Stanford Foundation Model Index Stanford has released this year's Foundation Model Transparency Index, which ranks the transparency of AI model governance across tech giants and AI research labs: Some of the models in scope for this report incude META's Llama2, Mistral's 7B, Anthrophic Claude3, OpenAI GPT4, Google Gemini and many others across Amazon, Adept, IBM, etc. Key results showcase a general improvement across the board on transparency, however highlighting there is still a way to go - certainly an interesting space to keep watch given the large gap. |
|
|---|
|
Diffusion Models Compendium Stable Diffusion continues to make waves in high-quality generative AI image generation - this resource provides a great overview on the intuition on the intricacies behind stable difussion models. This article dives into how these models generate data, as well as the broader applications of these models in music, video, 3D modeling, and even computational biology. Furthermore it provides relevant perspectives on the ethical concerns regarding dataset sourcing as well as practical examples for ML practitioners. |
|
|---|
|
Continuous Delivery of AI Systems The Outerbounds team shares a deep dive on how to organise continuous delivery of ML/AI systems through a 10-stage matury model. Produciton ML systems face challenges that go beyond traditional software systems, such as extensive computational needs, data unpredictability, and post-deployment validation; this demands specialised approaches when adopting standard methodologies such as CI/CD. Some of the key principles include GitOps integration, scalable compute resources, robust data and change management, and isolated environments for safe experimentation. |
|
|
|---|
|
Automated Vehicles Act UK An insightful development in nation-wide embracing of AI with the UK's Self-Driving Vehicles Act enacted last week, with an exciting 2-year ambition: Quite insightful to see how governments progress in the adoption of production AI systems to integrate with society with safety in mind, specifically this regulatory framework introducing ambitious plans to have self-driving vehicles operating on UK roads by 2026. This legislation specifically establishes rigorous safety standards, independent incident investigations, and clear liability frameworks - it also highlights the creation of over 38,000 jobs by 2035, providing a practical view on the potential opportunity in the industry. This of course comes with key challenges, as well as critical considerations which have arisen in other contexts where (semi-)self-driving cars have slowly been introduced. |
|
|
|---|
|
GenAI Red Teaming Report 2024 The Generative AI Red Teaming Challenge Transparency Report 2024 showcasing AI biases and ML vulnerabilities: This report brings together key insights discovered through various red teaming exercises by HumaneAI in collaboration with various leading tech organisations Nvidia, META, OpenAI, Stability, etc. Red teaming for ML systems is becoming growingly critical due to the cybersecurity risks posed in production AI models. Key findings in this 2024 report include the introduction of biases via challenge design, geographic and linguistic biases favoring U.S. and English-speaking contexts, and inconsistent model responses due to overcorrection for minority groups. |
|
|---|
|
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 conferences where we're speaking:
Other upcoming MLOps conferences in 2024:
In case you missed our talks:
|
|
|---|
| | |
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! |
|
|---|
| | |
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!
|
|
|---|
| | |
| | | | The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning. | | | | |
|
|
|---|
|
|
This email was sent to You received this email because you are registered with The Institute for Ethical AI & Machine Learning's newsletter "The Machine Learning Engineer"
|
| | | | |
|
|
|---|
|
© 2023 The Institute for Ethical AI & Machine Learning |
|
|---|
|
|
|
The Institute for Ethical AI & Machine Learning
