Human-machine Partnership Research Articles

Evolution of Production Spaces: A Historical Review for Projecting Smart Factories

Factories are transforming not only mechanically and technologically but also architecturally due to emerging developments in the industry and fabrication: This new process, called the Second Machine Age or Industry 4.0, a new model is designed in production by providing the human-machine partnership over a virtual network. It is aimed that the machines used in production and the people participating in different stages of production can work in different spaces. In time, jobs that require human power will be replaced by robots, and a new order is being considered where there will be no people in production spaces, and they can work in the virtual environment. Production for human beings is mostly from material production to digital production; labour will turn into digital labour. For this reason, it is thought that production spaces will turn into smart factories with only machines and production robots and no workers. And now the question is: what is a smart factory? The revolutions in the industry history started with the invention of the steam engine; then, new technological revolutions were experienced with the use of electricity in production, the development of automation systems and internet-based systems. While technology and production tools are constantly changing, these developments also affect production spaces. Factories are also transforming to keep up with these rapid and continuous physical and fictional innovations. This study focuses on the architectural evolution of factories by following the technological revolutions of the industry. It examines the main criteria in the process of change and transformation of factories and spatial reflections of the revolutions. It establishes a relationship between production technology and the needs of the production spaces and seeks references from past samples. The study aims to review the historical background for generating a projection to new production spaces and to be a new discussion for future factories.

Deep Learning for Cardiovascular Imaging

Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology. At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI. Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.

Bayesian Optimization of photonic curing process for flexible perovskite photovoltaic devices

Photonic curing is a thin-film processing technique that can enable high-throughput perovskite solar cell (PSC) manufacturing. However, photonic curing has many variables that can affect the processing outcome, making optimization challenging. Here, we introduce Bayesian Optimization (BO), a machine-learning framework, to optimize the power conversion efficiency (PCE) of photonically cured MAPbI3 PSCs on ITO-coated Willow Glass. We apply BO with four input variables—MAPbI3 concentration, additive CH2I2 volume, pulse voltage, and pulse length. These input variables were dynamically adjusted in response to the new data, an example of a human-machine partnership. With the limited experimental budget of 48 conditions, we achieved a champion PCE of 11.42% and predicted 14 new conditions resulting in >10% PCE. Beyond simple optimization, we examined the relationships between pairs of inputs with two-dimensional contour plots and investigated the relative importance of each input to gain insight into photonic curing. We demonstrate that BO is a powerful tool in process optimization and can be adapted to other PSC manufacturing cases.

Designing Deep Reinforcement Learning for Human Parameter Exploration

Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this article, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users for sound design. We describe a series of user-centred studies to probe the creative benefits of these agents and adapting their design to exploration. Preliminary studies observing users’ exploration strategies with parametric interfaces and testing different agent exploration behaviours led to the design of a fully-functioning prototype, called Co-Explorer, that we evaluated in a workshop with professional sound designers. We found that the Co-Explorer enables a novel creative workflow centred on human–machine partnership, which has been positively received by practitioners. We also highlight varied user exploration behaviours throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications.

COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence

PurposeTo investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. Materials and methodsAn AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. ResultsDiscrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007). ConclusionsOur results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.

Emerging materials intelligence ecosystems propelled by machine learning

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human–machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human–machine partnerships for fast and efficient virtual materials screening, development and discovery.

A mind model for intelligent machine innovation using future thinking principles

Purpose The purpose of this paper is to address the possible future evolution of innovation from a human-only initiative, to human–machine co-innovation, to autonomous machine innovation and to arrive at a conceptual mind model that outlines the role of innovation regimes and innovation agents. Design/methodology/approach This is a concept paper where a theoretical “thought experiment” is done, using future thinking principles and data that originate from the literature. Findings A conceptual mind model is developed to facilitate a better understanding of complexity at the edge of innovation where intelligent machines will emerge as innovators of the cyber world. It was found that innovation will gradually evolve from a human-only activity, to human–machine co-innovation, to incidences of autonomous machine innovation, based on the growth of machine intelligence and the adoption of human–machine partnership management models in future. Research limitations/implications Very little information is available in the literature on intelligent machines doing innovation. The work is based on a theoretical approach that presents new concepts to be debated, but have not been tested in engineering and technology management practice, except for a conference presentation and academic discussion. Practical implications The current world view is that future “smartness” is only possible through the creative abilities that humans have, but as machines are entering the workplace and our daily lives, not only as static robots on a manufacturing line, but as intelligent systems with the potential to replace lawyers and accountants, doctors and teachers, companions and partners, their role in innovation in complex environments needs to be explored. Social implications Human–machine interaction is often an emotional social issue of concern in terms of the replacement of human intelligence with machine intelligence. It should be asked whether humans will or should remain in control of innovation? Artificial intelligence (AI) may complement and even substitute human intelligence, but huge value is embedded in the new goods, services and innovations AI will enable, especially in manufacturing, where value embedded in the project becomes complex and dynamic. Originality/value The thinking presented in this paper is original and should lead to debate to question the way innovation systems will work in future and inspires thinking about AI and innovation.

