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Issue #113
This week in Issue #113:
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Announcing the Linux Foundation principles for Trusted AI, a fantastic and exciting initiative we have been contributing to for the last couple of months is finally live. These principles provide a strong foundation for open source and AI practitioners and researchers through the renowned Linux Foundation ecosystem.
A deep dive into GPU processing in Python using the Vulkan Kompute framework. This talk covers the motivations and trends in GPU processing, as well as the high level concepts and architecture of the Vulkan SDK, as well as how the Kompute framework extends it to provide a Python interface by implementing a simple logistic regression algorithm from scratch that can run in cross-vendor GPUs (AMD, Qualcomm, NVIDIA & Friends).
A fantastic in-depth hands on introduction into developing a Chess AI engine from scratch, covering the intuition, methodology and code required to get up and running. A full demo is also available for anyone looking to test their Chess skills.
The Data Exchange Podcast brings a conversation with LynxMD CEO Omer Dror, a startup that enables data exchanges and markets in the health and life sciences. In this podcast they delve into insights around datasets, privacy preserving tools, NLP, trends in the field and more.
An interesting article that delves into the intersection between SREs and production machine learning systems at scale by proposing the resposibilities that would encompass a "Machine Learning Reliability Engineering" role.
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