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Why Can't A Computer Be More Like A Brain?

Why Can't A Computer Be More Like A Brain?. Outline. Introduction Turning Test HTM A. Theory B. Applications & Limits Conclusion. Introduction. Brain allows: conversation, cat or dog, play catch Robots & Computers: NONE Wrong start despite 50 years of research?

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Why Can't A Computer Be More Like A Brain?

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  1. Why Can't A Computer Be More Like A Brain?

  2. Outline • Introduction • Turning Test • HTM • A. Theory • B. Applications & Limits • Conclusion

  3. Introduction • Brain allows: conversation, cat or dog, play catch • Robots & Computers: NONE • Wrong start despite 50 years of research? • Neglect of human brain in research • Neural network programming techniques

  4. Turning Test • It asked if a computer hidden away, could converse and be indistinguishable from a human • Today, the answer is NO, Behavioral frame? • Understand then replicate • Jeff Hawkins: Palm Computing, Hand Spring, Numenta • HTM (Hierarchical Temporal Memory) theory

  5. HTM: Theory Cerebral Cortex

  6. HTM: Theory • Neocortex & How it works • Motor control, language, music, vision, different jobs • Uniform structure, suggest common algorithm code • General purpose learning machine • 6 sheets, 30 billion neurons • Learning depends on size, senses, experiences

  7. HTM: Theory

  8. HTM: Theory • Different sheets connected by bundles of nerve fibers • A map reveals a hierarchical design • Input directly to regions, feed others • Info also flows down the hierarchy • Low level nodes, simple input, high more complex • HTM similarly built on hierarchy of nodes

  9. HTM: Theory • Memory not stored in a single location • Example: Cat • Ears, fur, eyes: low level nodes • Head, torso: high level nodes • Takes time, but can learn dog with less memory • Reuse knowledge, unlike AI and neural networks

  10. HTM: Theory • Time is the teacher • Patterns that occur together in generally have common cause • Hear sequence of notes, recognize melody • Memory: hierarchical, dynamic, memory systems • Not computer memory or single instance • Train HTM: Sensory input through time

  11. HTM: Theory • Machine learning difference? • Hybrid with a twist • Hierarchy: HHMM (Hierarchical Hidden Markov Models) • Spatial variation problem • Similarity means same conclusion

  12. HTM: Theory • Biological model: Accurate, neuroanatomy and physiology for direction • HTMs work: Can identify dogs in various forms • Bayesian network • Numenta: Three components • 1) Run-time engine: C++ routines, create, train, run • From small laptops to multi-core PC • Runs on Linux, can use Mac

  13. HTM: Theory • Automatically handles the message processing back and forth in nodes • 2) Tools: Python scripting language, train and test • Sufficient, but could modify/enhance: visualization • 3) Plug-in API and associated source code • Create new kinds of nodes • Two kinds: Basic learning (appears anywhere in net) • Interface node (out of the net to sensors input or effectors that output).

  14. HTM: Applications & Limits • What can you do with Numenta? • Car manufacturers: Spatial inference, data from camera/laser • Social networks, machine net, oil exploration • Work best when hierarchical structure in data (e.g.?) • What sensory data to train with? • Present them in time-varying form

  15. HTM: Applications & Limits • Applications that cannot be solved today: • Long memory sequences or specific timing • Example: Spoken Language, music, robotics require precise timing • Limitation because of tools & algorithms, not platform • Takes time to learn, never learned to program • Not humanlike, not brain, not to pass test

  16. Conclusion • Difference between brain and computer • Wrong approach, Turning test, Neocortex • HTM: Hierarchy, nodes, reuse data, learns on its own • Numenta platform: Run-time engine, Tools, Plug-in API • Applications: Spatial inference, networks • Limits: Long sequence, specific timing

  17. Question Time ?

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