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Welcome

Welcome. To the. Edinburgh University Young Scientific Researchers Association. Line Up : Introduction – Nicholas Groth Merrild Investigating Precognitive Dreaming – Dr. Caroline Watt

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Welcome

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  1. Welcome To the Edinburgh University Young Scientific Researchers Association

  2. Line Up:Introduction – Nicholas GrothMerrildInvestigating Precognitive Dreaming – Dr. Caroline Watt Computers and Human Language: The Artificial Intelligence and Cognitive Neuroscience side of it – Laurentiu Prodan Principia Scientifica – Jan LyczakowskivsAndrew Mooney

  3. New Heads of Departments Biology Chemistry Eleanor Drinkwater Adam Michalchuk

  4. Vacant Positions Medicine Physics/Mathematics

  5. Possibilities for New Departments Geoscience Veterinarian Medicine Psychology

  6. In the Eye of the Beholder? Investigating Precognitive Dreaming Caroline WattPerrott-Warrick Senior ResearcherKoestler Parapsychology UnitUniversity of Edinburgh

  7. ‘Psi’ Hypothesis • Dreams of major disasters • Prospective dream studies • Controlled studies

  8. e.g. of how to run a controlled test:Online dream precognition study Task: to dream about video clip that will be viewed at end of week 72 clips to form 18 pools of 4 orthogonal clips METHOD 1. Questionnaire Ambiguity tolerance Belief in precog dreams Prior precog dream experience Frequency of dream recall 2. Weekly dream summary submitted by dreamer

  9. Online dream precognition study: Method cont. 3. Dream summary & pool URLs to independent judge (1 of 2) 4. Judge submits ratings 5. Target randomly selected & sent to participant 4x, until 50 participants = 200 trials (MCE = 25%)

  10. Online dream precognition study: Hypothesis & Results Hypothesis 1: The judges will give significantly higher ratings to target videos than to decoy videos PARTICIPANTS 20M, 30F, mean age 42.8, range 21-82 years 66% believed in precog dreams, 26% ‘unsure’ 72% reported at least one prior precog dream exp 88% recalled dreams at least once/week

  11. Online dream precognition study:Results Hypothesis 1 64 hits out of 200 trials 32% hitrate (25% MCE) Exact binomial p = .015 (1-t) No relationship btwnhitrate & psychological variables Alternative explanations for above-chance hitrate?

  12. Alternative explanations  1. Experimenter leaked target info to judges 2. Exptr’stgt selection biased by knowledge of judges’ ratings 3. Participants leaked target info to judges 4. Dream summaries contained cues about previous week’s targets 5. Coordinating experimenter cheated 6. Non-random assignment of targets that coincided with judging bias = ‘stacking effect’     ?

  13. Computers and Human Language The Artificial Intelligence and Cognitive Neuroscience side of it LaurentiuProdan Master of Informatics Candidate School of Informatics, University of Edinburgh

  14. What is AI? Source: http://www.learnartificialneuralnetworks.com/ai.html

  15. What is Cognitive Neuroscience? http://www.abovetopsecret.com/forum/thread606040/pg1

  16. Sleep and the Brain http://www.nature.com/nrn/journal/v3/n9/fig_tab/nrn915_F7.html

  17. How is AI inspired by “brain studies”? http://www.ica.luz.ve/~dfinol/NeuroCienciaCognitiva/What%20is%20Cognitive%20Neuroscience%20-%20an%20introduction%20by%20Jamie%20Ward.htm

  18. How much do we know about our brain? Source: http://www.learnartificialneuralnetworks.com/ai.html

  19. Machine Translation http://specgram.com/CLII.4/09.phlogiston.cartoon.iv.html

  20. Statistical Machine Translation http://nlp.postech.ac.kr/research/previous_research/smt/

  21. Automatic Speech Recognition http://ispl.korea.ac.kr/Research/speech/autosr.html

  22. Complex systems – IR and QA http://archives.limsi.fr/RS2005/chm/lir/lir3/

  23. Dividing the probability space http://research.ics.tkk.fi/speech/research.shtml

  24. Neural Network Language Models http://archives.limsi.fr/RS2005/chm/tlp/tlp11/index.html

  25. A Neural Probabilistic Language Model and its successors http://www.iro.umontreal.ca/~bengioy/yoshua_en/research.html

  26. Thank you! Any Questions? Source: http://www.learnartificialneuralnetworks.com/ai.html

  27. Principia Scientifica Artificial Intelligence Jan LyczakowskvsMohib Hassan

  28. Artificial Intelligence - there is nothing to be afraid of. By: Jan Lyczakowski

  29. Artificial Intelligence vs. Artificial Life

  30. 1. Financial services AI as a manager

  31. 2. Industry and Army - AI is there where we could be in danger

  32. 3. Simulations - training and risk management.

  33. 4. Everyday example - SPAM mail recognition

  34. Artificial IntelligenceI.Robot, the Alternative Ending By: Andrew Mooney

  35. “Artificial intelligence is the study of how to make real computers act like the ones in the movies.”

  36. Thank You For Your Logo Submissions!

  37. Time to Sign Up And Join a Department! Contact: EUYSRA@gmail.com Facebook Group: EUYSRA

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