Machine Autonomy Research Articles

The impact of technology development on the future of the labour market in the Slovak Republic

One of the current open labour market issues is to analyze how the technology progress in production processes will affect the labour market, especially in the context of the replacement of human workers with machines. This paper aims to identify potential threats faced by the labour market in Slovakia, which based on the analysis, employs a significant number of workers in areas susceptible to automation. According to the results, industrial production in Slovakia is divided into several areas, where almost 41% of all industrial workers are employed in the manufacture of metal constructions, machinery and equipment, motor vehicles, semitrailers and trailers and the number of people employed in this sector is increasing linearly over the reference period from 2008 to 2016. The paper focuses on potential scenarios that may arise in the labour market in the near future, using Cobb-Douglas production function. In the original model, the production function of the company is based on production factors in which labour plays an important role. However, with the growth of automation, the original model of companies is changing, transforming, and workforce who has played the key role in the production process of goods or services are becoming redundant due to the growing autonomy of machines. The analyzed scenarios show situations that may arise in the labour market if a company changes the scale of the use of production factors in its production process. In other words, given the current situation as labour costs increase, the power and capabilities of machines are growing and a company wants to stay competitive on the global market.

On conflicts between ethical and logical principles in artificial intelligence

Artificial intelligence is nowadays a reality. Setting rules on the potential outcomes of intelligent machines, so that no surprise can be expected by humans from the behavior of those machines, is becoming a priority for policy makers. In its recent Communication “Artificial Intelligence for Europe” (EU Commission 2018), for instance, the European Commission identifies the distinguishing trait of an intelligent machine in the presence of “a certain degree of autonomy” in decision making, in the light of the context. The crucial issue to be addressed is, therefore, whether it is possible to identify a set of rules for data use by intelligent machines so that the decision-making autonomy of machines can allow for humans’ traditional informational self-determination (humans provide machines only with the data they decide to), as enshrined in many existing legal frameworks (including, for personal data protection, the EU’s General Data Protection Regulation) (EU Parliament and Council 2016) and can actually turn out to be further beneficial to individuals. Governing the autonomy of machines can be a very ambitious goal for humans since machines are geared first to the principles of formal logic and then—possibly—to ethical or legal principles. This introduces an unprecedented degree of complexity in how a norm should be engineered, which requires, in turn, an in-depth reflection in order to prevent conflicts between the legal and ethical principles underlying humans’ civil coexistence and the rules of formal logic upon which the functioning of machines is based (EU Parliament 2017).

A knowledge-based approach for representing jobholder profile toward optimal human–machine collaboration in cyber physical production systems

Increasing number of AI-enhanced approaches provide helpful “know-how” for reproducing and imitating skills and finally substituting human jobs with algorithms and intelligent machines. However, complementarity of human and machine, especially in hybrid man-machine settings is still not sufficiently explored. The main objective of this paper is to establish a twofold qualitative and quantitative methodology for optimal selection of a competent jobholder(s) to perform a certain task by semantic modeling and analysis of jobholder (human and machine) profiles corresponding to the task characteristics and learning requirements including knowledge, skills and competences (KSCs). The proposed knowledge-based approach comprises semantic modeling and quantitative methods focusing on measuring and correlating the level of human competences and machine autonomy, and identifying the extent of human–machine complementarity in performing an assigned task. The Vector of Competence and Autonomy (VCA) is built for identifying the extent of human–machine collaboration. The quantitative analysis involves several human factors in association to various combinations of technological components of Digital Assistance Systems with different automation degrees, under certain aspects of complexity of products and workplaces. Applying a set of rules and considering jobholder profiles, VCA values are interpreted and the current state of complementarity is inferred. Furthermore, feasible radical or incremental managerial transition pathways are identified as an initial step to reach the optimal (desired) level of human–machine collaboration in the example of TU Wien Pilot Factory Industry 4.0.

Uncertainties in the Algorithmic Image, xCoAx Special Issue 2019

The incorporation of algorithmic procedures into the automation of image production has been gradual, but has reached critical mass over the past century, especially with the advent of photography, the introduction of digital computers and the use of artificial intelligence (AI) and machine learning (ML). Due to the increasingly significant influence algorithmic processes have on visual media, there has been an expansion of the possibilities as to how images may behave, and a consequent struggle to define them. This algorithmic turnhighlights inner tensions within existing notions of the image, namely raising questions regarding the autonomy of machines, author- and viewer- ship, and the veracity of representations. In this sense, algorithmic images hover uncertainly between human and machine as producers and interpreters of visual information, between representational and non-representational, and between visible surface and the processes behind it. This paper gives an introduction to fundamental internal discrepancies which arise within algorithmically produced images, examined through a selection of relevant artistic examples. Focusing on the theme of uncertainty, this investigation considers how algorithmic images contain aspects which conflict with the certitude of computation, and how this contributes to a difficulty in defining images.

