AI & Machine Learning Security Following the Crowdstrike global incident we are reminded of the risks in critical infrastructure, making it a great opportunity to dive into some of the risks in AI security. The operation and maintenance of large scale production machine learning systems has uncovered new challenges which require fundamentally different approaches to that of traditional software. In this talk I dive into a set of practical examples showcasing "Flawed Machine Learning Security", together with best practices to tackle these. |
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Goldman on GenAI Value Gap Goldman Sachs talks about the GenAI elephant in the room, point out to the estimated ~$1tn AI capex spend from tech companies on GenAI and foundation models with the key need to see results: Goldman brings pragmatic insights on the opportunities and gaps in the AI gold rush, covering even the already-known supply constraints in AI chip production which is expected to lag with shortages beyond 2025. Even then, investment phases indicate immediate gains for companies like Nvidia producing, closley with infrastructure firms following - both which are bringing the "pick-axes in the gold rush". Even with that in mind, there is a stern warning on economic risks due to high valuations, highlighting the obvious on the remaining need to see substantial productivity gains from AI to show the potential across the S&P 500 and beyond. |
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Yoshua Bengio on AI Safety Yoshua Bengio weighs in on the safety risks and considerations of Large Language Models, covering hallucinations, lack of confidence estimates, and missing citations. Hallucinations are incorrect yet plausible-sounding answers - a proposed solution to mitigate these issues involves a bootstrapping approach, namely curating a high-quality text corpus, training a base model, and using it to classify and expand training data iteratively. |
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The Future of AI in Engineering GitHub's Chief Product Officer Inbal Shani dives into the transformative impact of AI on software development, covering the impact of AI augmenting (as opposed to replacing) developers soon, improving productivity through developer tools. Inbal highlights the underappreciated potential of AI-driven testing and predicts increased AI integration in development over the next few years, as well as shairng insights on fostering innovation and effective AI adoption within product teams.
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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 is a European research centre that carries out world-class research into responsible machine learning. | | | | |
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The Institute for Ethical AI & Machine Learning
