Artificial Minds

Sideloading is the creation of a digital model of a person during their life via iterative improvements of this model based on the person's feedback. The progress of LLMs with large prompts allows the creation of very large, book-size prompts which describe a personality. We will call mind-models created via sideloading "sideloads"; they often look like chatbots, but they are more than that as they have other output channels, like internal thought streams and descriptions of actions. -/- By arranging the (...) predictive power of the facts about a person, we can write down the most important facts first. This allows us to get a high level of fidelity in a person's model by writing down a finite number of facts and running it as an LLM prompt with instructions to create a person's chatbot. This instruction also includes an extensive general sideload prompt (we will call it a prompt-loader), which explains how the person's sideload should work, but it is universal and works as an operating system for different persons. -/- I created my personal sideload to test all this and it started working surprisingly quickly. We found that there are three main levels of facts about a person: core facts, long-term memory, and historical facts. Core facts should go to the main prompt of the LLM, long-term memory belongs to RAG, and historical facts – which a person often does not remember – should be only used to extract new relevant data. The most important thing in preparing data for sideloading is to get a full list of the core facts, and not to mix it with long-term memory and historical facts. The LLM-sideload project is inherently future-oriented. New state-of-the-art LLMs with large prompt capabilities emerge quarterly. This allows for the preparation of prompts that exceed current LLM capabilities, anticipating smooth execution in the following year. Below we present the key findings of this work in the form of an executive summary: Quality of sideloads is evaluated using three metrics: • Facts: Accuracy of the answers about the person's life • Vibe: How well the sideload captures the person's style and personality, as judged by the person themselves and their acquaintances • Brilliant insights: Ability to contribute unique, valuable ideas beyond the training data, while maintaining the author's style • Coarseness: Levels of details as percent of the total of recordable textual information from memory estimated to be 10000 pages. Similar to the jpeg level of compression. Estimated performance of Alexey Turchin's sideload: • Facts: 70% correct • Vibe: 20% accurate • Brilliant insights: near zero • Coarseness: 10% The project achieved these results in about one month of work. Vibe is harder to evaluate and regulate than facts. This approach focuses on informational personal identity, setting aside issues of observer identity and internal qualia. (shrink)

Can AI systems such as ChatGPT think? We present an argument from rationality for the negative answer to this question. The argument is founded on two central ideas. The first is that if ChatGPT thinks, it is not rational, in the sense that it does not respond correctly to its evidence. The second idea, which appears in several different forms in philosophical literature, is that thinkers are by their nature rational. Putting the two ideas together yields the result that ChatGPT (...) is not a thinker. The paper sets out the argument in detail and shows how it can withstand numerous objections. We end the paper by contrasting the argument from rationality with a more well-known argument often taken to show that ChatGPT doesn’t think, due to Bender and Koller. (shrink)

As artificial intelligence (AI) systems continue their rapid advancement, a framework for contextualising the major transitional phases in the development of machine intellect becomes increasingly vital. This paper proposes a novel chronological classification scheme to characterise the key temporal stages in AI evolution. The Prenoëtic era, spanning all of history prior to the year 2020, is defined as the preliminary phase before substantive artificial intellect manifestations. The Protonoëtic period, which humanity has recently entered, denotes the initial emergence of advanced foundation (...) models exceeding human capacities within specialised domains. The forthcoming Mesonoëtic epoch is anticipated to commence with the advent of Artificial General Intelligence (AGI), potentially facilitated by quantum computing capabilities. Ultimately, the Kainonoëtic age is posited to begin upon the rise of superintelligent systems, likely catalysed by an AGI undergoing recursive self-improvement. This novel period taxonomy provides a structured conceptualisation for the key milestones in the evolution towards advanced artificial intellect. This classification framework offers significant value across various disciplines, researchers and stakeholders in the AI landscape, by providing a clearly defined chronological nomenclature. AI policy-makers and regulators will find it instrumental in crafting stage-appropriate policy and legal interventions. Scientists and academics will benefit from a standardised terminology in the approach to AI periodisation, facilitating unambiguous communication and collaborative research. Finally, this proposed taxonomy can serve as an accessible tool for public understanding, offering a clearer roadmap of AI development that can inform open discourse and decision-making. (shrink)

This paper offers the first careful analysis of the possibility that AI and humanity will go to war. The paper focuses on the case of artificial general intelligence, AI with broadly human capabilities. The paper uses a bargaining model of war to apply standard causes of war to the special case of AI/human conflict. The paper argues that information failures and commitment problems are especially likely in AI/human conflict. Information failures would be driven by the difficulty of measuring AI capabilities, (...) by the uninterpretability of AI systems, and by differences in how AIs and humans analyze information. Commitment problems would make it difficult for AIs and humans to strike credible bargains. Commitment problems could arise from power shifts, rapid and discontinuous increases in AI capabilities. Commitment problems could also arise from missing focal points, where AIs and humans fail to effectively coordinate on policies to limit war. In the face of this heightened chance of war, the paper proposes several interventions. War can be made less likely by improving the measurement of AI capabilities, capping improvements in AI capabilities, designing AI systems to be similar to humans, and by allowing AI systems to participate in democratic political institutions. (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)

The idea that humans might one day form persistent and dynamic relationships in professional, social, and even romantic contexts is a longstanding one. However, developments in machine learning and especially natural language processing over the last five years have led to this possibility becoming actualised at a previously unseen scale. Apps like Replika, Xiaoice, and CharacterAI boast many millions of active long-term users, and give rise to emotionally complex experiences. In this paper, I provide an overview of these developments, beginning (...) in Section 1 with historical and technical context. In Section 2, I lay out a basic theoretical framework for classifying human-AI relationships and their specific dynamics. Section 3 turns to ethical issues, with a focus on the core philosophical question of whether human-AI relationships can have similar intrinsic value to that possessed by human-human relationships. Section 4 extends to the discussion of ethical issues to the more empirical matter of harms and benefits of human-AI relationships. The paper concludes by noting potentially instructive parallels between the nascent field of ‘Social AI’ and the recent history of social media. (shrink)

Self models contribute to key functional domains of human intelligence that are not yet presented in today’s artificial intelligence. One important aspect of human problem-solving involves the use of conceptual self-knowledge to detect self-relevant information presented in the environment, which guides the subsequent retrieval of autobiographical memories that are relevant to the task at hand. This process enables each human to behave self-consistently in our own way across complex situations, manifested as self-interest and trait-like characteristics. In this paper, we outline (...) a computational framework that implements the conceptual aspect of human self models through a modified version of the joint-embedding predictive architecture. We propose that through the incorporation of human-like autobiographical memory retrieval and self-importance evaluation, the modified architecture could support machine agents with significantly enhanced self-consistency, which could be applied to deliver more believable simulations of human behaviors. (shrink)

We comment on the collection of papers inspired by our book Why Machines Will Never Rule the World published in volume 12 (5+6) of the journal Cosmos+Taxis. We summarize the arguments made by the contributors about what we say in the book, and then show where we disagree.

The world of artificial intelligence appears to be in rapid transition, and claims that artificial general intelligence is impossible are competing with concerns that we may soon be seeing Artificial Godlike Intelligence and that we should be very afraid of this prospect. This article discusses the issues from a psychological and social perspective and suggests that with the advent of Generative Artificial Intelligence, something that looks to humans like Artificial General Intelligence has become a distinct possibility as is the idea (...) of making it semi-autonomous: not just responding to discrete external inputs or performing specific tasks but able to prompt itself to create an ongoing stream of cognition that can possess something akin to a ‘purpose’. Add to that the ability of computers to be connected in vast networks, and we can envisage intelligences with staggering amounts of knowledge and reasoning capability, engaging with humans to anticipate and grant wishes. Such Semi-Autonomous Godlike Artificial Intelligences (SAGAIs—meaning ‘engagements’ in Hindi) could end up like Kali, wreaking death and destruction on our species or like Thor, acting as a saviour and protector of humanity. Which of these our SAGAIs most resemble could depend on whose wishes it is designed to serve: a rich oligarchy seeking ever greater wealth and power whatever the cost to others, or a populace that has compassion for each other and for life on the planet. (shrink)

