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 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)

According to a Kantian conception truthfulness is characterised as a requirement of respect for the agency of another. In lying we manipulate the other’s rational capacities to achieve ends we know or fear they may not share. This is paradigmatically a failure of respect. In this paper we argue that the importance of truthfulness also lies in significant part in the ways in which it supports our agential need to make sense of the world, other people, and ourselves. Since sense-making (...) is something we do together, and that we can support or undermine, it generates norms of interaction that constitute a further, distinct, mode of recognition and respect for another’s agency. But the requirements of truthfulness and support for sense-making sometimes conflict. Through a series of cases, we analyze why and when a rigid insistence on truthfulness is disrespectful of the other and undermining of their agency. (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)

Modular learning was used to assist elementary learners in continuing their education during the pandemic. This modular learning posed academic challenges. This phenomenological study delved into the academic challenges of modular learning of public-school elementary school learners. Validated In-depth interview protocol (IDI) was used to collect the data from ten (10) Grade six learners. Findings revealed that these academic challenges include unfavorable learning environments, reading comprehension issues, lack of motivation, and poor quality of printed learning modules that affect the learners (...) ability to comprehend and finish the module effectively. Consequently, modular learning allows learners to take responsibility for their learning by utilizing the available answers key, optimizing internet usage, scheduling time properly, and guessing answers. Considerably, the learners used face-to-face conversations, personal messages, and phone calls to communicate their educational queries. Moreover, the structure of self-learning modules facilitates independent study and complicates learners understanding. It is recommended that there is a need to enhance policies for improving module quality, allocate sufficient funds to improve the printed module quality, conduct relevant teacher training and workshops for developing quality printed modules, and sustain the home visitation program to monitor students modular learning progress. (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 tears of Odysseus -- History : music gives voice to the ineffable -- Tears, chills, and broken bones -- The music itself -- Explaining strong emotional responses to music I -- Explaining strong emotional responses to music II -- The sublime, revisited -- Conclusion : values.

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)

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.

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)

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)

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

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)

Self-control is integral to successful human agency. Without it we cannot extend our agency across time and secure central social, moral, and personal goods. But self-control is not a unitary capacity. In the first part of this paper we provide a taxonomy of self-control and trace its connections to agency and the self. In part two, we turn our attention to the external conditions that support successful agency and the exercise of self-control. We argue that what we call moral security (...) is a critical foundation for agency. Parts three and four explore what happens to agency when moral security is lacking, as in the case of those subject to racism, and those living in poverty. The disadvantages suffered by those who are poor, in a racial minority or other oppressed group, or suffering mental illness or addiction, are often attributed to a lack of individual self-control or personal responsibility. In particular, members of these groups are often seen as irresponsibly focused on short-term pleasures over long-term good, a view underwritten by particular psychological theories of self-control. We explore how narratives about racism and poverty undermine moral security, and limit and distort the possibility of synchronic and diachronic self-control. Where moral security is undermined, the connection between self-control and diachronic goods often fails to obtain and agency contracts accordingly. We close with some preliminary reflections on the implications for responsibility. (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)

Tarlac State University (TSU) is a multi-ethnic and multicultural institution with a student population that is predominated by the Kapampangan, Ilocano, and Tagalog ethnolinguistic groups. Using a modified Katz and Braly trait checklist, a comparative study was able to: 1) profile the stereotypes of these three ethnolinguistic groups, 2) determine their uniformity indices, 3) determine their positivity/negativity indices, 4) compare and contrast their profiled stereotypes, 5) compare and contrast their uniformity indices, and 6) compare and contrast their positivity/negativity indices. This (...) paper was able to establish that (1) the salient traits of the Kapampangan ethnic stereotype are mayabang, masarap magluto, and galante/magastos; those of the Ilocano ethnic stereotype are kuripot, baduy, and madiskarte; and those of the Tagalog ethnic stereotype are maka-Diyos, maganda/guwapo, (2) the Ilocano and the Tagalog ethnic stereotypes shared the most number of traits, while the Kapampangan and Ilocano ethnic stereotypes shared the least number of traits, (3) the Ilocano ethnic stereotype is the sharpest, while the Tagalog ethnic stereotype is the blurriest, and (4) the Tagalog ethnic stereotype is the most positive, while the Kapampangan ethnic stereotype is the most negative. Understanding the dynamics among these ethnolinguistic groups would hopefully lead towards building a more cohesive student body in TSU, or citizens of Tarlac City, or inhabitants of Tarlac Province. This paper is also important in providing a model study that can be replicated in other multicultural institutions and locations in the country. (shrink)

