Download as PDF, PPTX
More Related Content
Similar to Google Cloud - Google's vision on AI
![RTP_AR_Basic_Learners' Workbook_KS2 [FOR REPRODUCTION] (1).pdf](https://cdn.slidesharecdn.com/ss_thumbnails/rtparbasiclearnersworkbookks2forreproduction1-251016024943-e51a16ac-thumbnail.jpg?width=600ounds&width=560&fit=bounds)
![Matrix and determinant URT [Autosaved].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/matrixanddeterminanturtautosaved-251018190340-9e6a6deb-thumbnail.jpg?width=600ounds&width=560&fit=bounds)
Google Cloud - Google's vision on AI
- 1. Google's vision onAI Big Data Expo 19th September 2018 Rokesh Jankie rokesh@google.com
- 2.
- 3. 4th of October2016
- 4. 4th of October2016
- 5. Putting things inperspective
- 6. Machine learning islearning from examples and experience
- 7. Let’s try some human-poweredimage detection
- 8. How would wedo this without ML?
- 9. How would wedo this without ML?
- 10. How would wedo this without ML?
- 11. What about adog... and a mop? Easy, right?
- 12. Not so fast... CC-BY-SA-2.5Wikimedia 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 YouGet 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 youwere 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.
- 19.
- 20. History of OpenSource @Google
- 21. You mentioned DeepLearning? What ?! • The first question to answer: What’s a Neural Network ? • Inspired by Biology: • Two flavors: Supervised an Unsupervised
- 22. This is whatwe are trying to solve f(x) = y
- 23. Neural Networks • Manykinds op NNs
- 24. What happened inthe 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.
- 28. TensorFlow History: TheCat This was in 2012 It took 16,000 computers to identify a cat!
- 29. Linear Regression &Classification
- 30.
- 32.
- 33. what just happenedin Linear Regression ?
- 34. We need morethan just software
- 36. Confidential & ProprietaryGoogleCloud Platform 36 11.5 petaflops per pod
- 39.
- 41. On device machinelearning
- 42. Examples • Image2Text • “Lipreading” • Human-like sound: WaveNet • Skin cancer detection • AI Experiments by Google • Learn how to fly
- 43.
- 44.
- 45.
- 46.
- 47. Possibilities…many, but Iwant to highlight one specifically
- 49.
- 50. – Aja Huang “Thepossibilities and power are innumerable”
- 54.
- 56. Google AI ResearchCenters
- 57. Google AI ResearchCenters
- 60.
- 61.
- 62.
- 63. 4th of October2016
- 64. Final Note, bythe man himself!
- 65.