Gödel argued that the incompleteness theorems entail that the mind is not a machine, or that certain arithmetical propositions are absolutely undecidable. His view was that the mind is not a machine, and that no arithmetical propositions are absolutely undecidable. I argue that his position presupposes that the idealized mathematician has an ability which I call the recursive-ordinal recognition ability. I show that we have this ability if, and only if, there are no absolutely undecidable arithmetical propositions. I argue that (...) there are such propositions, but that no recognizable example of one can be identified, even in principle. (shrink)

SummaryJ.R. Lucas has argued that it follows from Godel's Theorem that the mind cannot be a machine or represented by any formal system. Although this notorious argument against the mechanism thesis has received considerable attention in the literature, it has not been decisively rebutted, even though mechanism is generally thought to be the only plausible view of the mind. In this paper I offer an analysis of Lucas's argument which shows that it derives its persuasiveness from a subtle confusion. In (...) particular, I examine Benacerraf's reconstruction of Lucas's argument, thereby permitting a precise re‐statement of my own analysis and reinforcement of my conclusions regarding Lucas's mistake. This examination of Benacerraf's reconstruction permits us to evaluate certain radical and highly paradoxical conclusions concerning empirical psychology which Benacerraf draws, and this, in turn, permits us to consider certain suggestive implications for the mind which may, after all, follow from Godel's Theorem.RésuméJ.R. Lucas a dérivé du théorème de Gödel la conclusion que ľesprit ne peut pas être une machine ou être représenté par un systeme formel. Bien que cet argument célèbre contre la thése du mécanisme ait éveillé un interêt considérable dans la littérature, il ňa pas été réfuté de façon décisive, même si ľopinion générate prévaut que le mécanisme est la seule conception possible de ľesprit. Dans cet article, je propose une analyse du raisonnement de Lucas qui montre que sa force persuasive provient ?on;une subtile confusion. En particulier, j'examine la reconstruction, par Benacerraf, de la démonstration de Lucas, ce qui me pérmet de préciser ma propre analyse et de confirmer mes conclusions quant àľerreur de Lucas. Cet examen de la reconstruction de Benacerraf nous fournit ľoccasion de discuter certaines conclusions radicales et hautement paradoxals qu'il en tire concernant la psychologie expérimentale et nous conduit à certaines implications suggestives concernant ľesprit et qui pourraient finalement decouler du théorème de Gödel.ZusammenfassungJ.R. Lucas hat argumentiert, aus Gödels Theorem mUsse folgen, dass der menschliche Geist keine Maschine sein oder durch ein formales System dargestellt werden kann. