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Artificial general intelligence research project at Keen Software House (3/2015)

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Artificial general intelligence research project at Keen Software House (3/2015)

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Artificial general intelligence research project at Keen Software House (3/2015)

  1. 1. Artificial Intelligence Research at Keen Software House Technical Report
  2. 2. Shortly about AGI • Artificial General Intelligence - Autonomous agent - Able to perceive and change its environment - Able to remember, reason and plan - Adaptable and able to learn - Able to communicate
  3. 3. What to use for AGI? • Classical AI? - Symbolic architectures - Inference machines, expert systems - Planners and solvers, STRIPS • Artificial neural networks? - Spiking Networks - FFN, RNN - DeepNets • Multi agent systems? • All of them!
  4. 4. Suitable tool for experiments • Rapid model prototyping - Integrate existing model - Create (or recreate) new model • Model insight - Rich GUI & Visualization possibilities - Model structure view (oriented graph?) - Runtime view & execution control • Heterogeneous architecture - Connect different models together - Able to use various hardware • Parallel execution - GPU based solution - Cluster solution
  5. 5. Existing tools & inspiration • Graph of connected modules - ROS - Matlab / Simulink - Maya material editor - Nengo (Eliasmith) • Specialized libraries (modules) - Caffe, OpenCV, cuBLAS, cuDNN, - ROS modules
  6. 6. Our solution – Brain Simulator • Model structure - Nodes, tasks, memory blocks, worlds • Model view - Graph view (model structure) - Observers (model data) • simple, numeric, 3D, custom • Experiments & debugging - Model parameters exposed to GUI - Adjustable observers - Simulation control • Parallel computing - CUDA (Intel Phi support in progress) - Multi GPU support
  7. 7. Brain Simulator – screenshots
  8. 8. Brain Simulator – modules • Implemented modules - Feed-forward nets (FFN, RNN, convolution nets, auto associators) - Self-organizing networks (SOM, GNG, K-means …) - Vector symbolic architectures (HRR, BSC) - Hierarchical temporal memory (spatial & temporal poolers) - Spiking networks & STDP - Computer vision (filters, segmentation, tracking, optical flow) - Hopfield network, SVD, SLAM, PID, Differential evolution and many others • Imported modules - Caffe, BLAS, BEPU Physics, Space Engineers, Gameboy emulator • Planned modules - Deep learning & RBMs, Hierarchical Q-Learning
  9. 9. BS Screenshots – SOM
  10. 10. Development methodology • Iterative/agile approach - Early implementation and experiments - Separated experiments with mockup parts - Milestone oriented (global model iterations) • Separated experiments (proofs of concept) - Data representation, memory models, temporal data encoding - Learning strategies, goal inference, action selection - Spatial awareness, visual working memory, navigation - Computer vision • Milestone examples - 6-legged robot agent (integration test) - Breakout/Pong game (reinforcement learning & vision test) - Autonomous agent game (PacMan, Nethack)
  11. 11. Example 1 – walking robot • Physical world emulation - Connected to Space Engineers game - 6-legged robot body - Runtime visual data processing & body control • Learning from mentor - Hardwired movements - Learning body state associated with high level movement commands - Simple vision to action associations - Totally supervised system
  12. 12. Video of 6-legged robot
  13. 13. Example 2 – Pong / Breakout • Pong / Breakout game - From bitmap to buttons - Reinforced learning (reward and punishment) - Image processing towards object tracking - Vector symbolic architecture - Goal states extraction - Action learning & action selection • Existing solutions - Not Q-learning (DeepMind and others before them) - Modular, engineered system - Better insight (faster learning?), sacrificed flexibility
  14. 14. Pong / Breakout model
  15. 15. Visual Processing
  16. 16. Pong / Breakout model
  17. 17. Pong / Breakout BS inspection
  18. 18. Future work • Next milestone – 2D egocentric game - Advanced visual working memory - Navigation & inner spatial representation of environment - Environment variables extraction, hierarchical Q-learning - Multiple goals and motivations, goal chaining - Motoric systems (bipedal balancing) • Future milestones - Same model playing different games - Same model instance playing different games - Motoric systems (command sequences unrolling & execution) • Computing platform improvements - Brain Simulator release (with remote module repository) - HPC solution - Unix systems release
  19. 19. The end • You can invest in AI companies • Every $1 invested today will return 1,000,000 times • Join our team – we are always hiring • AI Programmers / Researchers • SW Engineers / Architects • PR Manager / Evangelist • Follow us: • http://blog.marekrosa.org/ • http:// www.keenswh.com/ Thank you. Questions?

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