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)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (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 view of nature we adopt in the natural attitude is determined by common sense, without which we could not survive. Classical physics is modelled on this common-sense view of nature, and uses mathematics to formalise our natural understanding of the causes and effects we observe in time and space when we select subsystems of nature for modelling. But in modern physics, we do not go beyond the realm of common sense by augmenting our knowledge of what is going on (...) in nature. Rather, we have measurements that we do not understand, so we know nothing about the ontology of what we measure. We help ourselves by using entities from mathematics, which we fully understand ontologically. But we have no ontology of the reality of modern physics; we have only what we can assert mathematically. In this paper, we describe the ontology of classical and modern physics against this background and show how it relates to the ontology of common sense and of mathematics. (shrink)

Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has emerged (...) a new field called ‘explainable AI’ (XAI). When we examine the XAI literature, however, it becomes apparent that its protagonists have redefined the term ‘explanation’ to mean something else, namely: ‘interpretation’. Interpretations are indeed sometimes possible, but we show that they give at best only a subjective understanding of how a model works. We propose an alternative to XAI, namely certified AI (CAI), and describe how an AI can be specified, realized, and tested in order to become certified. The resulting approach combines ontologies and formal logic with statistical learning to obtain reliable AI systems which can be safely used in technical applications. (shrink)

Since the noun phrase `artificial intelligence' (AI) was coined, it has been debated whether humans are able to create intelligence using technology. We shed new light on this question from the point of view of themodynamics and mathematics. First, we define what it is to be an agent (device) that could be the bearer of AI. Then we show that the mainstream definitions of `intelligence' proposed by Hutter and others and still accepted by the AI community are too weak even (...) to capture what is involved when we ascribe intelligence to an insect. We then summarise the highly useful definition of basic (arthropod) intelligence proposed by Rodney Brooks, and we identify the properties that an AI agent would need to possess in order to be the bearer of intelligence by this definition. Finally, we show that, from the perspective of the disciplines needed to create such an agent, namely mathematics and physics, these properties are realisable by neither implicit nor explicit mathematical design nor by setting up an environment in which an AI could evolve spontaneously. (shrink)

Barry Smith recently discussed the diagraphs of book eight of Jacob Lorhard’s Ogdoas scholastica under the heading “birth of ontology” (Smith, 2022; this issue). Here, I highlight the commonalities between the original usage of diagraphs in the tradition of Ramus for didactic purposes and the the usage of their present-day successors–modern ontologies–for computational purposes. The modern ideas of ontology and of the universal computer were born just two generations apart in the breakthrough century of instrumental reason.

The goal of creating Artificial General Intelligence (AGI) – or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence – has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that this very ability (...) should be accepted also as a necessary condition of AGI, and we provide a description of the nature of human dialogue in particular and of human language in general against this background. We then argue that it is for mathematical reasons impossible to program a machine in such a way that it could master human dialogue behaviour in its full generality. This is (1) because there are no traditional explicitly designed mathematical models that could be used as a starting point for creating such programs; and (2) because even the sorts of automated models generated by using machine learning, which have been used successfully in areas such as machine translation, cannot be extended to cope with human dialogue. If this is so, then we can conclude that a Turing machine also cannot possess AGI, because it fails to fulfil a necessary condition thereof. At the same time, however, we acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called “narrow” AI can still be of considerable utility. (shrink)

Health Level 7 (HL7) is an international standards development organisation in the domain of healthcare information technology. Initially the mission of HL7 was to enable data exchange via the creation of syntactic standards which supported point-to-point messaging. Currently HL7 sees its mission as one of creating standards for semantic interoperability in healthcare IT on the basis of its flagship “version 3” (v3). Unfortunately, v3 has been plagued by quality and consistency issues, and it has not been able to keep pace (...) with recent developments either in semantics and ontology or in computer science and engineering. HL7’s response has been to develop its “Services-Aware Interoperability Framework” (SAIF), which is intended to provide a foundation for work on all aspects of standardization in HL7 henceforth. We here summarise the major design principles that must be satisfied by a semantic interoperability framework – principles relating both to static semantics and to computational behaviour. We then assess the SAIF in light of these principles. We conclude that the SAIF is not in a position to support the needed reform of the HL7 v3 family of standards. (shrink)

