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Issue #111
This week in Issue #111:
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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!
An interesting analysis of key stats and trends of jupyter notebooks on Github, together with statistics on the popularity of python libraries for ML used, and relevant linked trends from youtube/google/data sources.
OpenAI shares insights on how they've pushed their Kubernetes cluster towards massive scale for their large models like GPT-3, CLIP and DALL-E, but also for rapid smaller-scale iterative research initiatives such as scaling laws for neural language models.
An analysis of 30,000 unique data science blog posts from 2020 to identify the most discussed topics and technologies. This blog post provides deeper insights on software tools, programming languages, cloud platforms and data science platforms.
This short video series aims to introduce core concepts in software architecture and software requirements planning into production machine learning systems. It covers the motivations, approaches towards requirements gathering, methodologies to take technical decisions, software architecture diagram approaches, communication, documentation, skills and personal development
The Montreal AI Ethics Institute has published "The State of AI Ethics Report (January 2021)" which captures the most relevant developments in AI Ethics since October of 2020. In this report they cover 8 key themes including: 1) Algorithmic Injustice, 2) Discrimination, 3) Ethical AI, 4) Labor Impacts, 5) Misinformation, 6) Privacy, 7) Risk & Security, and 8) Social Media.
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 Vulkan compute framework optimized for advanced GPU 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