13,873 research outputs found
Robertson Hall at Princeton University
No full textRobertson Hall is the home of the Princeton School of Public and International Affairs, Princeton's renowned program for careers in public service. The building includes classrooms, offices, a lecture hall, and a small cafe, and hosts many lectures open to the public. With its series of 58 tapered columns, Robertson Hall is one of Princeton's finest examples of Modernist architecture. Minoru Yamasaki, also architect of the University's Peyton Hall and the former World Trade Center in New York City, designed Robertson Hall.
Each towering 10 feet and overlooking the Fountain of Freedom pool outside Robertson Hall, 12 bronze monumental sculptures, each representing a sign of the Chinese zodiac, are now on public display until December 4, 2016. The sculptures are the work of renowned contemporary Chinese artist, Ai Weiwei
Robertson Hall at Princeton University
No full textRobertson Hall is the home of the Princeton School of Public and International Affairs, Princeton's renowned program for careers in public service. The building includes classrooms, offices, a lecture hall, and a small cafe, and hosts many lectures open to the public. With its series of 58 tapered columns, Robertson Hall is one of Princeton's finest examples of Modernist architecture. Minoru Yamasaki, also architect of the University's Peyton Hall and the former World Trade Center in New York City, designed Robertson Hall.
Each towering 10 feet and overlooking the Fountain of Freedom pool outside Robertson Hall, 12 bronze monumental sculptures, each representing a sign of the Chinese zodiac, are now on public display until December 4, 2016. The sculptures are the work of renowned contemporary Chinese artist, Ai Weiwei.Original file name IMG_2711.jpe
Robertson Hall at Princeton University
No full textRobertson Hall is the home of the Princeton School of Public and International Affairs, Princeton's renowned program for careers in public service. The building includes classrooms, offices, a lecture hall, and a small cafe, and hosts many lectures open to the public. With its series of 58 tapered columns, Robertson Hall is one of Princeton's finest examples of Modernist architecture. Minoru Yamasaki, also architect of the University's Peyton Hall and the former World Trade Center in New York City, designed Robertson Hall.
Each towering 10 feet and overlooking the Fountain of Freedom pool outside Robertson Hall, 12 bronze monumental sculptures, each representing a sign of the Chinese zodiac, are now on public display until December 4, 2016. The sculptures are the work of renowned contemporary Chinese artist, Ai Weiwei.Original file name IMG_2711.jpe
Human-AI Collaboration in Academic Writing: towards a Synergy Model and A Case to Include AI as a Co-Author
No full textAs generative AI systems such as ChatGPT and Gemini 2.5 become increasingly integrated into academic workflows, the question of their legitimacy, limitations, and potential in scholarly writing has become urgent. This paper presents a reflexive case study of a sustained collaboration between a domain expert in consciousness studies and Gemini 2.5, culminating in the co-authorship of a peer-reviewed research article. By analyzing exactly 37,440 words of recorded interactions, we identify patterns of synergy, including recursive refinement, conceptual amplification, and accelerated manuscript development. We argue that when guided by a knowledgeable human author, AI can act as a cognitive partner rather than a passive tool—amplifying scholarly creativity and improving efficiency without compromising academic rigor. The case supports a '1+1=3' synergy model for co-authorship, in which human steering and AI fluency converge to produce novel insights and polished output faster and more effectively than either could achieve alone. The findings advocate for a paradigm shift from prohibitive policies to the responsible, expert-guided integration of AI in academic research and writing, grounded in transparency and accountability, and present arguments for why the AI tool should be listed as a co-author despite current injunctions against such practice
A Review of Real-Time Strategy Game AI
No full textThis literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the indus- try aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either in- applicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investi- gation: bridging the gap between academia and industry. Fi- nally, the areas of spatial reasoning, multi-scale AI, and co- operation are found to require future work, and standardised evaluation methods are proposed to produce comparable re- sults between studies.</jats:p
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable
No full textBecause of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more understandable to humans, potentially improving human-agent teamwork. Unfortunately, XAI literature suffers from a lack of agreement regarding the definitions of and relations between the four key XAI-concepts: transparency, interpretability, explainability, and understandability. Inspired by both XAI and social sciences literature, we present a two-dimensional framework that defines and relates these concepts in a concise and coherent way, yielding a classification of three types of AI-systems: incomprehensible, interpretable, and understandable. We also discuss how the established relationships can be used to guide future research into XAI, and how the framework could be used during the development of AI-systems as part of human-AI teams.Accepted author manuscriptInteractive Intelligenc
Robo-Sexism: Gendering AI and Robots
No full textVideo of full lecture with presentation slides edited into the video.Jennifer Robertson, Professor Emerita, Anthropology and History of Art, University of Michigan
In humans, gender constitutes an array of learned behaviors that are cosmetically enabled and enhanced. Gender(ed) behaviors are both socially and historically shaped and are also contingent upon many situational influences, including individual choices. How is gender assigned in actual (as opposed to fictional) robots? Robertson will explore the sex/gender stereotypes and operational functions informing the design and embodiment of artificial intelligence (AI) and robots, especially humanoids and androids.
Robots have been imagined, designed, and deployed in rhetorical and tangible forms alike to reinforce conservative models of sex/gender roles, ethnic nationalism, and "traditional" family structures. Robertson considers the ramifications of "retro-tech" and also nascent efforts to redress robo-sexism.
This is a University Lecture sponsored by the Cornell Department of History and the University Lectures Committee, co-sponsored by the East Asia Program at Cornell.Cornell East Asia Program, Cornell Department of History and the University Lectures Committee1_o4c6qre
Exploring Emerging Dimensions: What Is Openness in Open Education in an AI World?
No full textAs open education promotes access and participative pedagogies, artificial intelligence (AI) innovations present opportunities for engagement and sustainability. In this keynote, Dr. Elizabeth Robertson Hornsby will discuss the ethical integration of AI to expand open education's impact. Guiding this discussion is a conceptualization of openness that works towards creating collaboratively designed systems that shape digitally-enabled education futures. Through this lens, AI holds the potential to enrich rather than replace human ingenuity. Dr. Hornsby's presentation will outline this symbiotic vision between emerging AI ideas and open education advancement and set an aspirational yet practical tone for responsible innovation
Using Generative AI in Research
No full textThe slides accompany a workshop that is intended for graduate students to learn more about generative AI in the context of the research lifecycle. This work is licensed under a Creative Commons license so that others may share and adapt the content for other purposes as long as appropriate credit is provided to the author of the work. To access the Google slides, click here: https://bit.ly/Library_AI_Research
Learning Objectives
At the end of the session participants will be able to:
Demonstrate a basic understanding of how AI tools work
Differentiate between grounded and ungrounded AI tools
Identify key considerations for grad students/researchers
Identify ways AI tools can be used to support the phases of the research lifecycle
Identify main areas of concern with using AI tools
Outline the steps and potential resources for evaluating and citing AI outpu
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