In this chapter we focus on the structure of close personal relations and diagnose how these relationships are disrupted by addiction. We draw upon Peter Strawson’s landmark paper ‘Freedom and Resentment’ (2008, first published 1962) to argue that loved ones of those with addiction veer between, (1) reactive attitudes of blame and resentment generated by disappointed expectations of goodwill and reciprocity, and (2) the detached objective stance from which the addicted person is seen as less blameworthy but also as less (...) fit for ordinary interpersonal relationships. We examine how these responses, in turn, shape the addicted person’s view of themselves, their character and their capacities, and provide a negative narrative trajectory that impedes recovery. We close with a consideration of how these effects might be mitigated by adopting less demanding variations of the participant stance. (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)

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)

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)

In this chapter we draw comparisons between Kass’ views on the normative authority of repugnance and social intuitionist accounts of moral judgement which are similarly sceptical about the role of reasoned reflection in moral judgement. We survey the empirical claims made in support of giving moral primacy to intuitions generated by emotions such as repugnance, as well as some common objections. We then examine accounts which integrate intuition and reflection, and argue that plausible accounts of wisdom are in tension with (...) Kass’ claim that our inarticulable emotional responses can be the expression of deep wisdom. We conclude that while repugnance and other emotions have a role to play in informing deliberation and judgement, we have reason to be cautious in giving them normative authority. Affective responses alone cannot discharge the burden of justification for moral judgement and are just one tool relied upon by those we consider wise. (shrink)

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)

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)

Journal of Applied Philosophy, EarlyView.

In this paper we look at two kinds of relations that give rise to reasons for action of a distinctive sort: friendship and erotic love. We argue that what is common to these different relations of affection is that the people in them exhibit dispositions toward mutual direction by one another and interpretation of one another (in a sense that we describe in detail below). This mutual responsiveness is, in part, a matter of responding to reasons that arise from the (...) relation of love or friendship. These reasons are frequently distinct from – and may be opposed to – normative reasons arising from prudence or even morality. Yet reasons of love are normative: they too may obligate us. They may make demands on us that conflict with our present desires and require considerable sacrifice. (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)

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)

A dominant view of guilt and shame is that they have opposing action tendencies: guilt- prone people are more likely to avoid or overcome dysfunctional patterns of behaviour, making amends for past misdoings, whereas shame-prone people are more likely to persist in dysfunctional patterns of behaviour, avoiding responsibility for past misdoings and/or lashing out in defensive aggression. Some have suggested that addiction treatment should make use of these insights, tailoring therapy according to people’s degree of guilt-proneness versus shame-proneness. In this (...) paper, we challenge this dominant view, reviewing empirical findings from others as well as our own to question (1) whether shame and guilt can be so easily disentangled in the experience of people with addiction, and (2) whether shame and guilt have the opposing action tendencies standardly attributed to them. We recommend a shift in theoretical perspective that explains our main finding that both emotions can be either destructive or constructive for recovery, depending on how these emotions are managed. We argue such management depends in turn on a person’s quality of self-blame (retributive or ‘scaffolding’), impacting upon their attitude towards their own agency as someone with fixed and unchanging dispositions (shame and guilt destructive for recovery) or as someone capable of changing themselves (shame and guilt productive for recovery). With an eye to therapeutic intervention, we then explore how this shift in attitude towards the self can be accomplished. Specifically, we discuss empathy-driven affective and narratively-driven cognitive components of a process that allow individuals to move away from the register of retributive self-blame into a register of scaffolding ‘reproach’, thereby enabling them to manage their experiences of both shame and guilt in a more generative way. (shrink)

