Intelligence Augmentation Research Articles

A risk-based model for human-artificial intelligence conflict resolution in process systems

The conflicts stemming from discrepancies between human and artificial intelligence (AI) in observation, interpretation, and action have gained attention. Recent publications highlight the seriousness of the concern stemming from conflict and models to identify and assess the conflict risk. No work has been reported on systematically studying how to resolve human and artificial intelligence conflicts. This paper presents a novel approach to model the resolution strategies of human-AI conflicts. This approach reinterprets the conventional human conflict resolution mechanisms within AI. The study proposes a unique mathematical model to quantify conflict risks and delineate effective resolution strategies to minimize conflict risk. The proposed approach and mode are applied to control a two-phase separator system, a major component of a processing facility. The proposed approach promotes the development of robust AI systems with enhanced real-time responses to human inputs. It provides a platform to foster human-AI collaborative engagement and a mechanism of intelligence augmentation.

Generative AI for Intelligence Augmentation: Categorization and Evaluation Frameworks for Large Language Model Adaptation

Generative AI for Intelligence Augmentation: Categorization and Evaluation Frameworks for Large Language Model Adaptation

Attribute Feature Perturbation-Based Augmentation of SAR Target Data.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?

AbstractThe growth of artificial intelligence (AI) has allowed industries to automate and improve their efficiency in operations. Especially in process industries, AI helps to develop intelligent models and tools to proactively monitor and predict equipment or system failures, minimize downtime, and optimize maintenance schedules. With the advancements in AI and its ability to perform tasks, there is a growing belief that AI may eventually replace humans. However, the absence of human involvement in operations in the process industry raises safety concerns. Therefore, AI should collaborate with humans rather than replace them in processing facility operations. This technology is referred to as intelligence augmentation (IA). This article (i) presents a detailed comparison between AI and IA's potential in process systems, (ii) identifies the feasibility of using AI and IA in process safety, and (iii) identifies the risk associated with the implementation of AI or IA in process industries.

Artificial intelligence – Human intelligence conflict and its impact on process system safety

In the Industry 4.0 revolution, industries are advancing their operations by leveraging Artificial Intelligence (AI). AI-based systems enhance industries by automating repetitive tasks and improving overall efficiency. However, from a safety perspective, operating a system using AI without human interaction raises concerns regarding its reliability. Recent developments have made it imperative to establish a collaborative system between humans and AI, known as Intelligent Augmentation (IA). Industry 5.0 focuses on developing IA-based systems that facilitate collaboration between humans and AI. However, potential conflicts between humans and AI in controlling process plant operations pose a significant challenge in IA systems. Human-AI conflict in IA-based system operation can arise due to differences in observation, interpretation, and control action. Observation conflict may arise when humans and AI disagree with the observed data or information. Interpretation conflicts may occur due to differences in decision-making based on observed data, influenced by the learning ability of human intelligence (HI) and AI. Control action conflicts may arise when AI-driven control action differs from the human operator action. Conflicts between humans and AI may introduce additional risks to the IA-based system operation. Therefore, it is crucial to understand the concept of human-AI conflict and perform a detailed risk analysis before implementing a collaborative system. This paper aims to investigate the following: 1. Human and AI operations in process systems and the possible conflicts during the collaboration. 2. Formulate the concept of observation, interpretation, and action conflict in an IA-based system. 3. Provide a case study to identify the potential risk of human-AI conflict.

The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums

The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums

Intelligence Augmentation and capability co-elevation in healthcare enabled by reasoned transparency

Artificial intelligence (AI) systems in healthcare can have a significant impact on the performance of the actors involved, encouraging, for example, increasingly early diagnoses, personalized treatments and more accurate data management and processing techniques. However, these results may depend on increasingly profitable human-machine interactions, Intelligence Augmentation, and potential in terms of capability co-elevation. Starting from this assumption, this study aims to understand which can be the Intelligence Augmentation and capability co- elevation driver in healthcare. This conceptual paper has been carried out with the conceptual goal of delineating, so, to address the research question, a deductive reasoning approach was applied and the methodological approach followed has been based on the description of the theoretical background, definition of evidence from an illustrative case, Livongo Health, addressed by analyzing secondary data extrapolated from the website contents, and development conclusions. From the illustration case, insights have been outlined to understand how to achieve the objectives of Intelligence Augmentation and capability co-elevation: it has been observed how the reasoned transparency in AI systems can be understood as an enabling factor.

