Richard Feynman, Winner of the 1965 Nobel Prize in Physics, gives us an insightful lecture about computer heuristics: how computers work, how they file information, how they handle data, how they use their information in allocated processing in a finite amount of time to solve problems and how they actually compute values of interest to human beings. These topics are essential in the study of what processes reduce the amount of work done in solving a particular problem in computers, giving them speeds of solving problems that can outmatch humans in certain fields but which have not yet reached the complexity of human driven intelligence. The question if human thought is a series of fixed processes that could be, in principle, imitated by a computer is a major theme of this lecture and, in Feynman's trademark style of teaching, gives us clear and yet very powerful answers for this field which has gone on to consume so much of our lives today. No doubt this lecture will be of crucial interest to anyone who has ever wondered about the process of human or machine thinking and if a synthesis between the two can be made without violating logic. ---

https://www.youtube.com/watch?v=ipRvjS7q1DI&fbclid=IwAR1ysEkCG2hcjuGw9TOZHMkOU35wSAOvXv6bEfEi4U8yPQiXKy0pUElLfnU

A school in China is monitoring students with facial-recognition technology that scans the classroom every 30 seconds
https://www.businessinsider.com/china-school-facial-recognition-technology-2018-5

A free resource for researchers and practitioners to find and follow the latest state-of-the-art ML papers and code.
https://paperswithcode.com/sota?fbclid=IwAR0r0_RDwcrNUFjNCXlVkQXkarmXlgEKk4Vy5Zn6XQ7nAGq1D1yBlCZhXY8

This is my neck of the woods. - DL
https://www.wbur.org/news/2019/11/25/boston-dynamics-robot-dog-massachusetts-state-police

DeepSynth: Program Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning. https://arxiv.org/abs/1911.10244

SWAG: Item Recommendations using Convolutions on Weighted Graphs. https://arxiv.org/abs/1911.10232

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring. https://arxiv.org/abs/1911.09785

Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP

Causality for Machine Learning
Bernhard Schölkopf : https://arxiv.org/abs/1911.10500
#MachineLearning #DeepLearning #ArtificialIntelligence

Richard S. Sutton: intelligence is trying to build a model of the world.

Exactly.

https://www.youtube.com/watch?v=XqzT-rBDTPU&fbclid=IwAR3jWkhrWorNDr9Wyvd_zS71O3jrspT3aPzYCJ93ussUPiOnOd7ZRO_EWUc

This is an exhaustive list of Monte Carlo tree search papers from major conferences including NIPS, ICML, and AAAI. Some of them with publicly available implementations.

https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers

#datascience #machinelearning #deeplearning #python #ai #analytics #datamining

Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcem... https://arxiv.org/abs/1911.10334

Compressing Representations for Embedded Deep Learning. https://arxiv.org/abs/1911.10321

PlantDoc: A Dataset for Visual Plant Disease Detection. https://arxiv.org/abs/1911.10317

Deep learning achieved great success in modeling sensory processing. However, such models raise questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (biological circuit) with another (a deep net), without understanding either? https://papers.nips.cc/paper/9060-from-deep-learning-to-mechanistic-understanding-in-neuroscience-the-structure-of-retinal-prediction https://t.iss.one/ArtificialIntelligenceArticles

Deep learning from the topological, metric, information, causal, physics, computational, and neuroscience perspective. A nice assay by Raul Vicente: "The many faces of deep learning:" https://arxiv.org/abs/1908.10206