This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which this (...) Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (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)

A pluralistic approach to folk psychology must countenance the evaluative, regulatory, predictive, and explanatory roles played by attributions of intelligence in social practices across cultures. Building off of the work of the psychologist Robert Sternberg and the philosophers Gilbert Ryle and Daniel Dennett, I argue that a relativistic interpretivism best accounts for the many varieties of intelligence that emerge from folk discourse. To be intelligent is to be comparatively good at solving intellectual problems that an interpreter deems worth solving.

This article presents seven principles that have guided our thinking about emotional intelligence, some of them new. We have reformulated our original ability model here guided by these principles, clarified earlier statements of the model that were unclear, and revised portions of it in response to current research. In this revision, we also positioned emotional intelligence amidst other hot intelligences including personal and social intelligences, and examined the implications of the changes to the model. We discuss the present and future (...) of the concept of emotional intelligence as a mental ability. (shrink)

The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents (...) and primates). However, the interpretation of a psychometricgfactor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength. (shrink)

Degree‐sentences, i.e. sentences that seem to refer to things that allow of degrees, are widely used both inside and outside of philosophy, even though the metaphysics of degrees is much of an untrodden field. This paper aims to fill this lacuna by addressing the following four questions: [A] Is there some one thing, such that it is degree sensitive? [B] Are there things x, y, and z that stand in a certain relation to each other, viz. the relation that x (...) has more y than z? [C] In those cases in which degree sentences do not refer to phenomena that are degree sensitive, what is responsible for their prima facie seeming to do so? [D] If there are degree sensitive things, to which ontological categories do they belong? We answer each of these questions by arguing that there are, metaphysically speaking, different phenomena that degree sentences refer to: some refer to determinates that emanate from a certain determinable, others to tokens that are instantiations of a certain type, and yet others to what we call ‘complex, resultant properties that are constituted by stereotypical properties’. Finally, we show the relevance of our answers by applying them to the notions of freedom and belief. (shrink)

Cognitive enhancement takes many and diverse forms. Various methods of cognitive enhancement have implications for the near future. At the same time, these technologies raise a range of ethical issues. For example, they interact with notions of authenticity, the good life, and the role of medicine in our lives. Present and anticipated methods for cognitive enhancement also create challenges for public policy and regulation.

Following Snyderman and Rothman, we surveyed expert opinions on the current state of intelligence research. This report examines expert opinions on causes of international differences in student assessment and psychometric IQ test results. Experts were surveyed about the importance of culture, genes, education, wealth, health, geography, climate, politics, modernization, sampling error, test knowledge, discrimination, test bias, and migration. The importance of these factors was evaluated for diverse countries, regions, and groups including Finland, East Asia, sub-Saharan Africa, Southern Europe, the Arabian-Muslim (...) world, Latin America, Israel, Jews in the West, Roma, and Muslim immigrants. Education was rated by N = 71 experts as the most important cause of international ability differences. Genes were rated as the second most relevant factor but also had the highest variability in ratings. Culture, health, wealth, modernization, and politics were the next most important factors, whereas other factors such as geography, climate, test bias, and sampling error were less important. The paper concludes with a discussion of limitations of the survey. (shrink)

There exists a significant disparity within society between individuals in terms of intelligence. While intelligence varies naturally throughout society, the extent to which this impacts on the life opportunities it affords to each individual is greatly undervalued. Intelligence appears to have a prominent effect over a broad range of social and economic life outcomes. Many key determinants of well-being correlate highly with the results of IQ tests, and other measures of intelligence, and an IQ of 75 is generally accepted as (...) the most important threshold in modern life. The ability to enhance our cognitive capacities offers an exciting opportunity to correct disabling natural variation and inequality in intelligence. Pharmaceutical cognitive enhancers, such as modafinil and methylphenidate, have been shown to have the capacity to enhance cognition in normal, healthy individuals. Perhaps of most relevance is the presence of an ‘inverted U effect’ for most pharmaceutical cognitive enhancers, whereby the degree of enhancement increases as intelligence levels deviate further below the mean. Although enhancement, including cognitive enhancement, has been much debated recently, we argue that there are egalitarian reasons to enhance individuals with low but normal intelligence. Under egalitarianism, cognitive enhancement has the potential to reduce opportunity inequality and contribute to relative income and welfare equality in the lower, normal intelligence subgroup. Cognitive enhancement use is justifiable under prioritarianism through various means of distribution; selective access to the lower, normal intelligence subgroup, universal access, or paradoxically through access primarily to the average and above average intelligence subgroups. Similarly, an aggregate increase in social well-being is achieved through similar means of distribution under utilitarianism. In addition, the use of cognitive enhancement within the lower, normal intelligence subgroup negates, or at the very least minimises, several common objections to cognitive enhancement. Subsequently, this paper demonstrates that there is a compelling case for cognitive enhancement use in individuals with lower, normal intelligence. (shrink)

