Presented live at FITC Toronto 2018 More info at http://fitc.ca/event/to18/ Presented by Xavier Snelgrove, Element AI Overview The last couple years have seen an explosion in research using AI and machine learning techniques for creative purposes, especially for image creation. If you’ve seen apps like Prisma, or images from Google’s Deep Dream, then you’ve seen these algorithms in action. It’s early days, and while some of the new aesthetics are beautiful, the tools and approaches are still embryonic. In this talk Xavier will show work he and others have made with new algorithms, and explain at a high level how some of these neural-network based tools actually work. In particular he’ll explain his “multiscale neural texture synthesis” algorithm that has allowed for the creation of much more varied and high-resolution images. Many of these techniques are not yet usable by people without a deep technical background, and Xavier will discuss directions he is taking his work to try to make the algorithms more accessible and expressive. Just as you don’t need to be an audio engineer to create electronic music on a synthesizer, neither should you need to be an AI researcher to create electronic images with a neural net. Just as you don’t expect a synthesizer to write your music, neither should we look to algorithms to create our images. Objective To share some of the advances in using AI to create images, and explain how the algorithms work. Target Audience Technically curious creatives who want to better understand how visual neural algorithms like style transfer and deep dream actually work. Assumed Audience Knowledge None: the talk will be mostly accessible, with just occasional more hardcore diversions Five Things Audience Members Will Learn How a neural network works (at a very high level) How style transfer algorithms (like Prisma) work Some tools they can use today to create neural images Current frontiers in creative AI Interesting artists working with AI today

1. Using AI as a tool in
artistic creation
Xavier Snelgrove (@wxswxs)
FITC Toronto, April 8, 2018

2. https://www.reddit.com/r/MachineLearning/comments/3a1ebc/image_generated_by_a_convolutional_network/
June 2015

3. August 2015
Gatys, L. A., Ecker, A. S., & Bethge, M. (2015, August 26). A Neural Algorithm of Artistic Style.

4. July 2016

5. July 2016
"Photoshop Filter Effect"

6. Explosion of work

7. Olah, et al., "The Building Blocks of Interpretability", Distill, 2018.

8. Miyato, T. et al, "cGANs with Projection Discriminator" ICLR 2018

9. Robbie Barrat: art-DCGAN
https://github.com/robbiebarrat/art-DCGAN

10. Robbie Barrat: art-DCGAN
https://github.com/robbiebarrat/art-DCGAN

11. Robbie Barrat: art-DCGAN
https://github.com/robbiebarrat/art-DCGAN

12. Helena Sarin 2018: @glagolista

13. Mario Klingemann 2018
https://twitter.com/quasimondo/status/982711735001010176

14. Mario Klingemann 2018
https://twitter.com/quasimondo/status/982711735001010176

15. My (slightly out of fashion) technique
(https://wxs.ca/research/multiscale-neural-synthesis/)

16. Layer 1
Layer 7
Layer 17

17. Layer 1
Layer 7
Layer 17

18. Layer 1
Layer 7
Layer 17
Generalization of
colour!

19. Gram Matrix
How often is the "fur" feature active at the same time
as the "stripy" feature?
Fur
Eyes
Grass
Stripy
Smooth Fur
Eyes
Grass
Stripy
Smooth
0.8
2.3 0.1 0.03
...
(This is an over-simplification…)

20. Optimization
Create a new image that matches the Gram matrix
of a target image (at multiple scales)

29. Open up a new possibility space
Provide tools to explore this space

30. Levers of control

31. Initialization

34. Gram combination
+

39. Gram modification

42. New directions:
untargeted synthesis

46. New directions:
animation

51. Code open-source

52. This is a microcosm for, more broadly,
how we will interact with these algorithms.

53. Questions?
Xavier Snelgrove
http://wxs.ca
@wxswxs

54. Subjective Function x Lionize
Collaboration with Leo Krukowski: https://www.leokru.com/

55. Subjective Function x Lionize
Collaboration with Leo Krukowski: https://www.leokru.com/