AI Weekly October 1st 2018

Hi! New AI Weekly is here! Enjoy your weekend reading AI news and don’t forget to share it with your friends 😉


Why building your own Deep Learning Computer is 10x cheaper than AWS – If you’ve used, or are considering, AWS/Azure/GCloud for Machine Learning, you know how crazy expensive GPU time is. And turning machines on and off is a major disruption to your workflow. There’s a better way. Just build your own Deep Learning Computer. It’s 10x cheaper and also easier to use.

Building safe artificial intelligence: specification, robustness, and assurance – Building a rocket is hard. Each component requires careful thought and rigorous testing, with safety and reliability at the core of the designs. Rocket scientists and engineers come together to design everything from the navigation course to control systems, engines and landing gear. Once all the pieces are assembled and the systems are tested, we can put astronauts on board with confidence that things will go well. If artificial intelligence (AI) is a rocket, then we will all have tickets on board some day. And, as in rockets, safety is a crucial part of building AI systems. Guaranteeing safety requires carefully designing a system from the ground up to ensure the various components work together as intended, while developing all the instruments necessary to oversee the successful operation of the system after deployment.


Introduction to Machine Learning for Coders: Launch

ICLR 2019 Submissions


How to visualize decision trees – decision trees are the fundamental building block of gradient boosting machines and Random Forests™, probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models.

albumentations – fast image augmentation library and easy to use wrapper around other libraries

Release TensorFlow 1.11.0


Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting – Robots and autonomous agents often complete goal-based tasks with limited resources, relying on imperfect models and sensor measurements. In particular, reinforcement learning (RL) and feedback control can be used to help a robot achieve a goal. Taking advantage of this body of work, this paper formulates general computation as a feedback-control problem, which allows the agent to autonomously overcome some limitations of standard procedural language programming: resilience to errors and early program termination.


Kaggle “Quick, Draw!” Doodle Recognition Challenge


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