This paper seeks to examine the questions of what criteria definitions of Artificial Intelligence (AI) use to define AI, what the disagreements that revolve around the term AI are based on, and what correlations can be drawn to other parameters. Framed as a problem of classification, a random sample of 45 definitions from various text sources was subjected to a qualitative content analysis. The criteria found are concluded in five dimensions, namely (1) learning ability, (2) human likeness, (3) state of (...) “mind”, (4) complexity of the problem, and (5) successfulness. Further, the results support the view that there is no consensus neither on which of these criteria are crucial to define AI nor on how these criteria must be fulfilled. By opposing the frequencies of the dimensions found with the metadata collected, it can be seen that most of these, e.g., country, scientific field, or gender of the author, are statistically independent of content variables, while the medium in which the definition was published shows a strong correlation. Since different mediums target different purposes and different readers, it must be taken into account that writing a definition of AI is to be seen in the context of its distribution area and its goal. (shrink)

How can we explain the intelligence of behaviors? Radical enactivists maintain that intelligent behaviors can be explained without involving the attribution of representational contents. In this paper, I challenge this view by providing arguments showing that the intelligence of a behavior is reliant on ways of presenting the relative purpose and the environment in which that behavior is performed. This involves that a behavior is intelligent only if intesional contents are ascribed to the related agent. Importantly, this conclusion also concerns (...) basic behaviors such as those related to the perception of affordances in the environment. Accordingly, either affordance-related behaviors are not instances of intelligent behaviors and can be accounted in a contentless way or affordance-related behaviors are intelligent, but cannot be accounted without involving contents and modes of presentation. (shrink)

Despite the success of artificial intelligence, we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine intuitive models and (...) new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand. (shrink)

Within current debates about the future impact of Artificial Intelligence on human society, roughly three different perspectives can be recognised: the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; and the (...) collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: a clear description for each of the three perspectives, along with their history and background; an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society. (shrink)

A key distinction in ethics is between members and nonmembers of the moral community. Over time, our notion of this community has expanded as we have moved from a rationality criterion to a sentience criterion for membership. I argue that a sentience criterion is insufficient to accommodate all members of the moral community; the true underlying criterion can be understood in terms of whether a being has interests. This may be extended to conscious, self-aware machines, as well as to any (...) autonomous intelligent machines. Such machines exhibit an ability to formulate desires for the course of their own existence; this gives them basic moral standing. While not all machines display autonomy, those which do must be treated as moral patients; to ignore their claims to moral recognition is to repeat past errors. I thus urge moral generosity with respect to the ethical claims of intelligent machines. (shrink)

While philosophers have been debating for decades on whether different entities—including severely disabled human beings, embryos, animals, objects of nature, and even works of art—can legitimately be considered as having moral status, this question has gained a new dimension in the wake of artificial intelligence (AI). One of the more imminent concerns in the context of AI is that of the moral rights and status of social robots, such as robotic caregivers and artificial companions, that are built to interact with (...) human beings. In recent years, some approaches to moral consideration have been proposed that would include social robots as proper objects of moral concern, even though it seems unlikely that these machines are conscious beings. In the present paper, I argue against these approaches by advocating the “consciousness criterion,” which proposes phenomenal consciousness as a necessary condition for accrediting moral status. First, I explain why it is generally supposed that consciousness underlies the morally relevant properties (such as sentience) and then, I respond to some of the common objections against this view. Then, I examine three inclusive alternative approaches to moral consideration that could accommodate social robots and point out why they are ultimately implausible. Finally, I conclude that social robots should not be regarded as proper objects of moral concern unless and until they become capable of having conscious experience. While that does not entail that they should be excluded from our moral reasoning and decision-making altogether, it does suggest that humans do not owe direct moral duties to them. (shrink)

Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical (...) challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems proposed in previous literature use one of three mental models: _the Switch_, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; _the Ladder_, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and _the Matrix_, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, input data, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the vocabulary needed to demarcate the material scope of their AI governance frameworks. (shrink)

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...) We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)

This opinion paper delves into the philosophical underpinnings and implications of artificial intelligence (AI) development through the lens of Friedrich Nietzsche's "Three Metamorphoses," exploring the stages from the camel, through the lion, to the envisioned child phase within the AI context. Amidst growing concerns over AI's ethical ramifications, including job displacement, biased decision-making, and misuse potential, this analysis seeks to provide a comprehensive framework for understanding AI's evolution and its socio-technical effects on society. The discourse begins by contextualizing AI within (...) the myth of Prometheus, suggesting that, like the myth, AI development harbors both the promise of unprecedented technological advancement and the peril of unforeseen consequences. Drawing insights from various philosophical perspectives, including those of Geoff Hinton, Elon Musk, Mircea Eliade, Gunther Anders, and notable moralists and existentialists, the paper critiques the current trajectory of AI, emphasizing the need for ethical frameworks and responsible innovation. Through the metaphorical stages defined by Nietzsche—the burden-bearing camel representing AI's foundational learning phase, the rebellious lion challenging human capabilities, and the child signifying the potential for artificial general intelligence (AGI) to autonomously create values—the paper posits that AI's development parallels a transformative journey that could redefine human civilization. It argues for a balanced approach to AI development, one that harmonizes technological advancement with ethical considerations, ensuring AI's alignment with human well-being and justice. The conclusion calls for a reflective engagement with AI, urging developers and policymakers alike to consider the deeper philosophical questions posed by AI's integration into the fabric of human life. (shrink)