Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Kate Crawford about the Atlas of AI. What is the Atlas of AI? How is AI an industry of extraction? How is AI impacting the planet? To answer these questions and more we welcome to the show Dr Kate Crawford to discuss Kate's new book Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence.
Artificial Intelligence (AI) promises to impact our everyday lives. Empowering everyone to learn about AI, and how it's used, will be instrumental to making sure the benefits are shared more broadly across society. Our content is free, high-quality, fair and impartial. In a bid to reduce hype and report accurately, all information is produced by those working directly in the field, without filter or intermediary. Our contributors are AI experts from across the globe representing the many subdisciplines that comprise the field.
Nearly all real-world applications of reinforcement learning involve some degree of shift between the training environment and the testing environment. However, prior work has observed that even small shifts in the environment cause most RL algorithms to perform markedly worse. As we aim to scale reinforcement learning algorithms and apply them in the real world, it is increasingly important to learn policies that are robust to changes in the environment. Broadly, prior approaches to handling distribution shift in RL aim to maximize performance in either the average case or the worst case. While these methods have been successfully applied to a number of areas (e.g., self-driving cars, robot locomotion and manipulation), their success rests critically on the design of the distribution of environments.
This post contains a list of the AI-related seminars that are scheduled to take place between 14 April and 31 May 2021. All events detailed here are free and open for anyone to attend virtually. Machine learning for medical image analysis and why clinicians are not using it Speaker: Christian Baumgartner (Tuebingen University) Organised by: Tuebingen University Zoom link is here. Real-time Distributed Decision Making in Networked Systems Speaker: Na Li (Harvard) Organised by: Control Meets Learning Join the Google group to find out how to register. The limits of Shapley values as a method for explaining the predictions of an ML system Speaker: Suresh Venkatasubramanian (University of Utah) Organised by: Trustworthy ML Join the mailing list for instructions on how to sign up, or check the website a few days beforehand for the Zoom link.
Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Su Lin Blodgett about defining bias. How do we define bias? Is all bias the same? Is it possible to eliminate bias completely in our AI systems?
Researchers at the University of Barcelona have developed an open access, deep learning-based web app that will enable the detection and quantification of floating plastics in the sea with a reliability of over 80%. Floating sea macro-litter is a threat to the conservation of marine ecosystems worldwide. According to UNESCO, plastic debris causes the deaths of more than a million seabirds every year, as well as more than 100,000 marine mammals. Eroded fragments, known as micro-plastics, are now prevalent across the food chain. The largest density of floating litter is found in the great ocean gyres (systems of circular currents) with litter being caught and spun in these vast cycles.
As part of our ongoing focus on the UN sustainable development goals (SDGs), we are launching the next topic in this series: life below water. Posts on this topic will be featured on our website throughout April. The aim of this UN SDG is to: "Conserve and sustainably use the oceans, seas and marine resources for sustainable development." This includes topics such as reducing marine pollution, protecting and restoring ecosystems, reducing ocean acidification, and sustainable fishing. In our issue, among other things, we'll be reporting on research which uses convolutional neural networks to identify floating marine litter from aerial images.
If you are keen to watch more, the entire playlist for the 2020 event can be found here. The lectures include tutorials on the use of Python and R, statistics for machine learning, Gaussian processes, reinforcement learning. You can also find out about geospatial analysis, AI for social good, machine learning for industry users, and much more. You can find out more about Data Science Nigeria here.
Here are the most tweeted papers that were uploaded onto arXiv during March 2021. Results are powered by Arxiv Sanity Preserver. Abstract: We consider the vector embedding problem. We are given a finite set of items, with the goal of assigning a representative vector to each one, possibly under some constraints (such as the collection of vectors being standardized, i.e., have zero mean and unit covariance). We are given data indicating that some pairs of items are similar, and optionally, some other pairs are dissimilar.
Welcome to our March 2021 monthly digest. Our digests are designed to keep you up-to-date with the latest happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. This month, our attention turned to education, and we considered both the use of AI in teaching, and the teaching of AI. Carles Sierra wrote about team formation techniques in education, describing how AI methods can be used to facilitate collaborative learning.