The decline in autobiographical memory function in people with Alzheimer’s dementia has been argued to cause a loss of self-identity. Prior research suggests that people perceive changes in moral traits and loss of memories with a “social-moral core” as most impactful to the maintenance of identity. However, such research has so far asked people to rate from a third-person perspective, considering the extent to which hypothetical others maintain their identity in the face of various impairments. In the current study, we (...) examined the impact of perspective, comparing first- and third-person perspectives, as well as memory type. This online study asked 201 participants to consider hypothetical scenarios in which either themselves or another person experienced different types of memory failures associated with a diagnosis of AD. For each scenario, participants rated the degree to which the depicted individual remained the same person, and how impactful the impairment was. Social semantic memory failures – involving failures to recognise a loved one – were rated as most detrimental to self-continuity, and procedural memory failures the least. Averaged across all memory types, people considered their own and their partner’s self-continuity to be more resilient to memory failures than that of a parent or stranger. However, this pattern was reversed for some memory types: forgetting semantic or episodic information about close relationships was rated as more detrimental from a first-person than third-person perspective. Our findings suggest that perspective and type of memory impairment interact to impact judgements about the extent to which people maintain their identity when they experience dementia, and highlight the importance of social relationships to maintaining a sense of self. (shrink)

Tarlac State University (TSU) is a multi-ethnic and multicultural institution with a student population that is predominated by the Kapampangan, Ilocano, and Tagalog ethnolinguistic groups. This paper is a comparative study of the ethnic stereotypes and self-stereotypes of these three ethnolinguistic groups. Using a modified Katz and Braly trait checklist, this paper was able to: 1) profile the ethnic stereotypes and self-stereotypes of these three ethnolinguistic groups, 2) determine their uniformity indices, 3) determine their positivity/negativity indices, 4) compare and contrast (...) their profiled ethnic stereotypes and self-stereotypes, 5) compare and contrast the uniformity indices of their ethnic stereotypes and self-stereotypes, and 6) compare and contrast the positivity/negativity indices of their ethnic stereotypes and self-stereotypes. This paper was also able to establish that the Kapampangan ethnic stereotype and self-stereotype shared the most number of traits in common, while the Tagalog ethnic stereotype and self-stereotype shared the least number of traits in common. This paper was also able to establish that the uniformity indices of the Kapampangan ethnic stereotype and self-stereotype are closest to each other; while those of the Tagalog ethnic stereotype and self-stereotype are farthest from each other. Finally, this paper was able to establish that the positivity/ negativity indices of the Kapampangan and Tagalog ethnic stereotype and self-stereotype are both closest to each other; while those of the Ilocano ethnic stereotype and self-stereotype are farthest to each other. This paper is significant not only in knowing whether there is a difference between how the three ethnolinguistic groups construct each other’s stereotypes and their respective self-stereotypes, but more so in laying down the preliminary information that would lead towards understanding the dynamics among these same ethnolinguistic groups, and towards building a more cohesive student body in TSU, or citizens of Tarlac City, or inhabitants of Tarlac Province. This paper is also important in providing a model study that can be replicated in other multicultural institutions and locations in the country. (shrink)

