Find out more about our session on Wednesday 21 January.

We're pleased to announce that we will be giving an introduction to science communication for AI researchers at AAAI this year. This will be held on Wednesday 21 January from 13:00 - 14:30. The session is part of the Undergraduate Consortium programme. However, if you are attending the conference and fancy finding out how you can communicate your research to a general audience in different formats, then you are more than welcome to join us. The session will comprise a talk, a Q&A, and the opportunity to try some of the activities presented in the tutorial. You will have the opportunity to receive advice on any science communication ideas or questions you have.

Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science communicators struggle to anticipate how audiences will perceive and interact with scientific news. In this paper, we introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness. Using this framework, we create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations, providing valuable insights into public responses to scientific information across domains. We further develop NLP models that predict public perception scores with a strong performance. Leveraging the dataset and model, we examine public perception of science from two perspectives: (1) Perception as an outcome: What factors affect the public perception of scientific information? (2) Perception as a predictor: Can we use the estimated perceptions to predict public engagement with science? We find that individuals' frequency of science news consumption is the driver of perception, whereas demographic factors exert minimal influence. More importantly, through a large-scale analysis and carefully designed natural experiment on Reddit, we demonstrate that the estimated public perception of scientific information has direct connections with the final engagement pattern. Posts with more positive perception scores receive significantly more comments and upvotes, which is consistent across different scientific information and for the same science, but are framed differently. Overall, this research underscores the importance of nuanced perception modeling in science communication, offering new pathways to predict public interest and engagement with scientific content.

Democratic societies need accessible, reliable information. Videos and Podcasts have established themselves as the medium of choice for civic dissemination, but also as carriers of misinformation. The emerging Science Communication Knowledge Infrastructure (SciCom KI) curating non-textual media is still fragmented and not adequately equipped to scale against the content flood. Our work sets out to support the SciCom KI with a central, collaborative platform, the SciCom Wiki, to facilitate FAIR (findable, accessible, interoperable, reusable) media representation and the fact-checking of their content, particularly for videos and podcasts. Building an open-source service system centered around Wikibase, we survey requirements from 53 stakeholders, refine these in 11 interviews, and evaluate our prototype based on these requirements with another 14 participants. To address the most requested feature, fact-checking, we developed a neurosymbolic computational fact-checking approach, converting heterogenous media into knowledge graphs. This increases machine-readability and allows comparing statements against equally represented ground-truth. Our computational fact-checking tool was iteratively evaluated through 10 expert interviews, a public user survey with 43 participants verified the necessity and usability of our tool. Overall, our findings identified several needs to systematically support the SciCom KI. The SciCom Wiki, as a FAIR digital library complementing our neurosymbolic computational fact-checking framework, was found suitable to address the raised requirements. Further, we identified that the SciCom KI is severely underdeveloped regarding FAIR knowledge and related systems facilitating its collaborative creation and curation. Our system can provide a central knowledge node, yet a collaborative effort is required to scale against the imminent (mis-)information flood.

We're pleased to announce that we will be giving an introduction to science communication for AI researchers at AAAI this year. This will be held on Wednesday 26 February from 13:00. If you are attending the conference and fancy finding out how you can communicate your research to a general audience in different formats, then please do join us. Following an hour-long introductory talk, there will be an optional, open, drop-in session where you can try out some of the things you learnt in the course, ask any sci-comm questions, and chat about your ideas and stories. It helps demystify AI for a broad range of people including policy makers, business leaders, and the public. As a researcher, mastering this skill can not only enhance your communication abilities but also expand your network and increase the visibility and impact of your work.

Nuclear fusion is one of the most promising ways for humans to obtain infinite energy. Currently, with the rapid development of artificial intelligence, the mission of nuclear fusion has also entered a critical period of its development. How to let more people to understand nuclear fusion and join in its research is one of the effective means to accelerate the implementation of fusion. This paper proposes the first large model in the field of nuclear fusion, XiHeFusion, which is obtained through supervised fine-tuning based on the open-source large model Qwen2.5-14B. We have collected multi-source knowledge about nuclear fusion tasks to support the training of this model, including the common crawl, eBooks, arXiv, dissertation, etc. After the model has mastered the knowledge of the nuclear fusion field, we further used the chain of thought to enhance its logical reasoning ability, making XiHeFusion able to provide more accurate and logical answers. In addition, we propose a test questionnaire containing 180+ questions to assess the conversational ability of this science popularization large model. Extensive experimental results show that our nuclear fusion dialogue model, XiHeFusion, can perform well in answering science popularization knowledge. The pre-trained XiHeFusion model is released on https://github.com/Event-AHU/XiHeFusion.

The thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024) will take place in Vancouver, Canada, from Tuesday 10 December to Sunday 15 December. There is a bumper programme of events, including invited talks, orals, posters, tutorials, workshops, and socials, not to mention AIhub's session on science communication. There are seven invited talks this year: Alison Gopnik – The Golem vs. stone soup: Understanding how children learn can help us understand and improve AI Sepp Hochreiter – Toward industrial artificial intelligence Fei-Fei Li – From seeing to doing: Ascending the ladder of visual intelligence Lidong Zhou – A match made in silicon: The co-evolution of systems and AI Arnaud Doucet – From diffusion models to Schrödinger bridges Danica Kragic – Learning for interaction and interaction for learning Rosalind Picard – How to optimize what matters most? We (AIhub) will be running a short course on science communication on Tuesday 10 December. There will be a total of 14 tutorials this year.

We're pleased to announce that we will be giving an introduction to science communication for AI researchers at NeurIPS this year. This will be held on Tuesday 10 December from 14:00. If you are attending the conference and fancy finding out how you can communicate your research to a general audience in different formats, then please do join us. Following an hour-long introductory talk, there will be an optional, open, drop-in session where you can try out some of the things you learnt in the course, ask any sci-comm questions, and chat about your ideas and stories. It helps demystify AI for a broad range of people including policy makers, business leaders, and the public. As a researcher, mastering this skill can not only enhance your communication abilities but also expand your network and increase the visibility and impact of your work.

Digital media platforms (e.g., social media, science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with given degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Given these findings, we discuss several implications of AI-personalized translation tools in facilitating communication in collaborative contexts.

We're pleased to announce that we will be giving a short introduction to science communication for roboticists at the International Conference on Intelligent Robots and Systems (IROS) this year. This will be held in person and via a livestream on Tuesday 15 October from 13:00 GST (09:00 UTC), and will be run in collaboration with IEEE Spectrum. If you fancy finding out how you can communicate your research to a general audience in different formats, then please do join us. Following an hour-long introductory talk, there will be an optional, open, drop-in session where you can try out some of the things you learnt in the course, ask any sci-comm questions, or chat about your ideas and stories. You can also watch online via a livestream.