Deep Learning State of the Art (2020)

Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series.

Website: https://deeplearning.mit.edu
Slides: http://bit.ly/2QEfbAm
References: http://bit.ly/deeplearn-sota-2020
Playlist: http://bit.ly/deep-learning-playlist

OUTLINE:
0:00 - Introduction
0:33 - AI in the context of human history
5:47 - Deep learning celebrations, growth, and limitations
6:35 - Deep learning early key figures
9:29 - Limitations of deep learning
11:01 - Hopes for 2020: deep learning community and research
12:50 - Deep learning frameworks: TensorFlow and PyTorch
15:11 - Deep RL frameworks
16:13 - Hopes for 2020: deep learning and deep RL frameworks
17:53 - Natural language processing
19:42 - Megatron, XLNet, ALBERT
21:21 - Write with transformer examples
24:28 - GPT-2 release strategies report
26:25 - Multi-domain dialogue
27:13 - Commonsense reasoning
28:26 - Alexa prize and open-domain conversation
33:44 - Hopes for 2020: natural language processing
35:11 - Deep RL and self-play
35:30 - OpenAI Five and Dota 2
37:04 - DeepMind Quake III Arena
39:07 - DeepMind AlphaStar
41:09 - Pluribus: six-player no-limit Texas hold'em poker
43:13 - OpenAI Rubik's Cube
44:49 - Hopes for 2020: Deep RL and self-play
45:52 - Science of deep learning
46:01 - Lottery ticket hypothesis
47:29 - Disentangled representations
48:34 - Deep double descent
49:30 - Hopes for 2020: science of deep learning
50:56 - Autonomous vehicles and AI-assisted driving
51:50 - Waymo
52:42 - Tesla Autopilot
57:03 - Open question for Level 2 and Level 4 approaches
59:55 - Hopes for 2020: autonomous vehicles and AI-assisted driving
1:01:43 - Government, politics, policy
1:03:03 - Recommendation systems and policy
1:05:36 - Hopes for 2020: Politics, policy and recommendation systems
1:06:50 - Courses, Tutorials, Books
1:10:05 - General hopes for 2020
1:11:19 - Recipe for progress in AI
1:14:15 - Q&A: what made you interested in AI
1:15:21 - Q&A: Will machines ever be able to think and feel?
1:18:20 - Q&A: Is RL a good candidate for achieving AGI?
1:21:31 - Q&A: Are autonomous vehicles responsive to sound?
1:22:43 - Q&A: What does the future with AGI look like?
1:25:50 - Q&A: Will AGI systems become our masters?

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