“Only a small community has concentratedon general intelligence. No one has tried to make a thinking machine... The bottom line is that we really haven’t progressed too far toward a truly intelligent machine. We have collections of dumb specialists in small domains; the true majesty of general intelligence still awaits our attack.... We have got to get back to the deepest questions of AI and general intelligence... ” –MarvinMinsky as interviewed in Hal’s Legacy, edited (...) by David Stork, 2000. Our goal in creating this edited volume has been to?ll an apparent gap in the scienti?c literature, by providing a coherent presentation of a body of contemporary research that, in spite of its integral importance, has hitherto kept a very low pro?le within the scienti?c and intellectual community. This body of work has not been given a name before; in this book we christen it “Arti?cial General Intelligence”. What distinguishes AGI work from run-of-the-mill “arti?cial intelligence” research is that it is explicitly focused on engineering general intelligence in the short term. We have been active researchers in the AGI?eld for many years, and it has been a pleasure to gather together papers from our colleagues working on related ideas from their own perspectives. In the Introduction we give a conceptual overview of the AGI?eld, and also summarize and interrelate the key ideas of the papers in the subsequent chapters. (shrink)

What will be the next huge leap in humanity's progress? We cannot know for sure, but I am reasonably confident that it will involve the radical extension of technology into the domain of thought. Ray Kurzweil (2000, 2005) has eloquently summarized the arguments in favor of this position.

This chapter discusses nine ways to bias open‐source artificial general intelligence (AGI) toward friendliness. There is no way to guarantee that advanced AGI, once created and released into the world, will behave according to human ethical standards. The primary objective of the chapter is to suggest some potential ways to do so. First it discusses an engineer the capability to acquire integrated ethical knowledge, and provides rich ethical interaction and instruction, respecting developmental stages. The chapter creates stable, (...) hierarchy‐dominated goal systems, and ensures that the early stages of recursive self‐improvement occur relatively slowly and with rich human involvement. It tightly links AGI with the Global Brain, and focuses on foster deep, consensus‐building interactions and commensurability between divergent viewpoints. The chapter creates a mutually supportive community of AGIs, and encourages measured co‐advancement of AGI software and AGI ethics theory. Finally it develops advanced AGI sooner. (shrink)

Special Issue “Risks of artificial general intelligence”, Journal of Experimental and Theoretical Artificial Intelligence, 26/3 (2014), ed. Vincent C. Müller. http://www.tandfonline.com/toc/teta20/26/3# - Risks of general artificial intelligence, Vincent C. Müller, pages 297-301 - Autonomous technology and the greater human good - Steve Omohundro - pages 303-315 - - - The errors, insights and lessons of famous AI predictions – and what they mean for the future - Stuart Armstrong, Kaj Sotala & Seán (...) S. Ó hÉigeartaigh - pages 317-342 - - - The path to more general artificial intelligence - Ted Goertzel - pages 343-354 - - - Limitations and risks of machine ethics - Miles Brundage - pages 355-372 - - - Utility function security in artificially intelligent agents - Roman V. Yampolskiy - pages 373-389 - - - GOLEM: towards an AGI meta-architecture enabling both goal preservation and radical self-improvement - Ben Goertzel - pages 391-403 - - - Universal empathy and ethical bias for artificial general intelligence - Alexey Potapov & Sergey Rodionov - pages 405-416 - - - Bounding the impact of AGI - András Kornai - pages 417-438 - - - Ethics of brain emulations - Anders Sandberg - pages 439-457. (shrink)

An arms race for an artificial general intelligence would be detrimental for and even pose an existential threat to humanity if it results in an unfriendly AGI. In this paper, an all-pay contest model is developed to derive implications for public policy to avoid such an outcome. It is established that, in a winner-takes-all race, where players must invest in R&D, only the most competitive teams will participate. Thus, given the difficulty of AGI, the number of competing (...) teams is unlikely ever to be very large. It is also established that the intention of teams competing in an AGI race, as well as the possibility of an intermediate outcome, is important. The possibility of an intermediate prize will raise the probability of finding the dominant AGI application and, hence, will make public control more urgent. It is recommended that the danger of an unfriendly AGI can be reduced by taxing AI and using public procurement. This would reduce the pay-off of contestants, raise the amount of R&D needed to compete, and coordinate and incentivize co-operation. This will help to alleviate the control and political problems in AI. Future research is needed to elaborate the design of systems of public procurement of AI innovation and for appropriately adjusting the legal frameworks underpinning high-tech innovation, in particular dealing with patenting by AI. (shrink)

The Conference on Artificial General Intelligence is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. Its second installation, AGI-09, in Arlington, Virginia, March 6-9, 2009, attracted 67 paper submissions, which is a substantial increase from the previous year. Of these submissions, 33 (i.e., 49%) were accepted as full papers for presentation at the conference. Additional 13 papers were included (...) as position papers. The program also included a keynote address by Juergen Schmidhuber on 'The New AI', a post-conference workshop on 'The Future of AI', and a number of pre-conference tutorials on various topics related to AGI. (shrink)

We’ve seen a significant increase in the attention AI research is receiving this past decade, in large part due to some of the impressive feats of machine learning, particularly deep learning. This has resulted in something of a hype in the ability of AI’s in tackling various issues. The aim of the current essay is to examine the speculative questions “what would it mean for systems to transition from merely intelligently executing a task to knowledgably executing a task?” and “what (...) is the relation between consciousness and intelligence, such that specific evaluations about conscious AI might be made?” We offer what we hope is a roadmap for how to navigate asking these questions and a route forward in understanding how AI goes from intelligence to knowledge and potentially to some types of consciousness. (shrink)

Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In (...) particular, we challenge the scientific community to develop intelligent systems that have human-friendly values that they provably retain, even under recursive self-improvement. (shrink)

A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure (...) is instructive: in exploring the argument, we gain insight into how to model advanced AI systems. (shrink)

Pei Wang, Ben Goertzel. [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [ 18] [19] [20] [21] [22] [23] [24] [25] [26] [27] Bach, J. (2009). Principles ofSynthetic Intelligence PSI: An Architecture ofMotivated Cognition (Oxford University Press,  ...

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly (...) constructed “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices. (shrink)

The article explores the transformative impact on human and social sciences in response to anticipated societal shifts driven by the forthcoming proliferation of artificial systems, whose intelligence will match human capabilities. Initially, it was posited that artificial intelligence (AI) would excel beyond human abilities in computational tasks and algorithmic operations, leaving creativity and humanities as uniquely human domains. However, recent advancements in large language models have significantly challenged these conventional beliefs about AI’s limitations and strengths. It (...) is projected that, in the near future, generative AI models will adeptly replicate individual qualities, desires, beliefs, opinions, and the essence of human identity and consciousness to a degree that is nearly indistinguishable from that of humans. This lends support to the connectionist approach to understanding consciousness, suggesting an inherent similarity between biological and artificial neural networks. The discussion posits two innovative areas of scientific inquiry: “mathematical anthropology” and “multi-dimensional calculus of value.” The former suggests that viewing human nature through a mathematical and calculative lens not only preserves but enriches our understanding of the complexity of anthropological experience and its perceived contradictions. The latter hypothesis explores how mathematical models could facilitate various social interactions. The advanced simulation capabilities of neural networks suggest that traditional social and political frameworks face a growing vulnerability to AI-driven manipulations. This trend underscores the urgency of developing social interaction models that incorporate explicitly defined calculative rules. In conclusion, the paper advocates for a paradigm shift in how mathematics is perceived – not merely as a tool for computation but as a foundational science for crafting sophisticated models. In conclusion, the article advocates for transitioning from perceiving mathematics solely as a science of computation to viewing it as a discipline dedicated to constructing various formalized models, thereby deepening our insight into the complexities of human and social phenomena. (shrink)

This article interrogates the challenge artificial general intelligence (AGI) poses to religion and human societies, in general. More specifically, it seeks to respond to “Singularity”—when machines reach a level of intelligence that would put into question the privileged position humanity enjoys as imago Dei . Employing the Bemba notion of mystico‐relationality in dialogue with the concepts of the “created co‐creator” and Christ the Key, it argues for the possibility of AI participating in imago Dei . (...) The findings show that imaging is a fluid, participatory activity that aims at likeness, but also social harmony. It also argues that God is the only original creator, humans are created creators, and that every aspect of visible existence, including AI, is inherently divine imaging. However, strong imaging is only attainable based on the only One and True Image—Christ, whose union of the material and the divine means that all creation can image, excluding nothing, even AI. (shrink)

