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Issue #151
This week in Issue #151:
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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
The keynotes for the NeurIPS 2021 LXAI Workshop have been published πŸŽ‰ 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.
The HuggingFace team has published an online course that ill teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem β€” πŸ€— Transformers, πŸ€— Datasets, πŸ€— Tokenizers, and πŸ€— Accelerate β€” as well as the Hugging Face Hub. It’s completely free and without ads.
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.
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 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.
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
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:
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!
Β© 2018 The Institute for Ethical AI & Machine Learning