Welcome to our June 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we hear about solving problems by combining deep learning and automated reasoning, find out how to learn physics from videos, and congratulate the IJCAI award winners.
What does solving a Sudoku puzzle have to do with protein design? Marianne Defresne reveals all in this blog post where she talks about work, with Sophie Barbe and Thomas Schiex, combining deep learning with automated reasoning to solve complex problems.
In their work 3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes, Haotian Xue, Antonio Torralba, Joshua Tenenbaum, Daniel Yamins, Yunzhu Li and Hsiao-Yu Tung present a framework for learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids. In this interview, Haotian tells us about this work and their methodology.
In their paper Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning, Safa Alver and Doina Precup introduce models that allow for performing scalable and robust planning in lifelong reinforcement learning scenarios. In this interview, Safa tells us more.
The second VISION AI Open Day took place on Thursday 1 June in Prague. The focus was on “trustworthy AI” and the programme featured a roundtable discussion which was live-streamed. You can find out more about this event, and watch the panel discussion, here.
The winners of three IJCAI awards have been announced. These three distinctions, and their recipients are:
You can find out more about these awards on the IJCAI webpage.
A important milestone in the process of EU AI legislation was taken on 14 June when the European parliament voted in favour of adoption, with 499 votes in favour, 28 against and 93 abstentions. The next step will involve talks with EU member states on the final form of the law. This document provides more information.
In their paper Science in the age of large language models, Abeba Birhane, Atoosa Kasirzadeh, David Leslie and Sandra Wachter write about large language models and their potential risks. They call for careful consideration and responsible usage to ensure that good scientific practices and trust in science are not compromised. You can watch the authors discuss their work in this video.
A report from the Center for Democracy and Technology (CDT) examines new models that companies claim can analyse text across languages. Authors Gabriel Nicholas and Aliya Bhatia explain how these language models work and explore their capabilities and limits.
Microsoft’s 26 lesson ML for beginners course now has an accompanying video series, which walks students through selected lessons. The course covers the basics of techniques such as clustering, classification, and reinforcement learning and includes short projects and quizzes.
In this YouTube video, Cyber Simplicity explains the basics of artificial intelligence in the style of a British 1960’s public information film.