One key question in the philosophy of artificial intelligence (AI) concerns how we can recognize artificial systems as intelligent. To make the general question more manageable, I focus on a particular type of AI, namely one that can prove mathematical theorems. The current generation of automated theorem provers are not understood to possess intelligence, but in my thought experiment an AI provides humanly interesting proofs of theorems and communicates them in human-like manner as scientific papers. I then ask what the (...) criteria could be for recognizing such an AI as intelligent. I propose an approach in which the relevant criteria are based on the AI’s interaction within the mathematical community. Finally, I ask whether we can deny the intelligence of the AI in such a scenario based on reasons other than its (non-biological) material construction. (shrink)

En este artículo nos planteamos las cuestiones de si realmente la IA ha alcanzado el nivel de la inteligencia humana, algunas de las razones que nos llevan a este estado de opinión, así como varias de las diferencias fundamentales entre la IA y la inteligencia humana. Realizamos un breve recorrido del desarrollo histórico de la IA, para después revisar las capacidades reales e importantes limitaciones de las técnicas de aprendizaje profundo en las que se basan los avances recientes en IA. (...) Abordamos particularmente el argumento de que las capacidades cognitivas complejas son indisociables de un cuerpo biológico en interacción con un mundo físico y sociocultural, frente a una IA basada en un axioma dualista y cognitivista que ha sido señalado como incompleto o parcial. Finalizamos con considerando los riesgos reales de la IA en la actualidad, así como algunas especulaciones sobre su futuro desarrollo. (shrink)

Nuestro objetivo es unir la Teoría de las Inteligencias Múltiples a la metodología organizativa del trabajo por rincones en las aulas del segundo ciclo de Educación Infantil. Llevando a cabo una investigación- revisión de los distintos conceptos abordados y una investigación en la acción, a través de grupo focal con muestra in- tencional de grupos de expertos, para conseguir una propuesta integradora, donde se propone un modelo de educación en el que el niño sea sujeto activo de su propio aprendizaje (...) y se potencie su talento innato junto con el desarrollo de las distintas habilidades. (shrink)

In this chapter, I propose the mechanism of cognitive enhancement based on studies of cognitive-enhancing drugs and behavioral training. I argue that there are mechanistic differences between cognitive-enhancing drugs and behavioral training due to their different enhancing effects. I also suggest possible mechanisms for cognitive-enhancing drugs and behavioral training and for the synergistic effects of their simultaneous application.

Starting from the assumption that working memory capacity is an important predictor of general fluid intelligence, we asked which aspects of working memory account for this relationship. Two theoretical stances are discussed. The first one posits that the important explanatory factor is storage capacity, roughly defined as the number of chunks possible to hold in the focus of attention. The second one claims that intelligence is explained by the efficiency of executive control, for instance, by prepotent response inhibition. We investigated (...) 96 children at the age between 10 and 13. They completed a version of the n-back task that allows assessment of both storage capacity and inhibitory control. They also completed Raven’s Progressive Matrices as the fluid intelligence test and the Test for Creative Thinking - Drawing Production, for control purposes. We found that Raven’s scores correlated negatively with the number of unnecessary responses to irrelevant stimuli but they did not correlate with the number of signal detections. We conclude that children’s fluid intelligence depends on inhibitory control, with no relationship with storage capacity. (shrink)

In this essay I explore efforts at regulating race-related speech on publicly funded colleges and universities. In the first section, I present the scope of the current debate about the topic: what speech is, contexts in which it is found, etc. In the second section, I present the case for unrestricted speech on campuses for the advancement of knowledge and social progress. The third section addresses standard problem cases for free speech like the non-scientific nature of racist epithets, existential threats (...) to the university, and involuntary exposure to racist speech. The fourth section explores arguments for regulating speech coming from critical race theorists and those who question the cultural narrative surrounding speech these days. In the fifth section, I lay bare the irreconcilable incompatibilities of free speech proponents and advocates of speech restrictions in several areas. The conclusion points the way forward toward by calling attention to the lingering questions about the values of knowledge and social progress. (shrink)

