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

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PowerPoint Presentation Artificial Intelligence Research at Keen Software House Technical Report 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 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! 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 Existing tools & inspiration Graph of connected modules ROS Matlab / Simulink Maya material editor Nengo (Eliasmith) Specialized libraries (modules) Caffe, OpenCV, cuBLAS, cuDNN, ROS modules 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 Brain Simulator – screenshots 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 BS Screenshots – SOM 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) 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 Video of 6-legged robot 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 Pong / Breakout model Visual Processing Pong / Breakout model Pong / Breakout BS inspection 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 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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