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Issue #112
This week in Issue #112:
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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!
This week we are organising an online meetup on "Misinformation and Bias", where Dr. Magdalena Liz will cover how are governments and companies tackling issues related to this topic. Dr. Magdalena is an advisor for the Centre for Data Ethics & Innovation, a body established by the government to advise on challenges & opportunities of AI.
As companies embrace digital technologies to transform their operations and products, many are using best-of-breed software, open source tools, and software as a service (SaaS) platforms to rapidly and efficiently integrate new technologies which results in growing metadata sources - this blog post provides an overview of the ecosystem of metadata management systems.
PapersWithCode has launched their datasets component; they are now indexing 3000+ research datasets from machine learning. This enables users to find datasets by task and modality, compare usage over time, browse benchmarks, and more. You can now explore the live dataset in their new section.
A comprehensive analysis of machine learning platforms in 2021. This article dives into machine learning frameworks and plaforms across a broad range of quadrants, covering from cloud / onprem, to specialised / end-to-end.
Synced has compiled a global list of proposals, rules and regulatory frameworks for AI introduced in 2020, covering Ai governance frameworks, whitepapers, executive orders and beyond.
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