Nowadays, artificial intelligence (AI) is becoming a powerful tool to process huge volumes of data generated in scientific research and extract enlightening insights to drive further explorations. The recent trend of human-in-loop AI has promoted the paradigm shift in scientific research by enabling the interactive collaboration between AI models and human experts. Inspired by these advancements, this chapter explores the transformative role of AI in accelerating scientific discovery across various disciplines such as mathematics, physics, chemistry, and life sciences. It provides a comprehensive overview of how AI is reshaping the scientific research – enabling more efficient data analysis, enhancing predictive modeling, and automating experimental processes. Through the examination of case studies and recent developments, this chapter underscores AI’s potential to revolutionize scientific discovery, providing insights into current applications and future directions. It also addresses the ethical challenges associated with AI in science. Through this comprehensive analysis, the chapter aims to provide a nuanced understanding of how AI is facilitating scientific discovery and its potential to accelerate innovations while maintaining rigorous ethical standards.

With the rapid development of artificial intelligence technology, human–AI interaction and collaboration have become important topics in the field of contemporary technology. The capabilities of AI have gradually expanded from basic task automation to complex decision support, content creation, and intelligent collaboration in high-risk scenarios. This technological evolution has provided unprecedented opportunities for industries in different fields, but also brought challenges, such as privacy protection, credibility issues, and the ethical and legal relationship between AI and humans. This book explores the role and potential of AI in human–AI interaction and collaboration from multiple dimensions and analyzes AI’s performance in privacy and credibility, knowledge sharing, search interaction, false information processing, and high-risk application scenarios in detail through different chapters.

Informal caregivers such as family members or friends provide much care to people with physical or cognitive impairment. To address challenges in care, caregivers often seek information online via social media platforms for their health information wants (HIWs), the types of care-related information that caregivers wish to have. Some efforts have been made to use Artificial Intelligence (AI) to understand caregivers’ information behaviors on social media. In this chapter, we present achievements of research with a human–AI collaboration approach in identifying caregivers’ HIWs, focusing on dementia caregivers as one example. Through this collaboration, AI techniques such as large language models (LLMs) can be used to extract health-related domain knowledge for building classification models, while human experts can benefit from the help of AI to further understand caregivers’ HIWs. Our approach has implications for the caregiving of various groups. The outcomes of human–AI collaboration can provide smart interventions to help caregivers and patients.

Misinformation on social media is a recognized threat to societies. Research has shown that social media users play an important role in the spread of misinformation. It is crucial to understand how misinformation affects user online interaction behavior and the factors that contribute to it. In this study, we employ an AI deep learning model to analyze emotions in user online social media conversations about misinformation during the COVID-19 pandemic. We further apply the Stimuli–Organism–Response framework to examine the relationship between the presence of misinformation, emotions, and social bonding behavior. Our findings highlight the usefulness of AI deep learning models to analyze emotions in social media posts and enhance the understanding of online social bonding behavior around health-related misinformation.

Generative AI based on large language models (LLM) currently faces serious privacy leakage issues due to the wide range of parameters and diverse data sources. When using generative AI, users inevitably share data with the system. Personal data collected by generative AI may be used for model training and leaked in future outputs. The risk of private information leakage is closely related to the inherent operating mechanism of generative AI. This indirect leakage is difficult to detect by users due to the high complexity of the internal operating mechanism of generative AI. By focusing on the private information exchanged during interactions between users and generative AI, we identify the privacy dimensions involved and develop a model for privacy types in human–generative AI interactions. This can provide a reference for generative AI to avoid training private data and help it provide clear explanations of relevant content for the types of privacy users are concerned about.

As generative AI technologies continue to advance at a rapid pace, they are fundamentally transforming the dynamics of human–AI interaction and collaboration, a phenomenon that was once relegated to the realm of science fiction. These developments not only present unprecedented opportunities but also introduce a range of complex challenges. Key factors such as trust, transparency, and cultural sensitivity have emerged as essential considerations in the successful adoption and efficacy of these systems. Furthermore, the intricate balance between human and AI contributions, the optimization of algorithms to accommodate diverse user needs, and the ethical implications of AI’s role in society pose significant challenges that require careful navigation. This chapter will delve into these multifaceted issues, analyzing both user-level concerns and the underlying technical and psychological dynamics that are critical to fostering effective human–AI interaction and collaboration.

AI is evolving rapidly and is poised to have far-reaching societal and global impacts, including in the military domain. AI offers cognitive reasoning and learning about problem domains –processing large quantities of data to develop situational awareness, generate solution goals, recommend courses of action, and provide robotic systems with the means for sense-making, guidance, actions, and autonomy. This chapter explores metacognition – an emerging and revolutionary technology that is enabling AI to become self-aware – to think and reason about its own cognition. This chapter explores metacognition applications in the military domain, focusing on four areas: (1) improving human interaction with AI systems, (2) providing safe and ethical AI behavior, (3) enabling autonomous systems, and (4) improving automated decision aids. The chapter begins with an overview of foundational AI and metacognition concepts, followed by a discussion of the potential contribution of metacognition to improve military operations. The chapter concludes with speculations concerning the more distant future of metacognition and its implications on AI systems and warfare.