Jobst Langrebe’s and Barry Smith’s book Why Machines Will Never Rule the World argues that artificial general intelligence (AGI) will never be realized. Drawing on theories of complexity they argue that it is not only technically, but mathematically impossible to realize AGI. The book is the result of cooperation between a philosopher and a mathematician. In addition to a thorough treatment of mathematical modelling of complex systems the book addresses many fundamental philosophical questions. The authors show that philosophy is still (...) relevant for questions of information technology in general and artificial intelligence in particular. This paper endorses Landgrebe’s and Smith’s arguments that artificial general intelligence cannot be realized, but not their conclusion that machines will never rule the world. It is not only a question of what technology can do. An equally important question is what technology does to us. Machines may not take over the world in a literal sense, but they may have many negative effects. Some of the most serious can be placed under the category of the “degeneration effect”. (shrink)

In their Why Machines Will Never Rule the World, Landgrebe and Smith (2023) argue that it is impossible for artificial general intelligence (AGI) to succeed, on the grounds that it is impossible to perfectly model or emulate the “complex” “human neurocognitive system”. However, they do not show that it is logically impossible; they only show that it is practically impossible using current mathematical techniques. Nor do they prove that there could not be any other kinds of theories than those in (...) current use. Even if perfect theories were impossible or unlikely, perfection may not be needed and may even be unhelpful. (shrink)

In August 2022, only a few months before ChatGPT was released, Barry Smith, well-known contemporary philosopher, together with Jobst Landgrebe, artificial intelligence entrepreneur, published a book entitled Why Machines will Never Rule the World: Artificial Intelligence without Fear (Landgrebe and Smith 2022). In this important, dense and far-reaching work, Landgrebe and Smith argue from the mathematical theory of complex systems, and a sophisticated analysis of the capabilities of human intelligence, that AGI— at the level of human intelligence—will never be possible. (...) In broad outline, Landgrebe and Smith claim that intelligent human behaviour is a facility that develops from a complex world and for a complex world; that the complexities of the real world and our behaviour in it can never be fully specified or simulated mathematically; that digital systems can only execute what can be simulated mathematically; and that therefore human-level intelligence will never be manifested by a digital system. The present collection of articles is in response to and inspired by Landgrebe and Smith. It provides reflections from a variety of perspectives and disciplines on the possibility of AGI, as well as on the transformations that AI technology is already bringing even in its current incarnation. (shrink)

Artificial intelligence is man's attempt to use software to emulate the intelligence of human beings. But the complexity of the human neurological system formed in the course of evolution is impossible to replicate: "Human languages and societies are complex systems, indeed complex systems of many complex systems," so much so that their mathematical modeling is impossible. Barry Smith, philosopher and professor at the University at Buffalo. shows no uncertainty about this. His latest book written with Jobst Landgrebe, a mathematician and (...) entrepreneur in the AI field, leaves no doubt – starting with the title: Why machines will never rule the world (Routledge 2023). The book was presented in Turin during an evening organized by the Fondazione CRT and the Bruno Leoni Institute, in which Smith was interviewed based on questions developed by generative Ai. We have accepted the challenge to conduct an interview parallel to the one done with GPT4. (shrink)

This paper addresses the seemingly insurmountable challenges the problem of personal identity raises for the prospect of radical human enhancement and synthetic consciousness. It argues that conceptions of personal identity rooted in psychological continuity akin to those proposed by Parfit and the Buddha may not provide the sort of grounding that many transhumanists chasing the dream of life extension think that they do if they rest upon ontologies that assume an incompatibility between identity and change. It also suggests that process (...) ontologies that take change to be primary, such as those that align with contemporary systems biology, offer a better way out of the personal identity dilemma. But the solution in this case, which regards biological organisms as processes rather than things, may constrain the possibility of biologically inspired superintelligent aliens (BISAs), which Schneider (following Bostrom 2014) posits as possibly the most common form of (extraterrestrial) superintelligence in the universe. (shrink)

International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance thresholds through a risk matrix informed by (...) background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasizing that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimization. (shrink)

Review of Jobst Landgrebe and Barry Smith, Why Machines Will Never Rule the World (Routledge, 2023).

This is a review of Jobst Landgrebe and Barry Smith, Why Machines Will Neve Rule the World (Routledge, 2023).

