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Chapter 2

Chapter 2. Alper Aydemir CVAP, KTH. Origins of Artificial Intelligence. Classical approach: “the brain” is the one and only holy grail of AI 1956 Darthmouth meeting Good old Fashioned AI Expert systems were thought of replacing human intelligence “soon”

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Chapter 2

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  1. Chapter 2 AlperAydemir CVAP, KTH

  2. Origins of Artificial Intelligence • Classical approach: “the brain” is the one and only holy grail of AI • 1956 Darthmouth meeting • Good old Fashioned AI • Expert systems were thought of replacing human intelligence “soon” • Symbol processing, set of rules, when we have enough rules  success!

  3. Origins of Artificial Intelligence • The world state can be described as a configuration of clear, unambiguous set of symbols. • This is how computers work, everything should be as precise as possible: not in nature! • The more complex the model is the more care to maintain it • GOFAI has failed to live up to its promises • Let alone human-like, even insect-like intelligence is not achieved yet

  4. Origins of Artificial Intelligence • Common sense: “surely a hallmark of intelligence” (same as chess?) • Very large set of “rules” is crucial to teach robots common sense • Speech is also another area where GOFAI failed • Google Voice • Asking the right question

  5. Robots with bodies • Brooks: “The world itself is its own best model” • Studying insects • Simply act and react, no need to model the world to its atoms • Decision theoretic planning  super expensive • Truly autonomous robots need to learn the environment on their own • Kuiper’s garbled pixels

  6. Robots with bodies • Interaction with the real world is never clean but messy and ill defined. • Modeling everything is simply impossible • Effect of embodiment idea on AI • Research on animal intelligence • More collaboration with biology, neuroscience

  7. Neuroscience • 80’s: The rise of Artificial Neural Networks • Simulation/abstraction neurons and their connections • Some success in computer vision (classification, pattern recognition) and language acquisition • Connectionist approach • “ I am my connectome”, S. Seung • However this was mostly without embodiment • Recently, taken interest in embodiment, neuroscience gained popularity again • Computational Neuroscience, Neuro-Informatics etc.

  8. Multidisciplinary AI • Computer Science • Linguistics / Computational linguistics • Philosophy • A. Sloman • Biology, robotics, biomechanics • Embodied AI called robotics, biomimetics, adaptive locomotion et cetera. • Various conferences sprung up

  9. Biorobotics • Build robots which mimics certain organisms (typically simple and non-human) • Example: Sahabot mimicking Tunisian desert ants • Snapshot model by Sussex Univ. • Short-range navigation based on horizon appearance • Long-range navigation based on light polarization • Empirically proven model

  10. Biorobotics • No need for a detailed map, course, completely imprecise navigation and localization • SLAM! • Snakebots by Hirose Lab. – Univ. of Tokyo • AukeIjspeert’s salamander – EPFL • ...

  11. Developmental Robotics • Brooks falsely claimed “we have reached insect-like intelligence” and moved to a “sexier” topic: how does human learning work? • Humanoid robotics became popular in Japan • However mostly concentrated in mechanics • With Brook’s “Cog” project a new interest in human-like intelligence flamed up • HRP humanoid project • Ishiguro’s baby robot, RoboCub

  12. Ubiquitous computing • Moving away from interacting with computers with only mouse/keyboard • “Scatter” sensors everywhere • More recently researchers became interested in not only sensing but changing the world as well • Various ways of interacting with computers, “wearable computers” and so gained popularity

  13. Multi-agent systems • Important insight: complex behavior can emerge from very simple rules in the individual level • Cellular automata: the Game of life, movement of bird flocks • Swarm intelligence, self-organization • Book: “Order out of Chaos” by Prigogine

  14. Self organization • Emergence of a pattern from the local interactions of many individuals • Ant trails • Marked by pheromones • P(followingTrail(X)) ~ trail scent • Shorter paths = more ants prefers  shorthest food source gets consumed first! • Modular robotics, self-configuration and self-assembly • Murata, Tokyo Inst. of Technology

  15. Multi agent systems • Rather than emergence of pattern aimed at solving a particular task • RoboCup: a team of robots playing soccer • Various leagues: small, humanoid, middle • 100.000 spectators in Fukuoka match!

  16. Evolutionary Robotics • Trying to understand and simulate natural evolution • Genetic algorithms for designing electronic circuits • Only “the brain” evolves so far in previous work, though there are some few examples of the body evolving.

  17. Summary • The journey of AI has changed significantly from GOFAI to embodied systems • Embodied intelligence is now the artificial intelligence (or has become) • By building synthetic systems (robots) we can learn a lot about the nature of intelligence • Also they allow testing of concrete ideas rather than just thought experiments • “Put your money where your mouth is!”

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