Over the past ten years, we have worked in a collaboration between educators and computer scientists at the University of Illinois to imagine futures for education in the context of what is loosely called “artificial intelligence.” Unhappy with the first generation of digital learning environments, our agenda has been to design alternatives and research their implementation. Our starting point has been to ask, what is the nature of machine intelligence, and what are its limits and potentials in education? This paper (...) offers some tentative answers, first conceptually, and then practically in an overview of the results of a number of experimental implementations documented in greater detail elsewhere. Our key finding is that artificial intelligence—in the context of the practices of electronic computing developing over the past three quarters of a century—will never in any sense “take over” the role of teacher, because how it works and what it does are so profoundly different from human intelligence. However, within the limits that we describe in this paper, it offers the potential to transform education in ways that—counterintuitively perhaps—make education more human, not less. (shrink)

The debate on the right to be forgotten on Google involves the relationship between human information processing and digital processing by algorithms. The specificity of digital memory is not so much its often discussed inability to forget. What distinguishes digital memory is, instead, its ability to process information without understanding. Algorithms only work with data without remembering or forgetting. Merely calculating, algorithms manage to produce significant results not because they operate in an intelligent way, but because they “parasitically” exploit the (...) intelligence, the memory, and the attribution of meaning by human actors. The specificity of algorithmic processing makes it possible to bypass the paradox of remembering to forget, which up to now blocked any human-based forgetting technique. If you decide to forget some memory, the most immediate effect is drawing attention to it, thereby activating remembering. Working differently from human intelligence, however, algorithms can implement, for the first time, the classical insight that it might be possible to reinforce forgetting not by erasing memories but by multiplying them. After discussing several projects on the web which implicitly adopt this approach, the article concludes by raising some deeper problems posed when algorithms use data and metadata to produce information that cannot be attributed to any human being. (shrink)

The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry out an (...) epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents. (shrink)

Artificial intelligence experts are currently divided into “presentist” and “futurist” factions that call for attention to near-term and long-term AI, respectively. This paper argues that the presentist–futurist dispute is not the best focus of attention. Instead, the paper proposes a reconciliation between the two factions based on a mutual interest in AI. The paper further proposes realignment to two new factions: an “intellectualist” faction that seeks to develop AI for intellectual reasons and a “societalist faction” that seeks to develop AI (...) for the benefit of society. The paper argues in favor of societalism and offers three means of concurrently addressing societal impacts from near-term and long-term AI: advancing societalist social norms, thereby increasing the portion of AI researchers who seek to benefit society; technical research on how to make any AI more beneficial to society; and policy to improve the societal benefits of all AI. In practice, it will often be advantageous to emphasize near-term AI due to the greater interest in near-term AI among AI and policy communities alike. However, presentist and futurist societalists alike can benefit from each others’ advocacy for attention to the societal impacts of AI. The reconciliation between the presentist and futurist factions can improve both near-term and long-term societal impacts of AI. (shrink)

This paper uses a case study of a 1970s controversy in artificial-intelligence research to explore how scientists understand the relationships between research and practical applications. It is part of a project that seeks to map such relationships in order to enable better policy recommendations to be grounded empirically through historical evidence. In 1972 the mathematician James Lighthill submitted a report, published in 1973, on the state of artificial-intelligence research under way in the United Kingdom. The criticisms made in the report (...) have been held to be a major cause behind the dramatic slowing down of such research. This paper has two aims, one narrow and one broad. The narrow aim is to inquire into the causes, motivations and content of the Lighthill report. I argue that behind James Lighthill's criticisms of a central part of artificial intelligence was a principle he held throughout his career – that the best research was tightly coupled to practical problem solving. I also show that the Science Research Council provided a preliminary steer to the direction of this apparently independent report. The broader aim of the paper is to map some of the ways that scientists have articulated and justified relationships between research and practical, real-world problems, an issue previously identified as central to historical analysis of modern science. The paper therefore offers some deepened historical case studies of the processes identified in Agar's ‘working-worlds’ model. (shrink)

This paper addresses the question of how the ups and downs in the development of artificial intelligence (AI) since its inception can be explained. It focuses on the development of artificial intelligence in Germany since the 1970s, and particularly on its current dynamics. An assumption is made that a mere reference to rapid advances in information technologies and the various methods and concepts of artificial intelligence in recent decades cannot adequately explain these dynamics, because from a social science perspective, this (...) is an oversimplified, technology-centred explanation. Drawing on ideas from social scientific innovation research, the hypothesis is rather that artificial intelligence should be understood as a “promising technology”. Its various stages of development have always been driven by technological promises about its special powers and capabilities when applied to solving economic and societal challenges. (shrink)

