ΑΙhub.org
 

AIhub blogpost highlights 2024


by
30 December 2024



share this:
laptop and cup of coffee on a table

Over the course of the year, we’ve had the pleasure of working with many talented researchers from across the globe. As 2024 draws to a close, we take a look back at some of the excellent blog posts from our contributors.


Generating physically-consistent local-scale climate change projections


Jose González-Abad reports on work on statistical downscaling for climate models, and introduces a framework which encodes physical constraints to improve consistency and robustness.


Theoretical remarks on feudal hierarchies and reinforcement learning

Two agents and a planet
Diogo Carvalho writes about research on hierarchical reinforcement learning, work that won him and his colleagues a best paper award at ECAI 2023.


Crowdsourced clustering via active querying


Yi Chen, Ramya Korlakai Vinayak and Babak Hassibi write about crowdsourced clustering: finding clusters in a dataset with unlabelled items by querying pairs of items for similarity.


The moderating effect of instant runoff voting


Kiran Tomlinson considers the instant runoff voting system and provide a mathematical backing for the argument that it favours moderate candidates in a way that plurality doesn’t.


An iterative refinement model for PROTAC-induced structure prediction


Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally “undruggable” proteins by binding simultaneously to their targets and degradation-associated proteins. Bo Qiang, Wenxian Shi, Yuxuan Song and Menghua Wu detail their work on PROTAC-induced structure prediction.


Learning programs with numerical reasoning


Inductive logic programming is a form of program synthesis that can learn explainable programs from small numbers of examples. Current approaches struggle to learn programs with numerical values. Céline Hocquette writes about work introducing a novel approach to dealing with these numerical values.


Are models biased on text without gender-related language?


Catarina G Belém and colleagues audited 28 popular language models to find out whether such models display gender biases in stereotype-free contexts. They found that, contrary to prior assumptions, gender bias does not solely stem from the presence of gender-related words in sentences.


Bridging the gap between user expectations and AI capabilities: Introducing the AI-DEC design tool


Christine Lee presents AI-DEC, a participatory design tool that enables users and AI systems to communicate their perspectives and collaboratively build AI explanations.


Proportional aggregation of preferences for sequential decision making


Nikhil Chandak and Shashwat Goel address the challenge of ensuring fairness in sequential decision making, leveraging proportionality concepts from social choice theory.


Building trust in AI: Transparent models for better decisions


Danial Dervovic is working on improving the interpretability of logistic regression models, proposing an augmentation to such models, which makes decisions made by them more understandable.


Enhancing controlled query evaluation through epistemic policies


A significant data challenge concerns the sharing of information without compromising sensitive details. Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati and Domenico Fabio Savo present the controlled query evaluation framework – an approach that safeguards confidentiality whilst still providing answers to queries.


Dynamic faceted search: from haystack to highlight


The number of scholarly articles is growing rapidly, and finding the most relevant information from this vast collection of data can be daunting. Mutahira Khalid, Sören Auer and Markus Stocker utilise facet generation, an advanced search method that allows users to filter and refine search results.


Improving calibration by relating focal loss, temperature scaling, and properness


A key factor influencing both the accuracy and calibration of a model is the choice of the loss function during training. Viacheslav Komisarenko explores how to choose a loss function to achieve good calibration.


Multi-agent path finding in continuous environments


How can a group of agents minimise their journey length whilst avoiding collisions? Kristýna Janovská and Pavel Surynek explain all.




Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.




            AIhub is supported by:



Related posts :



What’s on the programme at #AIES2025?

  17 Oct 2025
The conference on AI, ethics, and society will take place in Madrid from 20-22 October.

Generative AI model maps how a new antibiotic targets gut bacteria

  16 Oct 2025
Researchers used a GenAI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria.

What’s coming up at #IROS2025?

  15 Oct 2025
Find out what the International Conference on Intelligent Robots and Systems has in store.

Applying machine learning to chip design and manufacturing: interview with Lorenzo Servadei

  14 Oct 2025
Find out how Lorenzo and his team are using ML and Electronic Design Automation.

Why we should be skeptical of the hasty global push to test 15-year-olds’ AI literacy in 2029

  13 Oct 2025
Are schools set to become testing grounds for AI developments?

Machine learning for atomic-scale simulations: balancing speed and physical laws

How much underlying physics can we safely “shortcut” without breaking a simulation?

Policy design for two-sided platforms with participation dynamics: interview with Haruka Kiyohara

  09 Oct 2025
Studying the long-term impacts of decision-making algorithms on two-sided platforms such as e-commerce or music streaming apps.

The Machine Ethics podcast: What excites you about AI? Vol.2

This is a bonus episode looking back over answers to our question: What excites you about AI?



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence