Welcome to our September 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. In this edition we cover the release of the latest AI100 report, an award winning paper from IJCAI, some useful AI resources, and more.
In this interesting article, IJCAI 2021 invited speaker Edith Elkind writes about the continuing quest to bring the theory of fair land division closer to practice. This is work that won Edith, and co-authors Erel Segal-Halevi and Warut Suksompong, a distinguished paper award at IJCAI 2021. Their article “Keep your distance: land division with separation“ investigates fair land allocation under separation constraints.
Our latest focus series affordable and clean energy (as part of our wider series on the UN sustainable development goals) was launched this month. So far, we’ve heard from Mustafa Sinan Mustafaoğlu, Gülgün Kayakutlu and M. Özgür Kayalica, who wrote about finding the optimum amount of free electricity to tackle energy poverty, and from Mathijs de Weerdt who introduced a recent report from the Netherlands which considers opportunities for AI as an accelerator of the energy transition.
Don’t forget to check out content from all of our other focus collections:
Good health and well-being
Life below water
Affordable and clean energy
We recently launched Tutorial Tuesdays where we collect, in a Twitter thread, useful AI resources on a particular theme. So far we’ve covered:
Resources for AI basics
Resources for neural network basics (and beyond)
Resources for ethical AI
AI tools to get creative with
If there are any types of resources you’d like us to cover, then please do get in touch.
This month, inspired by the Olympics and Paralympics, our experts discussed sports and the role AI and robotics could play. There are two aspects to this. Firstly, building AI-based robots to play sports (as is being done with RoboCup). Secondly, using AI techniques for performance analysis and improvement. Plus, find out which sports we would most like to see an AI system attempt.
On 16 September, the One Hundred Year Study on Artificial Intelligence (AI100) 2021 report was released. The mission of AI100 is to launch a study every five years, over the course of a century, to better track and anticipate how artificial intelligence propagates through society, and how it shapes different aspects of our lives. The first report was published in 2016, and, like that inaugural document, the 2021 edition has been written by a team of AI experts, all with much experience in the field.
Stephen José Hanson is chatting to AI researchers about their work, and the field in general. In this video he talks to Michael I Jordan, about AI as an engineering discipline, what people call AI, so-called autonomous cars, and more.
Another month, another AI strategy. This time it was the turn of the UK government who released a 10 year plan. You can read the html version here. The three key aims for the strategy are to: 1) Invest and plan for the long-term needs of the AI ecosystem, 2) Support the transition to an AI-enabled economy, 3) Ensure the UK gets the national and international governance of AI technologies right.
Lena Wang, intern at Radical AI Podcast this summer, has created a technology and power curriculum, which you can find here. This course examines the interactions of technology and power, in particular, how technology enforces and extends both state and privatized forms of power. It explores how under the existing context of capitalism and colonialism, state and corporate interests are inextricably linked and perpetuated through technological developments, and investigates how such technologies systemically harm marginalised communities.
On October 27 and 28, Barcelona will play host to an AI and music festival. There will be an in-person element, but those interested will be able to watch the event live here. The programme includes concerts, research demos and talks.
Max Noichl has been extracting some accidental ‘haikus’ from the Stanford Encyclopaedia of Philosophy, and illustrating them using machine learning algorithms VQGAN and CLIP. See some of the results here.