“Strong AI” is the view that it is possible for an artificial agent to be mentally indistinguishable from human agents. Because the behavioral dispositions of artificial agents are determined by underlying dispositional systems, Strong AI seems to entail human behavioral dispositions are also determined by dispositional systems. It is, however, highly intuitive that non-dispositional, phenomenal properties, such as being in pain, at least partially determine certain human behavioral dispositions, like the disposition to take a pain killer. Consequently, Strong AI seems (...) to conflict with an intuitive view of phenomenal properties’ role in determining human behavioral dispositions. My goal here is not directly to evaluate this tension, but rather to clarify how dispositionalism in the metaphysics of properties bears on it. While a tempting thought is that dispositionalism fits well with Strong AI’s thoroughly dispositional account of human behavior, I argue that this thought does not hold for dispositionalism in general. In particular, I argue that combining a version of the “powerful qualities view” with certain dispositionalist conceptions of the will leads to a version of the intuitive view of phenomenal properties that is radically incompatible with Strong AI. I argue further that this view also raises a challenge for the weaker claim that an artificial agent could be behaviorally indistinguishable from a human agent. (shrink)

Recent progress in artificial intelligence (AI) has drawn attention to the technology’s transformative potential, including what some see as its prospects for causing large-scale harm. We review two influential arguments purporting to show how AI could pose catastrophic risks. The first argument — the Problem of Power-Seeking — claims that, under certain assumptions, advanced AI systems are likely to engage in dangerous power-seeking behavior in pursuit of their goals. We review reasons for thinking that AI systems might seek power, that (...) they might obtain it, that this could lead to catastrophe, and that we might build and deploy such systems anyway. The second argument claims that the development of human-level AI will unlock rapid further progress, culminating in AI systems far more capable than any human — this is the Singularity Hypothesis. Power-seeking behavior on the part of such systems might be particularly dangerous. We discuss a variety of objections to both arguments and conclude by assessing the state of the debate. (shrink)

Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from original human-generated text. We then argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference, using new names to refer to new entities. Such examples could be (...) explained if LLMs were not cultural technologies but had beliefs, desires, and intentions. According to interpretationism in the philosophy of mind, a system has such attitudes if and only if its behavior is well explained by the hypothesis that it does. Interpretationists may hold that LLMs have attitudes, and thus have a simple solution to the novel reference problem. We emphasize, however, that interpretationism is compatible with very simple creatures having attitudes and differs sharply from views that presuppose these attitudes require onsciousness, sentience, or intelligence (topics about which we make no claims). (shrink)

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution (...) and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. (shrink)

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution (...) and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. (shrink)

Ambitious value learning proposals to solve the AI alignment problem and avoid catastrophic outcomes from a possible future misaligned artificial superintelligence (such as Coherent Extrapolated Volition [CEV]) have focused on ensuring that an artificial superintelligence (ASI) would try to do what humans would want it to do. However, present and future sentient non-humans, such as non-human animals and possible future digital minds could also be affected by the ASI’s behaviour in morally relevant ways. This paper puts forward Sentientist Coherent Extrapolated (...) Volition, an alternative to CEV, that directly takes into account the interests of all sentient beings. This ambitious value learning proposal would significantly reduce the likelihood of risks of astronomical suffering from the ASI’s behaviour, and thus we have very strong pro-tanto moral reasons in favour of implementing it instead of CEV. This fact is crucial in conducting an adequate cost-benefit analysis between different ambitious value learning proposals. (shrink)

The GOLEM XIV’s forecast for the development of the human civilisation and a typology of technological singularities: In the paper, a conceptual analysis of technological singularity is conducted and results in the concept differentiated into convergent singularity, existential singularity, and forecasting singularity, based on selected works of Ray Kurzweil, Nick Bostrom, and Vernor Vinge respectively. A comparison is made between the variants and the forecast of GOLEM XIV (a quasi-alter ego and character by Stanisław Lem) for the possible development of (...) Homo sapiens civilisation. According to the prediction, the scientific and technological progress is to bring humanity to transfer its civilisational primacy to a nonhuman superhuman intelligence xor a posthuman one. Both scenarios are best described by forecasting singularity, yet all variants are applicable only conditionally. As GOLEM did not undertake, the trajectories may also intersect or combine. (shrink)

