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/

Mathematics for Artificial Intelligence
https://rubikscode.net/2019/04/29/mathematics-for-artificial-intelligence-linear-algebra/

Meta-Sim: Learning to Generate Synthetic Datasets
Kar et al.: https://arxiv.org/abs/1904.11621 @ArtificialIntelligenceArticles
#ComputerVision #PatternRecognition #ArtificialIntelligence

Conversation between Lex Fridman and Oriol Vinyals about DeepMind AlphaStar, StarCraft, and Language
Artificial Intelligence podcast: https://youtu.be/Kedt2or9xlo
#AlphaStar #ArtificialIntelligence #DeepLearning #ReinforcementLearning

Invertible Residual Networks
Behrmann et al.: https://arxiv.org/abs/1811.00995
#MachineLearning #ArtificialIntelligence #ComputerVision

A first in medical robotics: Autonomous navigation inside the body

https://techxplore.com/news/2019-04-medical-robotics-autonomous-body.html?fbclid=IwAR3ragCWgmUkIpQffYdtebY6mnRO7U6d__4uK_nsZKuaoTtv7JHBY0ZNUNo

Taming Recurrent Neural Networks for Better Summarization
https://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html

Unsupervised Data Augmentation
Xie et al.: https://arxiv.org/abs/1904.12848
#DeepLearning #MachineLearning #ArtificialIntelligence #UnsupervisedLearning

This is lecture 3 in the series on Wasserstein GAN.. In this lecture, basic understanding of Wasserstein Generative Adversarial Network (WGAN) is discussed
https://youtu.be/aenOaiQPSYA
You can subscribe channel for more such videos
https://www.youtube.com/user/kumarahlad/featured?sub_confirmation=1

Claude Shannon, John McCarthy, Ed Fredkin and Joseph Weizenbaum
@ArtificialIntelligenceArticles
#AI in the 60's

Cornell University - Machine Learning for Intelligent Systems (CS4780/ CS5780)

I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.


Youtube Video Lectures:
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
Course Lecture Notes:
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

#artificialintelligence #machinelearning #deeplearning #AI #algorithms #computerscience #datascience

The once-hot robotics startup Anki is shutting down after raising more than $200 million
It’s a hard, hard fall.
By Theodore Schleifer: https://www.vox.com/2019/4/29/18522966/anki-robot-cozmo-staff-layoffs-robotics-toys-boris-sofman
#ArtificialIntelligence #MachineLearning #Robotics

Yining Shi just made three Doodle Classifier experiments with Tensorflow.js:
1. Train a doodle classifier with tf.js
2. Train a doodle classifier with 345 classes
3. KNN doodle classifier
Code and demo: https://github.com/yining1023/doodleNet
#MachineLearning #TensorFlow #tensorflowjs #doodles

Activation Atlases: a new technique for visualizing what interactions between neurons can represent

By Google and OpenAI.

Blog: https://blog.openai.com/introducing-activation-atlases/
Paper: https://distill.pub/2019/activation-atlas
Code: https://github.com/tensorflow/lucid/…
Demo: https://distill.pub/2019/activation-atlas/app.html

#artificialintelligence #deeplearning #machinelearning #neuralnetworks

FOUR Productivity FEYNMAN- strategies:
i) Stop trying to know-it-all.
ii) Don't worry about what others are thinking.
iii) Don't think about what you want to be, but what you want to do.
iv) Have a sense of humor and talk honestly. @ArtificialIntelligenceArticles

The field of #machinelearning seeks to answer the question "How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?," Tom Mitchell on the Discipline of Machine Learning

Story: https://mld.ai/6b76a
Paper: https://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf

#ML #artificialintelligence #research #carnegiemellon #scsatcmu [ machine learning ] [ artificial intelligence ] #AI #education

#ArtificialIntelligence #DeepLearning #MachineLearning

A wonderful comprehensive read from #Google_Brain and #DeepmindAI on the challenges which we can come across while implementing RL on real-world systems.

Paper-Title: Challenges of Real-World Reinforcement learning
Link to the paper: https://arxiv.org/abs/1904.12901

They highlighted 9 most important challenges as follows:

1. Training off-line from the fixed logs of an external behavior policy.
2. Learning on the real system from limited samples.
3. High-dimensional continuous state and action spaces.
4. Safety constraints that should never or at least rarely be violated.
5. Tasks that may be partially observable, alternatively viewed as non-stationary or stochastic.
6. Reward functions that are unspecified, multi-objective,or risk-sensitive.
7. System operators who desire explainable policies and actions.
8. Inference that must happen in real-time at the controlfrequency of the system.
9. Large and/or unknown delays in the system actuators,sensors, or rewards.

Wave Physics as an Analog Recurrent Neural Network
Hughes et al.: https://arxiv.org/abs/1904.12831
#ComputationalPhysics #DeepLearning #MachineLearning #EvolutionaryComputing #Physics