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Applying Machine Learning to
Psi Research:
An Example of using a Deep Machine Learning
Image Classifier to Analyze Seeming...
Machine Learning (ML)
ML is a method of data analysis that
automates model building.
ML algorithms iteratively learn from ...
Typical Machine Learning Network
Inputs
Nodes
Outputs
Deep Machine Learning Network
Inputs
Nodes
Outputs
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
Some Current ML Applications
Natural language processing (text and speech)
Computer vision
Medical diagnosis
User preferen...
Investment in ML Tools
• Google: TensorFlow
• IBM: Watson
• Microsoft: Computational Network Toolkit
• Open Source Librari...
Visualizing FieldREG Data
10011
10010
11100
01101
Group engaged in a
common task or event
Visualizing FieldREG Data
10011
10010
11100
01101
Idea or
Intention
Custom Software
Maps data to a 3D space
Love vs. Hate
Love Hate
Love Hate
Develop & test a
sorting/scoring task
Recruit, consent, and
train participants
Collect data
Analyze data
Formula...
Deep Machine Learning Image
Classifier
http://clarifai.com/
Image concept and feature tagging
Love vs. Hate: Results
What did we learn?
• The software made this test possible to complete
in only 2 hours.
• There was a considerable time/cos...
ML: Cautionary Tales
Google Flu Trends was wrong for 100 out of 108 weeks.
The Parable of Google Flu: Traps in Big Data An...
ML: Cautionary Tales
“The AI chatbot Tay is a machine learning
project, designed for human engagement.
As it learns, some ...
ML Solves 100-Year-Old Biology Problem
“This problem, and our approach, is nearly universal.
It can be used with anything,...
ML and Parapsychology Data
Global Consciousness Project
FieldREG
Physiology/EEG
Dream telepathy/precognition
Remote viewin...
Summary
• Researchers now have access to new, powerful, low
cost/free ML tools.
• Exploratory test using visualized FieldR...
Acknowledgments
Julie Beischel
Windbridge Institute volunteers
Friends & Supporters of the Windbridge Institute
PsiForm se...
For more information about the FieldREG visualization project and to download a
free PDF version of the book, please visit...
Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seeming...
Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seeming...
Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seeming...
Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seeming...
Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seeming...
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Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seemingly Random Visualized FieldREG Data Collected during Sessions with Meditators

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Slides from Mark Boccuzzi's talk at:
Accessing the Exceptional, Experiencing the Extraordinary:
An Integration of the 59th Annual Convention of the Parapsychological Association and 35th Annual Conference of the Society for Scientific Exploration
Boulder, Colorado, USA, June 20-24, 2016

Published in: Science
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Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seemingly Random Visualized FieldREG Data Collected during Sessions with Meditators

  1. 1. Applying Machine Learning to Psi Research: An Example of using a Deep Machine Learning Image Classifier to Analyze Seemingly Random Visualized FieldREG Data Collected during Sessions with Meditators SSE/PA Conference June 23, 2016 Boulder, CO Mark Boccuzzi The Windbridge Institute, LLC mark@windbridge.org
  2. 2. Machine Learning (ML) ML is a method of data analysis that automates model building. ML algorithms iteratively learn from data, allowing computers to find connections in data without the need for them to be explicitly programmed.
  3. 3. Typical Machine Learning Network Inputs Nodes Outputs
  4. 4. Deep Machine Learning Network Inputs Nodes Outputs … … … … … … … … … … … … … … … …
  5. 5. Some Current ML Applications Natural language processing (text and speech) Computer vision Medical diagnosis User preferences Image recognition and feature extraction Virtual assistants Self driving cars
  6. 6. Investment in ML Tools • Google: TensorFlow • IBM: Watson • Microsoft: Computational Network Toolkit • Open Source Libraries: – Java /JavaScript – R – Lua – C++ – Python
  7. 7. Visualizing FieldREG Data 10011 10010 11100 01101 Group engaged in a common task or event
  8. 8. Visualizing FieldREG Data 10011 10010 11100 01101 Idea or Intention Custom Software Maps data to a 3D space
  9. 9. Love vs. Hate
  10. 10. Love Hate
  11. 11. Love Hate Develop & test a sorting/scoring task Recruit, consent, and train participants Collect data Analyze data Formulate Conclusions
  12. 12. Deep Machine Learning Image Classifier http://clarifai.com/ Image concept and feature tagging
  13. 13. Love vs. Hate: Results
  14. 14. What did we learn? • The software made this test possible to complete in only 2 hours. • There was a considerable time/cost savings as compared to traditional approaches that rely on research participants. • This exploratory test demonstrates the potential value of machine learning in efficiency and potential hypothesis generation.
  15. 15. ML: Cautionary Tales Google Flu Trends was wrong for 100 out of 108 weeks. The Parable of Google Flu: Traps in Big Data Analysis David Lazer, Ryan Kennedy, Gary King, Alessandro Vespignani Science 14 Mar 2014: Vol. 343, Issue 6176, pp. 1203-1205 DOI: 10.1126/science.1248506 2008
  16. 16. ML: Cautionary Tales “The AI chatbot Tay is a machine learning project, designed for human engagement. As it learns, some of its responses are inappropriate and indicative of the types of interactions some people are having with it. We're making some adjustments to Tay.” ~ Microsoft March 23, 2016
  17. 17. ML Solves 100-Year-Old Biology Problem “This problem, and our approach, is nearly universal. It can be used with anything, where functional data exist but the underlying mechanism is hard to guess” M. Levin http://www.popularmechanics.com/science/a15886/computer-scientific-theory/
  18. 18. ML and Parapsychology Data Global Consciousness Project FieldREG Physiology/EEG Dream telepathy/precognition Remote viewing Environmental monitoring Ganzfeld data Mediumship Near-Death Experiences Instrumental Transcommunication/EVP
  19. 19. Summary • Researchers now have access to new, powerful, low cost/free ML tools. • Exploratory test using visualized FieldREG data showed the potential benefits offered by ML in terms of efficiency and hypothesis development. • Although we need to be cautious, parapsychology may benefit from ML analysis of existing psi databases.
  20. 20. Acknowledgments Julie Beischel Windbridge Institute volunteers Friends & Supporters of the Windbridge Institute PsiForm session volunteers Thank you for your attention! mark@windbridge.org www.Windbridge.org
  21. 21. For more information about the FieldREG visualization project and to download a free PDF version of the book, please visit: http://www.windbridge.org/vibook/ For research updates you can follow us on social media: https://www.facebook.com/mark.boccuzzi https://www.facebook.com/Windbridge.Institute/ https://twitter.com/Windbridge

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