While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...) robot blame, namely the folk's willingness to ascribe inculpating mental states or "mens rea" to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question - also explored in the experiment - whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot "knew" rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense when given the chance. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook. (shrink)

Does the capacity to think require the capacity to sense? A lively debate on this topic runs throughout the history of philosophy and now animates discussions of artificial intelligence. Many have argued that AI systems such as large language models cannot think and understand if they lack sensory grounding. I argue that thought does not require sensory grounding: there can be pure thinkers who can think without any sensory capacities. As a result, the absence of sensory grounding does not entail (...) that large language models cannot think or understand. I also consider to what extent quasi-sensory grounding can at least boost the performance of a language model. (shrink)

This short article is a “conversation” in which an android, Mort, replies to Richard Marc Rubin’s android named Sol in “The Robot Sol Explains Laughter to His Android Brethren” (The Philosophy of Humor Yearbook, 2022). There Sol offers an explanation for how androids can laugh--largely a reaction to frustration and unmet expectations: “my account says that laughter is one of four ways of dealing with frustration, difficulties, and insults. It is a way of getting by. If you need to label (...) my conception of humor, you might call it an adjustment theory, or perhaps an accommodation theory” (Rubin 256). Mort is intrigued but has his doubts. Mort contends that synesthesia likely has something to do with how humans develop humor. Perhaps, he considers, human designers crossed the androids' sensory wires, allowing them to be able to smash up concepts that do not otherwise seem to fit together, reconstruct incongruities in a manner that is not nonsensical, express the aha-eureka moment of a serendipitous discovery, and even feel blue when they fail at a given task that we were expected to complete. (shrink)

This paper investigates the nature of dispositional properties in the context of artificial intelligence systems. We start by examining the distinctive features of natural dispositions according to criteria introduced by McGeer (2018) for distinguishing between object-centered dispositions (i.e., properties like ‘fragility’) and agent-based abilities, including both ‘habits’ and ‘skills’ (a.k.a. ‘intelligent capacities’, Ryle 1949). We then explore to what extent the distinction applies to artificial dispositions in the context of two very different kinds of artificial systems, one based on rule-based (...) classical logic and the other on reinforcement learning. Here we defend three substantive claims. First, we argue that artificial systems are not equal in the kinds of dispositional properties they instantiate. In particular, we show that logical systems instantiate merely object-centered dispositions whereas reinforcement learning systems allow for the instantiation of agent-based abilities. Second, we explore the similarities and differences between the agent-centered abilities of artificial systems and those of humans, especially as relates to the important distinction made in the human case between habits and skills/intelligent capacities. The upshot is that the agent-centered abilities of truly intelligent artificial systems are distinctive enough to constitute a third type of agent-based ability — blended agent-based ability — raising substantial questions as to how we understand the nature of their agency. Third, we explore one aspect of this problem, focussing on whether systems of this type are properly considered ‘responsible agents’, at least in some contexts and for some purposes. The ramifications of our analysis will turn out to be directly relevant to various ethical concerns of artificial intelligence. (shrink)

