Stanford State of AI Report The 2024 Stanford State of AI Report is out: Another fantastic annual report from Stanford providing a comprehensive overview of the evolving landscape of the AI ecosystem. Some key highlights this year includes noting the significant dominance of AI development in industry, and the steeply rising costs of training advanced models like Google’s Gemini Ultra and OpenAI’s GPT-4. Other key findings highlight the United States' leadership in global AI development and investment, alongside a growing concern over the lack of standardization in responsible AI practices which complicates the evaluation of AI systems' safety and fairness. |
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Stripe Feature Store Airbnb OSS Following last week's announcement of Airbnb's newly open sourced Feature Store, Stripe releases a deep dive into how they've adopted Chronon at scale: Stripe has partnered with Airbnb to adapt and implement the Chronon OSS framework to create their internal ML feature platform called "Shepherd". They showcase Shepherd as next-gen feature engineering platform that enhances feature development across massive datasets, meeting strict latency and freshness requirements. Stripe highlights that this has improved fraud detection by integrating over 200 new features and significantly reducing fraud. They also cover key adaptations to their internal systems, including leveraging Flink for streaming jobs and employing a dual key-value store system for efficient data handling. |
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NSA Security Framework for AI The NSA has released a comprehensive cybersecurity guideline titled "Deploying AI Systems Securely: Best Practices for Deploying Secure and Resilient AI Systems": This security framework outlines essential security practices for AI systems, including securing deployment environments, maintaining continuous protection through monitoring and updates, enforcing strict access controls, and fostering collaboration and compliance with international cybersecurity standards. This framework is aimed at entities within the National Security System and applications with high-threat and sensitive operational contexts, and is designed to safeguard AI systems against theft and misuse. |
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LLMs as Effective Regressors LLMs can effectively execute both linear and non-linear regression, often outperforming traditional supervised methods like Random Forest and Gradient Boosting: This research paper explores the ability of LLMs to perform regression tasks using in-context examples without additional training. The results suggest that LLMs inherently develop a sophisticated numerical reasoning ability during the training across these large text corpora, which allows them to adapt and refine their regression capabilities as they process more in-context examples. This insight is particularly interesting in context to the ongoing trend of foundation (transformer) model announcements from various of the major tech companies. |
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Llama 3 Latest META's Release Meta has launched Llama 3: This is META's latest iteration of its open large language model, featuring two models with 8 billion and 70 billion parameters that set new benchmarks in reasoning, code generation, and instruction-following. This release builds on a standard transformer architecture, leveraging a vast 15 trillion token dataset for training; it is also interesting to see the outline of safety tools (aka "Llama Guard 2"). Kudos for the availability as Meta Llama 3 is now available across major platforms such as AWS and Google Cloud, which promotes a spirit of benchmarking across the AI community. |
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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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