Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. Prior to joining Facebook in August 2014, he worked at MuseAmi, where he built deep learning models for music and vision targeted at mobile devices. He holds a Masters in CS from NYU, and spent time in Yann LeCun’s NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.
Abstract Summary:
Dynamic Deep Learning: a paradigm shift in AI research and tools:
AI research has seen many shifts in the last few years. We’ve seen research go from using static datasets such as Imagenet to being more dynamic and online in self-driving cars, robots and game-playing.Many dynamic environments such as Universe and Starcraft are being used in AI research to solve problems pertaining to reinforcement learning and online learning. In this talk, I shall discuss these shifts in research. Tools such as PyTorch, DyNet and Chainer have popped up to cope up with the paradigm shift, enabling cutting-edge AI, and I shall discuss these as well.
7. Today's AI Future AI Tools for AI
Today's AI
Text Classification (sentiment analysis etc.)
Text Embeddings
Graph embeddings
Machine Translation
Ads ranking
12. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Self-driving Cars
13. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Agents trained in many environments
Cars Video games
Internet
14. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Dynamic Neural Networks
self-adding new memory or layers
changing evaluation path based on inputs
15. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Live
data
BatchNorm
ReLU
Conv2d
Prediction
Continued Online Learning
16. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Sample-1
BatchNorm
ReLU
Conv2d
Prediction
Data-dependent change in model structure
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
17. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Sample-2
BatchNorm
ReLU
Conv2d
Prediction
Data-dependent change in model structure
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
18. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Sample
BatchNorm
ReLU
Conv2d
Prediction
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
Change in model-capacity at runtime
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
19. Today's AI Future AI Tools for AI
Current AI Research / Future AI
Sample
BatchNorm
ReLU
Conv2d
Prediction
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
Change in model-capacity at runtime
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
BatchNorm
ReLU
Conv2d
20. Today's AI Future AI Tools for AI
A next-gen framework for AI
•Interop with many dynamic environments
- Connecting to car sensors should be as easy as training on a dataset
- Connect to environments such as OpenAI Universe
•Dynamic Neural Networks
- Change behavior and structure of neural network at runtime
•Minimal Abstractions
- more complex AI systems means harder to debug without a simple API
21. Today's AI Future AI Tools for AI
Tools for AI research and deployment
Many machine learning tools and deep learning frameworks
22. Today's AI Future AI Tools for AI
Tools for AI research and deployment
Static graph frameworks Dynamic graph frameworks
23. Today's AI Future AI Tools for AI
Dynamic graph Frameworks
•Model is constructed on the fly at runtime
•Change behavior, structure of model
•Imperative style of programming