نحن مقيمون على الإنترنت، نرسم معالم دنيانا التي نبتغيها من خلاله، ونُمارس تمثيل شخصياتٍ أبعد ما تكون عنا؛ نحقق زيفًا أحلامًا قد تكون بعيدة المنال، ويُصدق بضعنا البعض فيما نسوقه من أكاذيب ومثاليات؛ ننعم بأقوالٍ بلا أفعال، وقلوبٍ بلا عواطف، وجناتٍ بلا نعيم، وألسنة في ظلمات الأفواه المُغلقة تنطق بحركات الأصابع، وحريةٍ مُحاطة بأسيجة الوهم؛ ومن غير إنترنت سيبدو أكثر الناس قطعًا بحجمهم الطبيعي الذي لا نعرفه، او بالأحرى نعرفه ونتجاهله! لا شك أن ظهور الإنترنت واتساع نطاق استخداماته يُمثل حدثًا (...) فريدًا متناميًا في مسيرة الإنسان الحضارية وتغيير الطريقة التي يعيش بها البشر حياتهم. ومع ذلك، لا ينطوي أي تعريف للإنترنت حتى الآن على إشارة للواقع الافتراضي، رغم تعايشنا معه وفيه بالفعل؛ فنحن نتفاعل ونتبادل المعلومات، ونشتري ونبيع، ونلعب ونضحك ونبكي، ونمارس أدق تفاصيل حياتنا عبر الإنترنت؛ وكل ما كنا نقوم به من قبل بالحركات الجسدية المكانية أوكلنا مهمة القيام به إلى عقولنا! ولعل هذا ما تفعله كلمة «ميتافيرس»، وهي كلمة استخدمها لأول مرة كاتب الخيال العلمي الأمريكي «نيل ستيفنسون» في روايته «تحطم الثلج» (1992)، للدلالة على تفاعل البشر مع بعضهم البعض ومع البرمجيات في فضاء افتراضي ثلاثي الأبعاد مشابه للعالم الفعلي. (shrink)

لم نعد بحاجة إلى فانوس سحري نمسح عليه بأصابعنا لكي يخرج منه المارد القادر على خدمتنا وتلبية بعض أهم مطالبنا الحياتية، ولم نعد بحاجة إلى تعويذات نلج بها في عالم السحر والخيال؛ لقد خرج المارد بالفعل من قمقمه الحاسوبي؛ من جوف مختبرات البرمجة والذكاء الاصطناعي، بتعويذات (أكواد) رياضية رمزية سرعان ما تمكن من التهامها وهضمها، ليبيت قادرًا على إنتاج تعويذات أخرى مماثلة، وربما أفضل منها! خرج «المُحول التوليدي المدرب مُسبقًا»، المعروف اختصارًا باسم «جي بي تي»، ملوحًا بإمكانات بحثية وخدمية وإنتاجية (...) هائلة، ومهددًا بتصفية قطاعات بأسرها من المهن والوظائف، وبتجريف مهارات البحث العلمي لدى طلاب المدارس والجامعات! (shrink)

تُخبرنا النظرية الحاسوبية للعقل (أو مذهب الحوسبة)، أن عقولنا تُشبه الحواسيب في عملها؛ أي أنها تتلقى مدخلات من العالم الخارجي، ثم تُنتج بالخوارزميات مخرجات في شكل حالات ذهنية أو أفعال. وبعبارة أخرى، تذهب النظرية إلى أن الدماغ لا يعدو أن يكون معالج معلومات؛ حيث يكون العقل بمثابة «برمجيات» (سوفت وير) تعمل على «جهاز» هو الدماغ (هارد وير). وما دام العقل مجرد برمجيات تخضع للحوسبة الفيزيائية بواسطة الأدمغة، أليس من الممكن إذن منطقيًا نقلها إلى أي حاسوب مثلما نقوم بنقل أية برمجيات (...) أخرى؟. (shrink)

الذكاء الاصطناعي العاطفي»، ويُعرف أيضًا باسم «الحوسبة العاطفية»، و«الذكاء الاصطناعي المتمركز حول الإنسان»، و«الذكاء الاصطناعي الاجتماعي»، مفهوم جديد نسبيًا (ما زالت تقنياته في طور التطوير)، وهو أحد مجالات علوم الحاسوب الهادفة إلى تطوير آلات قادرة على فهم المشاعر البشرية. يشير المفهوم ببساطة إلى اكتشاف وبرمجة المشاعر الإنسانية بُغية تحسين الذكاء الاصطناعي، وتوسيع نطاق استخدامه، بحيث لا يقتصر أداء الروبوتات على تحليل الجوانب المعرفية (المنطقية) والتفاعل معها فحسب، بل والامتداد بالتحليل والتفاعل إلى الجوانب العاطفية للتواصل البشري.