Empowering service systems through Intelligence Augmentation (IA) in digital society

The paper explores the interpretative significance of relevance in the systemic qualification of “intelligence,” particularly in the context of Intelligence Augmentation (IA). Using the Viable System Approach (VSA) matrix, it delves into Informational Variety and Systemic Relevance to understand the incremental impact of AI in complex decision-making. The study reveals that, methodologically, the relevance of the viable system explains the synergistic collaboration between humans and machines, considering technology not just as an influential resource but also possessing structural criticality. “Intelligent” service systems, based on AI, transform into “wise” systems when components cooperate with technological resources. This interaction, rather than amplifying human capabilities, leads to cognitive enhancement, streamlining decision-making. The abstract emphasizes the incompleteness of current support systems, which focus solely on the quantitative aspect of information, suggesting that greater efficiency necessitates integrating human experiences, knowledge, and emotions. It underscores that the role of technology lies in how humans interact with it, and as technology becomes increasingly critical, algorithms will progressively shape AI decision-making processes. In summary, AI facilitates the transition of the business system from an intelligent to a wise configuration, integrating the rational component of AI with the emotional aspect of human intelligence.

How does AI promote design iteration? The optimal time to integrate AI into the design process

Design encompasses an iterative problem-solving process. While prior research has acknowledged the significance of mental iteration within this process, limited attention has been given to methods for enhancing it. This paper addresses this gap by investigating the impact of integrating artificial intelligence (AI) augmentation into the design iteration process. The primary objectives are to assess the potential augmentation of design by AI and to determine the optimal timing for AI integration. A comprehensive empirical investigation involving 48 designers and various AI-augmented conditions was conducted to substantiate the role of AI in design. The findings reveal that AI augmentation significantly enhances designer performance, improving both the efficiency of the iteration process and the quality of solutions generated in terms of depth and breadth. Comparative analyses with the Baseline demonstrate the positive impact of AI augmentation. Notably, performance under Real-time and On-demand conditions surpasses that of In-stage, emphasising the importance of selecting the right timing for AI integration. This study not only contributes theoretical support for the development of AI-augmented tools but also provides valuable insights for the advancement of design education.

A Research Framework Focused on AI and Humans instead of AI versus Humans

Despite lacking a shared understanding and a generally accepted definition, Artificial intelligence (AI) is promoted and credited with miraculous abilities to solve all problems. To gain a more nuanced and deeper understanding of the design trade-offs associated with AI, this paper proposes a research framework that contrasts two competing frameworks: (1) AI versus Humans (characterized by strong AI and Artificial General Intelligence) focused on replacing human beings and (2) AI and Humans (characterized by intelligence augmentation and human-centered AI) focused on empowering human beings as individuals and communities. The arguments in the paper are supported by research activities that explored conceptual frameworks and inspiring prototypes. These developments have resulted in gaining a deeper understanding of how AI-type systems can contribute to quality of life aspects with a specific focus on rethinking and reinventing learning, education, working, and collaboration in the digital age.

How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work

This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of ML-based decision support systems and also enriches theories on human-AI augmentation.

Augmentation and translation crowdsourcing

Abstract This paper critically discusses how crowdsourcing relate to the emergence of the so-called translation augmentation paradigm. As a broad generalization, these two technology-driven phenomena emerged as possible solutions to achieve better, more efficient or speedier solutions to complex problems than those that single individuals can solve on their own. They both can be contextualized within 4EA approaches to cognition, reframing problem-solving towards a distributed and extended perspective that goes beyond the limitations of the individual mind. Nevertheless, augmented translation refers to the coupling of human and machines in a technology-centric approach that attempts to increase the cognitive capabilities of translators, who remain in the loop and in charge (Lommel 2020; Angelone 2023). It relates to existing technological integrated systems beyond just the automation brought by TM and MT that learn and adapt from humans. The paper (1) interrelates distributed and extended cognitive approaches and crowdsourcing (Risku, Pein-Weber & Rogl 2016; Jiménez-Crespo 2017; Risku & Windhager 2020) in relation to AI and intelligence augmentation (IA), (2) analyzes the existing fuzziness in the theoretical and conceptual constructs to translation augmentation, (3) explores issues related to the locus of control and the center of the cognitive system in augmented crowdsourcing, as well as (4) looks ahead by introducing unexplored areas such as social augmentation. In doing so, the paper will attempt to answer whether human translators in crowdsourcing initiatives are, in fact, “in charge” and – or augmented.

Futuristic Dentistry: Intelligence Augmentation rather than Artificial Intelligence should be the Future Realm of Dentistry

Futuristic Dentistry: Intelligence Augmentation rather than Artificial Intelligence should be the Future Realm of Dentistry

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning

BackgroundVisual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot ‘see’ intravascular devices, which precludes artificial...

