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Lex Fridman :

New introductory lecture of the MIT Self-Driving Cars series with an overview of the industry from Tesla to Waymo and autonomous taxi services to flying cars and connected vehicles to human-centered autonomy. Many interesting talks to come!
https://www.youtube.com/watch?v=sRxaMDDMWQQ
The power of deeper networks for expressing natural functions”,

Rolnick and Tegmark: https://arxiv.org/abs/1705.05502

#artificalintelligence #deeplearning #machinelearning
AAAI Conference Analytics

Citation distribution by the top AAAI 20 authors, year by year:https://goo.gl/Z15LbW

#artificalintelligence #deeplearning #machinelearning
Lex Fridman : First blog post on Deep Learning Basics with TensorFlow (on the official TensorFlow page):
https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0
The Hanabi Challenge: A New Frontier for AI Research

Paper by Bard et al.: https://arxiv.org/abs/1902.00506

Code: https://github.com/deepmind/hanabi-learning-environment
Can neural networks learn commonsense reasoning?

ATOMIC | An Atlas of Machine Commonsense for If-Then Reasoning: https://homes.cs.washington.edu/~msap/atomic/
ArtificialIntelligenceArticles
Rust Detection Using Deep Learning https://www.youtube.com/watch?v=iYsSmnma8jA
Techniques for Detection of Rusting of Metals using Image Processing: A Survey

Most of industries around us make use of iron machines & tools for manufacturing their products. On the other
hand corrosion is a natural process that deteriorates the integrity
of iron surface. Therefore, rusting of iron takes place. To avoid unwanted accidents in industries, it is necessary to detect rusting in earlier stage, so that it can be prevented. Digital Image Processing for the detection of the rusting provides fast, accurate and objectives results. There have been many techniques for detection of rust. In this paper we are describing some existing techniques for detection of rusting. We have analyzed these
techniques and made comparison based o their approaches,
strengths and limitations.

https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.675.7088&rep=rep1&type=pdf