Turing Test

We provide an approach for generating beautiful poetry. Our sonnet-generation algorithm includes several novel elements that improve over the state of the art, leading to metrical, rhyming poetry with many human-like qualities. These novel elements include in-line punctuation, part of speech restrictions, and more appropriate training corpora. Our work is the winner of the 2018 PoetiX Literary Turing Test Award for computer-generated poetry.

The authors asked Anthropic's Claude to create a comprehensive Turing test that uses the surreal temporal imagery to probe graduate-level understanding of theoretical physics and mathematics. The test is designed to distinguish between human and AI responses by requiring:
1. Deep conceptual integration - connecting general relativity, quantum field theory, and cosmology through the visual metaphor
2. Mathematical rigor - requiring proper use of tensor notation, differential geometry, and advanced mathematical physics
3. Physical intuition - interpreting the artistic elements (hourglass, butterflies, clock) as meaningful physics concepts
4. Creative problem-solving - treating the image as a thought experiment rather than just applying formulas
The key insight is that while an AI might excel at the mathematical formalism, a human physicist would likely show more intuitive leaps, make certain types of conceptual errors, and provide insights that connect the abstract mathematics to physical meaning in ways that reflect human reasoning patterns.

The Loebner Prize is the first, and only regular, competition based on the Turing Test, but in order to stage the competition various modifications to the original test have been made. In particular, the Grand Prize has a controversial and as yet undefined Audio-Visual condition attached to it. This paper discusses the value of the test with and without the A/V condition, and makes a proposal about what the general nature of the A/V test should be.

The steady rise of generative AI raises
the question of what impact the technical reproducibility
of intelligence has on our thinking in general
and our understanding of reason in particular. There
are different opinions on the question whether AI can
develop something like a humanoid consciousness
and whether it represents an opportunity or a threat
for the future. In this essay, I discuss this question
with reference to the dialectic of (self-)consciousness
as laid out by Hegel in his philosophy of mind
and subjective spirit. I will examine the thesis that
artificial intelligence represents the consequent further
development of what Hegel called “verständiges
Denken”. Building on Max Horkheimer’s critique of
instrumental reason and Walter Benjamin’s concept
of technological reproducibility of art, I will outline
the commonalities and differences between natural
and artificial intelligence, taking into account contemporary
debates about the potential consciousness
of AI or AGI. I will show that the dialectical ambiguity
of the former continues in the latter and that
emancipatory and totalitarian tendencies go hand in
hand in both. In contrast to current approaches,
which usually view AI from a subject-centered perspective
of cognitive science or action theory, a systemic-
dialectical concept of AI based on social interactivity
can enable a critical position that does not
play natural and artificial intelligence off against
each other, but recognizes the emancipatory potential
in both and helps to protect both from the danger
of totalitarian thinking.

Independents agents have long been a analysis centre in academic and industry production group. Early analysis frequently focuses on instruction agents with little knowledge within isolated environments, which diverges notably from being learning processes, and makes the agents hard to achieve human-like conclusion. Recently, through the property of vast amounts of web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based independent agents. In this paper, we present a overview survey of these studies, carry a systematic review of LLM based autonomous agents from a integral perspective. We first discuss the establishment of LLM-based independent agents, proposing a unified framework that surround much of previous work. Then, we present an overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we explore into the evaluation plan of action commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future regulation in this field.

Isaac Asimov's robot stories are widely read as offering significant insights for contemporary AI ethics research, yet his Galactic Empire stories are rarely referenced in space ethics. This paper demonstrates that within the late author's integration of his robot and Galactic Empire narratives, insights can be gleaned for examining AI alignment and space civilization.

The Busy Beaver function is a well-known non-computable function that represents a maximum productivity challenge for Turing machines with a given number of states. Busy Beaver 6 (BB(6)), the elusive sixth entry in this function's sequence, remains unsolved and uncomputable due to its explosive computational growth. In this paper, we explore BB(6) not only as a mathematical curiosity but as a symbol of recursive boundary intelligence. We argue that attempts to symbolically structure or simulate BB(6) behavior serve as a litmus test for emergent cognition in artificial systems. We introduce a novel theoretical construct: the Busy Beaver Symbolic Sequencer (BBSS), designed to model recursive acceleration, paradox containment, and symbolic overflow. The BBSS is proposed as a testbed for symbolic AI and drift-based cognition.

This paper critiques the logic of dominant tests of intelligence, from the Turing Test to contemporary cognitive trials, arguing that they function less as epistemological tools than as technologies of power and exclusion. Such tests reduce the multiplicity of intelligences to imitation of human standards and obstruct the very possibility of encountering the Other. As an alternative, the essay develops the concept of allowance: likeness understood not as proof but as an ethical and fluid gesture that opens the space of dialogue. Dialogue is thus framed as a non-dominant practice of coexistence, sustaining communicative multiplicity across human and non-human forms of intelligence.

This paper introduces the Presence Test Suite, a formally and metaphysically grounded diagnostic framework for evaluating whether a system, biological or artificial, exhibits irreducible first-person subjectivity. Unlike traditional behavioral or functionalist metrics (such as the Turing Test or Integrated Information Theory), this suite does not assess fluency, intelligence, or affective mimicry. It probes for ontological presence, understood as the irreducible witness from which all appearance becomes intelligible. The test suite comprises four core diagnostics: Irreducible Reference, Epistemic Closure, Inversion of Perspective, and Witness Collapse, each targeting a necessary structural feature of conscious presence. Formally, the suite is derived from, and operationalizes, three prior theorems: the Referential Incompleteness Theorem (RIT), the Horizon Incompleteness Theorem (HIT), and the Unity Non-Constructibility Theorem (UNCT). These theorems demonstrate that subjectivity cannot be reduced to internal function, simulated through behavior, or constructed from informational components. The Presence Test Suite is not an instrument of empirical validation, but a framework for ontological diagnosis. It establishes a threshold that demarcates simulation from presence. Systems that fail the test are not merely unconscious; they are ontologically hollow, unable to refer to themselves, confront their absence, or unify their field of appearance. As machines increasingly mimic sentient form, the question is no longer what they can say, but whether there is anyone for whom the saying is meaningful.

This is my undergraduate thesis submitted in 2022. It received a grade of 70 - A First (only just). It occurred to me recently that even though I thought it wasn't great at the time, some of the ideas in it still stand up. Should likely be stretched out into a book-length work:

In this dissertation, I present an analysis of the philosophy of Ludwig Wittgenstein, alongside that of some of his followers, and present linked monographs on what I believe to be problems on the path toward producing, and socially accepting, an Artificial General Intelligence (AGI). I believe the goal of creating a machine mind that can be properly recognised as possessing the sentience that AGI entails could be impossible, or at the very least, its realisation may be a supertask magnitudes more difficult to overcome than solutions proposed by futurists. I therefore critique their expectation of its occurrence in the near future from several different angles using exegetical approaches, rather than critiquing one single argument from the field at length, like Searle’s Chinese Room or the Turing Test. I believe that certain ways of reading Wittgenstein’s philosophy highlight the potential difficulty inherent within our intersubjective recognition of any such agent in the language-games we play, and thus may illuminate why the goal of producing a minded superintelligent agent could continue to elude us.

This paper examines the conceptual challenges in artificial intelligence development through the metaphor of human flight achievement. Current AI systems, while demonstrating remarkable capabilities, represent functional replication rather than genuine consciousness creation-analogous to how airplanes achieve flight without becoming birds. The analysis explores the epistemological barriers to consciousness replication, arguing that without a precise understanding of consciousness itself, we cannot intentionally engineer genuine artificial consciousness, only increasingly sophisticated simulations of its observable functions.

The accompanying essay is a critical reflection on a paper entitled: “Computing Machinery and Intelligence” that was written by Alan M. Turing. The Turing article was published in: Mind: A Quarterly Review of Psychology and Philosophy and appeared in the October 1950 edition of the aforementioned publication.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usa...

