The Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI 2025), which took place alongside AAAI 2025, serves as a showcase for successful applications and novel uses of AI. One such application is the Onboard Artificial Intelligence Research (OnAIR) platform, introduced by Evana Gizzi and colleagues in their paper OnAIR: Applications of The NASA On-Board Artificial Intelligence Research Platform. This open-source software pipeline and cognitive architecture tool has been designed to aid space research and missions. We spoke to Evana, Artificial Intelligence Research Lead at NASA Goddard Space Flight Center, about the OnAIR platform, some of the particular challenges of deploying AI-based solutions in space, and how the tool has been used so far. OnAIR is an open-source software pipeline and cognitive architecture tool.

This research addresses the growing need to measure and understand AI literacy in the context of generative AI technologies. Through three sequential studies involving a total of 517 participants, we establish AI literacy as a coherent, measurable construct with significant implications for education, workforce development, and social equity. Study 1 (N=85) revealed a dominant latent factor - termed the "A-factor" - that accounts for 44.16% of variance across diverse AI interaction tasks. Study 2 (N=286) refined the measurement tool by examining four key dimensions of AI literacy: communication effectiveness, creative idea generation, content evaluation, and step-by-step collaboration, resulting in an 18-item assessment battery. Study 3 (N=146) validated this instrument in a controlled laboratory setting, demonstrating its predictive validity for real-world task performance. Results indicate that AI literacy significantly predicts performance on complex, language-based creative tasks but shows domain specificity in its predictive power. Additionally, regression analyses identified several significant predictors of AI literacy, including cognitive abilities (IQ), educational background, prior AI experience, and training history. The multidimensional nature of AI literacy and its distinct factor structure provide evidence that effective human-AI collaboration requires a combination of general and specialized abilities. These findings contribute to theoretical frameworks of human-AI collaboration while offering practical guidance for developing targeted educational interventions to promote equitable access to the benefits of generative AI technologies.

This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve this, the proposed conceptualization explains the differences among existing views by different computational paradigms, such as digital, analog, mechanical, or morphological computation. Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved. Intelligence is hypothesized as a multi-paradigmatic computation relying on specific computational principles. These principles distinguish intelligence from other, non-intelligent computations. The proposed conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence.

Between 2021 and 2023, AI-Olympics, a series of online AI competitions was hosted by the online evaluation platform Jidi in collaboration with the IJCAI committee. In these competitions, an agent is required to accomplish diverse sports tasks in a two-dimensional continuous world, while competing against an opponent. This paper provides a brief overview of the competition series and highlights notable findings. We aim to contribute insights to the field of multi-agent decision-making and explore the generalization of agents through engineering efforts.

In recent years, ML researchers have wrestled with defining and improving machine learning (ML) benchmarks and datasets. In parallel, some have trained a critical lens on the ethics of dataset creation and ML research. In this position paper, we highlight the entanglement of ethics with seemingly ``technical'' or ``scientific'' decisions about the design of ML benchmarks. Our starting point is the existence of multiple overlooked structural similarities between human intelligence benchmarks and ML benchmarks. Both types of benchmarks set standards for describing, evaluating, and comparing performance on tasks relevant to intelligence -- standards that many scholars of human intelligence have long recognized as value-laden. We use perspectives from feminist philosophy of science on IQ benchmarks and thick concepts in social science to argue that values need to be considered and documented when creating ML benchmarks. It is neither possible nor desirable to avoid this choice by creating value-neutral benchmarks. Finally, we outline practical recommendations for ML benchmark research ethics and ethics review.

If New Zealand does not invest in artificial intelligence research, its AI capabilities will only be efficient software running in the cloud of large overseas companies, creating risk for the country's technology and data sovereignty independence. This is a conclusion of the first white paper issued by New Zealand's Artificial Intelligence Researchers Association, which says our universities and research institutes have very strong AI research with "huge breadth and potential". "It is imperative to create and invest in an AI ecosystem where industry and research organisations can work together more closely for the benefit of Aotearoa New Zealand," said the paper. AI was profoundly changing how people live and work, and its cumulative impact was likely to be comparable to transformative technologies such as electricity or the internet. "As a result it is imperative that we take a strategic approach to realising the potential benefits offered by AI and to protecting people against the potential risks," the paper said.

Philip K. Dick's iconic 1968 sci-fi novel, "Do Androids Dream of Electric Sheep?" posed an intriguing question in its title: would an intelligent robot dream? In the 53 years since publication, artificial intelligence research has matured significantly. And yet, despite Dick being prophetic about technology in other ways, the question posed in the title is not something AI researchers are that interested in; no one is trying to invent an android that dreams of electric sheep. Why? Mainly, it's that most artificial intelligence researchers and scientists are busy trying to design "intelligent" software programmed to do specific tasks. There is no time for daydreaming.

The 390 labs and institutions that form the CLAIRE Research Network are committed to working together towards realising the vision of CLAIRE: European excellence across all of AI, for all of Europe, with a human-centred focus. Over 22,000 people work for the groups and institutions that form the CLAIRE Research Network in 36 countries (properly accounting for overlap between groups). Associated research groups and institutions can be seen here. To join the CLAIRE Research Network, first sign your support for CLAIRE, and then contact us at network@claire-ai.org to request the application form. To apply, we require that the lab, research group or institution be part of a not-for profit organisation that conducts research in AI, and regularly publishes that research, and have its own working website (not that of a larger entity of which it is part).

We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind Lab2D was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, but it may also be useful beyond that particular subfield.

MIT and SenseTime today announced that SenseTime, a leading artificial intelligence (AI) company, is joining MIT's efforts to define the next frontier of human and machine intelligence. SenseTime was founded by MIT alumnus Xiao'ou Tang PhD '96 and specializes in computer vision and deep learning technologies. The MIT-SenseTime Alliance on Artificial Intelligence aims to open up new avenues of discovery across MIT in areas such as computer vision, human-intelligence-inspired algorithms, medical imaging, and robotics; drive technological breakthroughs in AI that have the potential to confront some of the world's greatest challenges; and empower MIT faculty and students to pursue interdisciplinary projects at the vanguard of intelligence research. SenseTime is the first company to join a new Institute-wide initiative, the MIT Intelligence Quest, since its launch earlier this month. The MIT Intelligence Quest seeks to leverage the Institute's strengths in brain and cognitive science and computer science to advance research into human and machine intelligence in service to all humanity.