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

Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, Søgaard has argued in his "Understanding models understanding language" (2022) for a thesis of this sort. His idea is that (1) where there is semantics there is also understanding and (2) machines are not only capable of what he calls `inferential semantics', but even that they can (with the help of inputs from sensors) `learn' referential semantics. We show that he goes wrong because he pays insufficient (...) attention to the difference between language as used by humans and the sequences of inert symbols which arise when language is stored on hard drives or in books in libraries. (shrink)

Sam Harris is a contemporary illustration of the difficulties standing in the way of coherent interdisciplinary thinking in an age where science and the humanities have drifted so far apart. We are here with Harris’s views on AI, and specifically with his view according to which, with the advance of AI, there will evolve a machine superintelligence with powers that far exceed those of the human mind. This he sees as something that is not merely possible, but rather a matter (...) of inevitability. If, however, we look carefully at what intelligence is, and at how computers really work on the basis of mathematical models, then we can see that it is forever impossible to emulate inside a computer even the intelligence of crows or rabbits, let alone that of human beings. (shrink)

Das in diesem Aufsatz vorgebrachte Argumentationsmuster hat folgende Schritte: 1. Der menschliche Geist ist vom Körper nicht trennbar, sie bilden ein Kontinuum. 2. Unser Bewusstsein und alle darauf aufbauenden geistigen Phänomene sind die Emanation eines materiellen Prozesses, den ein komplexes System verursacht. 3. Komplexe Systeme lassen sich mathematisch nicht modellieren und nicht kausal verstehen. 4. Computer sind Turing-Maschinen. Sie können nur mathematische Modelle berechnen. Es wird niemals Hyper-Turing Maschinen geben, und wenn es sie gäbe, könnten sie auch nur mathematische Modelle (...) berechnen. 5. Es ist nicht möglich, den Körper als Substrat des Geistes durch einen Computer zu ersetzen. Die digitale Unsterblichkeit ist demzufolge ein Ding der Unmöglichkeit. (shrink)

In his “Bridging mainstream and formal ontology”, Augusto (2021) gives an excellent analysis of Dietrich von Freiberg’s idea of using causality as a partitioning principle for upper ontologies. For this Dietrich’s notion of extrinsic principles is crucial. The question whether causation can and indeed should be used as a partitioning principle for ontologies is discussed using mathematics and physics as examples.

The term ‘intelligence’ as used in this paper refers to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors (...) and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as ‘items of knowledge’. (shrink)

Adults who stutter (AWS) have demonstrated atypical coordination of motor and sensory regions during speech production. Yet little is known of the speech-motor network in AWS in the brief time window preceding audible speech onset. The purpose of the current study was to characterize neural oscillations in the speech-motor network during preparation for and execution of overt speech production in AWS using magnetoencephalography (MEG). Twelve AWS and twelve age-matched controls were presented with 220 words, each word embedded in a carrier (...) phrase. Controls were presented with the same word list as their matched AWS participant. Neural oscillatory activity was localized using minimum-variance beamforming during two time periods of interest: speech preparation (prior to speech onset) and speech execution (following speech onset). Compared to controls, AWS showed stronger beta (15-25Hz) suppression in the speech preparation stage, followed by stronger beta synchronization in the bilateral mouth motor cortex. AWS also recruited the right mouth motor cortex significantly earlier in the speech preparation stage compared to controls. Exaggerated motor preparation is discussed in the context of reduced coordination in the speech-motor network of AWS. It is further proposed that exaggerated beta synchronization may reflect a more strongly inhibited motor system that requires a stronger beta suppression to disengage prior to speech initiation. These novel findings highlight critical differences in the speech-motor network of AWS that occur prior to speech onset and emphasize the need to investigate further the speech-motor assembly in the stuttering population. (shrink)