‘Where are the people? What are they doing? Why are they doing it?’(Mindell) Situating artificial intelligence within a socio-technical framework

This article explores the sociology of artificial intelligence (AI), focusing on interactions between social actors and technological processes. The aim is to locate social actors in the key elements of Bell’s framework for understanding AI, featuring big data, algorithms, machine learning, sensors and rationale/logic. We dispute notions of human autonomy and machine autonomy, seeking alternatives to both anthropocentric and technological determinist accounts of AI. While human actors and technological devices are co-producers of the assemblages around AI, we challenge the argument that their respective contributions are symmetrical. The theoretical problem is to establish quite how human actors are positioned asymmetrically within AI processes. This challenge has strong resonances for issues of inequality, democracy, governance and public policy. The theoretical questions raised do not support the argument that sociology should respond to the rise of big data by becoming a primarily empirical discipline.

Public Service Chatbots: Automating Conversation with BBC News

Automation of journalistic tasks is growing with the development of increasingly sophisticated software for newsgathering, production, and distribution. Bots are one form of algorithmic technology that has found a place in the modern newsroom, with chatbots leading the way as news organisations seek to attract new audiences using conversational forms of journalism. Recent advances in artificial intelligence (AI) and machine learning (ML) have fuelled increasing experimentation with machine autonomy and there has been much hyperbole in the press about the extent and impact of this on journalism. Looking at on-the-ground trials in audience-facing bots at the UK’s largest public broadcaster, we find a significantly more restricted picture. News bots at The BBC to-date have been basic, do not use ML, and have rarely been integrated into news production. The organisation is laying groundwork for development of more interactive news formats with an increasingly conversational tone and individual mode of address as part of a strategy for increased personalisation, which is likely to involve growing levels of ML. In the process, bots are reconfiguring working practices and infrastructure, posing new editorial and technical challenges, and redefining relationships with audiences. We discuss the implications of this for public service media.

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Human–computer interaction (HCI) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI, more in particular the capacity of individuals and firms to apply state‐of‐the‐art techniques and to prevent an artificial divide, thereby increasing social sustainability.

Exploring Machine Autonomy and Provenance Data in Coffee Consumption

Technologies such as distributed ledgers and smart contracts are enabling the emergence of new autonomous systems, and providing enhanced systems to track the provenance of goods. A growing body of work in HCI is exploring the novel challenges of these systems, but there has been little attention paid to their impact on everyday activities. This paper presents a study carried out in 3 office environments for a 1-month period, which explored the impact of an autonomous coffee machine on the everyday activity of coffee consumption. The Bitbarista mediates coffee consumption through autonomous processes, presenting provenance data at the time of purchase while attempting to reduce intermediaries in the coffee trade. Through the report of interactions with and around the Bitbarista, we explore its implications for everyday life, and wider social structures and values. We conclude by offering recommendations for the design of community shared autonomous systems.

How swarming will change warfare

ABSTRACTWith advances in artificial intelligence and machine autonomy, a profound transformation in warfare is underway. The convergence of these technologies is enabling drone swarms, made up of cooperative, autonomous robots that react to the battlefield as one. Much as they do in colonies of insects, flocks of birds, or packs of wolves, swarm members will work together to create a unified whole that is greater than the sum of the individuals. They will allow militaries to field forces that are larger in number, faster, and better coordinated – not to mention more expendable – than would be possible with humans alone. Robotic swarms will benefit some countries more than others, decoupling military power from personnel limitations. The most profound and long-lasting impact of swarms will be in the way they change command-and-control, that is, the way militaries organize and fight on the battlefield.

Cloud Manufacturing as a new type of Product-Service System

ABSTRACTIndustry 4.0 (I4.0) technology developments in Cloud Manufacturing (CMfg) are challenging traditional business models, and adapting these is key to a sustainable competitive advantage. In parallel, pay-per-use strategies are being discussed as an enabler to future sustainable societies. The general benefits of CMfg services are clear, but to date the actual business implications from a service provider perspective have not been discussed. This article explores new business model opportunities based on the idea of providing CMfg as a completely new type of Product-Service Systems. Technology developments and business recommendations are defined, considering the proposed business model targets the manufacturing industry as a whole. Manufacturers make use of their spare capacity by purchasing time on networked equipment on a pay-per-use basis. This allows costs to be brought down, whilst creating new revenue streams. It also increases machine hosts’ competitiveness by reducing investment costs and enabling instant manufacturing scalability. CMfg is then classified into three levels of machine autonomy, arguing that as technology develops intermediaries may slowly integrate vertically and eventually replace manufacturers by completely autonomous equipment. The proposed business model presents both a first step and a baseline point of reference towards bridging the gap between advanced manufacturing technology and new business development in the context of I4.0 (Smart Manufacturing).

Assessing the Sense and Scope of Autonomyy in Emerging Military Weapon Systems

Lethal Autonomous Weapon Systems (LAWS) are essentially weapon systems that, once activated, can select and engage targets without further human intervention. While these are neither currently fielded nor officially part of any nation’s defence strategy, there is ample evidence that many States and defence contractors are currently developing LAWS for future deployment. Accordingly, this article will proceed in two main sections. Firstly, it offers a definition of LAWS, focusing on what it is about weapons autonomy that may call for these systems to be delineated, and subject to certain additional legal requirements in international humanitarian law (IHL), arms control and non-proliferation. Secondly, it considers what kinds of weapon systems are likely to emerge as LAWS, the unique challenges posed by them and some ongoing innovations to address these challenges. Three themes run throughout the paper. Firstly, ‘autonomy’ is a term of art, which must be narrowly and specifically applied to weapon systems, if it is to be useful in a LAWS context. Secondly, the purpose for which weapons autonomy and LAWS are defined is to delineate systems that may need to be subject to a) deployment restrictions, b) stronger and additional precautions in attack (in IHL) and/or c) commonly agreed rules to promote strategic stability (in arms control) and non-proliferation. Accordingly, a third theme is to distinguish between the strategic, operational and tactical levels; and to bear in mind that machine autonomy is technically more feasible at the tactical level, while it is necessary to have extensive (deliberative) human involvement at the operational and strategic levels. Indeed, the epitome of this is precisely the setting of strategic priorities and operational parameters within which tactical autonomy will operate. This is the first of a seven-part series. Future articles will build on it to address, more specifically: 1) whether and how LAWS can be deployed in compliance with IHL; 2) issues relating to the explosive remnants of war; 3) the lawful use of LAWS for targeted strikes; 4) legal and policy issues in the autonomous delivery of nuclear weapons; 5) arms control issues; and 6) non-proliferation.

Ethics and Autonomous Systems: Perils and Promises [Point of View

Examines the technology of autonomous systems and explores its ethical and social implications. Artificial intelligence (AI) and its role in autonomous systems have promised everything from utopian freedom to existential dystopia. The unfilled hyperbole surrounding past and present promises regarding AI futures has left many people skeptical, afraid, or just confused. Rational discussion is often left in the wake due to the fears and fantasy evoked by the press and Hollywood. Fortunately, as a byproduct, this has resulted in a blossoming of worldwide discourse on the ethical implications of the intelligent machines we are creating. Many near and mid-term ethical concerns have arisen with the advent of autonomous systems: particularly regarding driverless cars, privacy and drones, companionand intimate robotics, the displacement of jobs by intelligent machines, and warfighting robots among others. The IEEE Global Initiative on the Ethics of Autonomous Systems, the United Nations, the International Committee of the Red Cross, the White House, and the Future of Life Institute are among many responsible organizations that are now considering the ramifications of the real-world consequences of machine autonomy as we continue to stumble about trying to find a way forward.

Human Rights and the use of Autonomous Weapons Systems (AWS) During Domestic Law Enforcement

Much attention has been paid during the last couple of years to the emergence of autonomous weapons systems (AWS), weapon systems that allow computers, as opposed to human beings, to have increased control over decisions to use force. These discussions have largely centered on the use of such systems in armed conflict. However, it is increasingly clear that AWS are also becoming available for use in domestic law enforcement. This article explores the implications of international human rights law for this development. There are even stronger reasons to be concerned about the use of fully autonomous weapons systems--AWS without meaningful human control--in law enforcement than in armed conflict. Police officers--unlike their military counterparts--have a duty to protect the public. Moreover the judgments that are involved in the use of force under human rights standards require more personal involvement that those in the conduct of hostilities. Particularly problematic is the potential impact of fully autonomous weapons on the rights to bodily integrity (such as the right to life) and the right to dignity. Where meaningful human control is retained, machine autonomy can enhance human autonomy, but at the same time this means, higher standards of responsibility about the use of force should be applied because there is a higher level of human control. However, fully autonomous weapons entail no meaningful human control and, as a result, such weapons should have no role to play in law enforcement. Language: en

