Issue #263 🤖 🥳🥳🥳 Happy New Year 2024!! 🥳🥳🥳 How time flies! This month marks 5 years since starting this weekly Machine Learning newsletter 🎉 What started with just one commit, today now has almost 60,000 subscribers 🚀 And not a single Sunday missed 🤯 Thank you to everyone for your continued support - in today's newsletter we share a special edition celebrating our achievements throughout 2023! |
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Real Time AI Image Generation Open Source AI image generation is now possible in real-time with StreamDiffusion: StreamDiffusion is an innovative real-time diffusion pipeline designed for interactive image generation with significant performance improvements for live streaming and similar applications. Key features include Stream Batch for efficient batch processing, Residual Classifier-Free Guidance for reduced computational load, an Input-Output Queue for parallel processing, and a Stochastic Similarity Filter to optimize GPU usage. These advancements enable up to 1.5x faster processing and 91.07fps on an RTX4090 GPU, significantly reducing energy consumption. StreamDiffusion is ideal for real-time applications like game graphics and AI-assisted drawing, and is available as an open-source project for production machine learning practitioners. |
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Deep Learning 33 years vs now Deep Neural Nets: 33 years ago and 33 years from now - Andrej Karpathy on Yann LeCun et al.'s seminal 1989 paper: Andrej Karpathy revisit's Yann LeCunn's (et al.)'s paper on neural networks for handwritten zip code recognition, highlighting its modern relevance despite its small dataset and network size. Karpathy's reproduction in PyTorch is (obviously) significantly faster due to modern computational power & tooling, incorporates contemporary techniques like Adam optimizer, data augmentation, dropout, and ReLU activation, achieving notable (but expected) error rate reductions. This exercise underscores the enduring principles of neural network design while reflecting on the immense scale and efficiency advancements in machine learning, suggesting a future dominated by fine-tuning large, pre-trained models rather than training from scratch. |
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Cohere LLM Foundation Course Cohere's free online LLM course: A great resource offeriong a comprehensive online course covering foundational concepts and practical applications of Large Language Models in Natural Language Processing. The course covers hands-on learning together with real-world applications like semantic search, text generation, and classification. It's designed to be flexible, catering to various skill levels, and includes community support through Cohere's Discord. This makes it an ideal resource for those looking to deepen their understanding and practical skills in NLP and LLMs, particularly in a production environment. |
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Habits of Great Engineers 10(+) Habits of Great Software Engineers: A great article which outlines 10(+) key habits of great software engineers: 1) Focusing beyond the code, 2) antifragility, 3) joy of tinkering, 4) knowing the why, 5) thinking in systems, 6) tech detox, 7) the art of approximation, 8) transfering knowledge, 8) making hard things easy, 9) playing the long game, and 10+) encompasses a set of honourable mentions which are great to include into your tooling which you can check out in the article. These insights are particularly valuable for machine learning practitioners, who operate at the nexus of software engineering and data science. |
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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.
Upcoming MLOps conferences:
In case you missed our key talks:
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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.
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!
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| | | | The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning. | | | | |
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© 2023 The Institute for Ethical AI & Machine Learning |
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The Institute for Ethical AI & Machine Learning
