Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefore crucial to understand how they will impact climate action. On the one hand, AI can support applications in climate change mitigation (reducing or preventing greenhouse gas emissions), adaptation (preparing for the effects of a changing climate), and climate science. These applications have implications in areas ranging as widely as energy, agriculture, and finance. At the same time, AI is used in many ways that hinder climate action (e.g., by accelerating the use of greenhouse gas-emitting fossil fuels). In addition, AI technologies have a carbon and energy footprint themselves. This symposium brought together participants from across academia, industry, government, and civil society to explore these intersections of AI with climate change, as well as how each of these sectors can contribute to solutions.

Modern civilian and military systems have created a demand for sophisticated intelligent autonomous machines capable of operating in uncertain dynamic environments. Such systems are realizable thanks in large part to major advances in perception and decision-making techniques, which in turn have been propelled forward by modern machine learning tools. However, these newer forms of intelligent autonomy raise questions about when/how communication of the operational intent and assessments of actual vs. supposed capabilities of autonomous agents impact overall performance. This symposium examines the possibilities for enabling intelligent autonomous systems to self-assess and communicate their ability to effectively execute assigned tasks, as well as reason about the overall limits of their competencies and maintain operability within those limits. The symposium brings together researchers working in this burgeoning area of research to share lessons learned, identify major theoretical and practical challenges encountered so far, and potential avenues for future research and real-world applications.

Since the first AI-HRI held at the 2014 AAAI Fall Symposium Series, a lot of the presented research and discussions have emphasized how artificial intelligence (AI) developments can benefit human-robot interaction (HRI). This portrays HRI as an application, a source of domain-specific problems to solve, to the AI community. Likewise, this portrays AI as a tool, a source of solutions available for relevant problems, to the HRI community. However, members of the AI-HRI research community will point out that the relationship has a deeper synergy than matchmaking problems and solutions -- there are insights from each field that impact how the other one thinks about the world and performs scientific research. There is no greater opportunity for sharing perspectives at the moment than human-aware AI, which studies how to account for the fact that people are more than a source of data or part of an algorithm. We will explore how AI-HRI can change the way researchers think about human-aware AI, from observation through validation, to make even the algorithmic design process human-aware.

The use of AI to analyze, synthesize, and evaluate pathways to achieve carbon neutrality (e.g., energy sector transition plans from fossil fuels to low-carbon technologies) and for applications in climate change mitigation-related policy more broadly. The use of AI to understand and/or alleviate the effect of climate change on economies, society, production, conflict, and international trade, and for applications in climate change adaptation-related policy more broadly. Methodologies and frameworks for assessing the climate impacts of AI technologies in general (e.g., increased computational energy demand, the effects of applications, and broader systemic effects), including strategies for measurement and reporting. Governance and policies required to align the use of AI with societal climate change goals, the UN Sustainable Development Goals, and associated ESG frameworks. The use of AI to analyze, synthesize, and evaluate pathways to achieve carbon neutrality (e.g., energy sector transition plans from fossil fuels to low-carbon technologies) and for applications in climate change mitigation-related policy more broadly.

The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?