Notes on a New Kind of Art

I started working with robots applied to art around the turn of the century. Aiming at the most possible autonomy of the process, they were the next logical step after experimenting with algorithms confined to the computer environment. I was never interested in “digital art”. The first experiences, with an ant algorithm running on a computer connected to a robotic arm [fig. 1], showed the potential for a machine to create its own drawings and paintings as a kind of artificial creativity. The claim that these works represent a new kind of art, the art of machines, may be controversial in the context of the mainstream art world. But, actually, it is inscribed in the global evolution of robotics and artificial intelligence towards a greater autonomy of machines. Art announces what is about to arrive. DOI: http://dx.doi.org/10.14195/2182-8830_3-1_11 DOI: http://dx.doi.org/10.14195/2182-8830_3-1_11

The autonomy-safety-paradox of service robotics in Europe and Japan: a comparative analysis

Service and personal care robots are starting to cross the threshold into the wilderness of everyday life, where they are supposed to interact with inexperienced lay users in a changing environment. In order to function as intended, robots must become independent entities that monitor themselves and improve their own behaviours based on learning outcomes in practice. This poses a great challenge to robotics, which we are calling the "autonomy-safety-paradox" (ASP). The integration of robot applications into society requires the reconciliation of two conflicting aspects: increasing machine autonomy and ensuring safety in end-use. As the level of robot autonomy grows, the risk of accidents will increase, and it will become more and more difficult to identify who is responsible for any damage incurred. However, emphasizing safety impairs the autonomous functioning of the robot. This problem implies the need for a broadened concept of product safety. Our comparative study shows that the institutional framing of the ASP as well as concrete solutions to this problem differs between Europe and Japan in two respects: (1) the understanding of robot agency and (2) the concept of "appropriate" user---robot interaction.

The new killer drones: understanding the strategic implications of next-generation unmanned combat aerial vehicles

The expansive use of armed unmanned combat aerial vehicles (UCAV), or ‘drones’, by the United States over the past decade has occurred within a particular strategic context characterized by irregular warfare operations in permissive environments. Ongoing strategic, ethical and moral debates regarding specific uses of drones may well be overtaken by a new generation of armed combat drones able to survive and operate in contested airspace with design elements such as stealth and greater levels of machine autonomy. These design parameters, and the likely strategic context within which second generation UCAVs will be deployed, suggest a fundamentally different set of missions from those performed by the current generation of drones. The most beneficial characteristic of current unmanned systems has been the ability to combine persistent surveillance with the delivery of small precision-guided munitions. With a shift to more contested environments, this type of armed surveillance mission may become less practical and second generation UCAVs will instead focus on high intensity warfare operations. These new systems may have significant implications for deterrence, force doctrine and the conduct of warfare

ACM SIGGRAPH Distinguished Artist Award for Lifetime Achievement in Digital Art

The 2014 ACM SIGGRAPH Lifetime Achievement Award in Digital Art is awarded to Harold Cohen for his pioneering achievements in creating art through artificial intelligence and machine autonomy. Best known for his creation of the art-making system AARON, Cohen began his career as a painter after graduating from the Slade School of Fine Art in London in 1951. Exhibited widely in galleries and museums, Cohen’s work represented Great Britain in the Venice Biennale, Documenta 3, the Paris Biennale, and many other important international shows. In 1968, Cohen was invited to be a visiting lecturer at the University of California San Diego. Within two years, he became Professor and Chairman of the Visual Arts Department. During this time, his research interests turned to artificial intelligence, and in 1971 he was invited to be a visiting scholar at the Artificial Intelligence Laboratory at Stanford University. What grew out of this period of his life was AARON, a program that simulates the cognitive processes underlying the artist’s approach to drawing. The initial version of AARON produced autonomous black-and-white drawings and later incorporated color, an important element of Cohen’s creative research. In particular, he is interested in the cognitive processes behind representation that allow our vision to infer patterns and objects through line and color. His imagery is abstract, fluid, dense, and highly suggestive of natural forms. While mathematical in nature, his computer images are stylistically linked to his early paintings depicting figurative and plant elements as their subject matter. His creative practice continues to this day, and Cohen sees AARON as his creative partner, as evidenced by his recent exhibition Collaborations with My Other Self, at UC San Diego, 2011–2012. AARON has produced original autonomous drawings in museums and science centers in the US, Europe, and Asia, including the Tate Gallery and Victoria & Albert Museum in London, Documenta 6, Stedelijk Museum in Amsterdam, Los Angeles County Museum, San Francisco Museum of Modern Art, the IBM Gallery and the Brooklyn Museum in New York, among others. Science centers that have featured Cohen’s creative work with AARON include the Boston Science Museum, the Buhl Center in Pittsburgh, and the California Museum of Science and Technology. AARON is on permanent exhibit at the Computer Museum in Mountain View, California. Harold Cohen. Photograph courtesy of Tom Machnik, 2014.

