ΑΙhub.org
monthly digest
 

AIhub monthly digest: December 2024 – attending NeurIPS, multi-agent path finding, and tackling illegal mining


by
31 December 2024



share this:
Panda and tiger reading

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we look back at our week attending NeurIPS, hear about work localising illegal mining sites using machine learning and geospatial data, and discover how a group of agents can minimise their journey length whilst avoiding collisions.

AIhub at NeurIPS 2024

We were lucky enough to attend the thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024) which took place in Vancouver, Canada, from Tuesday 10 December to Sunday 15 December. On the first day of the event we held a session on science communication for AI researchers. It was great to see so many people there, and so many thoughtful questions following our presentation. You can find the webpage for the session here.

The 2024 awards for outstanding main track papers (and runners-up), outstanding datasets and benchmarks paper, and the test-of-time award were announced during the opening ceremony. You can find out who won here.

You can also find out what participants got up to in our two social media summaries: #NeurIPS2024 social media round-up part 1 | #NeurIPS2024 social media round-up part 2.

We’ll be posting more content from the conference over the coming weeks, so be sure to check out our NeurIPS collection page.

Interview with Andrews Ata Kangah: Localising illegal mining sites using machine learning and geospatial data

Andrews Ata Kangah is a team leader and researcher working on democratizing AI and AI solutions for environmental problems. We spoke to him about his research using machine learning and geospatial data to localise illegal mining sites in Ghana, and his experience attending the AfriClimate AI workshop at the Deep Learning Indaba.

Multi-agent path finding in continuous environments

Multi-agent path finding describes a problem where a group of agents (robots, vehicles, or even people) are each trying to get from their starting positions to their goal positions without colliding. In this blog post, Kristýna Janovská and Pavel Surynek write about their method for multi-agent path finding in continuous environments, where agents move on sets of smooth paths.

RoboCup teams up with Booster, Fourier and Unitree

The RoboCup Federation has announced new partnerships with three robotics companies: Booster Robotics, Fourier Intelligence and Unitree Robotics. The RoboCup Federation, an international initiative, uses the RoboCup competition series and challenges as a platform to promote and advance robotics and AI research. The aim is that the companies’ humanoid robot hardware will be used in future RoboCup competitions.

AI is not a “stochastic parrot,” it’s a mirror

In this interview in Vox, Shannon Vallor talks about some of the ideas from her new book, The AI Mirror. “One thing I hear in every country that I travel to to speak about AI is: Are humans really any different from AI? Aren’t we at the end of the day just predictive text machines? Are we ever doing anything other than pattern matching and pattern generation? That rhetorical strategy is actually what scares me. It’s not the machines themselves. It’s the rhetoric of AI today that is about gaslighting humans into surrendering their own power and their own confidence in their agency and freedom. That’s the existential threat, because that’s what will enable humans to feel like we can just take our hands off the wheel and let AI drive.”

How to benefit from AI without losing your human self

In this fireside chat from IEEE Computational Intelligence Society, Tayo Obafemi-Ajayi (Missouri State University) asks Hava T Siegelmann (University of Massachusetts, Amherst) about how to benefit from AI without losing your human self.

2024 AAAI / ACM SIGAI doctoral consortium interviews

Over the course of the year, we’ve had the privilege of meeting a number of the 2024 AAAI / ACM SIGAI doctoral consortium participants. In this collection we’ve compiled links to all of the interviews.

Looking back over 2024

We’ve had the opportunity to work with many talented researchers during 2024. In these two posts, we’ve collected some of our favourite interview and blog posts. AIhub interview highlights 2024 | AIhub blog post highlights 2024.


Our resources page
Our events page
Seminars in 2024
AAAI/ACM SIGAI Doctoral Consortium interview series
AAAI Fellows 2024 interview series
AI around the world focus series
New voices in AI series



tags: , , , , ,


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




            AIhub is supported by:



Related posts :

#AAAI2026 social media round up: part 2

  03 Feb 2026
Catch up on the action from the second half of the conference.

Interview with Zijian Zhao: Labor management in transportation gig systems through reinforcement learning

  02 Feb 2026
In the second of our interviews with the 2026 AAAI Doctoral Consortium cohort, we hear from Zijian Zhao.
monthly digest

AIhub monthly digest: January 2026 – moderating guardrails, humanoid soccer, and attending AAAI

  30 Jan 2026
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

The Machine Ethics podcast: 2025 wrap up with Lisa Talia Moretti & Ben Byford

Lisa and Ben chat about the prevalence of AI slop, the end of social media, Grok and explicit content generation, giving legislation more teeth, anthropomorphising reasoning models, and more.

Interview with Kate Larson: Talking multi-agent systems and collective decision-making

  27 Jan 2026
AIhub ambassador Liliane-Caroline Demers caught up with Kate Larson at IJCAI 2025 to find out more about her research.

#AAAI2026 social media round up: part 1

  23 Jan 2026
Find out what participants have been getting up to during the first few of days at the conference

Congratulations to the #AAAI2026 outstanding paper award winners

  22 Jan 2026
Find out who has won these prestigious awards at AAAI this year.

3 Questions: How AI could optimize the power grid

  21 Jan 2026
While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.


AIhub is supported by:







 













©2026.01 - Association for the Understanding of Artificial Intelligence