Critico la teoría hermenéutica de Landgrebe sobre el cartesianismo de Husserl mostrando la estructura argumentativa en la que se inserta, lo que pretende y lo que está forzada a pretender. Su núcleo duro radica en los conceptos de crítica inmanente y lógica interna y tiene como meta final promover una idea de trascendental no-entitativa voluntarista y correlacional opuesta a la de un yo absoluto. Para probar esto, analizo tres niveles del cartesianismo de Landgrebe: primero, la síntesis contradictoria entre (...) apodicticidad y experiencia; segundo, su interpretación de Filosofía Primera, y, tercero, el objetivismo de Husserl. Concluyo de la función que juega el cartesianismo en este esquema y del fracaso de documentar con ejemplos la idea que el concepto no describe una noción en la filosofía de Husserl, sino que es una herramienta hermenéutica para controlar el sentido de trascendental en esta filosofía.I criticize Landgrebe’s hermeneutical theory about Husserl’s Cartesianism describing the structure of the argument where this concept is rooted, what it claims and what it is forced to claim. The theory’s hard core is based on the concepts of immanent critique and internal logic and it’s final goal consists in advancing a non-entitative voluntaristic and correlational definition of transcendental opposite to that of an absolute I. To prove this, I analyze three levels inside Landgrebe’s concept of Cartesianism: first, the contradictory synthesis of experience and apodicticity; second, his interpretation of First Philosophy, and, third, Husserl’s objectivism. I conclude from the goal that Cartesianism plays in this argument and the failure in documenting this idea with examples that Landgrebe’s Cartesianism is rather an hermeneutical tool made to control the definition of transcendental in Husserl’s philosophy than a concept describing Husserl’s thought. (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)

This article is a response to the position of Landgrebe and Smith on the fundamental limitations that prevent the creation of Artificial General Intelligence (AGI), expressed in their book Why Machines Will Never Rule the World. The reasons for failures for attempts to create AGI using formal logic and algorithmic approaches to modeling intelligence are discussed. An attempt is made to define the future direction of intellectual systems development as hybrid evolving systems, as well as a revision of the (...) Turing test statement and language models role. (shrink)

Jeanette Edwards observes a pattern of questions of the form “Why do anthropology fieldwork in location X?” - she only hears the question posed of some places - and she explains this pattern by saying that some places are taken to be obviously legitimate for anthropology fieldwork whereas others are not. I draw distinctions between legitimate and illegitimate, obviously legitimate and not obviously legitimate, and better and worse. The distinctions lead to a different explanation.

Landgrebe and Smith provide an argument for the impossibility of Artificial General Intelligence based on the limits of simulating complex systems. However, their argument presupposes a very contemporary vision of artificial intelligence as a model trained on data to produce an algorithm executable in a modern digital computing system. The present contribution explores what it means to be artificial. Current artificial intelligence approaches on modern computing systems are not the only conceivable way in which artificial intelligence technology might be (...) created. If there are conceivable routes by which an artificial intelligence might be developed that are not constrained in the same way as the current generation of artificial neural networks, then these might be plausible routes towards the engineered generation of artificial general intelligence that are not precluded by the Landgrebe and Smith argument against the possibility of artificial general intelligence. (shrink)

Jeanette Edwards did fieldwork in the English town of Bacup. Why do fieldwork there? She writes that she is often asked this, whereas the question is unlikely to be asked of an anthropologist who does fieldwork in Papua New Guinea, because it is “axiomatically” an acceptable place for fieldwork. I present two responses to Edwards’ thinking, one of which concerns an asymmetry in how “skeptics” present their questions.

Jeanette Edwards tells us that she is often asked about why she did fieldwork in the English town of Bacup, whereas she has not heard anthropologists who did fieldwork in Papua New Guinea asked why there. She commits herself to a certain explanation for this: potential inquirers assume that non-Western societies are legitimate objects of study for social anthropology but this is not assumed for Western societies. I propose another explanation: it is not about the legitimacy of the object (...) of study, rather “your” levels of commitment. Would you do that for anthropology? (shrink)

Jeanette Edwards sounds as if she wishes to reject the question “Why did you do fieldwork there?” I propose a metaphysical route to this, which is to say, “The self before fieldwork is not my self,” but this conflicts with the traditional Lockean account of personal identity.

