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Issue #133
This week in Issue #133:
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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!
SHAP Author Scott Lundberg shares an insightful perspective in an article that advocates for best practices in explainable AI and raises awareness of the pitfalls of trying to extract causal insights from modern predictive machine learning models.
A comprehensive article that covers 7 high level themes in security related to MLOps systems. This first article covers the first one which is protecting data, and provides an intuition across multiple phases of the ML lifecycle.
The data exchange podcast dives into conversation with AI Researcher Connor Leathy, and discusses large language models, open datasets for language models, the state of natural language research, and more.
An interesting article that presents the motivations and tools available to introduce automation on data pre-processing, cleaning, curation, and labelling across open and closed datasets of all types and sizes.
A proposed conceptual framework advocating for best practices on programming, differentiating clever vs insightful code, showing snippets that provide an intuition on what can be both a practical and philosophical concept.
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:
  • Vulkan 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!
© 2018 The Institute for Ethical AI & Machine Learning