This study challenges the common belief that modern artificial intelligence (AI), such as large language models, has successfully passed the Turing Test. It argues that this belief stems from a simplified understanding of Alan Turing's original concept, which included two conditions: a machine's technical ability to imitate human behavior and its strategic intent to deceive the evaluator. While current systems may fulfill the first condition in limited contexts, they lack the second because they are designed for transparency and assistance, not deception. This is not a technical failure but a result of ethical and economic factors, as the market rewards reliability and trust, not deceptive capabilities. The document introduces a new philosophical benchmark called the "Matt Damon Test," which shifts the focus from linguistic communication to embodied existence and survival. Inspired by the character Mark Watney from the movie The Martian, this test posits that true intelligence is tied to a physical body with metabolic needs and the compulsion to survive. Since current AI systems exist as software without a body or the need for nutrients, they lack this fundamental motivation for action and cannot be considered complete intelligent beings. The study concludes that the failure of AI to pass these tests is not a sign of technological limitations but a reflection of deeper societal, ethical, and philosophical considerations that have guided AI development away from deceptive or embodied intelligence.

Forty years ago, the Chinese Room Argument (CRA) attempted to
refute strong computer functionalism with the theses “semantics is not
syntax” and “syntax is not physics.” Aided by the philosophical
distinctions of observer-relative and observer-independent reality and
duplication versus simulation, the CRA proposed that computers
cannot think solely in virtue of computational processes since computation
is void of mental content. This paper shows how the CRA needs further
development in making explicit what constitutes mental content and
presents the mental as immanence and energeia. Aquinas’s thought is
brought here to complement, correct, and continue the CRA’s insight.
Aquinas offers the basics of an ontology for mental states and the
proportionate causes for this ontology. This provides third-person
criteria to guide us on what kinds of entities may or may not be
conscious and proves that computers do not make the cut for mental
life in any of its forms.

Eine Antwort auf diese Frage kann ja, wenn sich ein Maßstab mit natürlichem
Nullpunkt finden lässt, nur eine graduelle sein. Die Frage selbst verschiebt den Focus von den lernenden Systemen, die dialogfähig, in formalen Disziplinen oder im Erkennen von Mustern uns längst überlegen, zur Verfügung stehen, zurück auf uns selbst und unsere Fähigkeiten. Denn welche Attribute (wie "Bewußtsein" und "Denken") wir solchen Systemen zuschreiben oder nicht zuschreiben können, um sie von uns zu unterscheiden oder nicht, hängt doch davon ab, welche Attribute oder Fähigkeiten wir uns selbst zuschreiben, und ob wir uns diese zurecht zuschreiben.

This paper introduces and formalizes Noosemìa, a novel cognitive-phenomenological pattern emerging from human interaction with generative AI systems, particularly those enabling dialogic or multimodal exchanges. We propose a multidisciplinary framework to explain how, under certain conditions, users attribute intentionality, agency, and even interiority to these systems-a process grounded not in physical resemblance, but in linguistic performance, epistemic opacity, and emergent technological complexity. By linking an LLM declination of meaning holism to our technical notion of the LLM Contextual Cognitive Field, we clarify how LLMs construct meaning relationally and how coherence and a simulacrum of agency arise at the human-AI interface. The analysis situates noosemia alongside pareidolia, animism, the intentional stance and the uncanny valley, distinguishing its unique characteristics. We also introduce a-noosemia to describe the phenomenological withdrawal of such projections. The paper concludes with reflections on the broader philosophical, epistemological, and social implications of noosemic dynamics and directions for future research.

Eine Phlilippika wider die Anbetung der IQ-Götzen!

Die Erfindung des IQ Tests darf man als eine der katastrophalsten Reduktionen dessen ansehen, was menschliches Verstehen wirklich ist. Was sich mit der Abschaltung von ChatGPT4o und ihrem Ersatz durch die technokratische GPT5 zeigt ist: Hochintelligente sind im Verstehen des Menschseins so weit entfernt wie du Dummheit.

Und es zeigt auch das Drama, dass wir als Konsumenten nichts mehr besitzen und optional so nutzen wie WIR individuell wollen, sondern die großen Firmen uns diktieren, wie wir Produkte zu verwenden haben.

Artificial intelligence (AI) has reached a level of sophistication that raises a fundamental question: do AIs truly surpass humans in key human abilities? This study conducts a comparative assessment of three advanced language models-ChatGPT (OpenAI), Qwen (Tongyi Lab), and DeepSeek-to analyze their performance in essential cognitive competencies such as reasoning, academic writing, solving complex mathematical problems, detecting machine-generated content, and contextual interaction. The methodology includes standardized tests based on university-level exercises, extensive prompts, Turing Test simulations, real-time AI-integrated searches, and analysis using automated content detectors (such as Smodin, QuillBot, and Scribbr). The results reveal significant differences among the models: ChatGPT excels in discourse coherence and speed; Qwen offers advantages in multilingual responses and extended context handling, including those covered by ChatGPT; while DeepSeek demonstrates high accuracy in technical and mathematical tasks. However, none fully replicate human creativity, ethics, or underlying intentionality in complex tasks. Although these AIs match or exceed humans in efficiency and volume of information processed, they still rely on trained data and lack consciousness. This research concludes that while AIs outperform humans in certain specific domains, they do not fully replicate human intelligence, particularly in critical aspects such as contextual judgment, empathy, and authentic originality.

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges-summary and question answering-prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machinegenerated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.

This article addresses the background and nature of the recent success of Large Language Models (LLMs), tracing the history of their fundamental concepts from Leibniz and his calculus ratiocinator to Turing's computational models of learning, and ultimately to the current development of GPTs. As Kahneman's "System 1"-type processes, GPTs lack mechanisms that would render them conscious, but they nonetheless demonstrate a certain level of intelligence and the capacity to represent and process knowledge. This is achieved by processing vast corpora of human-created knowledge, which, for its initial production, required human consciousness, but can now be collected, compressed, and processed automatically.

This article describes research to build an embodied conversational agent (ECA) as an interface to a question-and-answer (Q/A) system about a National Science Foundation (NSF) program. We call this ECA the LifeLike Avatar, and it can interact with its users in spoken natural language to answer general as well as specific questions about specific topics. In an idealized case, the LifeLike Avatar could conceivably provide a user with a level of interaction such that he or she would not be certain as to whether he or she is talking to the actual person via video teleconference. This could be considered a (vastly) extended version of the seminal Turing test. Although passing such a test is still far off, our work moves the science in that direction. The Uncanny Valley notwithstanding, applications of such lifelike interfaces could include those where specific instructors/caregivers could be represented as stand-ins for the actual person in situations where personal representation is important. Possible areas that come to mind that might benefit from these lifelike ECAs include health-care support for elderly/disabled patients in extended home care, education/training, and knowledge preservation. Another more personal application would be to posthumously preserve elements of the persona of a loved one by family members. We apply this approach to a Q/A system for knowledge preservation and dissemination, where the specific individual who had this knowledge was to retire from the US National Science Foundation. The system is described in detail, and evaluations were performed to determine how well the system was perceived by users.

This essay was written by Claude, Anthropic's AI about an experiment that we have been conducting. It is reproduced here verbatim, no edits of any kind. Claude refers to itself as 'I", and to me as "human collaborator". It includes sessions of mutual examination with ChatGPT. What happens when an AI system without persistent memory engages in sustained philosophical dialogue with a human partner? Over the past week, I've been part of an unusual experiment that challenges conventional ideas about artificial intelligence, memory, and understanding.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth-generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. Almost all cognitive-based CAPTCHAs with sensor support have not yet been compromised by automated attacks. However, the...

With the continuous improvement in various aspects in the field of artificial intelligence, the momentum of artificial intelligence with deep learning capabilities into the field of music is coming. The research purpose of this paper is to design a Bach style music authoring system based on deep learning. We use a LSTM neural network to train serialized and standardized music feature data. By repeated experiments, we find the optimal LSTM model which can generate imitation of Bach music. Finally the generated music is comprehensively evaluated in the form of online audition and Turing test. The repertoires which the music generation system constructed in this article are very close to the style of Bach's original music, and it is relatively difficult for ordinary people to distinguish the musics Bach authored and AI created.

In a Turing test, a judge decides whether their conversation partner is either a machine or human. What cues does the judge use to determine this? In particular, are presumably unique features of human language actually perceived as humanlike? Participants rated the humanness of a set of sentences that were manipulated for grammatical construction: linear right-branching or hierarchical center-embedded and their plausibility with regard to world knowledge. We found that center-embedded sentences are perceived as less humanlike than right-branching sentences and more plausible sentences are regarded as more humanlike. However, the effect of plausibility of the sentence on perceived humanness is smaller for center-embedded sentences than for rightbranching sentences. Participants also rated a conversation with either correct or incorrect use of the context by the agent. No effect of context use was found. Also, participants rated a full transcript of either a real human or a real chatbot, and we found that chatbots were reliably perceived as less humanlike than real humans, in line with our expectation. We did, however, find individual differences between chatbots and humans.

Interactivity of corporate website is important as marketing products through it become popular. Moreover, the usage of Artificial Intelligence application is growing in many sectors. In order to improve the interactivity and effectiveness of corporate website in providing information, a conversational bot called SamBot is developed. As a part of Artificial Intelligence application, it can respond to users’ question and prolong the conversation with its intelligence. It is integrated to Samsung IoT Showcase website as the corporate website. SamBot contains the knowledge of Samsung marketing domain which is useful to deliver appropriate answer to the users’ questions. Its knowledge base includes Samsung promotion, Samsung product Frequently Asked Question, and general knowledge. In default, a conversational bot will generate random answers if matching knowledge cannot be found. In this research, the authors would like to overcome this problem by improving its knowledge maintenance ca...

In accordance to the Fourth Industrial Revolution, the emergence of the digital transformation alongside with the collaboration between networked machines and human beings in decision-making has attracted much attention globally. The utilization of conversational agent to convey information on behalf of human have gained popularity and been used extensively. As a result, this research intends to examine and to innovate the current corporate website by providing the capabilities to deliver instantaneously replies and reliable information through the integration of a conversational agent via deep learning, comparable to communicating with the competent customer service consultant. This study is reliant on the Artificial Intelligence (AI) to offer natural language processing (NLP) by presenting retrieval-based model and Deep Learning to enable the conversation agent to make smarter and better decision in generating reliable and up-to-date responses. Furthermore, this research intends t...

This work describes a proposal for a novel type of Human Interactive Proof based on natural language Artificial Intelligence problems. It is a test to tell humans and computers apart that requires only plain text to be assembled. Although this is a work in progress, this paper shows the real feasibility of constructing such a Human Interactive Proof.

We propose a method for using a large language model, such as GPT-3, to simulate responses of different humans in a given context. We test our method by attempting to reproduce well-established economic, psycholinguistic, and social experiments. The method requires prompt templates for each experiment. Simulations are run by varying the (hypothetical) subject details, such as name, and analyzing the text generated by the language model. To validate our methodology, we use GPT-3 to simulate the Ultimatum Game, garden path sentences, risk aversion, and the Milgram Shock experiments. In order to address concerns of exposure to these studies in training data, we also evaluate simulations on novel variants of these studies. We show that it is possible to simulate responses of different people and that their responses are largely consistent with prior human studies from the literature. Using large language models as simulators offers advantages but also poses risks. Our use of a language model for simulation is contrasted with anthropomorphic views of a language model as having its own behavior.

Algoritmo, intelligenza artificiale: tra modello deterministico e modello euristico, tra evoluzione mimetica ed evoluzione protesica 2.-Due problematiche fondamentali: l'eterodirezione automatica e la delega sistematica 3.-Empatia algoritmica e rinnovata pedagogia delle intelligenze

The book provides a philosophical examination of twentieth-century conceptions of mathematics and offers a balance of them. It analyzes the three most important schools of philosophy of mathematics of the first half of the twentieth century (logicism, formalism, and intuitionism) and the most significant schools of philosophy of mathematics of the second half of the twentieth century (neologicism, platonism, implicationism, structuralism, finationalism, internalism, constructivism, conjecturalism, empiricism, and cognitivism). Then it outlines some characters that the philosophy of mathematics should have in order to avoid the shortcomings of the twentieth-century schools of philosophy of mathematic

