A Coactive Learning View of Online Structured Prediction in Statistical Machine Translation

We present a theoretical analysis of online parameter tuning in statistical machine translation (SMT) from a coactive learn-ing view. This perspective allows us to give regret and generalization bounds for latent perceptron algorithms that are com-mon in SMT, but fall outside of the stan-dard convex optimization scenario. Coac-tive learning also introduces the concept of weak feedback, which we apply in a proof-of-concept experiment to SMT, showing that learning from feedback that consists of slight improvements over predictions leads to convergence in regret and transla-tion error rate. This suggests that coactive learning might be a viable framework for interactive machine translation. Further-more, we find that surrogate translations replacing references that are unreachable in the decoder search space can be inter-preted as weak feedback and lead to con-vergence in learning, if they admit an un-derlying linear model.

Human-Agent Teamwork in Cyber Operations: Supporting Co-evolution of Tasks and Artifacts with Luna

Abstract. In this article, we outline the general concept of coactive emergence, an iterative process whereby joint sensemaking and decision-making activities are undertaken by analysts and software agents. Then we explain our rationale for the development of the Luna software agent framework. In particular, we focus on how we use capabilities for comprehensive policy-based governance to ensure that key requirements for security, declarative specification of task-work, and built-in support for joint activity within mixed teams of humans and agents are satisfied

AI and Immigrants

Immigrants have founded or cofounded nearly two-thirds (65% or 28 of 43) of the top AI companies in the United States, and 70% of full-time graduate students in fields related to artificial intelligence are international students, according to a new National Foundation for American Policy (NFAP) analysis. Seventy-seven percent of the leading U.S.-based AI companies were founded or cofounded by immigrants or the children of immigrants. Forty-two percent (18 of 43) of the top U.S.-based AI companies had a founder who came to America as an international student

That Others May Learn: Three Views on Vicarious Learning in Organizations.

Vicarious learning, the process by which an individual learns from another’s experience, has long been recognized as a source of development and performance improvement in organizations, at both individual and collective levels. Yet existing perspectives on this critical learning process have been fairly limited, typically casting vicarious learning as a simple process of observation and imitation, enabled by formal organizational knowledge-transfer conduits. Largely absent from prior approaches is a consideration of the interpersonal dynamics underlying vicarious learning, leaving unexplored important questions related to 1) the actual behaviors unfolding when individuals interact to learn from one another’s experience, 2) how people coordinate efforts to enact and facilitate these vicarious learning interactions, and 3) the performance impact of different patterns of engagement in these interactions. In this dissertation, I advance a perspective on vicarious learning that views it as relationally co-created, emergently organized, and dyadically reciprocal, exploring the issues identified above in three distinct chapters. First, I present a theoretical model of what I term coactive vicarious learning, integrating theories of experiential learning and symbolic interactionism to articulate a co-construction process of vicarious learning, arising from individuals’ discussion and shared meaning-making. I unpack the antecedents and underlying behaviors of these discursive vicarious learning interactions, and theorize that they not only increase individuals’ knowledge, but also build individual and relational capacity for future learning. Second, I present a qualitative study of how these vicarious learning interactions manifest at work, inductively exploring the organizing processes used to facilitate vicarious learning in air medical transport teams. I advance a view of vicarious learning not as wholly determined by formal structures, but rather as an emergently organized phenomenon, enacted through interpersonal storytelling and facilitated by the coalescence of informal practices and formal structures. Third, I present a quantitative examination of different distributions of vicarious learning in work teams. Specifically, I examine what leads individuals to engage in reciprocal vicarious learning relationships (where each individual learns from the other, in contrast to the prevailing view of vicarious learning as one-way information transfer) and demonstrate that greater reciprocation of vicarious learning within a team enhances performance.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113410/1/cgmyers_1.pd

Deep Reinforcement Learning Approaches for Technology Enhanced Learning

Artificial Intelligence (AI) has advanced significantly in recent years, transforming various industries and domains. Its ability to extract patterns and insights from large volumes of data has revolutionised areas such as image recognition, natural language processing, and autonomous systems. As AI systems become increasingly integrated into daily human life, there is a growing need for meaningful collaboration and mutual engagement between humans and AI, known as Human-AI Collaboration. This collaboration involves combining AI with human workflows to achieve shared objectives. In the current educational landscape, the integration of AI methods in Technology Enhanced Learning (TEL) has become crucial for providing high-quality education and facilitating lifelong learning. Human-AI Collaboration also plays a vital role in the field of Technology Enhanced Learning (TEL), particularly in Intelligent Tutoring Systems (ITS). The COVID-19 pandemic has further emphasised the need for effective educational technologies to support remote learning and bridge the gap between traditional classrooms and online platforms. To maximise the performance of ITS while minimising the input and interaction required from students, it is essential to design collaborative systems that effectively leverage the capabilities of AI and foster effective collaboration between students and ITS. However, there are several challenges that need to be addressed in this context. One challenge is the lack of clear guidance on designing and building user-friendly systems that facilitate collaboration between humans and AI. This challenge is relevant not only to education researchers but also to Human-Computer Interaction (HCI) researchers and developers. Another challenge is the scarcity of interaction data in the early stages of ITS development, which hampers the accurate modelling of students' knowledge states and learning trajectories, known as the cold start problem. Moreover, the effectiveness of Intelligent Tutoring Systems (ITS) in delivering personalised instruction is hindered by the limitations of existing Knowledge Tracing (KT) models, which often struggle to provide accurate predictions. Therefore, addressing these challenges is crucial for enhancing the collaborative process between humans and AI in the development of ITS. This thesis aims to address these challenges and improve the collaborative process between students and ITS in TEL. It proposes innovative approaches to generate simulated student behavioural data and enhance the performance of KT models. The thesis starts with a comprehensive survey of human-AI collaborative systems, identifying key challenges and opportunities. It then presents a structured framework for the student-ITS collaborative process, providing insights into designing user-friendly and efficient systems. To overcome the challenge of data scarcity in ITS development, the thesis proposes two student modelling approaches: Sim-GAIL and SimStu. SimStu leverages a deep learning method, the Decision Transformer, to simulate student interactions and enhance ITS training. Sim-GAIL utilises a reinforcement learning method, Generative Adversarial Imitation Learning (GAIL), to generate high-fidelity and diverse simulated student behavioural data, addressing the cold start problem in ITS training. Furthermore, the thesis focuses on improving the performance of KT models. It introduces the MLFBKT model, which integrates multiple features and mines latent relations in student interaction data, aiming to improve the accuracy and efficiency of KT models. Additionally, the thesis proposes the LBKT model, which combines the strengths of the BERT model and LSTM to process long sequence data in KT models effectively. Overall, this thesis contributes to the field of Human-AI collaboration in TEL by addressing key challenges and proposing innovative approaches to enhance ITS training and KT model performance. The findings have the potential to improve the learning experiences and outcomes of students in educational settings