Build your own robotic cat!

Blog by Alex Bate: https://www.raspberrypi.org/blog/robotic-cat-petoi-nybble/

#Arduino #ArtificialIntelligence #MachineLearning #RaspberryPi #Robotics

See arXiv’s Feedback on the Guidance on the Implementation of Plan S https://blogs.cornell.edu/arxiv/2019/02/04/arxivs-feedback-on-the-guidance-on-the-implementation-of-plan-s/ #PlanS #preprints

Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms

https://arxiv.org/abs/1904.10626

Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation. (link: https://arxiv.org/abs/1904.11102) arxiv.org/abs/1904.11102

PAN: Path Integral Based Convolution for Deep Graph Neural Networks. (link: https://arxiv.org/abs/1904.10996) arxiv.org/abs/1904.10996

Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks. link: https://arxiv.org/abs/1904.10082

Orientation Aware Object Detection with Application to Firearms. (link: https://arxiv.org/abs/1904.10032) arxiv.org/abs/1904.10032

A Fast, Semi-Automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imag... (link: https://arxiv.org/abs/1904.09978) arxiv.org/abs/1904.09978

The computational power of Quantum Computers: an intuitive guide
Blog by Karel Dumon: https://medium.com/@kareldumon/the-computational-power-of-quantum-computers-an-intuitive-guide-9f788d1492b6
#QuantumComputing #QuantumPhysics #MachineLearning

"The scientist is not a person who gives the right answers, he's one who asks the right questions."

-- Claude Lévi-Strauss

Modern Deep Learning Techniques Applied to Natural Language Processing
https://nlpoverview.com/index.html @ArtificialIntelligenceArticles

Mark Zuckerberg & Yuval Noah Harari in Conversation


https://www.youtube.com/watch?v=Boj9eD0Wug8&feature=youtu.be&fbclid=IwAR1YBK-WRhSDmdiY5HRpkGDPI4ytBaIn6mSPRn1RGslEJ9eXxWF9avxVFeg

UN Handbook on Privacy-Preserving Computation Techniques"
By the Privacy Preserving Techniques Task Team (PPTTT): https://docs.google.com/document/d/1GYu6UJI81jR8LgooXVDsYk1s6FlM-SbOvo3oLHglFhY/edit#
#computation #machinelearning #technology @ArtificialIntelligenceArticles

FOR.ai Reinforcement Learning Codebase
Generic reinforcement learning codebase in TensorFlow: https://github.com/for-ai/rl @ArtificialIntelligenceArticles
#reinforcementlearning #tensorflow #technology

GANs and Divergence Minimization

By Colin Raffel:

https://colinraffel.com/blog/gans-and-divergence-minimization.html

@ArtificialIntelligenceArticles
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

A Selective Overview of Deep Learning https://www.princeton.edu/~congm/Publication/DL_survey/DL_survey.pdf

#weekend_read
Paper-Title: Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Link to the paper: https://arxiv.org/pdf/1904.11483.pdf
#Stanford #HRI
TL;DR: [1] They presented a decision-making framework for autonomously navigating urban intersections.

[2] Secondly, they introduced a learned belief updater that uses an ensemble of RNNs to estimate the location of vehicles behind obstacles and is robust to perception errors.

[3] Further they improved upon pure reinforcement learning methods by using a model checker to enforce safety guarantees.

[4] Finally, through a scene decomposition method they demonstrated how to efficiently scale the algorithm to scenarios with multiple cars and pedestrians.

Machine Learning From Scratch

Bare bones #Python implementations of #MachineLearning models and algorithms with a focus on accessibility.

By Erik Linder-Noren: https://github.com/eriklindernoren/ML-From-Scratch...
See More

Deep Learning: AlphaGo Zero Explained In One Picture

By L.V.: https://api.ning.com/files/G3detyndwpXvT8Py3CFA1rtuPS549-KcvNCPjfyaORlWtrBVjnT7MSsnV5zQmlOYZg8n9cIqQqf2u4GMq0VHnN1AE-nlYFnx/porc.png

A Gentle Introduction to 1×1 Convolutions to Reduce the Complexity of Convolutional Neural Networks
https://machinelearningmastery.com/introduction-to-1x1-convolutions-to-reduce-the-complexity-of-convolutional-neural-networks/

Ironically, Yuval Noah Harari's equation of B X C X D= HH, where B=biological knowledge, C=computer power, D=data, HH=human hacking in days after the 1st report of direct #brain activity to speech.

Fei Fei Li to YNH : "Okay, can I be specific? First of all the birth of AI is AI scientists talking to biologists, specifically neuroscientists, right. The birth of AI is very much inspired by what the brain does. Fast forward to 60 years later, today's AI is making great improvements in healthcare. There's a lot of data from our physiology and pathology being collected and using machine learning to help us. But I feel like you're talking about something else."

https://www.wired.com/story/will-artificial-intelligence-enhance-hack-humanity/