Philosophy of AI, Misc

This article cohesively discusses three topics, namely color and its perception, the yet-to-be-solved hard problem of consciousness, and the theoretical possibility of strong AI. First, the article restores color back into the physical world by giving cross-species evidence. Secondly, the article proposes a dual-field with function Q hypothesis (DFFQ) which might explain the ‘first-person point of view’ and so the hard problem of consciousness. Finally, the article discusses what DFFQ might bring to artificial intelligence and how it might allow strong (...) AI to stay true. (shrink)

The discourse on emotional A.I., i.e., technologies that read, classify, identify human emotions, is currently dominated by Western ideas. Yet, even A.I. researchers in the West acknowledge there are cultural differences if neglected could magnify and affect A.I.'s accuracy.

The focus of this article is the "good-will theory", which explains the effect humans can have on the safety of AI, along with how it is in the best interest of a superintelligent AI to work alongside humans and not overpower them. Future papers dealing with the good-will theory will be published, but discuss different talking points in regards to possible or real objections to the theory.

LLMs are, in the first instance, models of the statistical distribution of tokens in the vast linguistic corpus they have been trained on. But their often surprising emergent capabilities raise the question of how much understanding of the extralinguistic world LLMs can glean from this statistical distribution of words alone. Here, I explore and evaluate the idea that the probability distribution of words in the public corpus offers a window onto the conditional structure of the world. To become a good (...) next-token predictor, an LLM has to become a good pattern completer, and the patterns laid up in language mirror the patterns embodied in the world to a considerable extent. When this is the case—and this is no empty condition—it allows LLMs to compress into their weights two distinct, but complementary forms of understanding of the world. The first form of understanding stored is an understanding of which properties tend to cluster together; I spell out this idea by drawing on Millikan’s account of a multidimensional clumpy world. The second is an understanding of the inferential structure that propositions about these property clusters tend to be enmeshed in; I spell out this idea by drawing on Brandom’s account of the isomorphism between deontic normative conceptual relations of incompatibility and consequence among commitments and alethic modal relations of incompatibility and consequence among states of affairs. On the resulting picture, word models can be world models insofar as linguistic patterns track real patterns between facts. (shrink)

LLMs are, in the first instance, models of the statistical distribution of tokens in the vast linguistic corpus they have been trained on. But their often surprising emergent capabilities raise the question of how much understanding of the extralinguistic world LLMs can glean from this statistical distribution of words alone. Here, I explore and evaluate the idea that the probability distribution of words in the public corpus offers a window onto the conditional structure of the world. To become a good (...) next-token predictor, an LLM has to become a good pattern completer, and the patterns laid up in language mirror the patterns embodied in the world to a considerable extent. When this is the case—and this is no empty condition—it allows LLMs to compress into their weights two distinct, but complementary forms of understanding of the world. The first form of understanding stored is an understanding of which properties tend to cluster together; I spell out this idea by drawing on Millikan’s account of a multidimensional clumpy world. The second is an understanding of the inferential structure that propositions about these property clusters tend to be enmeshed in; I spell out this idea by drawing on Brandom’s account of the isomorphism between deontic normative conceptual relations of incompatibility and consequence among commitments and alethic modal relations of incompatibility and consequence among states of affairs. On the resulting picture, word models can be world models insofar as linguistic patterns track real patterns between facts. (shrink)

Over the course of the twentieth century, the notion of the systematicity of thought has acquired a much narrower meaning than it used to carry for much of its history. The so-called “systematicity debate” that has dominated the philosophy of language, cognitive science, and AI research over the last thirty years understands the systematicity of thought in terms of the compositionality of thought. But there is an older, broader, and more demanding notion of systematicity that is now increasingly relevant again. (...) To recover this notion from under the shadow of the systematicity debate, I distinguish between (i) the systematicity of thinkable contents, (ii) the systematicity of thinking, and (iii) the ideal of systematic thought. I then deploy this distinction to critically evaluate Fodor’s systematicity-based argument for the language of thought hypothesis before recovering the notion of the systematicity of thought as a regulative ideal, which has historically shaped our understanding of what it means for thought to be rational, authoritative, and scientific. To assess how much systematicity we need from AI models, I argue that we must look to the rationales for systematizing thought. To this end, I recover five such rationales from the history of philosophy and identify five functions served by systematization. Finally, I show how these can be used to arrive at a dynamic understanding of the need to systematize thought that can tell us what kind of systematicity is called for and when. (shrink)

A key assumption fuelling optimism about the progress of Large Language Models (LLMs) in modelling the world is that the truth is systematic: true statements about the world form a whole that is not just consistent, in that it contains no contradictions, but cohesive, in that the truths are inferentially interlinked. This holds out the prospect that LLMs might rely on that systematicity to fill in gaps and correct inaccuracies in the training data: consistency and cohesiveness promise to facilitate progress (...) towards comprehensiveness in an LLM’s representation of the world. However, philosophers have identified reasons to doubt that the truth is systematic across all domains of thought, arguing that in normative domains, in particular, the truth is not necessarily systematic. I argue that insofar as the truth in normative domains is asystematic, this renders it correspondingly harder for LLMs to make progress, because they cannot rely on the consistency and cohesiveness of the truth to work towards comprehensiveness. And the less LLMs can rely on the systematicity of truth, the less we can rely on them to do our practical deliberation for us, as there is correspondingly more of a role for human agency in navigating asystematic normative domains. (shrink)

Contains a description of a generalized and constructive formal model for the processes of subjective and creative thinking. According to the author, the algorithm presented in the article is capable of real and arbitrarily complex thinking and is potentially able to report on the presence of consciousness.

The reasoning abilities of large language models (LLMs) are the topic of a growing body of research in AI and cognitive science. In this paper, we probe the extent to which twenty-nine LLMs are able to distinguish logically correct inferences from logically fallacious ones. We focus on inference patterns involving conditionals (e.g., 'If Ann has a queen, then Bob has a jack') and epistemic modals (e.g., 'Ann might have an ace', 'Bob must have a king'). These inferences have been of (...) special interest to logicians, philosophers, and linguists, since they play a central role in the fundamental human ability to reason about distal possibilities. Assessing LLMs on these inferences is thus highly relevant to the question of how much the reasoning abilities of LLMs match those of humans. All the LLMs we tested make some basic mistakes with conditionals or modals, though zero-shot chain-of-thought prompting helps them make fewer mistakes. Even the best performing LLMs make basic errors in modal reasoning, display logically inconsistent judgments across inference patterns involving epistemic modals and conditionals, and give answers about complex conditional inferences that do not match reported human judgments. These results highlight gaps in basic logical reasoning in today's LLMs. (shrink)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...) control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

Are the real numbers rich enough to measure intelligence? We generalize a result of Alexander and Hutter about the so-called Legg-Hutter intelligence measures of reinforcement learning agents. Using the generalized result, we exhibit a paradox: in one particular version of the Legg-Hutter intelligence measure, certain agents all have intelligence 0, even though in a certain sense some of them outperform others. We show that this paradox disappears if we vary the Legg-Hutter intelligence measure to be hyperreal-valued rather than real-valued.

Chapter 1 of the ongoing online publication "Artificial Aesthetics: A Critical Guide to AI, Media and Design", Lev Manovich and Emanuele Arielli -/- Book information: Assume you're a designer, an architect, a photographer, a videographer, a curator, an art historian, a musician, a writer, an artist, or any other creative professional or student. Perhaps you're a digital content creator who works across multiple platforms. Alternatively, you could be an art historian, curator, or museum professional. -/- You may be wondering how (...) AI will affect your professional area in general and your work and career. Our book offers intellectual tools to help us better see the future of creative fields. These tools come from philosophy of art, experimental psychology, media theory, digital culture studies and data science. The book is the first to combine these different perspectives together. -/- We started the work on the book in summer 2019, exchanging numerous messages, commenting on each other ideas, and sharing drafts of sections. The final book is a result of this process. Although each chapter is written by one author, it reflects the discussions we had over 27 months. (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)

Episodic memory is memory for past events. It’s characteristically associated with an experience of ‘mentally replaying’ one’s experiences in the mind’s eye. This biological phenomenon has inspired the development of several ‘experience replay’ algorithms in AI. In this chapter, I ask whether experience replay algorithms might shed light on a puzzle about episodic memory’s function: what does episodic memory contribute to the cognitive systems in which it is found? I argue that experience replay algorithms can serve as idealized models of (...) episodic memory for the purposes of addressing this question. Taking the DQN algorithm as a case study, I suggest that these algorithms provide some support for mnemonic accounts, on which episodic memory’s function lies in the storage, encoding and retrieval of information. By extending and adapting experience replay algorithms, we might gain further insight into episodic memory’s operations and contributions to cognition. (shrink)

Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two-premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of ‘survival (...) stories’, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of ‘P(doom)’, the probability that humanity will be destroyed by AI. (shrink)

1. 1.1. Suffering is bad. This is why, ceteris paribus, there are strong moral reasons to prevent suffering. Moreover, typically, those moral reasons are stronger when the amount of suffering at st...

Linguistic outputs generated by modern machine-learning neural net AI systems seem to have the same contents—i.e., meaning, semantic value, etc.—as the corresponding human-generated utterances and texts. Building upon this essential premise, Herman Cappelen and Josh Dever's Making AI Intelligible sets for itself the task of addressing the question of how AI-generated outputs have the contents that they seem to have (henceforth, ‘the question of AI Content’). In pursuing this ambitious task, the book makes several high-level, framework observations about how a (...) meta-semantic account of the content of AI-generated outputs should proceed. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...) provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

Uno de los principios éticos mencionados más frecuentemente en los lineamientos para el desarrollo de la inteligencia artificial (IA) es la transparencia algorítmica. Sin embargo, no existe una definición estándar de qué es un algoritmo transparente ni tampoco es evidente por qué la opacidad algorítmica representa un reto para el desarrollo ético de la IA. También se afirma a menudo que la transparencia algorítmica fomenta la confianza en la IA, pero esta aseveración es más una suposición a priori que una (...) tesis basada en evidencia empírica. Tampoco se discute mucho hasta qué punto es técnicamente posible volver transparente la caja negra de la IA a través de los métodos de explicabilidad. En este capítulo haré un análisis de la interrelación entre los conceptos de transparencia, explicabilidad y confianza. Inicialmente, analizaré los diferentes tipos de opacidad algorítmica para entender mejor cuál es el problema al que nos enfrentamos. En las secciones subsiguientes trazaré la relación entre explicabilidad y transparencia, y presentaré los límites de los métodos actuales de explicabilidad. En la sección final, examinaré la evidencia empírica acerca de la relación entre transparencia y confianza en las decisiones automatizadas basadas en sistemas de IA. (shrink)

Can AI developers be held epistemically responsible for the processing of their AI systems when these systems are epistemically opaque? And can explainable AI (XAI) provide public justificatory reasons for opaque AI systems’ outputs? Koskinen (2024) gives negative answers to both questions. Here, I respond to her and argue for affirmative answers. More generally, I suggest that when considering people’s uncertainty about the factors causally determining an opaque AI’s output, it might be worth keeping in mind that a degree of (...) uncertainty about conclusions is inevitable even in entirely human-based empirical science because in induction there’s always a risk of getting it wrong. Keeping this in mind may help appreciate that requiring full transparency from AI systems before epistemically trusting their outputs might be unusually (and potentially overly) demanding. (shrink)

Many ethical frameworks require artificial intelligence (AI) systems to be explainable. Explainable AI (XAI) models are frequently tested for their adequacy in user studies. Since different people may have different explanatory needs, it is important that participant samples in user studies are large enough to represent the target population to enable generalizations. However, it is unclear to what extent XAI researchers reflect on and justify their sample sizes or avoid broad generalizations across people. We analyzed XAI user studies (N = (...) 220) published between 2012 and 2022. Most studies did not offer rationales for their sample sizes. Moreover, most papers generalized their conclusions beyond their target population, and there was no evidence that broader conclusions in quantitative studies were correlated with larger samples. These methodological problems can impede evaluations of whether XAI systems implement the explainability called for in ethical frameworks. We outline principles for more inclusive XAI user studies. (shrink)

Forty-two years ago, Capra published “The Tao of Physics” (Capra, 1975). In this book (page 17) he writes: “The exploration of the atomic and subatomic world in the twentieth century has …. necessitated a radical revision of many of our basic concepts” and that, unlike ‘classical’ physics, the sub-atomic and quantum “modern physics” shows resonances with Eastern thoughts and “leads us to a view of the world which is very similar to the views held by mystics of all ages and (...) traditions.“ This article stresses an analogous situation in biology with respect to a new theoretical approach for studying living systems, Integral Biomathics (IB), which also exhibits some resonances with Eastern thought. Stepping on earlier research in cybernetics1 and theoretical biology,2 IB has been developed since 2011 by over 100 scientists from a number of disciplines who have been exploring a substantial set of theoretical frameworks. From that effort, the need for a robust core model utilizing advanced mathematics and computation adequate for understanding the behavior of organisms as dynamic wholes was identified. At this end, the authors of this article have proposed WLIMES (Ehresmann and Simeonov, 2012), a formal theory for modeling living systems integrating both the Memory Evolutive Systems (Ehresmann and Vanbremeersch, 2007) and the Wandering Logic Intelligence (Simeonov, 2002b). Its principles will be recalled here with respect to their resonances to Eastern thought. (shrink)

Abstract: One approach to alignment with human values in AI and robotics is to engineer artiTicial systems isomorphic with human beings. The idea is that robots so designed may autonomously align with human values through similar developmental processes, to realize project ideal conditions through iterative interaction with social and object environments just as humans do, such as are expressed in narratives and life stories. One persistent problem with human value orientation is that different human beings champion different values as ideal, (...) meaning that the values to which an AI should be aligned are ambiguous. Prior work considered human development of purpose and source of meaning in life in terms of project ideal conditions, in effect establishing lifelong value orientations according to which intermediate situations are evaluated. The present work compares views on motivating values from St. Augustine and from popular cognitive science. These accounts are described as divergent error theories which present to their proponents as mutually exclusive yet accurate accounts of personal experience due to differential development of variable innate potentials. SpeciTically, the hypothesis proposed is that differential development of spindle neural projections establishes enduring connections between prior established relatively immediate routine processes entrained during childhood and prioritized in popular cognitive science, and later developing higher-level social and self-processes entrained during adolescence and emphasized in Augustine's account, with these projections hard-wiring lifelong motivating value orientations more or less inaccessible to modiTication through material interaction. Robot experiments informed by this study may evaluate variable value orientation by design, with for example autonomous robots developing motivating associations with temporally distal project ideal conditions through love for humanity, as described by Augustine, and others pursuing adaptive Tit to passing norms consistent with popular contemporary accounts. (shrink)

In his groundbreaking work, Abstraction and Empathy, Wilhelm Worringer delved into the intricacies of various abstract and figurative artworks, contending that they evoke distinct impulses in the human audience—specifically, the urges towards abstraction and empathy. This article asserts the presence of empirical evidence supporting the extension of Worringer’s concepts beyond the realm of art appreciation to the domain of art-making. Consequently, it posits that abstraction and empathy serve as foundational principles guiding the production of both abstract and figurative art. This (...) holds particular significance in the 21st century, where artificial intelligence (AI) assumes a creative role that was absent during Worringer’s initial formulation of his theory. Thus, this paper postulates that AI inherently harbors a predisposition for the generation of abstract art, owing to its non-living and inorganic origins and functioning. (shrink)

The media often discuss artificial neural networks like ChatGPT or Amazon's Alexa, and policymakers grapple with regulating emerging technologies. However, the precise nature of "artificial neurons" remains ambiguous. Is this term to be understood merely metaphorically or does it refer to physical entities resembling biological neurons? While commonly understood as mathematical nodes in AI, the discussion extends deeper, particularly with the advent of neuromorphic engineering. This paper discusses whether artificial neurons are indeed neurons and what the potential implications are. Specifically, (...) the paper addresses the question: I discuss three typesAre artificial neurons functional analogs of biological neurons? of artificial neurons against the background of the two main theories of biological function, viz. Causal Role and Selected Effects Theory. My thesis contends that while artificial neurons lack the morphological structure of biological neurons, certain types, especially those in neural circuits, exhibit functional parity, and thus can be considered functional analogs. (shrink)

According to virtue epistemology, the main aim of education is the development of the cognitive character of students (Pritchard, 2014, 2016). Given the proliferation of technological tools such as ChatGPT and other LLMs for solving cognitive tasks, how should educational practices incorporate the use of such tools without undermining the cognitive character of students? Pritchard (2014, 2016) argues that it is possible to properly solve this ‘technology-education tension’ (TET) by combining the virtue epistemology framework with the theory of extended cognition (...) (EXT) (Clark and Chalmers, 1998). He argues that EXT enables us to consider tools as constitutive parts of the students’ cognitive system, thus preserving their cognitive character from technologically induced cognitive diminishment. The first aim of this paper is to show that this solution is not sufficient to solve the TET. Second, I aim to offer a complementary and more encompassing framework of tool-use to address the TET. Then, I apply it to the educational uses of ChatGPT as the most notable example of LLM, although my arguments can be extended to other generative AI systems. To do so, in Sect. 1.1, I present Pritchard’s framework of cognitive character and virtue epistemology applied in education, to which I am committed in this treatment. In Sects. 2 and 3, I respectively illustrate Pritchard’s (2014) solution to the TET, and I highlight the general limitations of his proposal. Thus, in Sect. 4.1 I characterize ChatGPT as a computational cognitive artifact using Fasoli’s (2017, 2018) taxonomy of cognitive artifacts. In Sect. 4.2, I introduce my proposal, which combines Pritchard’s account of virtue epistemology with Fasoli’s (2017, 2018) taxonomy of cognitive artifacts to address the TET. Finally, in Sect. 5.1, I present some epistemically virtuous uses of ChatGPT in educational contexts. To conclude, I argue in favor of a multidisciplinary approach for analyzing educational activities involving AI technologies such as ChatGPT. (shrink)

Many researchers and intellectuals warn about extreme risks from artificial intelligence. However, these warnings typically came without systematic arguments in support. This paper provides an argument that AI will lead to the permanent disempowerment of humanity, e.g. human extinction, by 2100. It rests on four substantive premises which it motivates and defends: first, the speed of advances in AI capability, as well as the capability level current systems have already reached, suggest that it is practically possible to build AI systems (...) capable of disempowering humanity by 2100. Second, due to incentives and coordination problems, if it is possible to build such AI, it will be built. Third, since it appears to be a hard technical problem to build AI which is aligned with the goals of its designers, and many actors might build powerful AI, misaligned powerful AI will be built. Fourth, because disempowering humanity is useful for a large range of misaligned goals, such AI will try to disempower humanity. If AI is capable of disempowering humanity and tries to disempower humanity by 2100, then humanity will be disempowered by 2100. This conclusion has immense moral and prudential significance. (shrink)

Advances in artificial intelligence (AI) create an increasing similarity between the performance of AI systems or AI-based robots and human communication. They raise the questions: whether it is possible to communicate with, understand, and even empathically perceive artificial agents; whether we should ascribe actual subjectivity and thus quasi-personal status to them beyond a certain level of simulation; what will be the impact of an increasing dissolution of the distinction between simulated and real encounters. (1) To answer these questions, the paper (...) argues that the precondition for actually understanding others consists in the implicit assumption of the subjectivity of our counterpart, which makes shared feelings and a „we-intentionality” possible. This assumption is ultimately based on the presupposition of a shared form of life, conceived here as „conviviality.” (2) The possibility that future artificial agents could meet these preconditions is refuted on the basis of embodied and enactive cognition, which links subjectivity and consciousness to the aliveness of an organism. (3) Even if subjectivity is in principle impossible for artificial agents, the distinction between simulated and real subjectivity might nevertheless become increasingly blurred. Here, possible consequences are discussed, especially using the example of virtual psychotherapy. Finally, the paper makes case for a mindful appproach to the language we use to talk about artificial systems and pleads for preventing a systematic pretense of subjectivity. (shrink)

Deepfakes and other forms of synthetic media are widely regarded as serious threats to our knowledge of the world. Various technological responses to these threats have been proposed. The reactive approach proposes to use artificial intelligence to identify synthetic media. The proactive approach proposes to use blockchain and related technologies to create immutable records of verified media content. I argue that both approaches, but especially the reactive approach, are vulnerable to a problem analogous to the ancient problem of the criterion—a (...) line of argument with skeptical implications. I argue that, while the proactive approach is relatively resistant to this objection, it faces its own serious challenges. In short, the proactive approach would place a heavy burden on users to verify their own content, a burden that is exacerbated by and is likely to exacerbate existing inequalities. (shrink)

In Reality+: Virtual Worlds and the Problems of Philosophy, David Chalmers argues, among other things, that: if we are living in a full-scale simulation, we would still enjoy broad swathes of knowledge about non-psychological entities, such as atoms and shrubs; and, our lives might still be deeply meaningful. Chalmers views these claims as at least weakly connected: The former claim helps forestall a concern that if objects in the simulation are not genuine (and so not knowable), then life in the (...) simulation is illusory and therefore, not as valuable as a non-simulated life. Taking up these questions, I argue that in general, the value of social knowledge for a meaningful life dramatically swamps the value of non-social knowledge for a meaningful life. Along the way, I propose a non-additive model of the meaningfulness of life, according to which the overall effect of some potential contributor of value to a life depends in part on what is already in a life. One upshot is that the vindication of non-social knowledge, absent a correlative vindication of social knowledge, contributes either not at all or scarcely at all to the claim that our lives in the simulation might be deeply meaningful. This is so even though the vindication of non-social knowledge does forestall the concern that in the simulation, our lives might be wholly meaningless. (shrink)

Bài viết này giới thiệu năm yếu tố tiền đề đó với mục đích gia tăng nhận thức về quan hệ giữa người và máy trong bối cảnh công nghệ ngày càng thay đổi cuộc sống thường nhật. Năm tiền đề bao gồm: Tiền đề về cấu trúc xã hội, văn hóa, chính trị, và lịch sử; về tính tự chủ và sự tự do của con người; về nền tảng triết học và nhân văn của nhân loại; về hiện (...) tượng tiếp biến văn hóa sinh ra từ tương tác giữa người và máy; về tam giác tương tác giữa con người với máy tính, giữa con người với con người, và giữa con người với thiên nhiên. Trên cơ sở giải thích rõ về năm tiền đề này, bài viết đã đưa ra ba gợi ý cho Việt Nam về việc phát triển, ứng dụng, và quản lý trí tuệ nhân tạo. (shrink)

In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their effect on (...) the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models. (shrink)

The purpose of this paper is to show that understanding AI hallucination requires an interdisciplinary approach that combines insights from epistemology and cognitive science to address the nature of AI-generated knowledge, with a terminological worry that concepts we often use might carry unnecessary presuppositions. Along with terminological issues, it is demonstrated that AI systems, comparable to human cognition, are susceptible to errors in judgement and reasoning, and proposes that epistemological frameworks, such as reliabilism, can be similarly applied to enhance the (...) trustworthiness of AI outputs. This exploration seeks to deepen our understanding of the possibility of AI cognition and its implications for the broader philosophical questions of knowledge and intelligence. (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)

Edward Wilson wrote in Consilience that “Human history can be viewed through the lens of ecology as the accumulation of environmental prostheses” (1999 p 316), with technologies mediating our collective habitation of the Earth and its complex, interdependent ecosystems. Wilson emphasized the defining characteristic of complex systems, that they undergo transformations which are irreversible. His view is now standard, and his central point bears repeated emphasis, today: natural systems can be broken, species—including us—can disappear, ecosystems can fail, and technological prostheses (...) potentiate rather than ameliorate disaster when the fragilities that they introduce into our evolutionary co-dependencies with natural systems, and with each other, are under-appreciated. (shrink)

Inspired by recent progress in multi-agent Reinforcement Learning (RL), in this work we examine the collective intelligent behaviour of theoretical universal agents by introducing a weighted mixture operation. Given a weighted set of agents, their weighted mixture is a new agent whose expected total reward in any environment is the corresponding weighted average of the original agents' expected total rewards in that environment. Thus, if RL agent intelligence is quantified in terms of performance across environments, the weighted mixture's intelligence is (...) the weighted average of the original agents' intelligences. This operation enables various interesting new theorems that shed light on the geometry of RL agent intelligence, namely: results about symmetries, convex agent-sets, and local extrema. We also show that any RL agent intelligence measure based on average performance across environments, subject to certain weak technical conditions, is identical (up to a constant factor) to performance within a single environment dependent on said intelligence measure. (shrink)

This research examines how computer-generated art (CG-art) is reshaping the notion of artistic creativity in the current age of Artificial Intelligence (AI). In this context, this study proposes to refine the concept of CG-art by delimiting what an AI-generated artwork is. This new term has its roots in Cognitive Science, Aesthetics and Computer Science, emphasising their intersections. It involves the conjunction of three elements: (1) an autonomous AI-production of a new and surprising idea or artefact, (2) which passes an internal (...) evaluation mechanism embedded in the very same AI, and (3) is considered a candidate of appreciation by a human audience. This definition helps to analyse several aspects of CG-art by determining if they meet the requirements for being considered AI-generated artworks. To illustrate this, the Deep Dream algorithm, the painting Portrait of Edmond de Belamy, and the music albums I AM AI and PROTO are discussed. (shrink)

La scorciatoia - Come le macchine sono diventate intelligenti senza pensare in modo umano -/- Le nostre creature sono diverse da noi e talvolta più forti. Per poterci convivere dobbiamo imparare a conoscerle Vagliano curricula, concedono mutui, scelgono le notizie che leggiamo: le macchine intelligenti sono entrate nelle nostre vite, ma non sono come ce le aspettavamo. Fanno molte delle cose che volevamo, e anche qualcuna in più, ma non possiamo capirle o ragionare con loro, perché il loro comportamento è (...) in realtà guidato da relazioni statistiche ricavate da quantità sovrumane di dati. Eppure possono essere in certi casi più potenti di noi: ci osservano continuamente, e prendono decisioni al nostro posto. E allora come incorporarle nella nostra società senza rischi ed effetti collaterali? Questo libro - rigoroso, pungente, originale nell'approccio - ci spiega come siamo arrivati sin qui, e indica il percorso che ci aspetta prima di poterci fidare di questi nuovi agenti «alieni». La tecnologia non basta, occorre un dialogo tra scienze naturali e umane: è il passaggio cruciale per una convivenza sicura con questa nuova forma di intelligenza. (Il Mulino). (shrink)

