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Issue #96
This week in Issue #96:
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A hands on tutorial that teaches you how to leverage your on-device smartphone GPU for accelerated data processing and machine learning edge use-cases. This article covers how to build a simple Android App using the Native Development Kit (NDK) and run on-device GPU accelerated processing using the Vulkan Kompute framework.
Pachyderm Chief Evangelist Dan Jeffries writes about the canonical stack in machine learning, proposed to be analogous to the LAMP/MEAN stack in web development. This includes data processing, experimentation, productisation and compliance capabilities - as well as the open source and enterprise tools that could be involved throughout the ML & data lifecycle.
A16z has put together a comprehensible report on emerging architectures for modern data infrastucture. In this report they cover the underlying patterns arising for analytics and operational systems around data-powered products.
Facebook Engineering Director Joaquin Candela joins the weights & biases podcast to discuss his thoughts around fairness, as well as his work scaling and democratising AI at facebook.
This week we'll be hosting the meetup, "AI Ethics - Whose Ethics? An Analysis Across Eastern & Western Philosophy". During this session, we will dive into the similarities and differences in foundational philosophical concepts such as the meaning of good, continuity & the self, and we'll analyse published resources in the space of AI Ethics & Principles across the globe.
The topic for this week's featured production machine learning libraries is Privacy Preserving ML. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:
  • Google's Differential Privacy - This is a C++ library of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.
  • Intel Homomorphic Encryption Backend - The Intel HE transformer for nGraph is a Homomorphic Encryption (HE) backend to the Intel nGraph Compiler, Intel's graph compiler for Artificial Neural Networks.
  • Microsoft SEAL - Microsoft SEAL is an easy-to-use open-source (MIT licensed) homomorphic encryption library developed by the Cryptography Research group at Microsoft.
  • PySyft - A Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch.
If you know of any libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request
As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:
  • ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018
  • From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request
© 2018 The Institute for Ethical AI & Machine Learning