We propose that, for the purpose of studying theoretical properties of the knowledge of an agent with Artificial General Intelligence (that is, the knowledge of an AGI), a pragmatic way to define such an agent’s knowledge (restricted to the language of Epistemic Arithmetic, or EA) is as follows. We declare an AGI to know an EA-statement φ if and only if that AGI would include φ in the resulting enumeration if that AGI were commanded: “Enumerate all the (...) EA-sentences which you know.” This definition is non-circular because an AGI, being capable of practical English communication, is capable of understanding the everyday English word “know” independently of how any philosopher formally defines knowledge; we elaborate further on the non-circularity of this circular-looking definition. This elegantly solves the problem that different AGIs may have different internal knowledge definitions and yet we want to study knowledge of AGIs in general, without having to study different AGIs separately just because they have separate internal knowledge definitions. Finally, we suggest how this definition of AGI knowledge can be used as a bridge which could allow the AGI research community to import certain abstract results about mechanical knowing agents from mathematical logic. (shrink)

The science of consciousness has made great strides in recent decades. However, the proliferation of competing theories makes it difficult to reach consensus about artificial consciousness. While for purely scientific purposes we might wish to adopt a ‘wait and see’ attitude, we may soon face practical and ethical questions about whether, for example, agents artificial systems are capable of suffering. Moreover, many of the methods used for assessing consciousness in humans and even non-human animals are not straightforwardly applicable (...) to artificial systems. With these challenges in mind, I propose that we look for ecumenical heuristics for artificial consciousness to enable us to make tentative assessments of the likelihood of consciousness arising in different artificial systems. I argue that such heuristics should have three main features: they should be (i) intuitively plausible, (ii) theoretically-neutral, and (iii) scientifically tractable. I claim that the concept of general intelligence — understood as a capacity for robust, flexible, and integrated cognition and behavior — satisfies these criteria and may thus provide the basis for such a heuristic, allowing us to make initial cautious estimations of which artificial systems are most likely to be conscious. -/- . (shrink)

The problem of controlling an artificial general intelligence has fascinated both scientists and science-fiction writers for centuries. Today that problem is becoming more important because the time when we may have a superhuman intelligence among us is within the foreseeable future. Current average estimates place that moment to before 2060. Some estimates place it as early as 2040, which is quite soon. The arrival of the first AGI might lead to a series of events that we (...) have not seen before: rapid development of an even more powerful AGI developed by the AGIs themselves. This has wide-ranging implications to the society and therefore it is something that must be studied well before it happens. In this paper we will discuss the problem of limiting the risks posed by the advent of AGIs. In a thought experiment, we propose an AGI which has enough human-like properties to act in a democratic society, while still retaining its essential artificial general intelligence properties. We discuss ways of arranging the co-existence of humans and such AGIs using a democratic system of coordination and coexistence. If considered a success, such a system could be used to manage a society consisting of both AGIs and humans. The democratic system where each member of the society is represented in the highest level of decision-making guarantees that even minorities would be able to have their voices heard. The unpredictability of the AGI era makes it necessary to consider the possibility that a population of autonomous AGIs could make us humans into a minority. (shrink)

Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining (...) the emergence of background knowledge processes. In order to explain background knowledge, we present a non-similarity-based post-Skinnerian theory of human language called Relational Frame Theory which is rooted in a philosophical world view called functional contextualism. This theory offers a very different interpretation of how categories emerge through the functions of behavior and through contextual cues, which may be of some benefit to existing categorization theories. Specifically, RFT may be able to offer a novel explanation of how background knowledge arises, and we provide some mathematical considerations in order to identify a formal model. Finally, we discuss much of this work within the broader context of general semantic knowledge and artificial intelligence research. (shrink)

This article examines the relationship between artificial general intelligence and the image of God. After identifying various models that Christian theologians use to classify or define the imago Dei, particular attention will be given to the ‘created co-creator’ model. Scholars have interpreted this model in different ways, based on the nature of human creative action. This action is seen as either subordinate to divine creation action or the human creative action is truly cooperative with divine creative action. (...) Whether AGI would be made in the image of God in these models is then explored, highlighting the differences between humans as sub-creators versus humans as cooperative co-creators. If human creative action is cooperative, then the question arises as to whether AGI can be made in ‘the image of humanity’. Some elements of this image are explored, and then the discussion turns to whether AGI would be made in ‘the image of humanity’, and if so, could AGI still be made in the image of God?Contribution: The argument concludes by pointing to future work using the various models of imago Dei to help inform the relationship between humans and AGI by briefly mentioning two examples. (shrink)

Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social (...) significance of new AI achievements, especially regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues. (shrink)

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and (...) b) what is the relationship, if any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach. (shrink)

This paper examines a case where Amazon’s cloud-based AI assistant Alexa accidentally ordered a dollhouse for a 6-year-old girl. In the press, the case was defined as a technical recognition problem. Building on this idea, we argue that the dollhouse case helps us to analyze the limits of current AI applications. By drawing on the writings of Gilles Deleuze and François Laruelle, we argue that these limits are not merely technical but more deeply embedded in the structures where the thinking (...) of AI can potentially happen. We point out that AI research has been compromised by the concepts of what constitutes both ‘artificial’ and by what constitutes ‘intelligence’. First, we use the notion of artificial non-intelligence to explain how different modes of digital capitalism such as voice commerce establish limits for AI. Second, we use the notion of non-artificial intelligence to illustrate the limits of associating AI’s modes of thinking with human thought. (shrink)

This is the editorial for a special volume of JETAI, featuring papers by Omohundro, Armstrong/Sotala/O’Heigeartaigh, T Goertzel, Brundage, Yampolskiy, B. Goertzel, Potapov/Rodinov, Kornai and Sandberg. - If the general intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity – so even if we estimate the probability of this event to be fairly low, it is necessary to think about it now. We need to estimate what progress we (...) can expect, what the impact of superintelligent machines might be, how we might design safe and controllable systems, and whether there are directions of research that should best be avoided or strengthened. (shrink)

This paper considers challenges resulting from the use of advanced artificial judicial intelligence (AAJI). We argue that these challenges should be considered through the lens of value alignment. Instead of discussing why specific goals and values, such as fairness and nondiscrimination, ought to be implemented, we consider the question of how AAJI can be aligned with goals and values more generally, in order to be reliably integrated into legal and judicial systems. This value alignment framing draws on AI (...) safety and alignment literature to introduce two otherwise neglected considerations for AAJI safety: specification and assurance. We outline diverse research directions and suggest the adoption of assurance and specification mechanisms as the use of AI in the judiciary progresses. While we focus on specification and assurance to illustrate the value of the AI safety and alignment literature, we encourage researchers in law and philosophy to consider what other lessons may be drawn. (shrink)

The article analyzes the possibilities and limitations of artificial intelligence. The article considers the subjectivity of artificial intelligence, determines its necessity for solving intellectual problems depending on the possibility of representing the real world as a deterministic system. Methodological limitations of artificial intelligence, which is based on the use of big data technologies, are stated. These limitations cause the impossibility of forming a holistic representation of the objects of cognition and the world as a (...) whole. As a tool for deterministic description of the universe it is proposed to use empirical-metaphysical general theory of systems, which is an extension of existing general theories of systems due to ontological justification of the phenomenon of isomorphism and definition of a limited set of laws, rules, patterns and primitives of forms and relations of objects in the universe. The distinction of natural (human) and artificial intelligence is considered, including the realization of multisystem integration of intelligence in physical, biological, social and spiritual systems. A philosophically grounded approach to ensuring the evolutionary properties of artificial intelligence is formulated, based on the inclusion of non-equilibrium mechanisms through which stability is realized. (shrink)

In the current era, robots and artificial agents can do jobs that could only be achieved with human creativity in the past, therefore successfully stepped into the sacred territory that once considered exclusive to human. Given this situation, Mark Amerika made two significant contributions with his new book, “My life as an Artificial Creative Intelligence”. This book added to the emerging literary genre collaborated by human artists and AI, telling the story about the advanced ACI developed in (...) the project FATAL ERROR and its relationship with Amerika as its co-author and creator. Moreover, this book documented an in-depth investigation into the nature of creativity and its processes, asking whether creativity is exclusive to the human mind. (shrink)

I survey four categories of factors that might give a digital mind, such as an upload or an artificial general intelligence, an advantage over humans. Hardware advantages include greater serial speeds and greater parallel speeds. Self-improvement advantages include improvement of algorithms, design of new mental modules, and modification of motivational system. Co-operative advantages include copyability, perfect co-operation, improved communication, and transfer of skills. Human handicaps include computational limitations and faulty heuristics, human-centric biases, and socially motivated cognition. The (...) shape of hardware growth curves, as well as the ease of modifying minds, are found to have a major impact on how quickly a digital mind may take advantage of these factors. (shrink)

