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)

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)

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)

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

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)

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)

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)

What is essential to language? Two thinkers active in Vienna in the 1930's, Karl Bühler and Ludwig Wittgenstein, gave apparently incompatible answers to this question. I compare what Wittgenstein says about language and reference at the beginning of his Philosophical Investigations with some aspects of the descriptive analysis of language worked out by Bühler between 1907 and 1934, a systematic development of the philosophies of mind and language of such heirs of Brentano as Martinak, Marty, Meinong, Landgrebe and Husserl. (...) Y a-Uil quelque chose qui est essentiel au langage? Deux penseurs actifs à Vienne dans les années trente, Karl Bühler et Ludwig Wittgenstein, donnent à cette question des réponses qui sont apparemment incompatibles. Je compare ce que Wittgenstein dit du langage et de la référence au début de ses Investigations Philosophiques avec quelques aspects de l'analyse descriptive élaborée par Bühler entre 1907 et 1934, un développement systématique des philosophies de l'esprit et du langage des héritiers de Brentano tels que Martinak, Marty, Meinong, Landgrebe et Husserl. (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)

This is a review of Jobst Landgrebe and Barry Smith, Why Machines Will Neve 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)

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)

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

The article presents edmund Husserl’s idea of transcendental phenomenology in the perspec‐ tive of the project of science. this aspect of Husserl’s writings was pointed out by, for example, rudolf Bernet, man ed Brelage, Iso Kern, eduard marbach, eugen Fink, Ludwig Landgrebe, and roman Ingarden. the main aim, realised in the article is to prove the main hypothesis claiming that transcendental phenomenology is closely connected with Husserl’s conception of philosophy, the idea of philosophy as a science (eidetic and transcendental). (...) the project of transcendental science and knowledge is also connected with Husserl’s conception of transcen‐ dental reduction taken as a mode of revealing those constitutive achievements of consciousness which were covered in the natural attitude. In the paper, I focus on presenting the most im‐ portant points (epistemological, methodological, and transcendental) connected with Husserl’s evolution of phenomenology in the perspective of the project of science. (shrink)

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

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

The recent remarkable success of generative AI models to create text and images has already started altering our perspective of intelligence and the “uniqueness” of humanity in this world. Simultaneously, arguments on why AI will never exceed human intelligence are ever-present as seen in Landgrebe and Smith (2022). To address whether machines may rule the world after all, this paper zooms in on one of the aspects of intelligence Landgrebe and Smith (2022) neglected to consider: creativity. Using Rhodes (...) four Ps of creativity as a starting point, this paper evaluates the creative ability in visual generative AI models with respect to the state of the art in creativity theory. The most part of the reflective evaluation is performed through a case study in generating illustrations of dogs using the generative AI tool Midjourney. (shrink)