AIhub monthly digest: August 2024 – IJCAI, neural operators, and sequential decision making

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 find out about Neural Operators, take a virtual trip to IJCAI, and try to bridge the gap between user expectations and AI capabilities.

Interview with Anima Anandkumar – Neural Operators for science problems

Anima Anandkumar is the inventor of Neural Operators which extend deep learning to modelling multi-scale processes in many scientific domains, including weather and climate modelling, drug discovery, and engineering design problems. In the next in our series of interviews with the 2024 AAAI Fellows, Anima tells us about Neural Operators and how she has applied them to many important science and engineering problems.

Interview with Gautam Kamath – considerations for differential privacy

Florian Tramer, Gautam Kamath and Nicholas Carlini won an International Conference on Machine Learning (ICML 2024) best paper award for their work Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining, in which they challenge the paradigm of pretraining models with public data, and then privately fine-tuning the weights with sensitive data. In this interview, Gautam summarises some of the key findings of the paper.

Proportional aggregation of preferences for sequential decision making

In their blog post Proportional aggregation of preferences for sequential decision making, Nikhil Chandak and Shashwat Goel write about work that won them an outstanding paper award at AAAI 2024. Their paper addresses the challenge of ensuring fairness in sequential decision making, leveraging proportionality concepts from social choice theory.

Bridging the gap between user expectations and AI capabilities

In this blog post, Christine Lee writes about AI-DEC, a participatory design tool she developed with colleagues. The AI-DEC is a card-based design method that enables users and AI systems to communicate their perspectives and collaboratively build AI explanations.

The International Joint Conference on Artificial Intelligence

This month saw the running of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024). The programme included six keynote talks, panel discussions, tutorial and workshops. We summarised the event in these two social media round-ups: #IJCAI2024 – tweet round-up of the tutorials and workshops | #IJCAI2024 – tweet round-up from the main conference.

During the conference, the distinguished paper award winners were announced. You can find out who won here. We have more interviews and blog posts from IJCAI to come, so keep an eye on our collection for new content.

How do you solve a problem like conference reviewing?

In our latest AIhub coffee corner discussion, our trustees tackled the topic of conference reviewing, outlining some of the current problems and suggesting possible solutions to improve the process.

Climate Change AI Innovation Grants 2024 – call for proposals

Climate Change AI have announced funding of up to USD 1.4M for projects at the intersection of AI and climate change. They are offering up to USD 150K per proposal, for projects of 12 months in duration. Researchers have until 15 September to apply. More information is available here.

Visuals of AI in the military domain

In the latest blog post from Better Images of AI, Anna Nadibaidze explores the main themes found across common visuals of AI in the military domain. She argues for the need to discuss and find alternatives to images of humanoid “killer robots”.

Free AI courses from the Turing Institute

If you are interested in learning more about different aspects of artificial intelligence and data science, the Alan Turing Institute’s resources could be a good place to start. They have a number of free courses that cover topics such as standards, fairness on social media, operationalising ethics, and mitigating bias. We summarised the different courses here.

AI companies are pivoting from creating gods to building products. Good.

In their essay “AI companies are pivoting from creating gods to building products. Good.”, Arvind Narayanan and Sayash Kapoor write about the challenges of building products based on large language models (LLMs).

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Lucy Smith , Managing Editor for AIhub.

Lucy Smith , Managing Editor for AIhub.