Synthesis-planning program relies on human-machine partnership

Computer-aided synthesis-planning (CASP) programs aim to replicate what synthetic chemists do when tackling a synthesis: start with a target molecule and then work backward to trace a synthetic route, including an efficient and achievable series of reactions and reagents. Work in this field goes back 50 years, but successful examples have emerged only in the past several years. These rely either on chemistry rules written by human chemists or on machine-learning algorithms that have assimilated synthesis knowledge from databases of reactions. Researchers now report that one CASP program that combines human knowledge and machine learning performs better than one that uses only artificial intelligence, particularly for synthetic routes involving rarely used reactions (Angew. Chem., Int. Ed. 2019, DOI: 10.1002/anie.201912083). The program is an update to Chematica, which was developed by Bartosz A. Grzybowski of the Ulsan National Institute of Science and Technology and the Polish Academy of Sciences and is

Modeling with the crowd: Optimizing the human-machine partnership with Zooniverse

AbstractLSST and Euclid must address the daunting challenge of analyzing the unprecedented volumes of imaging and spectroscopic data that these next-generation instruments will generate. A promising approach to overcoming this challenge involves rapid, automatic image processing using appropriately trained Deep Learning (DL) algorithms. However, reliable application of DL requires large, accurately labeled samples of training data. Galaxy Zoo Express (GZX) is a recent experiment that simulated using Bayesian inference to dynamically aggregate binary responses provided by citizen scientists via the Zooniverse crowd-sourcing platform in real time. The GZX approach enables collaboration between human and machine classifiers and provides rapidly generated, reliably labeled datasets, thereby enabling online training of accurate machine classifiers. We present selected results from GZX and show how the Bayesian aggregation engine it uses can be extended to efficiently provide object-localization and bounding-box annotations of two-dimensional data with quantified reliability. DL algorithms that are trained using these annotations will facilitate numerous panchromatic data modeling tasks including morphological classification and substructure detection in direct imaging, as well as decontamination and emission line identification for slitless spectroscopy. Effectively combining the speed of modern computational analyses with the human capacity to extrapolate from few examples will be critical if the potential of forthcoming large-scale surveys is to be realized.

Smart Humanitarianism: Re-imagining Human Rights in the Age of Enterprise

A paradigmatic shift around the central role of ‘social entrepreneurs’ is captivating a broad, diverse range of social actors refashioning the institutional landscape of human rights and humanitarian practices. For this special issue dedicated to ‘Re-imagining Human Rights’, we explore some of the implications of these revolutionary changes in human rights practices, and their consequences for sociological study and political critique in the 21st century. Following a discussion of the state of the sociology of human rights practices, we describe the remaking of the human rights arena into a site of technocratic organizations with an emphasis on the ‘triple bottom line’ (financial, social and environmental sustainability). This market-led rights paradigm also promotes a new kind of empathy required for social problem-solving and humanitarian action – one less sentimental, much more technocratic and managerial. We offer some critical observations on this ‘smart humanitarianism’, which emphasizes the human-machine partnership via online technologies, apps, and expert systems management strategies; they redistribute the cognitive responsibilities of determining and delivering goods for greatest measurable impact with a quid-pro-quo of reframing inequality. We introduce the other contributing articles, signposting notable elements and the implications for wider socio-political critique, especially regarding ‘smart humanitarianism’.