The goal of this paper is to describe the mechanism of the public perception of risk of artificial intelligence. For that we apply the social amplification of risk framework to the public perception of artificial intelligence using data collected from Twitter from 2007 to 2018. We analyzed when and how there appeared a significant representation of the association between risk and artificial intelligence in the public awareness of artificial intelligence. A significant finding is that the image of the risk of (...) AI is mostly associated with existential risks that became popular after the fourth quarter of 2014. The source of that was the public positioning of experts who happen to be the real movers of the risk perception of AI so far instead of actual disasters. We analyze here how this kind of risk was amplified, its secondary effects, what are the varieties of risk unrelated to existential risk, and what is the dynamics of the experts in addressing their concerns to the audience of lay people. (shrink)

This article reflects on securitization efforts with respect to ‘killer robots’, known more impartially as autonomous weapons systems (AWS). Our contribution focuses, theoretically and empirically, on the Campaign to Stop Killer Robots, a transnational advocacy network vigorously pushing for a pre-emptive ban on AWS. Marking exactly a decade of its activity, there is still no international regime formally banning, or even purposefully regulating, AWS. Our objective is to understand why the Campaign has not been able to advance its disarmament agenda (...) thus far, despite all the resources, means and support at its disposal. For achieving this objective, we challenge the popular assumption that strong stigmatization is the universally best strategy towards humanitarian disarmament. We investigate the consequences of two specifics present in AWS, which set them apart from processes and successes of the campaigns to ban anti-personnel landmines, cluster munitions, and laser-blinding weapons: the complexity of AWS as a distinct weapons category, and the subsequent circumvention of its complexity through the utilization of pop-culture, namely science fiction imagery. We particularly focus on two mechanisms through which such distortion has occurred: hybridization and grafting. These provide the conceptual basis and heuristic tools to unpack the paradox of over-securitization: success in broadening the stakeholder base in relation to the first mechanism and deepening the sense of insecurity in relation to the second one does not necessarily lead to the achievement of the desired prohibitory norm. In conclusion, we ask whether it is not the time for a more epistemically-oriented expert debate with a less ambitious, lowest common denominator strategy as the preferred model of arms control for such a complex weapons category. (shrink)

Media exposure can shape audience perceptions surrounding novel innovations, such as artificial intelligence (AI), and could influence whether they share information about AI with others online. This study examines the indirect association between exposure to AI in the media and information sharing about AI online. We surveyed 567 US citizens aged 18 and older in November 2020, several months after the release of Open AI’s transformative GPT-3 model. Results suggest that AI media exposure was related to online information sharing through (...) psychological proximity to the impacts of AI and positive AI performance expectancy in serial mediation. This positive indirect association became stronger the more an individual perceived society to be changing due to new technology. Results imply that public exposure to AI in the media could significantly impact public understanding of AI, and prompt further information sharing online. (shrink)

The expression “AI” has become as commonplace as “computer.” While many people have a relatively clear idea of what an AI system or a computer does or can do, fewer have an idea of how precisely th...

A blockchain can be considered a technological phenomenon that is made up of different interconnected and autonomous systems. Such systems are referred to here as cyber-physical systems: complex interconnections of cyber and physical components. When cyber-physical systems are interconnected, a new whole consisting of a system of systems is created by the autonomous systems and their intercommunication and interaction. In a blockchain, individual systems can independently make decisions on joint information transactions. The decision-making procedures needed for this are executed based (...) on fault-tolerant communication and voting and consensus procedures, while the results of these decision-making procedures are stored in distributed ledgers. Due to the intercommunication, interaction, and independent decision making by autonomous systems, the new whole of a blockchain is a complex entity. Complexity science rather than the usual reductionist scientific approach can help us better understand the behaviour of the new and continuously developing whole of a blockchain as a technological phenomenon. (shrink)

Ever since the use of artificial intelligence in the space sector, there have been progressive changes that brought both benefits and risks. AI technologies have started to influence human rights and freedoms, relationships with public authorities and the private sector. Therefore, the use of AI involves legal obligations caused by the influence and consequences of the dependency on the specified technologies. This indicates the need to investigate the issue of the legal problems and risks of using AI in space activity (...) to identify, describe and explain legal issues and dilemmas in general and in the space sector in particular. The author has formulated theoretical definitions in the defined area through the logical-semantic approach. The comparative method has allowed us to carry out a comparative analysis of the AI legal regulation in the space sector in the USA, China and the EU. The authors have gone into rethinking the issue of action, cause-effect relationship, legal personality in matters of prosecution for damage caused by autonomous space objects. As well as combining the existing space law and its state-oriented responsibility with the space activity of private players using autonomous objects. (shrink)

Machine awareness is a disputed research topic, in some circles considered a crucial step in realising Artificial General Intelligence. Understanding what that is, under which conditions such feature could arise and how it can be controlled is still a matter of speculation. A more concrete object of theoretical analysis is algorithmic iteration for computational intelligence, intended as the theoretical and practical ability of algorithms to design other algorithms for actions aimed at solving well-specified tasks. We know this ability is already (...) shown by current AIs, and understanding its limits is an essential step in qualifying claims about machine awareness and Super-AI. We propose a formal translation of algorithmic iteration in a fragment of modal logic, formulate principles of transparency and faithfulness across human and machine intelligence, and consider the relevance to theoretical research on -AI as well as the practical import of our results. (shrink)