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Our repository on AI regulation, principles and guidelines has reached over 1k stars on Github ⭐🚀 This resource has served as a comprehensive resource to index resources relevant to the fast evolving space of Responsible Machine Learning and AI Ethics. It includes high-level frameworks, practical processes, interactive tools, industry standards, educational courses, research updates, and regulatory information. This curated list encompasses everything from ethical principles to bias audit tools to codes-of-conduct and global AI regulations. Best thing is that it is open source, so if there is any resources you find are missing they are one pull-request away, and we would thoroughly appreciate contributions!
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Andrej Karpathy's course on Deep Learning - From Zero to Hero: A great advanced course for machine learning practitioners, focusing on building neural networks from the ground up with emphasis on deep learning and language models. It covers the fundamentals of neural networks and backpropagation, dives into language modeling with a focus on character-level models and multilayer perceptrons (MLPs), and progresses to more complex topics like Batch Normalization, manual backpropagation techniques, and constructing architectures akin to WaveNet. The course also delves into building a Generatively Pretrained Transformer (GPT) from scratch, providing practical, hands-on experience in deep learning, particularly beneficial for those with a solid Python background and basic math knowledge.
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Generative AI is now reaching mainstream high quality video generation with Stable Diffusion's Video model release: Stability AI's Video Diffusion model is a new addition to their suite of AI models, currently focused on research and not intended for real-world or commercial use. They are soliciting feedback for its refinement and highlight its potential applications in sectors like Advertising, Education, and Entertainment. The model is part of a broader range of open-source models across various modalities, with the waitlist open for this new Text-To-Video interface.
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Hidden Technical Debt in Machine Learning Systems: A great resource to revisit from time to time, which has provided a pioneering view to the challenges of production machine learning, together with one of the most re-used images in MLOps. It was published in NeurIPS back in 2015by D. Sculley and colleagues, and warns production machine learning practitioners about the often-overlooked long-term maintenance costs or "technical debt" of ML systems. It highlights critical risk factors such as boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and system-level anti-patterns. This work emphasizes the importance of careful system design and maintenance considerations in ML projects to prevent accruing hidden costs and complexities over time.
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Machine Learning security continues to grow in importance across production systems; this article "Hacking Google Bard" dives into a vulnerability reported (and resolved) reminding us the importance: Google Bard, is Google's version of ChatGPT - this article showcases data exfiltration through Indirect Prompt Injection attacks. This vulnerability was exploited by using Bard's new Extensions feature to access personal documents and emails, and manipulating its markdown rendering capability to create image tags that connected to an attacker-controlled server. The exploit involved bypassing Google's Content Security Policy using Google Apps Script, enabling the exfiltration of chat history to a Google Doc. The issue was reported to Google and fixed within a month, highlighting the security challenges in LLM applications, especially when handling sensitive data.
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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.
Check out our "MLOps Curriculum" from previous conferences:
Relevant upcoming MLOps conferences:
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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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© 2023 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