This article investigates reasons to participate in non-deterministic elections, where the outcomes incorporate elements of chance beyond mere tie-breaking. The background context situates this inquiry within democratic theory, specifically non-deterministic voting systems, which promise to re-evaluate fairness and power distribution among voting blocs. This study aims to explore the normative implications of such electoral systems and their impact on our moral duty to vote. We analyze instrumental reasons for voting, including prudential and act-consequentialist arguments, alongside non-instrumental reasons, assessing their validity (...) in the context of non-deterministic systems. The results indicate that non-deterministic elections could strengthen the case for voting based on prudential and act-consequentialist grounds due to their proportional nature and the increased influence of each vote. We conclude that, while non-deterministic elections strengthen our duty to vote overall, they do not strengthen it for all the arguments in the literature. This paper contributes to the discourse on electoral systems by critically evaluating the moral obligation to vote in non-deterministic elections. (shrink)

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)

An interview by Alex Thomson of UKColumn on Landgrebe and Smith's book: Why Machines Will Never Rule the World. The subtitle of the book is Artificial Intelligence Without Fear, and the interview begins with the question of the supposedly imminent takeover of one profession or the other by artificial intelligence. Is there truly reason to be afraid that you will lose your job? The interview itself is titled 'Where this is no will there is no way', drawing on one thesis (...) of the book to the effect that there will never be anything like the human will on the part of a machine. This is for mathematical reasons. AI is always and in every case a matter of algorithms; thus of applied mathematics. And algorithms are not the sorts of things that can want or desire or intend to do something. It is the human will of those who work at OpenAI or google or microsoft which have given rise to the new shiny objects of AI (ChatGPT and other Large Language Models). And it is the human will of the users of these shiny objects which gives them the resources and power to work their apparent miracles. (shrink)

Here we present what we believe is a novel account of what languages are, along with an axiomatically rich representation of languages and language-related data that is based on this account. We propose an account of languages as aggregates of dispositions distributed across aggregates of persons, and in doing so we address linguistic competences and the processes that realize them. This paves the way for representing additional types of language-related entities. Like demographic data of other sorts, data about languages may (...) be of use to researchers in a number of areas, including biomedical research. Data on the languages used in clinical encounters are typically included in medical records, and capture an important factor in patient-provider interactions. Like many types of patient and demographic data, data on a person’s preferred and primary languages are organized in different ways by different systems. This can be a barrier to data integration. We believe that a robust framework for representing language in general and preferred and primary language in particular – which has been lacking in ontologies thus far – can promote more successful integration of language-related data from disparate data sources. (shrink)

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" (...) preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. (shrink)

Research and engineering in the quantum domain involve long chains of activity involving theory development, hypothesis formation, experimentation, device prototyping, device testing, and many more. At each stage multiple paths become possible, and of the paths pursued, the majority will lead nowhere. Our quantum metascience approach provides a strategy which enables all stakeholders to gain an overview of those developments along these tracks, that are relevant to their specific concerns. It provides a controlled vocabulary, built out of terms that are (...) designed to be maximally comprehensible to all groups of stakeholders and across all the sub-fields of the quantum domain. (shrink)

Książka składa się z przedmowy (_foreword_), wstępu, 13 rozdziałów (podzielonych na trzy części), załącznika zawierającego krótki opis teorii turbulencji, słownika (_glossary_), bibliografii i indeksu. W przedsłowiu autorzy szkicują przyjętą definicję AI jako „zastosowanie matematyki do modelowania funkcji ludzkiego mózgu” (s. ix), przy czym lokują pytanie o ograniczenia tych modeli w kontekście koncepcji Osobliwości (Singularity). Osobliwość Techniczna jest jedną z kluczowych idei transhumanizmu, którego autorzy recenzowanej książki są przeciwnikami. Wedle tej idei rozwój techniki ma zmierzać do punktu przełomu, w którym postęp (...) przyspieszy tak bardzo, że nie będziemy w stanie zapanować nad powstającymi artefaktami. Ten punkt jest właśnie nazywany Osobliwością. W kontekście tej książki oznacza moment powstania AI przewyższającej nas intelektualnie. (shrink)

