Reverse Eng Github's App AI Simon Willison shared last week a masterclass on reverse engineering with GitHub's new prompt-to-app agentic service: Recently GH released GitHub Spark to enable users to turn prompts into into React + TypeScript micro‑apps running in a Codespaces‑style container. Simon Willison was able to extract the system prompt as well as some of the internals such as the tools of the system through the prompt-to-app functionality itself by requesting a self‑documenting app that explains all its own internals. It is quite interesting to see the level of detail that it is possible to extract with these approaches, and ultimately reminds us of the challenges that arise when releasing a production service to the wild. |
|
|
|---|
|
Raschka's LLM Architectures Sebastian Raschka has put together an overview on the evolution of LLM architectures, together with the most comprehensive set of intuitive visual diagrams on LLMs: It is interesting to see that Sebastian Raschka argues that these models are still structurally similar to when they were initially released. Positional embeddings have evolved from absolute to rotational (RoPE), Multi-Head Attention has largely given way to Grouped-Query Attention, and the more efficient SwiGLU has replaced activation functions like GELU, however it is yet to be seen if there's been step-change breakthroughs since inception. Definitely worth diving into what seems to be one of the most comprehensive (intutive) overviews of LLMs out there. |
|
|
|---|
|
State of Homomorphic Encryption I've been keeping an eye on Homomorphic Encryption for a while, and it is exciting to see a moore's law-like evolution suggesting that we may be getting closer to broader applications: Fully Homomorphic Encryption basically lets servers run arbitrary computations encrypted data without decrypting it, which means that privacy is preserved through operations. The primarily disadvantage has been the costly performance hits required to perform computations, however it seems that as the field evolves, we see more production use-cases of FHE in industry, which could really be a game changer in today's privacy aware world. |
|
|---|
|
Ray Data, Train and Tune It is interesting to see end-to-end use-cases of MLOps infrastructure to process data at scale, and particularly it seems Ray has been seeing an increase in adoption in use-cases beyond purely ML. This is an interesting article showcasing the use-case of Ray Data, Ray Train, and Ray Tune across the entire ML workflow in a single Python stack. Namely with Ray Data they are ingesting and preprocessing 100 GB+ tables on elastic EC2 clusters, and scaling to 4 000 vCPUs. They then use Ray Train to wrap PyTorch code in distributed training, letting engineers scale from 4 to 64+ workers. They also use Ray Tune through Optuna to run thousands of hyper‑parameter optimization experiments and logging everything on MLFlow. |
|
|
|---|
|
Writing Toy Software to Improve In an era where we everything we hear is about vibe-coding, it's always nice to see some simple, fun-related projects arise to improve one's personal coding skills with toy projects. And there's no better way to learn things than by building quick stripped‑down versions of complex systems (regex engines, OS kernels, async runtimes, etc). Taking on some of these hobby-projects that don't have a particular end-goal in mind other than learning helps re-kindle the joy that first drew many into programming! |
|
|
|---|
|
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 2025:
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
