Issue #315 - The ML Engineer 🤖
AI Agents for Computers, Best Software Engineering Papers, Alibaba's Reasoning LLM, MLE ELI5, AMD GPU Inference + more 🚀
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This week in Machine Learning:
AI Agents to Control Computers
Alibaba's Reasoning LLM Model
Maximum Likelihood ELI5
AMD GPU Inference Optimization
Open Source ML Frameworks
Awesome AI Guidelines to check out this week
+ more 🚀
If you're looking for an interesting career opportunity, I'm hiring for a few roles including Data Science Manager (Forecasting), as well as Principal Product Manager (Forecasting) - check them out and please do share with your network!
AI Agents to Control Computers
Salesforce Research has released an AI vision foundation model that can control user interfaces across mobile, desktop and web 🤖 This is quite an interesting research breakthrough as this new model is able to locating elements in applications and take actions through reasoning workflows. This is quite an interesting two-stage training pipeline which separates the learning of element detection, and then optimize for the multi-step action planning on top of the applications without relying on specialized HTML inputs.
Best Software Engineering Papers
Here are the all-time-best research papers in software engineering for your end of year reading list: This is a great collection of research papers spanning across quite a range of topics, including programming paradigms, distributed systems, networking, cryptography, databases, and more. This list includes some of the early classics from Turing, Dijkstra, and Knuth, which are some of the historical foundations of programming that serve as the backbone of our systems today. Check it out!
Alibaba's QWEN model has been making the rounds with their surprisingly advanced reasoning foundation model: It is quite interesting to see the fast development of open source / open models, this time with the QwQ model, which brings together state-of-the-art innovations resulting in impressive results across mathematics and coding benchmarks. It seems this race to the top is only accelerating, so it is certainly an exciting space to keep an eye as further more capable open models are released and benchmarked across broader tasks.
It is often easy to dismiss some foundational concepts in machine learning; this is a great resource to brush up on fundamentals to build a strong understanding on core concepts such as the intuition behind Maximum Likelihood Estimation. This article does a great job of framing model training as "finding parameters that maximize the probability of observed data" - this makes it intuitive that one sees that minimizing MSE follows from assuming outputs come from a Gaussian distribution, minimizing Cross Entropy stems from assuming outputs follow a Bernoulli distribution, and that MLE is equivalent to minimizing the KL divergence between the true data distribution and the model’s distribution. For machine learning practitioners, understanding these foundational concepts can help you make informed choices about which loss functions to use and interpret how modeling assumptions align with real-world data.
AMD GPU Inference Optimization
NVIDIA CUDA has led the way on ML inference by quite a margin, however slowly other alternatives are reaching similar performance in real-world AI applications: This is a great practical optimization benchmark which explores how AMD compares and competes with NVIDIA GPUs on Llama2 inference. This is an interesting approach leveraging GPU compulation to automatically generates optimized GPU kernels for multiple backends by leveraging the Vulkan SDK, which is an abstraction layer that supports 1000s+ of GPUs beyond NVIDIA.
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:
TUM AI@WORK 10th October @ Germany
Other upcoming MLOps conferences in 2024:
ODSC West - 29th October @ USA
Data & AI Summit - 10th June @ USA
AI Summit London - 12th June @ UK
World Summit AI - 9th October @ Neatherlands
MLOps World - 8th November @ USA
In case you missed our talks:
The State of AI in 2024 - WeAreDevelopers 2024
Responsible AI Workshop Keynote - NeurIPS 2021
Practical Guide to ML Explainability - PyCon London
ML Monitoring: Outliers, Drift, XAI - PyCon Keynote
Metadata for E2E MLOps - Kubecon NA 2022
ML Performance Evaluation at Scale - KubeCon Eur 2021
Industry Strength LLMs - PyData Global 2022
ML Security Workshop Keynote - NeurIPS 2022
Open Source MLOps Tools
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!
OSS: Policy & Guidelines
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!
About us
The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.