Face Recognition and the Emergence of Smart Photography

This article examines face recognition as a key instance of the emergence of smart photography. Smart photography, drawing on Artificial Intelligence (AI) and Ambient Intelligence (AmI) manifests a ‘habit of mind’ (Karen Barad), or a way of thinking that is humanist in so far as it is predicated on human and machine autonomy, and representationalist in its quest for unmediated objects-in-themselves. Faces are among the objects that smart photography seeks (autonomously) to represent. By examining two of the principal algorithms of face recognition technology, the article will show how ways of seeing allied to ways of thinking that are also, ultimately, discriminatory and essentialist, materialise through software. Finally, if the ‘smart’ in smart photography means learning to discriminate between classes of faces that are fixed, essentialised and ultimately elusive (the stereotypical face of terror is both gendered and racialised) then how could smart be made smarter? This is a question of politics rather than progress.

Enhancing the Dgree of Autonomy on a ‘Tier 1’ Unmanned Aerial Vehicle Using a Visual Landing Framework

Humans continue to use tools to manually transform raw resources into valued outputs. The type of tool, amount of effort and form of energy required vary depending on the output; however they now enable industry to manufacture goods (with excellent quality and extremely high volume). Industry continues to invest heavily in machines so that people can operate productively. Similarly, researchers continue to pursue automation to increase the Degree of Autonomy (DOA) using Advanced Information Processing (AIP) techniques. Artificial Intelligence (AI), Computational Intelligence (CI) and Machine Intelligence (MI) now facilitate automation for numerous achievements. The proposed Visual Landing Framework (VLF) design uses a Multi-Agent System (MAS) to facilitate the development of components, that interoperate, via embedded business logic, to deliver the coordination and cooperation techniques required to automate a higher-level cognitive processing problem. As technology incorporates this ever increasing Level of Automation (LOA), humans remain in charge and are retained to make higher-order decisions. Unlike humans, heuristic and declarative logic systems suffers under these conditions and to need to adapt or make human-like decision to succeed. This paper discusses one possible avenue of enhancing the DOA on a ‘Tier 1’ Unmanned Aerial Vehicle (UAV) by reducing the need for the human to concentrate on a difficult cognitive task. The Machine to Machine (M2M) autonomy component uses an on-board camera, the Open Source Computer Vision Library (OpenCV) and Scale-Invariant Feature Transform (SIFT) algorithms to translate a fixed ground reference into positional commands. With this increased LOA the platform should be able to generate greater independence and enable more autonomous behaviour within unmanned control systems.

An efficient resource discovery framework for pure unstructured peer-to-peer systems

In this paper, we propose an efficient resource discovery framework allowing pure unstructured peer-to-peer systems to respond to requests at run time with a high success rate while preserving the local autonomy of member machines. There are five units in the proposed framework that respectively gather information about the status of resources, make decisions, detect the states of member machines, discover resources to respond to requests in normal and dynamic conditions, and balance the load of local machines. Efficient resource discovery is achieved by the deployment of a newly introduced mechanism that is placed on every machine allowing it to figure out its states before and after accepting other machines’ requests for its resources using a state model and deciding whether to accept or reject those requests. This state model accurately estimates the machine’s state based on the resources and processes of the machine before and after accepting the request. We have experimentally compared the proposed mechanism with random, learning-based, and state-based search mechanisms with regard to the number of missed requests, network bandwidth due to transferred messages, number of associated machines in a discovery operation, time required to process information in discovery operation, processing time in machines, and the number of faults per request. The results show significant improvement of some of these parameters, specially network bandwidth and the number of missed requests in a dynamic condition, under our framework.