Landgrebe and Smith (2022) have recently offered an important mathematical argument against the possibility of Artificial General Intelligence (AGI): human intelligence is a complex system; complex systems have some properties that cannot be modelled mathematically; hence we have no viable way to build an AI that would be able to emulate human intelligence. The issue of complexity is thus at the heart of the Landgrebe and Smith approach, and they tackle this issue by postulating a set of conditions, (...) derived from mathematics, for a system to be complex. I argue that Hayek’s “Theory of Complex Phenomena” offers an interesting alternative path to understanding what makes complex systems complex. This offers an argument complementary to that of Landgrebe and Smith, which approaches the issue of complexity from an ontological angle, and which is designed to show that AI systems are not the right kind of entity that may interact meaningfully with complex systems. (shrink)

The recent book by Landgrebe and Smith (2022) offers compelling arguments against the possibility of Artificial General Intelligence (AGI) as well as against the idea that machines have the abilities to master human language, human social interaction and morality. Their arguments leave open, however, a problem on the side of the imaginative power of humans to perceive more than there is and treat AIs as humans and social actors independent of their actual properties and abilities or lack thereof. The (...) mathematical and ontological arguments will not help against this strong human tendency to treat conversational AI (CAI) as if it was human. (shrink)

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

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

This short paper seeks to throw light on the sense in which the prior knowledge used by AlphaFold is to be understood in ontological terms. The paper is a comment on the 2022 ICBO presentation by Jobst Landgrebe entitled “What AlphaFold teaches us about deep learning with prior knowledge”.

Is the brain really a computer? In particular, is our intelligence a computational achievement: is it because our brains are computers that we get on in the world as well as we do? In this paper I will evaluate an ambitious new argument to the contrary, developed in Landgrebe and Smith (2021a, 2022). Landgrebe and Smith begin with the fact that many dynamical systems in the world are difficult or impossible to model accurately (inter alia, because it is (...) intractable to find exact solutions to the differential equations that describe them—meaning we have to approximate—but at the same time they are such that small differences in starting conditions lead to big differences in final conditions, thwarting accurate approximation). Yet we manage to survive and thrive in a world full of such systems. Landgrebe and Smith argue from these premises that it is not because our brains are computers that we get on as well as we do: instead it is because of the various ways that we dynamically couple with such systems, these couplings themselves impossible to model well enough to emulate in silico. Landgrebe and Smith thus defend a dynamical systems model in the lineage of Gibson (1979), Van Gelder (1995), and Thompson (2007), though their focus is on decisively refuting the computationalist alternatives rather than developing the positive account. Here I will defend the claim that human intelligence is genuinely computational (and that whole brain emulation and others forms of AGI may be possible) against this argument. (shrink)

The answer I shall sketch is not mine. Nor, as far as I can tell, is it an answer to be found in the voluminous literature inspired by Kripke’s work. Many of the elements of the answer are to be found in the writings of Wittgenstein and his Austro-German predecessors, Martinak, Husserl, Marty, Landgrebe and Bühler. Within this Austro-German tradition we may distinguish between a strand which is Platonist and anti-naturalist and a strand which is nominalist and naturalist. Thus (...) Husserl’s account of what he calls “directly referring” uses of singular terms invokes senses or individual concepts, albeit simple, not descriptive senses. But the account of reference fixing and reference given by Landgrebe, Bühler and Wittgenstein rejects senses.1 I confine further reference to these writers to footnotes since my aim here is to develop and unify some of their suggestions, in particular by comparing them with more recent work (cf. Mulligan 1997). (shrink)

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

In our book, Why Machines Will Never Rule the World, Jobst Landgrebe and I argue that we can engineer machines that can emulate the behaviours only of simple systems, which means: only of those systems whose behaviour we can predict mathematically. The human brain is an example of a complex system, and thus its behaviour cannot be emulated by a machine. We use this argument to debunk the claims of those who believe that large language models are poised to (...) achieve a level of intelligence that will equal or even surpass that of the human brain. The essay was published under the title "Why Machines Will Never Rule the World". (shrink)