Art, technology and the Internet of Living Things.

Intelligence augmentation was one of the original goals of computing. Artificial Intelligence (AI) inherits this project and is at the leading edge of computing today. Computing can be considered an extension of brain and body, with mathematical prowess and logic fundamental to the infrastructure of computing. Multimedia computing-sensing, analyzing, and translating data to and from visual images, animation, sound and music, touch and haptics, as well as smell-is based on our human senses and is now commonplace. We use data visualization and sonification, as well as data mining and analysis, to sort through the complexity and vast volume of data coming from the world inside and around us. It helps us 'see' in new ways. We can think of this capacity as a new kind of "digital glasses". The Internet of Living Things (IOLT) is potentially an even more profound extension of ourselves to the world: a network of electronic devices embedded into objects, but now with subcutaneous, ingestible devices, and embedded sensors that include people and other living things. Like the Internet of Things (IOT), living things are connected; we call those connections "ecology". As the IOT becomes increasingly synonymous with the IOLT, the question of ethics that is at the centre of aesthetics and the arts will move to the forefront of our experience of and regard for the world in and around us.

Executive-centered AI? Designing predictive systems for the public sector.

Recent policies and research articles call for turning AI into a form of IA ('intelligence augmentation'), by envisioning systems that center on and enhance humans. Based on a field study at an AI company, this article studies how AI is performed as developers enact two predictive systems along with stakeholders in public sector accounting and public sector healthcare. Inspired by STS theories about values in design, we analyze our empirical data focusing especially on how objectives, structured performances, and divisions of labor are built into the two systems and at whose expense. Our findings reveal that the development of the two AI systems is informed by politically motivated managerial interests in cost-efficiency. This results in AI systems that are (1) designed as managerial tools meant to enable efficiency improvements and cost reductions, and (2) enforced on professionals on the 'shop floor' in a top-down manner. Based on our findings and a discussion drawing on literature on the original visions of human-centered systems design from the 1960s, we argue that turning AI into IA seems dubious, and ask what human-centered AI really means and whether it remains an ideal not easily realizable in practice. More work should be done to rethink human-machine relationships in the age of big data and AI, in this way making the call for ethical and responsible AI more genuine and trustworthy.

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

We typically think of artificial intelligence (AI) as focusing on empowering machines with human capabilities so that they can function on their own, but, in truth, much of AI focuses on intelligence augmentation (IA), which is to augment human capabilities. We propose a framework for designing intelligent augmentation (IA) systems and it addresses six central questions about IA: why, what, who/whom, how, when, and where. To address the how aspect, we introduce four guiding principles: simplification, interpretability, human-centeredness, and ethics. The what aspect includes an IA architecture that goes beyond the direct interactions between humans and machines by introducing their indirect relationships through data and domain. The architecture also points to the directions for operationalizing the IA design simplification principle. We further identify some potential risks and emerging issues in IA design and development to suggest new questions for future IA research and to foster its positive impact on humanity.

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field

ChatGPT, a conversational AI interface that utilizes natural language processing and machine learning algorithms, is taking the world by storm and is the buzzword across many sectors today. Given the likely impact of this model on data science, through this perspective article, we seek to provide an overview of the potential opportunities and challenges associated with using ChatGPT in data science, provide readers with a snapshot of its advantages, and stimulate interest in its use for data science projects. The paper discusses how ChatGPT can assist data scientists in automating various aspects of their workflow, including data cleaning and preprocessing, model training, and result interpretation. It also highlights how ChatGPT has the potential to provide new insights and improve decision-making processes by analyzing unstructured data. We then examine the advantages of ChatGPT’s architecture, including its ability to be fine-tuned for a wide range of language-related tasks and generate synthetic data. Limitations and issues are also addressed, particularly around concerns about bias and plagiarism when using ChatGPT. Overall, the paper concludes that the benefits outweigh the costs and ChatGPT has the potential to greatly enhance the productivity and accuracy of data science workflows and is likely to become an increasingly important tool for intelligence augmentation in the field of data science. ChatGPT can assist with a wide range of natural language processing tasks in data science, including language translation, sentiment analysis, and text classification. However, while ChatGPT can save time and resources compared to training a model from scratch, and can be fine-tuned for specific use cases, it may not perform well on certain tasks if it has not been specifically trained for them. Additionally, the output of ChatGPT may be difficult to interpret, which could pose challenges for decision-making in data science applications.

A Critical Review on Factors Affecting the User Adoption of Wearable and Soft Robotics.

In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user's perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as "soft", "robot", "wearable", and "exoskeleton" was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted.