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

Performing Structured Improvisations with pre-trained Deep Learning Models
Pablo Samuel Castro: https://arxiv.org/abs/1904.13285
#deeplearning #technology #deepgenerativemodels #machinelearning

Has anyone checked out MIT's open-sourced introductory course in Deep Learning? Lectures and labs are included:

https://medium.com/tensorflow/mit-introduction-to-deep-learning-4a6f8dde1f0c

Limitations of adversarial robustness: strong No Free Lunch Theorem

theoretical paper on the impossibility of adversarial robustness

SLIDES
https://drive.google.com/file/d/1IOmZqLtujaqIywdPMtEOde6pT2bnlbg4/view
paper
https://arxiv.org/pdf/1810.04065.pdf

Detection Malaria with Deep learning: Health Care
https://towardsdatascience.com/detecting-malaria-with-deep-learning-9e45c1e34b60
Dataset with article...

AI paper of the day

Researchers recently developed and trained a CNN based on facial expression recognition, and explored its classification mechanism. Using a deconvolution visualization method, they project the extremum point of the CNN back to the pixel space of the original image. They also design the distance function to measure the distance between the presence of facial feature unit and the maximal value of the response on the feature map of CNN. Read more...

[https://medium.com/ai%C2%B3-theory-practice-business/ai-scholar-deep-learning-facial-expression-recognition-research-fcaa0a9984b6](https://medium.com/ai%C2%B3-theory-practice-business/ai-scholar-deep-learning-facial-expression-recognition-research-fcaa0a9984b6)

BoTorch: Bayesian Optimization in PyTorch




Official site: https://botorch.org

Github repo: https://github.com/pytorch/botorch

Techcruch article:
https://techcrunch.com/2019/05/01/facebook-open-sources-ax-and-botorch-to-simplify-ai-model-optimization/

Parallel and Distributed Deep Learning: A Survey
https://towardsdatascience.com/parallel-and-distributed-deep-learning-a-survey-97137ff94e4c

Best of (link: https://arXiv.org) arXiv.org for AI, Machine Learning, and Deep Learning – March 2019

https://insidebigdata.com/2019/04/09/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-march-2019/

Survey on Automated Machine Learning. (link: https://arxiv.org/abs/1904.12054) arxiv.org/abs/1904.12054

Real numbers, data science and chaos: How to fit any dataset with a single parameter

Laurent Boué: https://arxiv.org/abs/1904.12320

Code: https://github.com/Ranlot/single-parameter-fit/

#artificialintelligence #datascience #dataset #machinelearning

Statistical Physics of Liquid Brains


Liquid neural nets (or “liquid brains”) – class of cognitive living networks
characterised by the agents (ants or immune cells, for example) moving in space are compared with standard neural nets

https://www.biorxiv.org/content/10.1101/478412v1