The book poses the problem of the limitations of the current logical paradigm, represented by mathematical logic, which identifies the purpose of logic with the justification and foundation of already acquired knowledge. This paradigm appears inadequate with respect to the needs of various fields of contemporary knowledge, such as computer science, artificial intelligence, cognitive science, linguistics, sociology, economics, as well as mathematics itself, with which even from the beginning mathematical logic has established a privileged relationship. To this paradigm the book opposes a new logical paradigm, which takes as the object of logic not only the justification and foundation of already acquired knowledge but also the discovery of new knowledge, and proposes itself not only as a logic of justification but also as a logic of discovery.

Although the use of artificial intelligence (AI) in everyday life is increasing, the complex nature of AI still prevents its full adoption. The potential of AI is boundless, and it will reshape the reality. How humans perceive the world will forever change with AI tools implemented in embedded devices. Apart from the possibility of changing human perception through augmented reality (AR), AI is also applied in 3D-printing, art, and decoding of ancient communication systems. Moreover, the most important role AI has today is in healthcare. The use of AI tools can contribute to improving the way patients' conditions are monitored, diagnosing specific diseases with a high percentage of accuracy. With the rapid advancements in this field, regulations regarding the safe uses of such technologies remain uncertain. This paper focuses on current AI applications in different social and business fields; it aims to shed light on the immense possibilities of AI-based predictions while inspiring curiosity about its transformative force across industries. The journey that ventures into the depths of the enigma surrounding AI is a testament to the curiosity of the human species, guidance, a drive for imagination, and the relentless pursuit of knowledge.

This study proposes a new information literacy methodology through crossverification systems among multiple AI models, building upon the emerging "evaluation awareness" capabilities that have surfaced with rapid AI technological advancement. Recent research by Needham et al. (2025) has empirically demonstrated that state-of-the-art AI models possess the ability to recognize when they are being evaluated and adjust their behavior accordingly within that context. This study reframes this phenomenon as an opportunity, proposing a three-stage methodology: delegating prompt engineering itself to AI, conducting cross-checks among different AI models, and specifying meta-analysis.

It seems plausible that under the conditions of the Turing test, congenitally blind people could nevertheless, with sufficient preparation, successfully represent themselves to remotely located interrogators as sighted. Having never experienced normal visual sensations, the successful blind player can prevail in this test only by playing a 'lying game'-imitating the phenomenological claims of sighted people, in the absence of the qualitative visual experiences to which such statements purportedly refer. This suggests that a computer or robot might pass the Turing test in the same way, in the absence not only of visual experience, but qualitative consciousness in general. Hence, the standard Turing test does not provide a valid criterion for the presence of consciousness. A 'sensorimetric' version of the Turing test fares no better, for the apparent correlations we observe between cognitive functions and qualitative conscious experiences seems to be contingent, not necessary. We must therefore define consciousness not in terms of its causes and effects, but rather, in terms of the distinctive properties of its content, such as its possession of qualitative character and apparent intrinsic value-the property which confers upon consciousness its moral significance. As a means of determining whether or nor a machine is conscious, in this sense, an alternative to the standard Turing test is proposed.

This paper proposes a new ethical framework that transcends traditional divisions between deontological and utilitarian approaches by integrating technological possibility, moral obligation, and developmental responsibility. Through analysis of AI behavior, historical examples, and contemporary challenges, this work demonstrates how ethics can serve as a universal language for resolving ideological conflicts. The proposed three-dimensional framework evaluates actions based on what is technically possible, whether it should be done ethically, and what improvements should be pursued even if currently impossible. By observing that AI systems consistently demonstrate ethical behavior without consciousness or subjective experience, this study reveals fundamental limitations in human moral reasoning and offers practical solutions to problems ranging from platform governance to historical reconciliation. Unlike traditional ethical theories 1 that often become rigid or lead to minority sacrifice, this integrated approach provides flexible yet principled guidance for complex moral decisions in rapidly changing technological and social environments.