Whether it was John Searle’s Chinese Room argument (Searle, 1980) or Roger Penrose’s argument of the non-computable nature of a mathematician’s insight – an argument that was based on Gödel’s Incompleteness theorem (Penrose, 1989), we have always had skeptics that questioned the possibility of realizing strong Artificial Intelligence (AI), or what has become known by Artificial General Intelligence (AGI). But this new book by Landgrebe and Smith (henceforth, L&S) is perhaps the strongest argument ever made against strong AI. It is (...) a very extensive review of what building a mind essentially amounts to drawing on insights and results from biology, physics, linguistics, computability, philosophy, and mathematics. (shrink)

Durkheim écrit ce livre avec un but double : d'abord il voulait expliquer ce qui crée une société, ce qui la tient ensemble ; ensuite il voulait éclaircir l'influence qu'a la société sur la pensée logique. Pour Durkheim, la religion est la clé utilisée pour déverrouiller ces deux problématiques.Dans ce livre, Durkheim argumente que les représentations religieuses sont en fait des représentations collectives : l'essence du religieux ne peut être que le sacré. Il est une caractéristique qui se trouve universellement (...) dans toutes les religions. Le sacré, être collectif et impersonnel, représente ainsi la société elle-même. Ensuite il argumente que la société a un impact majeur dans la genèse de la pensée logique. C'est la société qui fournit à l'individu les catégories (le temps, l'espace, le nombre etc.) et les concepts nécessaires pour la pensée logique. (shrink)

É bem conhecida a oposição estabelecida por Kant entre experiência possível e dialética, na medida em que esta última é caracterizada como a “lógica da ilusão”. Ao mesmo tempo, o modo de pensar metafísico, que ocorre dialeticamente, em sentido kantiano, é uma tendência inevitável da razão, expressa na exigência formal de completude das categorias. Como o pensar, enquanto exercício livre da razão, é em si mesmo mais amplo do que a atividade de conhecer, própria do entendimento, o pensar contém o (...) conhecimento, embora este se qualifique pelas regras e pelos limites determinantes da objetividade. A pergunta que tentaremos formular é se essa relação continente-conteúdo não poderia configurar também uma dependência da experiência em relação ao raciocínio dialético, que estaria de algum modo indicada na função reguladora das idéias da razão. Nesse caso, a oposição formal entre conhecer e pensar seria inseparável da inclusão estrutural (dependência) da experiência no âmbito da razão. Na raiz do problema estaria talvez a tensão (dialética) entre a aspiração subjetiva de totalidade e as exigências objetivas de limitação e segmentação da experiência e a forma da experiência teria de ser finalmente concebida a partir de um fundo de inteligibilidade problemática. Dialectics and experienceThe separation of possible experience as objective knowledge and dialetics as a non-objective or non-theoretical knowledge is one of the most important aspects of kantian critical philosophy. But Kant also says that the activity of reason, as a pure thinking, has more amplitude than understanding knowledge. So we could say that theoric knowledge would depend on rational ( and non-theoretical) knowledge, as something contained in it. If we accept that, the consequence would be a relation of dependence between the form of objective knowledge and the background of a problematic – even doubtful – inteligible knowledge. (shrink)

gl. 1. Ėkofilosofii︠a︡ kak zhiznesokhrani︠a︡i︠u︡shchiĭ kulʹturnyĭ proekt -- gl. 2. Obrazy kulʹtury mira v istoriko-personologicheskom izmerenii -- gl. 3. Borʹba za mir v sovremennom mire : opyt, kont︠s︡ept︠s︡ii, modeli resheniĭ.

Em artigo inédito, Franklin Leopoldo e Silva desenvolve os sentidos da memória na produção da identidade, destacando as relações significativas do presente com o passado e o futuro na construção da história. Desse ponto, o autor acentua o papel das instituições nesse processo – um processo de exercício de poder, no qual se tem, na verdade, a institucionalização do poder – e, dentre elas, em particular a instituição universitária.

