Why Responsible
AI Development
Needs Cooperation
on Safety

By Amanda Askell, Miles Brundage and Jack Clark: https://openai.com/blog/cooperation-on-safety/

#ArtificialIntelligence #AIEthics #AISafety #AIGovernance #Governance

SLIDES - Cornell Tech, Learning Machines Seminar, New York City, March 2019
Anticipating the Unseen and Unheard for Embodied Perception
Kristen GraumanUniversity of Texas at Austin
Facebook AI Research

https://www.cs.utexas.edu/%7Egrauman/slides/grauman-cornell2019.pdf

Machine Learning for Health Postodoctoral Position


Open Postdoctoral Positions In Machine Learning for Health
The ML4H lab in in the Department of Computer Science at the University of Toronto, and at the Vector Institute is seeking motivated postdoctoral researcher for the Fall 2019 with a strong background in machine learning. The goal is to push the state-of-the-art in machine learning on the major challenges arising in health and health care.

Researchers will have the potential to participate in and create projects that target basic, translational science that bring novel machine learning techniques towards meaningful applications. Machine learning topics of interest include, but are not limited to, probabilistic modeling, representation learning, deep learning, time-series modelling, generative models, integrating multi-modal data, model interpretability, convex and non-convex optimization.

These topics are inspired by the challenges posed by biomedical data: high-dimensional, multi-modal datasets with missing data, collected under noisy and imperfect conditions, with complex temporal dynamics and a sensitive nature.

Applying
Candidates should send a research proposal, a CV and a cover letter/personal statement including the names of three referees to Dr. Marzyeh Ghassemi and use “ML4H Postdoc Application” in the subject line.

Qualifications
Interested postdoc applicants should have a Ph.D. in machine learning, fairness, applied causality, RL, or statistics with a strong publication record in top conferences such as NeurIPS, ICML, ICLR, AISTATS, AAAI, KDD, AMIA, MLHC, FAT*, etc.

Prior experience working on health-related data is not required, but you must be interested in meaningful applications of your work.

Fast Estimating Pedestrian Moving State Based on Single 2D Body Pose by Shallow Neural Ne... arxiv.org/abs/1907.04361

Bilevel Integrative Optimization for Ill-posed Inverse Problems. arxiv.org/abs/1907.03083

Video Question Generation via Cross-Modal Self-Attention Networks Learning. arxiv.org/abs/1907.03049

Head animation from single shot by #SamsungAI team

Samsung researchers have released a model that can generate faces in new poses from just a single image/frame (for each of face, pose). Done by building a well-trained landmark model in advance & one-shotting from that, using keypoints, adaptive instance norms and GANs. Model performs no 3D face modelling!

ArXiV: https://arxiv.org/abs/1905.08233v1
Youtube: https://www.youtube.com/watch?v=p1b5aiTrGzY

#GAN #CV #DL

21 Must-Know Open Source Tools for Machine Learning you Probably Aren’t Using (but should!)


https://www.analyticsvidhya.com/blog/2019/07/21-open-source-machine-learning-tools/

Five fundamental truths about algorithms for anyone living in the Digital Age. I would love to hear your thoughts! #machinelearning #bigdata #algorithms #artificialintelligence

https://www.youtube.com/watch?v=XYCq3K_XxZY&feature=share&fbclid=IwAR2CCOiLZgbNXG0AQC5hRfTNhETBlJ60ieQmVgXSYlXrwRHhugk_f01NSvg

Applied Machine Learning Engineer at Amazon
EAST PALO ALTO, CA, USA

https://www.marktechpost.com/job/applied-machine-learning-engineer-at-amazon/

https://t.iss.one/ArtificialIntelligenceArticles

Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science: MIT

Download Link: https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf

From Planck Area to Graph Theory: Topologically Distinct Black Hole Microstates. arxiv.org/abs/1907.03090

M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention

Ma et al.: https://arxiv.org/abs/1907.04378

#MachineLearning #DeepLearning #ArtificialIntelligence

Sparse Networks from Scratch: Faster Training without Losing Performance
Tim Dettmers and Luke Zettlemoyer: https://arxiv.org/abs/1907.04840
Paper: https://arxiv.org/abs/1907.04840
Blog post: https://timdettmers.com/2019/07/11/sparse-networks-from-scratch/
Code: https://github.com/TimDettmers/sparse_learning
#MachineLearning #NeuralComputing #EvolutionaryComputing

Interesting robotics application from CVPR 2019! paper: https://www.profillic.com/paper/arxiv:1901.04780

code:https://www.profillic.com/paper/arxiv:1901.04780/code

Densefusion: The model takes an RGB-D image as input and predicts the 6D pose of the each object in the frame.