Dreams, Drugs and ConvNets

Dreams, Drugs and ConvNets

Artefacts of human and artificial cognition.
at Studenckie Koło Naukowe Neurobiologii, the University of Warsaw


Piotr Migdał

March 02, 2017


  1. Dreams, Drugs & ConvNets Piotr Migdał, PhD deepsense.io / freelancer

    http://p.migdal.pl/ 2 Mar 2017, Studenckie Koło Naukowe Neurobiologii UW
  2. None
  3. None
  4. Deep Learning progress • image recognition, neural style, word analogies,

    per-char translations, playing ATARI games, Go, [no idea what’s next] • fast-paced (more than my quantum physics PhD):
 6 month ago a breakthrough, now a baseline • (no questions about Singularity please!)
  5. Artificial neural networks • Multidimensional arrays • Simple matrix operations

    (add, multiply) • Floats (not spikes) • Simple activation functions (sigmoid, ReLU) • Cost function and back-propagation • A lot of weights (e.g. 100M)
  6. Many layers https://research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/

  7. Convolutions http://setosa.io/ev/image-kernels/

  8. Patterns activating channels https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html

  9. Big questions • creating general AI or sentient beings •

    transferring our minds (mind uploading) “I mean, if we're able to save even just a small piece of ourselves, why wouldn't we do that?” - SOMA https://www.youtube.com/watch?v=BZTfi1jv-EE
  10. Flesh vs machines:
 practical questions • Cross-inspiration: • Machine Learning

    to Human Cognition • Human Cognition to Machine Learning • Common abstractions • Learning in general • Hierarchical signal processing
  11. NSFW, 18+, etc

  12. http://www.techrepublic.com/article/why-microsofts-tay-ai-bot-went-wrong/ Tay.AI (+ 4chan)

  13. The biggest lie of every
 parent, pet owner
 and neural

    network trainer: “I’ve never shown it that, it must have learned it by itself!”
  14. Stereotypes http://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html

  15. If you’ve never had a trip… https://youtu.be/cPKq7JuQDvg?t=256

  16. If you’ve never had a trip… https://youtu.be/z7_U6y8kJaY?t=72

  17. Deep dreams: forcing ConvNet
 to see things https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

  18. • What should I use:

    good GPU! http://alexgrey.com/shop/st-al.html
  19. Grocery trip https://www.youtube.com/watch?v=DgPaCWJL7XI

  20. Forcing networks to see things https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

  21. Neural style transfer also video: https://www.youtube.com/watch?v=Khuj4ASldmU

  22. With own photos to play with https://deepart.io/ or https://prisma-ai.com/

  23. What is sexual? http://blog.clarifai.com/what-convolutional-neural-networks-see-at-when-they-see-nudity/#.WLhGmRIrLLh

  24. What is sensual? http://blog.clarifai.com/what-convolutional-neural-networks-see-at-when-they-see-nudity/#.WLhGmRIrLLh

  25. And this? (yes, ConvNet is also fooled)

  26. Sketch to picture http://affinelayer.com/pixsrv/

  27. Sketch to picture: let’s get rogue! http://affinelayer.com/pixsrv/

  28. https://raw.githubusercontent.com/Newmu/dcgan_code/master/images/faces_arithmetic_collage.png https://github.com/255BITS/HyperGAN Picture analogies

  29. https://open_nsfw.gitlab.io/ Image generation

  30. Learning what is important • Object consistency • Toddler needs

    to learn, so do ConvNets http://blogs.scientificamerican.com/illusion-chasers/what-little-babies-see-that-you-no-longer-can/
  31. Uncertainty and learning http://www.nature.com/articles/ncomms11609

  32. Retrain your own network:
 McCollough effect http://www.michaelbach.de/ot/col-McCollough/

  33. Trypophobia
 (don’t google it!) visit here instead: https://en.wikipedia.org/wiki/Trypophobia

  34. Trypophobia detector https://github.com/grzegorz225/trypophobia-detector (a 5-day workshop was enough for 88%

  35. And if you
 don’t want to code… http://www.sciencemag.org/news/2015/11/pigeons-spot-cancer-well-human-experts https://www.youtube.com/watch?v=flzGjnJLyS0

  36. Questions? • If you want to learn: • http://lumiverse.io/series/neural-networks-demystified •

    https://gist.github.com/stared/7de2908b9bcba01c39ee3c591875a23c • http://www.deeplearningbook.org/ • Keras (on top of TensorFlow) in Jupyter Notebook • https://medium.com/@mateuszsieniawski/keras-with-gpu-on-amazon-ec2-a-step-by- step-instruction-4f90364e49ac#.4i1norivh • https://www.erowid.org/ http://p.migdal.pl/ + I do like emails: pmigdal@gmail.com