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Deep Learning State of the Art (2020) | MIT Deep Learning Series

This lecture is part of the MIT Deep Learning Lecture Series.Website: https://deeplearning.mit.eduSlides: http://bit.ly/2QEfbAmPlaylist: http://bit.ly/deep-learning-playlistOUTLINE:0:00 - Introduction0:33 - AI in the context of human history5:47 - Deep learning celebrations, growth, and limitations6:35 - Deep learning early key figures9:29 - Limitations of deep learning11:01 - Hopes for 2020: deep learning community and research12:50 - Deep learning frameworks: TensorFlow and PyTorch15:11 - Deep RL frameworks16:13 - Hopes for 2020: deep learning and deep RL frameworks17:53 - Natural language processing19:42 - Megatron, XLNet, ALBERT21:21 - Write with transformer examples24:28 - GPT-2 release strategies report26:25 - Multi-domain dialogue27:13 - Commonsense reasoning28:26 - Alexa prize and open-domain conversation33:44 - Hopes for 2020: natural language processing35:11 - Deep RL and self-play35:30 - OpenAI Five and Dota 237:04 - DeepMind Quake III Arena39:07 - DeepMind AlphaStar41:09 - Pluribus: six-player no-limit Texas hold'em poker43:13 - OpenAI Rubik's Cube44:49 - Hopes for 2020: Deep RL and self-play45:52 - Science of deep learning46:01 - Lottery ticket hypothesis47:29 - Disentangled representations48:34 - Deep double descent49:30 - Hopes for 2020: science of deep learning50:56 - Autonomous vehicles and AI-assisted driving51:50 - Waymo52:42 - Tesla Autopilot57:03 - Open question for Level 2 and Level 4 approaches59:55 - Hopes for 2020: autonomous vehicles and AI-assisted driving1:01:43 - Government, politics, policy1:03:03 - Recommendation systems and policy1:05:36 - Hopes for 2020: Politics, policy and recommendation systems1:06:50 - Courses, Tutorials, Books1:10:05 - General hopes for 20201:11:19 - Recipe for progress in AI1:14:15 - Q&A: what made you interested in AI1:15:21 - 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?

Deep learning differentiates small renal masses on multiphase CT

Between 2012 and 2016, researchers at Japan's Okayama University studied 1807 image sets from 168 pathologically diagnosed small (≤

Masses were classified as malignant (n = 136) or benign (n = 32) using a 5-point scale, and this dataset was then randomly divided into five subsets.

Finding no significant size difference between malignant and benign lesions, Tanaka's team did find that the AUC value of the corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022).

Deep Learning State of the Art (2020) | MIT Deep Learning Series

This lecture is part of the MIT Deep Learning Lecture Series.Website: https://deeplearning.mit.eduSlides: http://bit.ly/2QEfbAmPlaylist: http://bit.ly/deep-learning-playlistOUTLINE:0:00 - Introduction0:33 - AI in the context of human history5:47 - Deep learning celebrations, growth, and limitations6:35 - Deep learning early key figures9:29 - Limitations of deep learning11:01 - Hopes for 2020: deep learning community and research12:50 - Deep learning frameworks: TensorFlow and PyTorch15:11 - Deep RL frameworks16:13 - Hopes for 2020: deep learning and deep RL frameworks17:53 - Natural language processing19:42 - Megatron, XLNet, ALBERT21:21 - Write with transformer examples24:28 - GPT-2 release strategies report26:25 - Multi-domain dialogue27:13 - Commonsense reasoning28:26 - Alexa prize and open-domain conversation33:44 - Hopes for 2020: natural language processing35:11 - Deep RL and self-play35:30 - OpenAI Five and Dota 237:04 - DeepMind Quake III Arena39:07 - DeepMind AlphaStar41:09 - Pluribus: six-player no-limit Texas hold'em poker43:13 - OpenAI Rubik's Cube44:49 - Hopes for 2020: Deep RL and self-play45:52 - Science of deep learning46:01 - Lottery ticket hypothesis47:29 - Disentangled representations48:34 - Deep double descent49:30 - Hopes for 2020: science of deep learning50:56 - Autonomous vehicles and AI-assisted driving51:50 - Waymo52:42 - Tesla Autopilot57:03 - Open question for Level 2 and Level 4 approaches59:55 - Hopes for 2020: autonomous vehicles and AI-assisted driving1:01:43 - Government, politics, policy1:03:03 - Recommendation systems and policy1:05:36 - Hopes for 2020: Politics, policy and recommendation systems1:06:50 - Courses, Tutorials, Books1:10:05 - General hopes for 20201:11:19 - Recipe for progress in AI1:14:15 - Q&A: what made you interested in AI1:15:21 - 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?

Applying Occam’s razor to Deep Learning

What constitutes a model, as discussed by McCaullug (2002) in a statistical model context, is a different discussion, but here we assume a machine learning algorithm is considered a statistical model.

The surge in interest in using complex neural network architectures, i.e., deep learning due to their unprecedented success in certain tasks,  pushes the boundaries of 'standard' statistical concepts such as overfitting/overtraining and regularisation.

With the advent of Neural Architecture Search and new complexity measures that take the structure of the network into account, give rise to the possibility of practicing Occam's razor in deep learning.

Using a less complex deep neural network that would give similar performance is not practiced by the deep learning community due to the complexity of training and designing new architectures.

However, quantifying the complexity of similarly performing neural network architecture would bring the advantage of using less computing power to train and deploy such less complex models into production.

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