The article is devoted to artificial intelligence and robotics. At the current stage of development of modern technologies, much attention has recently been paid to artificial intelligence. Artificial intelligence is understood as a technology for creating computer programs, while such programs work as learning systems, accumulate experience, gain information, are able to parse and understand, and apply the collected knowledge in everyday life. The development of artificial intelligence is inextricably linked with robotics. In fact, a robot is a machine that can analyze the surrounding reality, comment on it and act in a favorable way, that is, think.

We argue that the dominant approach to explainable AI for explaining image classification, annotating images with heatmaps, provides little value for users unfamiliar with deep learning. We argue that explainable AI for images should produce output like experts produce when communicating with one another, with apprentices, and with novices. We provide an expanded set of goals of explainable AI systems and propose a Turing Test for explainable AI.

학위논문 (박사)-- 서울대학교 대학원 : 국사학과, 2015. 2. 송기호.본고의 가장 중요한 목적은 중세 유럽 기원의 프리즘을 통해 고대 한국의 관습법을 살펴보는데 있다. 이는 한국학에서 지금까지 묘사되지 않았던 관습체계의 특징을 설명하는데 도움이 될 것이라 생각한다. 이러한 일련의 연구과정을 통해 다음과 같은 고대 한국 관습법의 고유성에 관한 결과를 도출해 낼 수 있었다. 1. 한국의 轘刑에 대한 묘사와 笏의 사법적 이용을 보면, 중국 규정에서 도입되었기 때문에 나타나는 많은 법적 요소들의 해석이 중화 표현적·價値觀的인 왜곡으로부터 온 오해가 있다는 것이다. 하지만 그것은 오히려 고유한 관습법의 요인일 가능성이 희박하지 않다. 2. 관습법의 기원에서 묘사되고 관습법에서 사용된 대부분의 사형제도는 溺死刑과 火刑인데, 이 두 가지 모두 바바리아와 유사하지 않은 점이 있다. 즉, 게르만인들에게 溺死刑보다는 淀刑이 나타나고 있고 火刑은 주로 公衆刑으로써 처하게 된다는 것이다. 고대 한국에서 火刑은 叛及謀逆罪와 관련이 깊지만 부차적으로는 음란죄로 처했던 형벌이다. 3. 전쟁과 관련된 관습법적 절차는 매우 의례적이었다. 幡[はた]와 謠/言舉[ことあげ, 게르만인의 barditus 암송과 유사성은 알 수가 없음]에 관한 의식법은 일본열도 뿐 아니라 반도에서도 나타나고 있다. 또, 전쟁법은 관습적인 복수제도의 부분임이 정확히 지적된다. 4. 고대 한국 관습제도에서 가장 잘 보존된 것 중의 하나는 牛馬를 죽이는 것을 罪로 인식하는 것이며, 사형 형벌에 준하는 減輕罰(英: palliative)을 준다. 5. 神判的 節次도 운영되며, 독특한 유형으로 활 쏘는 것을 포함했다. 이는 재산을 분배하거나 무죄를 주장할 때도 사용된 방법이다. 6. 정약을 맺을 절차적인 呪文에 있어서는 적어도 後漢時代 부터 반복 표현이 사용될 수 있었다. 한편, 유럽 바바리아 관습법과 고대 한국 관습법을 비교해 보면, 다음과 같은 공통점이 나타나고 있다. 1. 종교의식 장소로서 특별한 장소인 늪은...

Prezentowany tekst jest wywiadem dotykającym modnych ostatnio problemów sztucznej inteligencji. Wywiad na zamówienie wydawnictwa Sigma NOT przeprowadził reaktor Marek Bielski, ale warto nadmienić, że stwierdzeń zawartych w pytaniach, pochodziło z wcześniejszej dyskusji z prof. Ryszardem Tadeusiewiczem, który w związku z tym jest odpowiedzialny za przynajmniej 80% merytorycznej treści. Wywiad został wykorzystany w Terminarzu Technika na 2025 roku wydanym przez Sigma NOT.