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THE ML ENGINEER 🤖
Issue #95
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This week in Issue #95:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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A hands on tutorial that covers how to train a machine learning model using the Reddit comment moderation dataset, and deploy it in a scalable infrastructure using Kafka and Seldon Core. It includes the accompanying code, together with the video format of the content that also covers core concepts in stream processing.
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Awful AI is a curated list to track current scary usages of AI. The authors put together this open source list, hoping to raise awareness to its misuses in society, as well as encourage best practices.
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Sebsatian Raschka has put together a comprehensible article that outlines core founcations when using NumPy and Matplotlib. This content is from the course he delivers on "introduction to ML and statistical pattern classification".
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A great repository that provides a broad range of jupyter notebooks to build and deep learning models that can be used across a broad range of applications. The models range across a lot of categories including Foundational Algorithms, GANs, Autoencoders, CNNs, and more.
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There has been a lot of applications of AI into the software development space itself - this paper has put together a comprehensive overview of the current state of the ecosystem around the use of deep learning in software engineering research.
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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.
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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
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About us |
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The Institute for Ethical AI & Machine Learning is a Europe-based research centre that carries out world-class research into responsible machine learning.
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© 2018 The Institute for Ethical AI & Machine Learning
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