DeepSeek AI Taking by Storm DeepSeek AI's model has taken the world by storm after beating OpenAI's O1 model with a pretty interesting Reinforcement Learning approach which they cover in their latest paper and it's quite interesting: The DeepSeek team published an in-depth overview of the innovations that are pushing the state-of-the-art in LLM reasoning models, which primarily introduces an approach that leverages large-scale reinforcement learning. DeepSeek-R1 leverages a multi-stage pipeline mixing RL to optimse for chain-of-thought reasoning that addresses cold-star issues and incorporates additional supervised training for writing and role-play task. This is quite a mindblowing open release, this really makes it clear that open source AI is really bringing the full potential to take over the global ML race. |
|
|
|---|
|
Add RecSys Engine to Your Apps The recent innovations in AI are also allowing day-to-day developers to make full use of state-of-the-art capabilities, and this is a great opportunity for anyone to bring these innovations to their existing applications: This is a great deep dive into how you can develop your own state-of-the-art recommender system by integrating vector search into existing database applications using good-old Postgres and the pgvector extension. This does not require re-engineering your entire data stack as you just need to introduce a table for embeddings and building a vector index. Definitely recommend exploring whether your existing applications can benefit from bringing some of these innovations into the mix! |
|
|
|---|
|
7 Most Influential CompSci Papers In computer science there has been monumental shifts in knowledge, and there are some research papers that have spearheaded these innovations - these are 7 papers thatlaid the foundation for modern computing: We can't get enough of the best papers in computer science, as these are also serving as the foundation on many innovations in machine learning - 7 of the papers worth checking out are: 1) Turing’s 1936 work defined what machines can compute, 2) Shannon’s 1948 paper formalized how information travels, 3) Codd’s relational model organized data storage, 4) Cook’s NP-completeness clarified problem complexity, 5) Vint Cerf and Kahn’s TCP/IP enabled global networking, 6) Tim Berners-Lee’s WWW proposal opened the internet to the masses, and 7) Brin/Page’s PageRank revolutionized information retrieval. This is certainly not an exhaustive list, but certainly some papers I had not checked out so definitely worth sharing! |
|
|---|
|
LLMOps in Prod Case Studies 457 real-world examlpes of LLM in production across industry -the ZenML team have done it again! It is great to see a applications and best practice of LLMOps in industry, and the team behind ZenML have brought an extensive piece that cove LLMops use-cases with deep dives across themes such as RAG to ground models in enterprise data, sophisticated prompt engineering and orchestration, and stringent evaluation pipelines as well as human-in-the-loop validation. With all the hype in LLMs it's good to see a list of use-cases that actually hit production - looking forward to seeing a list that shows the use-cases that are hitting bringing clear return on investments into the businesses! |
|
|
|---|
|
OpenAI's New Browser "Operator" OpenAI brings another innovation that they hope is going to drive similar hype as ChatGPT under "Operator", which aims to control your browser to perform action-based automations: OpenAI's new operator framework is a new browser automation agent powered by their new Computer-Using Agent model which combines GPT-4’s vision capabilities with reinforcement learning to interact with websites just as a human would. This basically means an OpenAI agent clicking, typing, and scrolling in your browser - it is interesting to think what are the considerations from a security, data, privacy perspective that will be opened up as users start enabling full access for AI models to perform actions across their workspaces. |
|
|
|---|
|
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