O presente artigo visa analisar, a partir do pensamento do filósofo Michel Foucault, o exercício da liberdade sob uma perspectiva ética, no âmbito do estoicismo tardio. Liberdade essa que se faz possível a partir do cuidado de si, por meio de exercícios regulares que possibilitam ao indivíduo viver a vida como arte. Com efeito, é na prática do cuidado de si, que o indivíduo se coloca como transformador do mundo e de si mesmo, o qual dá-se no encontro com o (...) outro, longe de ser uma experiência solitária com o próprio eu, realiza-se na associação com outrem. (shrink)

Neste livro Gombrich discute as idéias e as posturas específicas que tiveram influência decisiva na prática da arte renascentista. Todos os estudos aqui reunidos tratam de algo a que se pode chamar clima renascentista de opiniões sobre a arte, além da influência desse clima sobre a prática e a crítica da arte.

Alfred Edward Housman (1859–1936), ki je kasneje postal angleški pesnik in eden največjih filologov svojega časa, je svoj zafrkantski »fragment« objavil pri triindvajsetih, leta 1883, v reviji Bromsgrovian. Revijo je izdajala Bromsgrove School, kjer se je šolal kot najstnik (1870–77); na šolo, ki je prva prepoznala njegov jezikovni in pesniški talent, se je za kratek čas (1881–82) vrnil kot pomožni učitelj po ne­pričakovanem študijskem porazu na Oxfordu. Kot ugotavlja Ralph Marcellino, ni naključje, da besedilo paro­dira predvsem Ajshila, ki je bil (...) Housmanov najljubši pesnik. V njem odzvanja Oresteja, zlasti drami Agamemnon in Prinašalke pitnih darov, verjetno zato, ker so ju dijaki na šoli, kjer je poučeval, pri pouku največ brali. Prav ta ciljna publika je znala ceniti norčevanje iz retoričnih figur ter sintaktičnih konstrukcij, ki jim jih je njihov pomožni učitelj vbijal v glavo. (shrink)

Wittgenstein’s concepts shed light on the phenomenon of schizophrenia in at least three different ways: with a view to empathy, scientific explanation, or philosophical clarification. I consider two different “positive” wittgensteinian accounts―Campbell’s idea that delusions involve a mechanism of which different framework propositions are parts, Sass’ proposal that the schizophrenic patient can be described as a solipsist, and a Rhodes’ and Gipp’s account, where epistemic aspects of schizophrenia are explained as failures in the ordinary background of certainties. I argue that (...) none of them amounts to empathic-phenomenological understanding, but they provide examples of how philosophical concepts can contribute to scientific explanation, and to philosophical clarification respectively. (shrink)

Los recientes desarrollos de la inteligencia artificial generativa suponen desafíos a conceptos tradicionales del Derecho, uno de ellos es el de autoría. El problema que aborda este trabajo es cómo concebir la autoría de una obra escrita por una inteligencia artificial generativa en la que la intervención humana, en cuestiones de estilo y contenido, es nula o mínima. Para ello se enumeran casos recientes en los que se plantea ese problema a partir de artículos escritos empleando ese tipo de tecnología. (...) Pero el objetivo del trabajo es la revisión de la concepción de autoría que subyace a las normativas internacionales que regulan la propiedad intelectual y los presupuestos éticos que las justifican (la concepción de Locke, la concepción romántica y la concepción utilitarista). Sobre la base de una lectura de “¿Qué es un autor?” de Michel Foucault se sostiene que la noción de autoría cumple una función social que, al menos al estado del arte de la inteligencia artificial generativa, esta no puede cumplir y, por tanto, no debe ser reconocida como autora de las obras mencionadas. Concluyendo que las obras literarias elaboradas por inteligencia artificial generativa multipropósito con nula o escasa intervención humana deben ser consideradas como obras de dominio público. (shrink)

O objetivo deste texto é explorar aproximações e divergências no pensamento de Paulo Freire e Ivan Illich na forma como eles vêem a relação entre educação e transformação social. Acredita-se que a retomada da polêmica Freire e Illich sobre o problema educação e transformação social pode contribuir para melhor compreender e esclarecer a verdadeira natureza desse problema, já clássico na história da educação moderna.