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All questions answered: how CLAIRE shapes the future of AI in Europe

AIHub

On 18 January, the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) All Questions Answered (AQuA) series continued, with a one hour session focussing on CLAIRE itself. A panel, comprising members of the CLAIRE leadership team, were available to field questions about plans and events for 2023, the various CLAIRE Networks, and CLAIRE's Strategic Agenda for AI for Research and Innovation in Europe (SAFARI).


UrbanTwin: seeing double for sustainability

AIHub

A consortium of Swiss research institutes has begun working on UrbanTwin to make an AI-driven, ecologically-sensitive model of the energy, water and waste systems of the town of Aigle to help boost sustainability. Aigle has been chosen due to its size and because it has an extensive range of water sources and includes very detailed energy monitoring infrastructure previously developed by the Energy Center of EPFL. The UrbanTwin team aims to develop and validate a holistic tool to support decision-makers in achieving environmental goals, such as the Energy Strategy 2050 and the vision of climate-adaptive "sponge cities". The tool will be based on a detailed model of critical urban infrastructure, such as energy, water, buildings, and mobility, accurately simulating the evolution of these interlinked infrastructures under various climate scenarios and assessing the effectiveness of climate-change-related actions. "Urban areas are responsible for 75% of greenhouse gas emissions while rising temperatures significantly impact their liveability. They represent a natural integrator of several systems, including energy, water, buildings, and transport. So, they represent the ideal setting for implementing a coordinated, multi-sectoral response to climate changes leveraging digitalization as a systemic approach."


Counterfactual explanations for land cover mapping: interview with Cassio Dantas

AIHub

In their paper Counterfactual Explanations for Land Cover Mapping in a Multi-class Setting, Cassio Dantas, Diego Marcos and Dino Ienco apply counterfactual explanations to remote sensing time series data for land-cover mapping classification. In this interview, Cassio tell us more about explainable AI and counterfactuals, the team's research methodology, and their main findings. Our paper falls into the growing topic of explainable artificial intelligence (XAI). Despite the performances achieved by recent deep learning approaches, they remain black-box models with limited understanding of their internal behavior. To improve general acceptability and trustworthiness of such models, there is a growing need to improve their interpretability and make their decision-making processes more transparent.


Bottom-up top-down detection transformers for open vocabulary object detection

AIHub

We perform open vocabulary detection of the objects mentioned in the sentence using both bottom-up and top-down feedback. Object detection is the fundamental computer vision task of finding all "objects" that are present in a visual scene. However, this raises the question, what is an object? Typically, this question is side-stepped by defining a vocabulary of categories and then training a model to detect instances of this vocabulary. This means that if "apple" is not in this vocabulary, the model does not consider it as an object.


The Good Robot Podcast: featuring Arjun Subramonian

AIHub

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode we talk to Arjun Subramonian, a Computer Science PhD student at UCLA conducting machine learning research and a member of the grassroots organisation Queer in AI. In this episode we discuss why they joined Queer in AI, how Queer in AI is helping build artificial intelligence directed towards better, more inclusive, and queer futures, why'bias' cannot be seen as a purely technical problem, and why Queer in AI rejected Google sponsorship. Arjun Subramonian (pronouns: they/them) is a brown queer, agender PhD student at the University of California, Los Angeles. Their research focuses on graph representation learning, fairness, and machine learning (ML) ethics.


Applying AI to pathology reveals insights in endometrial cancer diagnostics

AIHub

Research at the Leiden University Medical Center (LUMC) Department of Pathology shows the power of artificial intelligence (AI) applied to endometrial carcinoma microscopy images. The group of Dr Tjalling Bosse offers insights that could improve diagnosis and treatment of uterine cancer. Their findings have been published in The Lancet Digital Health. Endometrial carcinoma is the most common cancer of the gynaecologic tract. At the LUMC both clinical trials and translational research is conducted to improve the care for these patients.


Natural Language Processing for low-resource languages

AIHub

Clearly, such an imbalance is undesirable, putting those who do not use English at a disadvantage. In this article, we highlight some of the work and initiatives being carried out on low-resource languages. Africa is one of the most linguistically diverse regions in the world. Despite this, African languages are barely represented in technology and research. Lanfrica aims to mitigate the difficulty encountered in the discovery of African language resources by creating a centralised hub.


Everyday AI podcast series

AIHub

In a new podcast series, Everyday AI, host Jon Whittle (CSIRO) explores the AI that is already shaping our lives. With the help of expert guests, he explores how AI is used in creative industries, health, conservation, sports and space. Episode 4: AI and citizen science – AI in ecology This episode features Jessie Barry from Cornell University's Macaulay Library and Merlin Bird ID, ichthyologist Mark McGrouther, and Google's Megha Malpani. Episode 6: The final frontier – AI in space This episode features Astrophysicist Kirsten Banks, NASA researcher Dr Raymond Francis, and Research Astronomer Dr Ivy Wong.


Using machine learning to forecast amine emissions

AIHub

Global warming is partly due to the vast amount of carbon dioxide that we release, mostly from power generation and industrial processes, such as making steel and cement. For a while now, chemical engineers have been exploring carbon capture, a process that can separate carbon dioxide and store it in ways that keep it out of the atmosphere. This is done in dedicated carbon-capture plants, whose chemical process involves amines, compounds that are already used to capture carbon dioxide from natural gas processing and refining plants. Amines are also used in certain pharmaceuticals, epoxy resins, and dyes. The problem is that amines could also be potentially harmful to the environment as well as a health hazard, making it essential to mitigate their impact.


The Good Robot Podcast: featuring Su Lin Blodgett

AIHub

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, Microsoft Senior Researcher Su Lin Blodgett explores whether you can use AI to measure discrimination, why AI can never be de-biased, and how AI shows us that categories like gender and race are not as clear cut as we think they are. Su Lin is a senior researcher in the Fairness, Accountability, Transparency, and Ethics in AI (FATE) group at Microsoft Research Montréal. She is broadly interested in examining the social and ethical implications of natural language processing technologies, and develops approaches for anticipating, measuring, and mitigating harms arising from language technologies, focusing on the complexities of language and language technologies in their social contexts, and on supporting NLP practitioners in their ethical work. She has also worked on using NLP approaches to examine language variation and change (computational sociolinguistics), for example developing models to identify language variation on social media.