META's V-JEPA vs OpenAI Sora As the world continues to make waves with OpenAI's text-to-video model "Sora", META has released V-JEPA as a proposed alternative to ML intelligence for the physical world: META's V-JEPA (Video Joint Embedding Predictive Architecture) is a novel model introduced by Meta. This architecture leverages a self-supervised learning framework which learns from unlabeled video data by predicting masked portions in an abstract representation space, focusing on understanding complex interactions between objects without the need for detailed pixel reconstruction. This approach enhances training and sample efficiency but also enables the model to adapt to various tasks with minimal additional training, and is being released as an open model to encourage research. |
|
|---|
|
The MLOps Bookshelf The MLOps Bookshelf Collection 📚 A great resource for practitioners looking to upgrade their knowledge in production machine learning operations. The list includes: 1) "Building Machine Learning Powered Applications" by Emmanuel Ameisen, 2) "Reliable Machine Learning" by Cathy Chen et al., 3) "Machine Learning Engineering in Action" by Ben Wilson, 4) "Effective Data Science Infrastructure" by Ville Tuulos, 5) "Machine Learning Design Patterns" by Valliappa Lakshmanan, 6) "Designing Machine Learning Systems" by Chip Huyen. The article also provides brief reviews and recommendations for each book. |
|
|
|---|
|
Google DeepMind Gemini 1.5 Google DeepMind introduces Gemini 1.5 with astonishing advancements standing up to the GPT Giant OpenAI ChatGPT: This release is announced directly bySundar Pichai and Demis Hassabis, highlighted as a significant advancement in AI with an alleged dramatically enhanced performance, efficiency, and groundbreaking long-context understanding capability, processing up to 1 million tokens. Gemini 1.5 Pro is built on a Mixture-of-Experts architecture and offers comparable quality to its predecessor while being more compute-efficient, excelling across a wide range of tasks and modalities. This model is initially available in a limited preview which will allow for further insights from the feedback. |
|
|---|
|
Unit Tests using LLMs at Meta Automated unit test generation using LLMs at Meta: Meta's TestGen-LLM tool leverages LLMs to automate the enhancement of existing unit tests for Android applications, focusing on increasing test coverage by identifying and covering previously missed scenarios. This tool aims to embody Assured Offline LLM-Based Software Engineering, generating test cases that try to offer improvements without regressing existing functionalities. This model was deployed during "test-a-thons" at Instagram and Facebook, showing significant efficacy of 75% of generated test cases correctly built, 57% passing reliably, and 25% with enhanced coverage, and with 73% of recommendations accepted for production. This initiative underscores the potential of integrating LLMs with software engineering workflows to augment human efforts in test development, marking a notable advancement in the automation and optimization of software testing processes at an industrial scale. |
|
|
|---|
|
Mistral-7B on an Acid Trip Control vectors in LLMs introduce a promising concept that can be used to direct and restrict the outputs of LLMs which can further augment other concepts such as RAG: The article introduces the concept of "control vectors" as a novel method for manipulating AI model behavior without prompt engineering or fine-tuning, showcasing its application on the Mistral-7B model. By adjusting model activations during inference, control vectors enable precise behavioral modifications, such as inducing states of happiness, laziness, or creativity. This article is very well written and intuitive, showcasing how these vectors can be used to enahnce model performance or bypass safety mechanisms. This approach presents a significant advancement for machine learning practitioners interested in AI transparency, interpretability, and customized model behavior, offering a practical and efficient tool for tailoring AI outputs to specific needs or research inquiries. |
|
|---|
|
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
