The MachineLearning EngineerIssue #151

This week in Issue #151:

• NeurIPS LX Workshop Keynotes
• HuggingFace Online NLP Course
• Neural Network for Chess Engine
• SpaCy vs NLTK. Normalization
• The State of AI-Generated Code
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

Forward email, or share the online version on 🐦 Twitter, 💼 Linkedin and 📕 Facebook!

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!

NeurIPS LX Workshop Keynotes

The keynotes for the NeurIPS 2021 LXAI Workshop have been published 🎉 We will be delivering a keynote on Responsible AI, together with two other Keynotes by Data Science Leader Ana Paula Appel on intersecting themes of industry & research, and Professor Joaquin Salas on the role of AI in challenging times.

HuggingFace Online NLP Course

The HuggingFace team has published an online course that ill teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It’s completely free and without ads.

Neural Network for Chess Engine

A great and comprehensive tutorial that shows how to first create a chess engine from scratch and then how to create and train an AI model that is able to run against the engine itself. It includes the underlying theory, code snippets and references.

SpaCy vs NLTK. Normalization

A comprehensive overview that takes two of the most popular python NLP libraries and evaluates them side by side to perform text normalization, sharing code snippets, comparisons and high level benchmarks including tradeoffs.

The State of AI-Generated Code

The AI O’Reilly team dives into the Github Copilot programme sharing their high level thoughts on practical and theoretical questions on the potential opportunities and challenges it presents.

OSS: GPU Accel. Frameworks

The topic for this week’s featured production machine learning libraries is GPU Acceleration Frameworks. 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:

• Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
• CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
• Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
• CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.

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!

OSS: Awesome AI Guidelines

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:

• AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems
• An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles
• 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!