In this session we'll dive into the journey that Google chooses to take in order focus on AI: what was the mindset, what were the challenges and what is the direction for the future.

1. Google's vision on AI
Big Data Expo
19th September 2018
Rokesh Jankie
rokesh@google.com

2. Shift in Computing

3. 4th of October 2016

4. 4th of October 2016

5. Putting things in perspective

6. Machine learning is learning
from examples and
experience

7. Let’s try some
human-powered image
detection

8. How would we do this without ML?

9. How would we do this without ML?

10. How would we do this without ML?

11. What about a dog... and a mop? Easy, right?

12. Not so fast...
CC-BY-SA-2.5 Wikimedia Commons https://commons.wikimedia.org/wiki/File:Komondor_Westminster_Dog_Show_crop.jpg
CC-BY-2.0 Wikimedia Commons https://commons.wikimedia.org/wiki/File:2014_Westminster_Kennel_Club_Dog_Show_(12487315865).jpg
CC-BY-2.0 Petful https://www.flickr.com/photos/petsadviser-pix/16395099127
CC-BY-SA-2.0 Jeffrey Beall https://www.flickr.com/photos/denverjeffrey/6903790333

14. How Can You Get Started with Machine Learning?
• Three ways, with varying complexity:
• Use a Cloud-based or Mobile API (Vision,
Natural Language, etc.)
• Use an existing model architecture, and retrain
it or fine tune on your dataset
• Develop your own machine learning models for
new problems
Moreflexible,butmoreeffortrequired

16. In case you were wondering...
CC-BY-SA-2.5 Wikimedia Commons https://commons.wikimedia.org/wiki/File:Komondor_Westminster_Dog_Show_crop.jpg
CC-BY-2.0 Wikimedia Commons https://commons.wikimedia.org/wiki/File:2014_Westminster_Kennel_Club_Dog_Show_(12487315865).jpg
CC-BY-2.0 Petful https://www.flickr.com/photos/petsadviser-pix/16395099127
CC-BY-SA-2.0 Jeffrey Beall https://www.flickr.com/photos/denverjeffrey/6903790333

17. What is TensorFlow
• Open source Machine Learning library
• Especially useful for Deep Learning
• For research and production
• Apache 2.0 license

18. TensorFlow History
• DistBelief

19. Jeff Dean!

20. History of Open Source @Google

21. You mentioned Deep Learning? What ?!
• The first question to answer: What’s a Neural Network ?
• Inspired by Biology:
• Two flavors: Supervised an Unsupervised

22. This is what we are trying to solve
f(x) = y

23. Neural Networks
• Many kinds op NNs

24. What happened in the last decade…
• Algorithms: This area has seen some improvements, but most of the early
wins came from fairly old ideas. Now that Deep Learning is showing success
we are seeing some good advances as well.
• Datasets: Training large networks is hard without large enough datasets.
MNIST can only go so far in pushing the limits. Having datasets like ImageNet
has really helped pushed the state of the art in vision.
• Compute: the biggest game changer in recent years.

25. 2012

28. TensorFlow History: The Cat
This was in 2012
It took 16,000
computers to
identify a cat!

29. Linear Regression & Classification

30. TensorFlow Playground

32. TensorBoard Projector
Recently launched
Projector

33. what just happened in Linear Regression ?

34. We need more than
just software

36. Confidential & ProprietaryGoogle Cloud Platform 36
11.5 petaflops per pod

39. TPU 3.0
8x faster

41. On device machine learning

42. Examples
• Image2Text
• “Lip reading”
• Human-like sound: WaveNet
• Skin cancer detection
• AI Experiments by Google
• Learn how to fly

43. Recent developments

44. Google Duplex

45. Generating voices

46. Google Lens

47. Possibilities…many, but I want to
highlight one specifically

49. Solution space

50. – Aja Huang
“The possibilities and power are innumerable”

54. What’s Next ?

56. Google AI Research Centers

57. Google AI Research Centers

60. Google & IoT
Edge TPU

61. Google Quantum.ai

62. https://ai.google

63. 4th of October 2016

64. Final Note, by the man himself!

65. That’s a wrap!
Thanks
rokesh@google.com
@rjankie