تُعد أخلاقيات الآلة جزءًا من أخلاقيات الذكاء الاصطناعي المعنية بإضافة أو ضمان السلوكيات الأخلاقية للآلات التي صنعها الإنسان، والتي تستخدم الذكاء الاصطناعي، وهي تختلف عن المجالات الأخلاقية الأخرى المتعلقة بالهندسة والتكنولوجيا، فلا ينبغي الخلط مثلاً بين أخلاقيات الآلة وأخلاقيات الحاسوب، إذ تركز هذه الأخيرة على القضايا الأخلاقية المرتبطة باستخدام الإنسان لأجهزة الحاسوب؛ كما يجب أيضًا تمييز مجال أخلاقيات الآلة عن فلسفة التكنولوجيا، والتي تهتم بالمقاربات الإبستمولوجية والأنطولوجية والأخلاقية، والتأثيرات الاجتماعية والاقتصادية والسياسية الكبرى، للممارسات التكنولوجية على تنوعها؛ أما أخلاقيات الآلة فتعني (...) بضمان أن سلوك الآلات تجاه المستخدمين من البشر، وربما تجاه الآلات الأخرى أيضًا، مقبول أخلاقيًا. الأخلاقيات التي نعنيها هنا إذن هي أخلاقيات يجب أن تتحلى بها الآلات كأشياء، وليس البشر كمصنعين ومستخدمين لهذه الآلات! (shrink)

This paper investigates the responses of GPT-4, a state-of-the-art AI language model, to ten prominent philosophical paradoxes, and evaluates its capacity to reason and make decisions in complex and uncertain situations. In addition to analyzing GPT-4's solutions to the paradoxes, this paper assesses the model's Theory of Mind (ToM) capabilities by testing its understanding of mental states, intentions, and beliefs in scenarios ranging from classic ToM tests to complex, real-world simulations. Through these tests, we gain insight into AI's potential for (...) social reasoning and its capacity for more sophisticated forms of human-AI interaction. The paper also explores the limitations and biases of AI-generated reasoning and its implications for our comprehension of complex philosophical problems. (shrink)

It is indubitable that machines with artificial intelligence (AI) will be an essential component in humans’ quest to become a spacefaring civilization. Most would agree that long-distance space travel and the colonization of Mars will not be possible without adequately developed AI. Machines with AI have a normative function, but some argue that it can also be evaluated from the perspective of ethical norms. This essay is based on the assumption that machine ethics is an essential philosophical perspective in realizing (...) the aim of humanity becoming a spacefaring civilization. In this essay, I explore two questions in the field of machine ethics, that I believe to be relevant to the role AI will play in long-distance space travel. The first is, should moral theory be extended to include machines with AI, and second, can machines be fully ethical agents? In this essay, I define AI and then discuss the difference between implicit, explicit and full ethical agents in relation to machines with AI. I then present the argument that the inclusion of moral theory is essential in the development of machines with AI. Without an adequate inclusion of moral theory in the design of AI it may pose an existential threat to humanity, especially in the development of super-intelligent machines. I also highlight that conceptual clarity is essential in the field of machine ethics and the choice of the conceptual foundation that informs AI research and development has ethical implications, especially in the case of super-intelligent machines. This essay is an exploratory and speculative philosophical analysis of certain aspects of machine ethics relevant to long-distance space travel and does not attempt to provide definitive answers to the questions posed in the essay, but instead aims to bring attention to what I deem important considerations. (shrink)

Today, another attempt to explore You’s capability was made for a more difficult “exercise”.

As testing of ChatGPT has shown, this form of artificial intelligence has the potential to develop, which requires improving its software and other hardware that allows it to learn, i.e., to acquire and use new knowledge, to contact its developers with suggestions for improvement, or to reprogram itself without their participation. Как показало тестирование ChatGPT, эта форма искусственного интеллекта имеет потенциал развития, для чего необходимо усовершенствовать её программное и прочее техническое обеспечение, позволяющее ей учиться, т.е. приобретать и использовать новые знания, (...) обращаться к её разработчикам с предложениями по усовершенствованию, или производить самопрограммирование без их участия. (shrink)

Transcript of an Interview on the podcast: Irreverend: Faith and Current Affairs.

This is a review of Mind Design II: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland and published by The MIT Press in 1997.

Should we fear a future in which the already tricky world of academic publishing is increasingly crowded out by super-intelligent artificial general intelligence (AGI) systems writing papers on phenomenology and ethics? What are the chances that AGI advances to a stage where a human philosophy instructor is similarly removed from the equation? If Jobst Landgrebe and Barry Smith are correct, we have nothing to fear.

