Cognitive Computing by Professor Gordon Pipa

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Professor Dr. Gordon Pipa, University of Osnabrueck, Germany is making this presentation for the Cognitive Systems Institute Speaker Series on May 26, 2016.

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Cognitive Computing by Professor Gordon Pipa

  1. 1. Institute of Cognitive ScienceCognitive Computing Prof. Dr. Gordon Pipa Chair of the Neuroinformatics Department Institute of Cognitive Science Osnabrück University gpipa@uos.de
  2. 2. Institute of Cognitive Science The Science of Our Minds By Philip Erpenbeck - Motion Designer
  3. 3. Institute of Cognitive Science At the Core of Cognitive Computing since 15 Years Technology Interaction Humans Artificial Intelligence Neurolinguistics Cognitive Modelling Neurobiopsychology Cognitive Robotics Neuroinformatics Neuroinspired Computer Vision Computational Linguistics Cognitive Psychology Philosophy of Mind 10 Full professors ~ 600 BSc ~ 200 MSc ~ 45 PhD students
  4. 4. Institute of Cognitive Science Influenza matters Prediction is important Delayed and too few data Why Cognitive Computing? A one year project by a core team of three master’s students
  5. 5. Institute of Cognitive Science Influenza matters Prediction is important Delayed and too few data Why Cognitive Computing? Social media analysis Data science methods Watson as medical expert
  6. 6. Institute of Cognitive Science Influenza matters Prediction is important Delayed and too few data Why Cognitive Computing? Social media analysis Data science methods Watson as medical expert Fully informed user Better prediction Cognitive Computing
  7. 7. Institute of Cognitive Science Influenza matters Prediction is important Delayed and too few data Why Cognitive Computing? Social media analysis Data science methods Watson as medical expert Fully informed user Better prediction
  8. 8. Institute of Cognitive Science Structured Causal Models • Disease spreads locally and via transportation hubs • Weather, vaccination, and seasonal events change spreading
  9. 9. Institute of Cognitive Science Structured Causal Models • Disease spreads locally and via transportation hubs • Weather, vaccination, and seasonal events change spreading
  10. 10. Institute of Cognitive Science Structured Causal Models • Disease spreads locally and via transportation hubs • Weather, vaccination, and seasonal events change spreading
  11. 11. Institute of Cognitive Science Structured Causal Models Direction and speed of spread NEEDS to be identified from data
  12. 12. Institute of Cognitive Science Identify Causal Interactions A driver influences and thereby leaves a trace that can be reconstructed B • Schumacher et al. (2015) - A Statistical Framework to Infer Delay and Direction of Information … • Sugihara et al. (2012) - Detecting Causality in Complex Ecosystems A C
  13. 13. Institute of Cognitive Science Identify Causal Interactions The model can be analyzed: When is New York going to be hit? • Schumacher et al. (2015) - A Statistical Framework to Infer Delay and Direction of Information … • Sugihara et al. (2012) - Detecting Causality in Complex Ecosystems B A C
  14. 14. Institute of Cognitive Science Identify Causal Interactions The model can be analyzed: Can vaccinations in Chicago stop the wave? • Schumacher et al. (2015) - A Statistical Framework to Infer Delay and Direction of Information … • Sugihara et al. (2012) - Detecting Causality in Complex Ecosystems B A C
  15. 15. Institute of Cognitive Science IBM Blue Mix & Watson at work Supported by:
  16. 16. Institute of Cognitive Science IBM Blue Mix & Watson at work Supported by:
  17. 17. Institute of Cognitive Science Influenza matters Prediction is important Delayed and too few data Why Cognitive Computing? Social media analysis Data science methods Watson as medical expert Fully informed user Better prediction
  18. 18. Institute of Cognitive Science Social Media Twitter gives realtime and anytime available data IBM Insights contains tweets since 2014 Twitter activity (geo tag + tweet)
  19. 19. Institute of Cognitive Science Close the Gap by Fusing Data Realtime fuzzy social media + Slow but relibale CDC data Twitter activity (geo tag + tweet) CDC – delayed influenca data Use the best from both worlds to improve prediction
  20. 20. Institute of Cognitive Science IBM Blue Mix & Watson at Work Supported by:
  21. 21. Institute of Cognitive Science IBM Blue Mix & Watson at Work Supported by:
  22. 22. Institute of Cognitive Science Influenza matters Prediction is important Delayed and too few data Why Cognitive Computing? Social media analysis Data science methods Watson as medical expert Fully informed user Better prediction
  23. 23. Institute of Cognitive Science Why Cognitive Computing? ... ? ? ? ?
  24. 24. Institute of Cognitive Science Watson at Work Supported by:
  25. 25. Institute of Cognitive Science Watson at Work Supported by:
  26. 26. Institute of Cognitive Science Summary Social media analysis Data science methods Watson as expert ● Data science allows identification of very complex causal relations ● Efficient use of BLUE Mix and Watson services ● Combine social media with other conventional data to get the best of both worlds  realtime and reliable ● Use large corpora to identify structure and relationships in your problem ● Use natural language interface for easy to use HCI
  27. 27. Institute of Cognitive Science Try It Yourself www.flu-prediction.com
  28. 28. Institute of Cognitive ScienceNEUROMORPHIC COMPUTING • Neuronal plasticity for local learning • Delays as a feature to render computation more efficent • Spatiotemporal computations of an excitable and plastic brain: neuronal plasticity leads to noise-robust and noise-constructive computations, H. Toutounji, G. Pipa - PLOS Comput Biol, 2014 • An introduction to delay-coupled reservoir computing, J. Schumacher, H. Toutounji, G. Pipa, Artificial Neural Networks, Vol 4, Springer Series in Bio-/Neuroinformatics pp 63-90 Linear task specific mapping Echo State Networks ESN (Jager, 2002) Liquid State Machines LSM (Maass et al 2003)
  29. 29. Institute of Cognitive ScienceNEUROMORPHIC COMPUTING • Unsupervised Representation learning based on neuronal plasticity (IP + STDP ) Here higher order markov transitions • Spatiotemporal computations of an excitable and plastic brain: neuronal plasticity leads to noise-robust and noise- constructive computations, H. Toutounji, G. Pipa; PLOS Computational Biology, 2014 • SORN: a self-organizing recurrent neural network, Frontiers in computational neuroscience 3, 23
  30. 30. Institute of Cognitive ScienceNEUROMORPHIC COMPUTING • An introduction to delay-coupled reservoir computing, J. Schumacher, H. Toutounji, G. Pipa, Artificial Neural Networks, Vol 4, Springer Series in Bio-/Neuroinformatics pp 63-90 • ‘Neuromorphic computation in multi-delay coupled models’, P. Nieters, J. Leugering, G. Pipa, IBM Research Journals (submitted) Recurrent network reservoir Delay coupled reservoir
  31. 31. Institute of Cognitive ScienceLEARNING TO FLY – LIKE A BIRD • Bioinspired motor control • Use of real-time recurrent networks • Reservoir computing system learns complex flight dynamics
  32. 32. Institute of Cognitive ScienceNEURO-INSPIRED SYSTEMS Spatiotemporal computations of an excitable and plastic brain: neuronal plasticity leads to noise-robust and noise- constructive computations, H. Toutounji, G. Pipa - PLOS Comput Biol, 2014 Linear mappingReservoirInput Current Position Current Yaw Current Pitch Current Roll Target Position
  33. 33. Institute of Cognitive Science Neuro-Inspired Self-Learning Systems
  34. 34. Institute of Cognitive Science Mobile Crowd EEG • Less than $99, 8 channel, LED stimulation, gyroscope • Mobile system with Bluetooth LE • Allows for nothing less than a new ERA of crowd EEG experiments
  35. 35. Institute of Cognitive Science The Future of ALP Crowd Bio-Signals You design your analysis that we perform for you. Pay for the service and the data you get. We store and process the data. We rent devices.
  36. 36. Institute of Cognitive Science The Cognitive ERA JOIN THE ADVENTURE LEARN HOW TO USE THE POWER OF THE BRAIN Supported by: Prof. Dr. Gordon Pipa gpipa@uos.de Osnabrück University
  37. 37. Institute of Cognitive Science The Moral/Ethical Turing Test “When the light turned green, the Google car waited for a few cars to pass and then began moving back into the middle of the lane to pass the sand bags. At the same time, a public transit bus was approaching from behind. The Google car expected the bus to stop or slow down to let it into the traffic flow. That didn't happen. Instead, the self-driving vehicle hit the side of the bus as it was moving back into the middle of the lane.”
  38. 38. Institute of Cognitive Science The Moral/Ethical Turing Test Naturalistic Condition Skulmowski A, Bunge A, Kaspar K and Pipa G (2014) Forced-choice decision-making in modified trolley dilemma situations: a virtual reality and eye tracking study. Front. Behav. Neurosci. 8:426. doi: 10.3389/fnbeh.2014.00426
  39. 39. Institute of Cognitive Science The Moral/Ethical Turing Test Study project: Moral decisions in the interaction of humans and a car driving assistant Abstract Condition
  40. 40. Institute of Cognitive Science The Moral/Ethical Turing Test Accepted behavior? Context dependence? Wish to interfere? Learning underlying rules Study project: Moral decisions in the interaction of humans and a car driving assistant
  41. 41. Institute of Cognitive Science Value of Life Naturalistic (embodied, situated) moral/ethical decisions are more reliable and distinguished than abstract ones

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