AN ATTEMPT FOR CONCEPTUAL FRAMEWORK FOR HUMAN-MACHINE COOPERATION

As technical processes become more and more complex and the need to support human operators in supervision or control tasks becomes increasingly crucial, the nature of the interactions between human operators and Decision Support Systems (DSS) tends towards Human-Machine cooperation in which the DSS facilitates a partnership with the human operators. In this paper, we first recall a definition for cooperation from the field of psychology: Two agents are cooperating if 1) each one strives towards goals and can interfere with the other (e.g., in terms of goals, resources, procedures) and 2) each agent tries to detect and process such interferences to make the other's activities easier. This definition, which was originally intended to describe human-human cooperation, can be extended to Human-Machine Cooperation if adjustments are made to compensate for the limitations of the machine's capabilities. We then present the four basic elements needed to design a cooperative Decision Support System:• sufficient know-how for solving problems autonomously• know-how-to-cooperate ability• an adequate organizational structure that integrates human and machine, and• a need-to-cooperate.Key studies in engineering have focused on know-how; others have concentrated on the ability to know-how-to-cooperate; still others, on the organizational structure of the Human-Machine partnership and the conditions that motivate human and artificial agents to cooperate to accomplish a task. Using a multi-disciplinary approach that associates research in cognitive psychology and human engineering, we bring these 4 basic elements together in a methodology for designing Human-Machine cooperative systems. This methodology is the result of more than twelve years of experiments at LAMIH in several fields of application, mainly Air Traffic Control and Telecommunication networks.

The human–machine partnership in UCAV operations

AbstractAt a time when a great deal of research on UCAVs is aimed at maximising their autonomy it should not be forgotten that human operators will, ultimately, remain ‘in control’ to some degree. The decision-sharing relationship between the operator and the UCAV depends on political constraints as well as the intelligence of the UCAV system. This in turn dictates the amount and type of information to be exchanged and the way in which it is communicated with the operator.Operational flexibility is a key military driver and in order to achieve it, a variable autonomy command interface, combined with information fusion and intelligent decision-support systems, will be required. To be effective the operator will need to work in concert with the UCAV system rather than act simply as a command source and an information sink. The implications of this ‘partnership’ for command and monitoring requirements and in particular for weapons release authorisation, are discussed in this paper.

The human-machine partnership in UCAV operations

AbstractAt a time when a great deal of research on UCAVs is aimed at maximising their autonomy it should not be forgotten that human operators will, ultimately, remain ‘in control’ to some degree. The decision-sharing relationship between the operator and the UCAV depends on political constraints as well as the intelligence of the UCAV system. This in turn dictates the amount and type of information to be exchanged and the way in which it is communicated with the operator.Operational flexibility is a key military driver and in order to achieve it, a variable autonomy command interface, combined with information fusion and intelligent decision-support systems, will be required. To be effective the operator will need to work in concert with the UCAV system rather than act simply as a command source and an information sink. The implications of this ‘partnership’ for command and monitoring requirements and in particular for weapons release authorisation, are discussed in this paper.

The Enterprise at the Edge:: Agents to the Rescue

There is a revolution in decision technology in the form of intelligent agent systems. Very soon, tools in the shape of distributed artificial intelligence will be available and be particularly applicable to the complexities of decision-making in the typical global enterprise. A number of issues will arise in the next few years as intelligent agents become the mainstream enablers of ‘real-time’ enterprise process, such as the need to ensure continuity in the transition phase by careful integration of intelligent agent systems into a firm's legacy systems. The authors discuss such issues and conclude with a look at the paradigm shift in enterprise philosophy made possible by a human–machine partnership.

Bridging across content and tools

Over the past 10 years, I have contributed to the development of two kinds of "multimedia environments." In the first, the human-machine partnership evolved as a means of accessing specific content for discovery and enjoyment - this was true for works such as "New Orleans in Transition" (1987), "Elastic Charles" (1988) as well as for several fictional narratives. In the second, the human-machine partnership was designed to allow users facilities for describing, managing and sequencing content. Both types of environments encourage the invention of richer human-readable pneumonics and incorporate machine-readable description as tools for content-look-ahead. What has fascinated me over the years is the complementarity which binds the generation of content and the design of tools. In fact we cannot talk about form without discussing content and the tools for accessing that content.

Man-Machine Interaction in Computer-Aided Design Systems

Many of the rapidly increasing numbers of computer users in the design environment are dissatisfied. The cardinal phases of design impose varying requirements, but recent research indicates that skill, style and comfort are the main categories for satisfaction. Advanced, long-term thinking is directed towards intelligent systems. However, a clearer perception of the essential features of interactive systems can help to achieve significant improvements even in the short term. Research on the symmetry (or its lack) of the human-machine partnership and on interfaces adaptable for and by the user, actions on collecting rules of thumb, on typography, on data-presentation, on experiments in complex environments and on establishing suitable levels of abstraction, can provide efficient means for rapid progress.