintaining a constant temperature in the hive • Keeping track of changing foraging conditions • Selecting the best possible nest site
  34. 34. @helenaedelson House Hunting Algorithm How does a colony solve the life or death problem of finding a new home? They hold a democratic debate. • Search: highly distributed searching by scouts • Assessment: evaluation of potential sites, based on criteria • Advertise: locally communicate information about a resource • Consensus: each evaluates for quorum threshold • Relocation: transport to the new home colonies and consensus
  35. 35. @helenaedelson Bees Encode Weighted Additive Strategy encoding distance, direction and weighted quality
  36. 36. @helenaedelson – Deborah Gordon, Biologist, Stanford “Ant algorithms have to be simple, distributed and scalable – the very qualities that we need in large engineered distributed systems"
  37. 37. @helenaedelson Chaos Or Pattern? The world's largest ant colony stretches over 2.7 km2 / 670 acres, contains approximately 306 million workers and 1 million queens across 45,000 interconnected nests.
  38. 38. @helenaedelson Hundreds of thousands of travelers speed along densely packed highways, transporting huge loads, without congestion. Congestion Avoidance Optimization Inbound Outbound Outbound Army ants have evolved a three-lane traffic system
  39. 39. @helenaedelson Exchanging Information Ants interact via smell with their antennae, or if it encounters a short- lived patch of pheromone deposited by another. With one quick touch, an ant can identify • A nest-mate - established trust • What task the other has been doing identity, trust and task
  40. 40. @helenaedelson One algorithm we work with was invented in the early stages of the internet because operating costs were high. Its goal was managing data congestion by gauging bandwidth availability. It is incredibly similar to one evolved by desert ants to gauge resource scarcity, many millions of years ago.
  41. 41. @helenaedelson • When an ant forages in the sun it loses water • It gets water back from seeds it eats • Would-be foragers wait at a narrow tunnel entrance to the nest • As returning food-bearing foragers pass, they drop their load to briefly touch antennae with those waiting (the positive feedback loop) Foraging Strategy Harvester ants evolved an algorithm for conserving water in the desert. They have to spend it to get it.
  42. 42. @helenaedelson The rate of interactions drive decisions of individuals. • It doesn't matter which ant it meets • Only the rate at which it meets other ants Foraging Optimization rate of interactions over content Additionally they had to solve searching for resources that are scattered (by wind and flooding), with unpredictable spatial dispersal versus predictably clustered.
  43. 43. @helenaedelson Acks that trigger transmission of the next data packet and indicates available bandwidth. A forager leaves the nest in response to the rate it meets returning foragers with food. TCP Three-Way Handshake congestion avoidance and determining availability Just as the rate of packet transmission increases/decreases with the rate of returned Acks, the rate of outbound foragers increases/decreases with the rate of successfully returning foragers.
  44. 44. @helenaedelson A source sends out a large wave of packets at the beginning of a transmission to gauge bandwidth Foraging harvester ants send out scout foragers to gauge food availability before auto- scaling the rate of outgoing foragers TCP slow start gauging bandwidth & elastic scaling
  45. 45. @helenaedelson Timeout when a data transfer link breaks or is disrupted, and the source stops sending packets When foragers are prevented from returning to the nest for more than 20 minutes foragers stop going out. TCP Timeout system stays stopped unless a positive event occurs
  46. 46. @helenaedelson Smart Swarms collective artificial intelligence of simulation agents
  47. 47. @helenaedelson Collective Artificial Intelligence creating artificial colonies
  48. 48. @helenaedelson what are the rules of engagement?
  49. 49. @helenaedelson - Radhika Nagpal, Professor of Computer Science,  Harvard University Wyss Institute for Biologically Inspired Engineering “The beauty of biological systems is that they are elegantly simple, and yet in large numbers, accomplish the seemingly impossible. At some level, you no longer even see the individuals; you just see the collective as an entity to itself.”
  50. 50. @helenaedelson Distributed Robotics • A single simple robot has many limitations, and can only do a few simple things • Yet, at scale, the smart algorithm overcomes its physical and mathematical limitations
  51. 51. @helenaedelson AI Algorithms At Scale • Schools of autonomous underwater vehicles coordinating with no central leadership to • gather data on ocean currents and ecology • monitor or clean up pollution • Hundreds of robots cooperating for quick disaster response • Millions of self-driving cars on our highways
  52. 52. @helenaedelson Signals In The Noise “We are drowning in information, while starving for wisdom.” – E. O. Wilson
  53. 53. @helenaedelson Algorithms Tuned By Evolution • Flexible roles • Decentralization, No leader • No reporting to one particular unit • Distributed consensus Unus pro omnibus, omnes pro uno • Simple rules and instructions • Local interactions and feedback loops • Self-organizing • Super-coordinators With the right organization, a group can solve cognitive problems with an ability that far exceeds that of its members. Resilience and Reduced complexity:
  54. 54. @helenaedelson Reliability and Resilience Resilience is a measure of a system’s ability to survive and persist within a variable environment.  • Societies like monkeys, swarms or proteins in a cell have evolved strategies to survive shock • Swarm networks respond efficiently to attack and disruption through simple interactions • These networks are easy to repair and can grow or shrink because they evolved to tolerate randomness how to thrive in a random world
  55. 55. @helenaedelson Adaptation and Rapid Exploitation The capacity of collectives to quickly learn, adapt and invent new patterns is much higher than top-down command/control. • In a flood of possibly conflicting neural signals, our brains have to quickly compute what we perceive and decide how respond • If ants or bees encounter a roadblock they quickly experiment with options and rapidly exploit a viable solution (like ensemble forecasting) a single visual neuron is like a single bee or ant scout
  56. 56. @helenaedelson – Buckminster Fuller “You never change things by fighting the existing reality.
 To change something, build a new model that makes the existing model obsolete.” @helenaedelson
  57. 57. @helenaedelson
  58. 58. @helenaedelson Resources • Pattern Discovery over Pattern Recognition: A New Way for Computers to See • The 1000 robot swarm • Swarm intelligence and neural network for data classification • Smart swarms of robots seek better algorithms • Neural Underpinnings of Decision Strategy Selection: A Review and a Theoretical Model • Collective Computation • A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems • The effect of individual variation on the structure and function of interaction networks in harvester ants • The Remarkable Self-Organization of Ants • The ants go marching, and manage to avoid traffic jams, Princeton Weekly Bulletin • The Regulation of Ant Colony Foraging Activity without Spatial Information • A Survey On Bee Colony Algorithms • Artificial Bee Colony Optimization: A New Selection Scheme and Its Performance Andrej Aderhold, Konrad Diwold, Alexander Scheidler, and Martin Middendorf • How and why trees talk to eachother • What Is Spacetime • Chaos Theory, The Butterfly Effect, And The Computer Glitch That Started It All • Scott Camazine, “Self Organization in Biological Systems” • Protein aggregation after heat shock is an organized, reversible cellular response • Chaos, Meaning, and Rabbits: Remembering Walter J. Freeman • Distributed House-Hunting in Ant Colonies Mohsen Ghaffari Cameron Musco Tsvetomira Radeva Nancy Lynch {ghaffari, cnmusco, radeva, lynch}, MIT • Phototactic Supersmarticles • How Nature Solves Problems Through Computation • How Ants Use Quorum Sensing To Estimate The Average Quality Of A Fluctuating Resource