We study the performance of a commercially available large language model (LLM) known as ChatGPT on math word problems (MWPs) from the dataset DRAW-1K. To our knowledge, this is the first independent evaluation of ChatGPT. We found that ChatGPT’s performance changes dramatically based on the requirement to show its work, failing $20\%$ of the time when it provides work compared with $84\%$ when it does not. Further, several factors about MWPs relate to the number of unknowns and number of operations that lead to a higher probability of failure when compared with the prior, specifically noting (across all experiments) that the probability of failure increases linearly with the number of addition and subtraction operations. We also have released the dataset of ChatGPT’s responses to the MWPs to support further work on the characterization of LLM performance and present baseline machine learning models to predict if ChatGPT can correctly answer an MWP.

This chapter introduces the concept of metacognition from a cognitive perspective, where it refers to knowledge and mental processes that operate on one’s own cognition. We review different forms of metacognition that involve distinct types of explicit reasoning and automatic processes, as well as various measures and functional benefits. We articulate four conjectures regarding the nature of metacognition in the specific context of the ACT-R cognitive architecture: (1) it involves extracting information about processes in cognitive modules; (2) the information is quantitative and approximate rather than symbolic; (3) the metacognitive information is available in working memory for cognitive processing; and (4) general cognitive processes are sufficient to respond to a situation detected by metacognitive monitoring. We illustrate these principles with examples of past work involving neuro-symbolic models of perception and introspection into declarative models of decision-making. Finally, we situate this approach within the context of theories such as predictive coding and the Common Model of Cognition encompassing other cognitive architectures.

Metacognitive AI is closely connected to certifiable AI and trustworthy AI, the two areas focusing on equipping AI with trustworthy guarantees in high-stake domains. This chapter provides a systematic overview, tutorial, and discussion of the certified approaches in trustworthy deep learning. The chapter introduces essential terminologies, core methodologies, and representative applications of certified approaches. We believe that certified approaches, as a prerequisite for deploying AI in high-stake and safety-critical applications, would be an essential tool in metacognitive AI, and we hope that this chapter can inspire readers to further advance the field of certifiable trustworthiness for metacognitive AI.

This chapter presents a metacognitive AI approach via formal verification and repair of neural networks (NNs). We observe that a neural network repair is a form of metacognition, where trained AI systems relearn until specifications hold. We detail Veritex, a tool for reachability analysis and repair of deep NNs (DNNs). Veritex includes methods for exact and over-approximative reachability analysis of DNNs. The exact methods can compute the exact output reachable domain, as well as the exact unsafe input space that causes safety violations of DNNs. Based on the exact unsafe input–output reachable domain, Veritex can repair unsafe DNNs on multiple safety properties with negligible performance degradation, by updating the DNN parameters via retraining. Veritex primarily addresses the synthesis of provably safe DNNs, which is not yet significantly addressed in the literature. Veritex is evaluated for safety verification and DNN repair. Benchmarks for verification include ACAS Xu, and benchmarks for the repair include an unsafe ACAS Xu and an unsafe agent trained in deep reinforcement learning (DRL), where it is able to modify the NNs until safety is proven.

In this chapter, we use task failure as a trigger to engage in metacognitive processes. We present a procedure by which an agent may exploit failure in the zero-shot outputs of LLMs as a trigger to investigate alternative solutions to the problem using object interactions and knowledge of the object semantics. We additionally propose a method through which knowledge gained from the object interactions can be distilled back into the LLM and avenues for future research.

We investigate the incorporation of metacognitive capabilities into Machine Learning Integrated with Network (MLIN) systems and develop machine Learning Integrated with Knowledge (mLINK) strata. This stratum is aimed at integrating knowledge obtained from multiple MLIN elements and reflecting on the ML application performance outcomes in order to provide feedback on metacognitive actions aimed at ensuring performance and improving ML application robustness towards Data Quality (DQ) variations. We discuss multiple use cases to show how the knowledge on the interrelationships between MLIN components, DQ, and ML application performance can be generated and employed by mLINK. We elaborate on how this knowledge is integrated into mLINK to produce metaknowledge, deemed as recommendations on adaptation actions or strategies needed. We define the process of employing these recommendations by mLINK as metacognition and describe multiple examples of utilizing these metacognitive strategies in practice, such as optimizing the data collection; reflection on DQ; DQ assurance; enhanced transfer learning; and Federated Learning for enhancing security, privacy, collaboration, and communication in MLIN.

To enhance understanding and collaboration with autonomous agents, it is crucial to construct a representation of their task strategies that integrates interpretability, monitoring, and formal reasoning. This dual-purpose representation fosters human comprehension and enables automated analytical processes. We achieve this balance by formalizing task strategies through temporal logic formulas. Recent trends emphasize inferring temporal logic formulas from data to explain system behaviors and assess autonomous agents’ competencies. Our methodology relies on positive and negative examples from system observations to construct a concise temporal logic formula consistent with the data. However, existing approaches often overlook real-world data’s noise and uncertainties, limiting practical deployment. Addressing this, we analyze labeled trajectories and aim to infer interpretable formulas that minimize misclassification loss. To tackle data uncertainties, we focus on labeled interval trajectories. Our algorithm maximizes the worst-case robustness margin, enhancing formula robustness and ensuring the adaptability and reliability of temporal logic inference in real-world applications.