Обговорюються революційні зміни в інтелектуальному розвитку людства. Це поява другої сигнальної системи людини, усвідомлення природи формування когнітивних здібностей людського мозку та формування високоінтелектуальних нейронних мереж. Природу формування другої сигнальної системи було зумовлено як збільшенням числа нейронів і синапів кори мозку, так і можливістю безпрецедентного інтерактивного спілкування всередині зростаючої чисельності людських громад, з урахуванням розвитку промови. Розглянуто болючу відмову нейрофізіологів та їх колег від релігійних міфічних уявлень про природу свідомості. Стало зрозумілим, що всі інтелектуальні здібності людини забезпечені роботою мозку та інших (...) розділів нервової системи. На цей відхід від містичних уявлень про свідомість вплинув розвиток нейронних мереж, мов програмування й діяльність нейрофізіологів спільно з нейрохірургами. Розвитку штучних нейронних мереж сприяли зусилля нейрофізіологів та їх колег із суміжних галузей науки, але передусім небачений технічний прогрес у створенні швидкодіючих обчислювальних засобів, які мають неймовірні обсяги пам'яті. Обговорюються аналогії революції, що виникла останнім часом у розвитку систем штучного інтелекту і природи появи другої сигнальної системи у людини. Розвиток революційних тенденцій у процесі створення багатопараметричних нейронних мереж дав змогу очікувати, що детальне вивчення цього процесу дозволить розібратися у природі появи другої сигнальної системи у вищих тварин. У роботі показані аналогії між цими двома явищами. Обговорюються можливості оцінки нейронною мережею свого внутрішнього стану та здатність усвідомлювати своє становище у зовнішньому оточенні, що є ключовими характеристиками самоідентичності та свідомості. (shrink)

Istoria paralelă a evoluției inteligenței umane și a inteligenței artificiale este o călătorie fascinantă, evidențiind căile distincte, dar interconectate, ale evoluției biologice și inovației tehnologice. Această istorie poate fi văzută ca o serie de evoluții interconectate, fiecare progres în inteligența umană deschizând calea pentru următorul salt în inteligența artificială. Inteligența umană și inteligența artificială s-au împletit de mult timp, evoluând în traiectorii paralele de-a lungul istoriei. Pe măsură ce oamenii au căutat să înțeleagă și să reproducă inteligența, IA a apărut (...) ca un domeniu dedicat creării de sisteme capabile de sarcini care necesită în mod tradițional intelect uman. Această carte analizează rădăcinile evolutive ale inteligenței, explorează apariția inteligenței artificiale, analizează istoria paralelă a inteligenței umane și a inteligenței artificiale, urmărind dezvoltarea lor, interacțiunile și impactul profund pe care l-au avut una asupra celeilalte, și imaginează peisajele viitoare în care converg inteligența umană și cea artificială. Să explorăm această istorie, comparând reperele cheie și evoluțiile din ambele tărâmuri. (shrink)

The parallel history of the evolution of human intelligence and artificial intelligence is a fascinating journey, highlighting the distinct but interconnected paths of biological evolution and technological innovation. This history can be seen as a series of interconnected developments, each advance in human intelligence paving the way for the next leap in artificial intelligence. Human intelligence and artificial intelligence have long been intertwined, evolving in parallel trajectories throughout history. As humans have sought to understand and reproduce intelligence, AI has emerged (...) as a field dedicated to creating systems capable of tasks that traditionally require human intellect. This book examines the evolutionary roots of intelligence, explores the emergence of artificial intelligence, examines the parallel history of human intelligence and artificial intelligence, tracing their development, interactions, and profound impact they have had on each other, and envisions future landscapes where intelligence converges human and artificial. Let's explore this history, comparing key milestones and developments in both realms. (shrink)

Consciousness and intelligence are properties that can be misunderstood as necessarily dependent. The term artificial intelligence and the kind of problems it managed to solve in recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following Russell’s analogy, if a machine can do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities (...) that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights than a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe the obvious answer is no, as problem-solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness, at least, cannot be modeled by computational intelligence and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence than human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals, and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals, and machines. (shrink)

This dissertation presents a philosophical inquiry into the concept of intelligence by reconstructing John Dewey’s theory of intelligence and investigating its educational implications. It presents three critiques of educational practices and theories that, from a Deweyan point of view, are built on misconceptions or oversimplifications of intelligence. While his theory of intelligence is primarily expressed in implicit terms, it offers a nuanced analysis of the sociality and contextuality of intelligence, questioning some of its traditional or mainstream conceptions. The reconstructed theory (...) presented in this dissertation implies that rather than holding onto the idea of individual intelligence, education (and those who are being educated) might benefit from focusing on the social practices and contexts that enable intelligence. The contextuality of intelligence suggests that conceiving intelligence as something that can be universalized, abstracted, and then redistributed is problematic. Dewey’s theory also suggests that people are rich with experience-based contextualized intelligence. To make the most of this, education needs to provide people with a robust theory of knowledge and an aspiration to be informed about the world in multiple and diverse ways. Learning the habit and processes of different kinds of inquiries could benefit societies by contributing to creative and intelligent democratic problem-solving in the inevitably unknown contexts of the future. (shrink)