1. AI for Beginners from a Beginner

2. About me AI beginner tomas@florian.ca IT Consulting Complex networking
Cloud / Virtualization systems
Cyber security

3. Demos
What got me started down this path is impressive demos that I’ve seen in the last couple of years

4. Question
Can anybody do this with now with open source software?
Yes

5. Lay of the Land End user apps CLI apps Frameworks Libraries Research
Open Source
Closed Cloud

6. Lay of the Land End user apps CLI apps Frameworks Libraries Research
Open Source
Closed Cloud

7. Lay of the Land End user apps CLI apps Frameworks Libraries Research
Open Source
Closed Cloud

8. Lay of the Land End user apps CLI apps Frameworks Libraries Research
Open Source
Closed Cloud

9. Lay of the Land End user apps CLI apps Frameworks Libraries Research
Open Source
Closed Cloud

11. Navigating Limits of AI
2x Demo
Show
Unwrap
Howto
Questions
Navigating Limits of AI

12. Demo #1 Object Detection

13. Object Detection

14. Object Detection

15. Unwrap: Black Box
./flow --imgdir sample_img/ --model cfg/yolo.cfg --load bin/yolo.weights

16. Neural Network
./flow --imgdir sample_img/ --model cfg/yolo.cfg --load bin/yolo.weights

17. Neural Network Model Weights (pre-trained)
./flow --imgdir sample_img/ --model cfg/yolo.cfg --load bin/yolo.weights

18. Darkflow Dependency Stack
model
weights
darkflow
TensorFlow
OpenCV
Python3
Anaconda
Ubuntu VM
i7 CPU, 4 GB RAM

19. Howto git clone https://github.com/thtrieu/darkflow
Create conda virtual env for the project
conda create -n NAME python=3.6
source activate NAME
Install dependencies
conda install tensorflow cython numpy
Add the repo with particular opencv version
conda config --add channels conda-forge
Install opencv
conda install opencv
Run setup
python3 setup.py build_ext --inplace
Download weight file for the model
and place it in bin/
Run
./flow --imgdir sample_img/ --model cfg/yolo.cfg --load bin/yolo.weights

20. Questions?

21. Demo #2 Generative Adversarial Network (GAN)

22. ThisPersonDoesNotExist.com

23. Demo #2

24. Unwrap: Black Box

25. GAN Generative Adversarial Network
Pre trained generator network

26. Transparent Latent GAN
Pretrained network model
Transparent latent GAN
Python3
CUDA toolkit
cuDNN
Jupyter
Anaconda
Ubuntu VM
2 CPU,6 GB RAM, K80 GPU with 12GB RAM,50 GB Disk

27. Howto
Git clone
Prepare anaconda
conda create -n NAME python=3.6
source activate NAME
cd transparent_latent_gan
Install dependencies
conda install pip
pip install -r requirements.txt
conda install cudatoolkit
conda install cudnn
conda install jupyter
Download pre-trained model (extract to same folder structure)
Run notebook
jupyter notebook
Navigate to URL shown at startup + notebooks/transparent_latent_gan/src/notebooks/tl_gan_ipywidgets_gui.ipynb

28. Hardware Notes Google Compute Engine preemptive K80 ~$0.20 CAD / Hour
I used vanilla Ubuntu and installed Nvidia drivers on it myself
Prebuilt images but more $ per hour (not preemptive)
REMEMBER TO TURN IT OFF
Nvidia GX 1060 (6 GB RAM … more is better) $300
Nvidia-smi

29. Expectations 80% of time dealing with building the stack
Poor documentation
Missing/incompatible pre-trained models
Dependency hell (much better with Anaconda)
Unhelpful error messages
20% real AI work

30. Path of least resistance
Anaconda
cuDNN 7
CUDA toolkit 9
Ubuntu 16.04
nVIDIA GPU > 6 GB RAM

31. You can do this now

32. Questions

33. Limits Too good to be true?

34. CSI zoom and enhance

35. CSI Zoom and Enhance for real

36. Zoom and Enhance

37. …Again

38. … Again

39. We got him – URL 937

40. We look up the registered owner
Cops go out
Shoot the guy
CSI Calgary saves the day
Case closed

41. Let’s run the same image through a different model

43. Zoom and Enhance

44. What?

45. Wait … what?

46. Side by side

49. Different model will CONVINCINGLY lead you to a different conclusion

50. Which license plate was it?
Maybe URL 937
Maybe BBL 3698
Maybe SOMETHIN ELSE
Even though we are seeing it in front of our own eyes there is a threshold at which AI can just make stuff up and make it look like the real thing

51. NN has learned to make convincing fakes
Those fakes may be rooted in reality or they may be purely hallucinated into existence
The degree to which it’s rooted in reality depends on the data it’s been exposed to during training
Garbage in – elaborate bullshit out

52. In the news: Woody Herrelson Look alike
The image of the suspect, taken from the surveillance footage, was extremely pixelated and turned up no results in Facial ID system
Investigators used high-quality images of Harrelson found on Google and submitted them in place of the suspect's more pixelated image. The result ended in a match.
An unidentified man was booked and charged for petty larceny.

53. So when is the AI apocolypse coming?

54. Sells well but the world doesn’t need more of it
Risk of AI
Create AI that succeeds at producing output that is apparently super-intelligent, super accurate, and super trustworthy
When in reality the output is elaborately disguised bullshit
Sells well but the world doesn’t need more of it

55. GAN Limit Demo

56. How GAN works

57. How GAN works

58. Be aware of AI limits and use it within those limits
Use AI within it’s limits

59. Best Open Source AI in 2019 https://medium. mybridge

60. Presentation copy: lab.florian.ca tomas@florian.ca
Questions?
Presentation copy: lab.florian.ca

61. Useful Links
AI Cheat sheet

62. Other cool stuff
Video Game graphics GAN
Style GAN