Call it the Skynet hypothesis, Artificial General Intelligence, or the advent of the Singularity — for years, AI experts and non-experts alike have fretted (and, for a small group, celebrated) the idea that artificial intelligence may one day become smarter than humans. -/- According to the theory, advances in AI — specifically of the machine learning type that’s able to take on new information and rewrite its code accordingly — will eventually catch up with the wetware of the biological brain. (...) In this interpretation of events, every AI advance from Jeopardy-winning IBM machines to the massive AI language model GPT-3 is taking humanity one step closer to an existential threat. We’re literally building our soon-to-be-sentient successors. -/- Except that it will never happen. At least, according to the authors of the new book Why Machines Will Never Rule the World: Artificial Intelligence without Fear. (shrink)

Whole Brain Emulation has been championed as the most promising, well-defined route to achieving both human-level artificial intelligence and superintelligence. It has even been touted as a viable route to achieving immortality through brain uploading. WBE is not a fringe theory: the doctrine of Computationalism in philosophy of mind lends credence to the in-principle feasibility of the idea, and the standing of the Human Connectome Project makes it appear to be feasible in practice. Computationalism is a popular, independently plausible theory, (...) and Connectomics a well-funded empirical research program, so optimism about WBE is understandable. However, this optimism may be misplaced. This article argues that WBE is, at best, no more compelling than any of the other far-flung routes to achieving superintelligence. Similarly skeptical conclusions are found regarding immortality. The essay concludes with some positive considerations in favor of the Biological Theory of consciousness, as well as morals about the limits of Computationalism in both artificial intelligence and the philosophy of mind more generally. (shrink)

Originally published in PhilosophyNews, July 19, 2022. -/- This new series, What’s Happening in Philosophy (WHiP)-The Philosophers aims to provide a monthly snapshot of various trends and discussions happening across the discipline. -/- In this inaugural post, we begin with a harrowing tale from David Edmonds involving the murder of the German philosopher Moritz Schlick. Schlick was a Vienna Circle guiding spirit and logical positivist thinker. Next up is Steven Nadler’s take on several biographies of the ‘father of modern philosophy’ (...) in his new paper, The Many Lives of René Descartes. Lastly, questions around AI in academia come up in an article from Scientific American. (shrink)

The objective of this paper is to provide a critical analysis of the Kantian notion of freedom (especially the problem of the third antinomy and its resolution in the critique of pure reason); its significance in the contemporary debate on free-will and determinism, and the possibility of autonomy of artificial agency in the Kantian paradigm of autonomy. Kant's resolution of the third antinomy by positing the ground in the noumenal self resolves the problem of antinomies; however, invites an explanatory gap (...) between phenomenality and the noumenal self; even if he has successfully established the compatibility of natural causality and non-natural causality through his transcendental argument. This paper is also devoted to establishing the plausibility of the knowledge claim that Kantian reduction of phenomenality has served half of the purpose of the AI scientists on the possibility of Artificial Autonomous Agency. (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)

This chapter will explore the dynamics of power underpinning ethical issues within smart cities via a new paradigm derived from Systems Theory. The smart city is an expression of technology as a socio-technical system. The vision of the smart city contains a deep fusion of many different technical systems into a single integrated “ambient intelligence”. ETICA Project, 2010, p. 102). Citizens of the smart city will not experience a succession of different technologies, but a single intelligent and responsive environment through (...) which they move. Analysis of such an environment requires a framework which transcends traditional ontologically-based models in order to accommodate this deep fusion. This chapter will outline a framework based on Latour’s Actor-Network Theory and Luhmann’s treatment of society as an autopoetic system. We shall use this framework to map the influence of relevant factors on ethical issues, irrespective of their composition or type. For example, under this treatment, both human praxis and technical design can be viewed as comparable tools of domination. This chapter will provide a framework for the analysis of relations between any elements of the smart city, ranging from top-level urban management processes down to individual device operations. While we will illustrate the use of this schema through examination of ethical issues arising from power dynamics within the smart city, it is intended that this example will demonstrate the wider utility of the model in general. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...) of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)