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Issue #210
THE ML ENGINEER 🎅
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❄️❄️❄️
We wish to thank every single one of our 20k subscribers for your support 💖 and wish you all happy holidays!
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If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
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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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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The history of Modern AI and Deep Learning has an ambiguous and bumpy lineage. Jürgen Schmidhuber has put together a fantastic annotated history timeline highlighting key "most important / relevant" events in the history of neural networks, deep learning, computer science, and mathematics in general, crediting those who laid the foundations of the field.
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The ability to data-mine the entirity of github.com can provide absolutely insightful results. The team behind the OSS Insights project has put together a fantstic analysis extracted from crunching the entirity of github.com 2022 data, showcasing insights on top programming languages, repositories, tech trends, activity and more.
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It is indeed hard to believe that only a few weeks ago, the OpenAI team released ChatGPT, a state-of-the-art chatbot model that has taken the world by storm. Now OpenAI has done it again with the release of Point-E, a new model for generating 3D models from text. This article from DagsHub provides a fantastic overview of this new model together with an interactive application.
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Geoffrey Hinton published the Forward Forward Algorithm last week, an interesting alternative approach to backpropagation which does not require calculating the gradient of the loss function with respect to the network parameters. This codebase provides an intuitive explanation together with an implementation of the algorithms, as well as insightul results against the traditional backprop algorithm.
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Developing production machine learning infrastructure is challenging, but finding individuals with the right skills to deliver these systems in an organisation is even harder. Exscientia's MLOps Lead Oleksandr Stasyk has put together insightful thoughts on their journey building MLOps teams and finding the right talent, described as the T-shaped machine learning + engineering + devops skillsets.
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Upcoming MLOps Events
The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.
Conferences we'll be speaking at:
Other relevant upcoming MLOps conferences:
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MLConf - 30th March @ New York
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MLSys - 4th June @ Florida
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Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. 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. Four featured libraries in the GPU acceleration space are outlined below.
- 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 open source and open community events that are not listed do give us a heads up so we can add them!
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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:
- MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
- AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
- An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
- ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.
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© 2018 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