As China and the United States strive to be the primary global leader in AI, their visions are coming into conflict. This is frequently painted as a fundamental clash of civilisations, with evidence based primarily around each country’s current political system and present geopolitical tensions. However, such a narrow view claims to extrapolate into the future from an analysis of a momentary situation, ignoring a wealth of historical factors that influence each country’s prevailing philosophy of technology and thus their overarching (...) AI strategies. In this article, we build a philosophy-of-technology-grounded framework to analyse what differences in Chinese and American AI policies exist and, on a fundamental level, why they exist. We support this with Natural Language Processing methods to provide an evidentiary basis for our analysis of policy differences. By looking at documents from three different American presidential administrations––Barack Obama, Donald Trump, and Joe Biden––as well as both national and local policy documents (many available only in Chinese) from China, we provide a thorough comparative analysis of policy differences. This article fills a gap in US–China AI policy comparison and constructs a framework for understanding the origin and trajectory of policy differences. By investigating what factors are informing each country’s philosophy of technology and thus their overall approach to AI policy, we argue that while significant obstacles to cooperation remain, there is room for dialogue and mutual growth. (shrink)

The General Data Protection Regulation is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR—and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is (...) therefore expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: Following compliance checklists and codes of conduct; Supporting risk assessments; Complying with the new regulations regarding technologies that perform automatic profiling; Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances. (shrink)

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily (...) focused on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

The movement of Artificial Intelligence (AI) research from universities to big corporations has had a significant impact on the development of the field. In the past, AI research was primarily conducted in academic institutions, which foster a culture of peer reviewing and collaboration to enhance quality improvements. The growing interest in AI among corporations, especially regarding Machine Learning (ML) technology, has shifted the focus of research from quality to quantity. Corporations have the resources to invest in large-scale ML (...) projects and they are often more interested in fast results than in ensuring that AI algorithms are reliable and safe. This paper proposes that the description of a Darwinian process made by the mathematician and physicist Freeman Dyson can be used to understand the implications of ML research scenario. The context of the Darwinian process—or lack thereof—can be used to draw a parallel between historical and current implications of technology expansion strongly driven by economic interests, making clear the consequences of shifting AI research from academia to industry. Finally, this paper indicates future directions for AI research to create a healthier environment for the evolution of ML technology. (shrink)

This paper explores and ultimately affirms the surprising claim that artificial intelligence (AI) can become part of the person, in a robust sense, and examines three ethical and legal implications. The argument is based on a rich, legally inspired conception of persons as free and independent rightholders and objects of heightened protection, but it is construed so broadly that it should also apply to mainstream philosophical conceptions of personhood. The claim is exemplified by a specific technology, devices that (...) connect human brains with computers and operate by AI-algorithms. Under philosophically reasonable and empirically realistic conditions, these devices and the AI running them become parts of the person, in the same way as arms, hearts, or mental capacities are. This transformation shall be called empersonification. It has normative and especially legal consequences because people have broader and stronger duties regarding other persons (and parts of them) than regarding things. Three consequences with practical implications are: (i) AI-devices cease to exist as independent legal entities and come to enjoy the special legal protection of persons; (ii) therefore, third parties such as manufacturers or authors of software lose (intellectual) property rights in device and software; (iii) persons become responsible for the outputs of the empersonified AI-devices to the same degree that they are for desires or intentions arising from the depths of their unconscious. More generally, empersonification marks a new step in the long history of human–machine interaction that deserves critical ethical reflection and calls for a stronger value-aligned development of these technologies. (shrink)

In March 2016, DeepMind's computer programme AlphaGo surprised the world by defeating the world‐champion Go player, Lee Sedol. AlphaGo exhibits a novel, surprising and valuable style of play and has been recognised as “creative” by the artificial intelligence (AI) and Go communities. This article examines whether AlphaGo engages in creative problem solving according to the standards of comparative psychology. I argue that AlphaGo displays one important aspect of creative problem solving (namely mental scenario building in the form of (...) Monte Carlo tree search), while lacking another (domain generality). This analysis has consequences for how we think about creativity in humans and AI. (shrink)