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Issue #231
THE ML ENGINEER 🤖
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If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter, 💼 Linkedin and 📕 Facebook!
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This week in the ML Engineer:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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Researchers from the Max Plank Institute for Informatics present DragGAN, a method for interactively manipulating GAN-generated images. Users can "drag" points within an image, controlling attributes such as pose, shape, expression, and layout across diverse object categories. Unlike previous methods that relied on manual annotation or 3D models, DragGAN provides a more flexible, precise, and general approach. The system has two components: a feature-based motion supervision and a novel point tracking approach. The DragGAN method results in realistic outputs, even for challenging scenarios, outperforming prior methods in image manipulation and point tracking tasks.
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The O'Reilly team has released an overview of key highlights identified in the areas of artificial intelligence, ethcis in technology, general programming, security, infrastructure, IOT, web and quantum computing. In this trends edition they dive into the increasingly accelerating growth of generative AI with a particular focus on Large Language Models.
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In a recent congressional hearing on AI policy titled "Oversight of A.I: Rules for Artificial Intelligence", experts from industry and academia testified to the Senate Judiciary Committee, calling for clear rules, regulatory measures, and guardrails to govern the rapid development and deployment of AI technology. OpenAI's Sam Altman, IBM's Christina Montgomery, and AI expert Gary Marcus proposed a series of measures to guide AI policy, including transparency requirements, privacy rules, and limits on compute and AI capability.
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A fantastic article from the authors of the SPACE and DORA frameworks for developer productivity, which advocates a developer-centric approach which emphasizes lived experiences and daily challenges. DevEx focuses on three dimensions: feedback loops, cognitive load, and flow state. Optimizing these can boost productivity and business performance. The article proposes as well a measurement framework combining developer feedback and engineering system data is proposed to assess DevEx.
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Lightning AI has released an insightful evaluation across large language models, assessing the performance of various Large Language Models (LLMs), including GPT-3, GPT-4, Flan-t5, and Lit-LLaMA. T GPT-3 and GPT-4 stood out for response quality but require a paid subscription and data sharing with OpenAI. Flan-t5 and Lit-LLaMA performed accurately and are publicly available. Models under OpenRail License, like Bloom, are notable but may present usage restriction challenges. LLaMA 7B was good at explaining but relied heavily on quoting for context. Private models like GPT-3 and GPT-4 provided detailed summaries and displayed humor, but they are costly and less suitable for handling sensitive information. Overall, the initiative of diving into model evaluation is something that can only support improving the GenAI ecosystem.
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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.
Check out our "MLOps Curriculum" from previous conferences:
Relevant upcoming MLOps conferences:
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MLSys - 4th June @ Florida
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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.
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© 2018 The Institute for Ethical AI & Machine Learning
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The Institute for Ethical AI & Machine Learning