There has been a push in recent years to provide better explanations for how AIs make their decisions. Most of this push has come from the ethical concerns that go hand in hand with AIs making decisions that affect humans. Outside of the strictly ethical concerns that have prompted the study of explainable AIs (XAIs), there has been research interest in the mere possibility of creating XAIs in various domains. In general, the more accurate we make our models the harder (...) they are to explain. Go playing AIs like AlphaGo and KataGo provide fantastic examples of this phenomenon. In this paper, I discuss a non-exhaustive list of the leading theories of explanation and what each of these theories would say about the explainability of AI-played moves of Go. Finally, I consider the possibility of ever explaining AI-played Go moves in a way that meets the four principles of XAI. I conclude, somewhat pessimistically, that Go is not as imminently explainable as other domains. As such, the probability of having an XAI for Go that meets the four principles is low. (shrink)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...) promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

Inteligența artificială este un domeniu transformator care a captat atenția oamenilor de știință, inginerilor, întreprinderilor și guvernelor din întreaga lume. Pe măsură ce avansăm mai departe în secolul 21, au apărut câteva tendințe proeminente în domeniul IA. Inteligența artificială și tehnologia de învățare automată sunt utilizate în majoritatea aplicațiilor esențiale ale anilor 2020. Propunerile de control al capabilității inteligenței artificiale, denumite și în mod mai restrictiv confinarea IA, urmăresc să sporească posibilitatea de a monitoriza și controla comportamentul sistemelor IA, inclusiv (...) inteligența generală artificială, propusă pentru a reduce pericolul pe care l-ar putea prezenta dacă sunt nealiniate. Cercetarea și dezvoltarea menite să valideze răspunsuri la toate aceste întrebări trebuie să includă filozofia pentru formalismele sale robuste. (shrink)

Can we build machines with which we can have interesting conversations? Observing the new optimism of AI regarding deep learning and new language models, we set ourselves an ambitious goal: We want to find out how far we can get in creating a digital replica of a philosopher. This project has two aims; one more technical, investigating of how the best model can be built. The other one, more philosophical, explores the limits and risks which are accompanied by the creation (...) of digital replicas. In cooperation with Daniel Dennett, we took his complete works to fine-tune a GPT-3. In this paper, we share our first results with some piloting models and discuss legal and ethical questions about creating and interacting with digital replicas. (shrink)

We present a game mechanic called pseudo-visibility for games inhabited by non-player characters (NPCs) driven by reinforcement learning (RL). NPCs are incentivized to pretend they cannot see pseudo-visible players: the training environment simulates an NPC to determine how the NPC would act if the pseudo-visible player were invisible, and penalizes the NPC for acting differently. NPCs are thereby trained to selectively ignore pseudo-visible players, except when they judge that the reaction penalty is an acceptable tradeoff (e.g., a guard might accept (...) the penalty in order to protect a treasure because losing the treasure would hurt even more). We describe an RL agent transformation which allows RL agents that would not otherwise do so to perform some limited self-reflection to learn the training environments in question. (shrink)

Since the beginning of the 21st century, technologies like neural networks, deep learning and “artificial intelligence” (AI) have gradually entered the artistic realm. We witness the development of systems that aim to assess, evaluate and appreciate artifacts according to artistic and aesthetic criteria or by observing people’s preferences. In addition to that, AI is now used to generate new synthetic artifacts. When a machine paints a Rembrandt, composes a Bach sonata, or completes a Beethoven symphony, we say that this is (...) neither original nor real art, but simply the complex imitation and reproduction of existing products of human culture. We face the old question concerning the nature of creativity: what kind of recombination of ideas, unusual analogies, and conceptual connections are considered the mark of originality? Can AI produce artworks? Could machines reach a point at which we consider them genuinely creative? We also need to investigate the challenges posed by AI-art to the notion of authoriality: Who is the author of an artificially generated artifact? An artificial system could be considered just an artist’s and a programmer’s tool. However, we are also fascinated by the idea of autonomous artificial creativity in the aesthetic domain, as a manifestation of highly intelligent behavior. This paper will try to define some key questions around what could it mean to consider a machine creative or even equipped with artistic intentionality. (shrink)