This week we celebrate a large milestone towards democratising AI inference with our Vulkan Kompute project being adopted as one of the backends for the LLama.cpp and GPT4ALL frameworks! |
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Google Forecast Foundation Model Google's new deep learning model for zero-shot time-series forecasting with 200m parameters pre-trained on 100B time-series data-points: The time-series forecasting space continues to see innovative developments, in this case we see Google showcasing "TimesFM", a novel 200 million parameter model pre-trained on 100 billion real-world time-points for time-series forecasting. This model is designed for zero-shot performance, which means that it's expected to provide predictions without being trained, only by providing the input similar to something like ChatGPT. This model leverages a decoder-only architecture adapted from large language models, enabling it to provide accurate forecasts across various domains without additional training. TimesFM demonstrates its efficacy by closely matching or outperforming state-of-the-art supervised and traditional statistical forecasting methods on diverse datasets, including the Monash Forecasting Archive. The model's success highlights its potential to significantly improve forecasting tasks in retail, finance, and other sectors by reducing the need for extensive model training and validation, making advanced forecasting more accessible to users. |
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Cyber-resilience Act and OSS EU's new Cyber-resilience Act: What does it mean for open source? The EU Cyber Resilience Act has raised concerns within the open source community regarding its impact on software development, testing, auditing, and support. However since its first inception, this policy has adopted feedback from the community, now explicitly targets commercial activities, exempting non-commercial open source projects and contributors from its regulations. It specifies conditions under which open source could be considered commercial, thereby subject to the CRA, and introduces a "light-touch" regulatory regime for open-source software stewards supporting commercial use. While the CRA does not directly regulate most open source software, it mandates due diligence from commercial users, potentially benefiting open source security. Production machine learning practitioners engaged in commercial open source activities should be aware of this new policy, which will most likely influence the security practices within the open source machine learning ecosystem. |
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Stanford's Modern Algorithms Stanford is providing a refresher of the traditional computer science course on "Data Structures and Algorithms" focusing on the Modern Algorithmic Toolbox: Stanford has ran a course in Spring 2023 focusing on the core algorithms that power modern machine learning and data analysis. This course is led by by Gregory Valiant and combines theoretical insights with practical applications to guide students through a series of weekly mini-projects covering topics such as hashing, dimension reduction, gradient descent, and linear-algebraic techniques like PCA and SVD. It is aimed at fostering a hands-on understanding of how and when to apply these algorithms. Additionally, the course structure encourages collaboration on projects, making it highly relevant for machine learning practitioners looking to deepen their algorithmic knowledge and apply it in real-world scenarios. |
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LLaVA 1.16 OCR & Reasoning LLaVA-1.6 is released with improved reasoning, OCR, and world knowledge: An interesting advancement in large multimodal ML models with the release of LLaVA's new model surpassing its predecessor and outperforming competitors like Gemini Pro in various benchmarks. Key enhancements include quadrupled image resolution, superior visual reasoning and OCR capabilities, expanded world knowledge, and efficient deployment with SGLang, all while maintaining the minimalist design and data efficiency of LLaVA-1.5. The model also was created with lower training costs, requiring less than 1M visual instruction tuning samples and about a day's training on 32 A100 GPUs. LLaVA-1.6 represents a leap forward in LMM efficiency, power, and versatility, offering new opportunities for research and application in the field. |
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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:
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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! |
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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!
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The Institute for Ethical AI & Machine Learning
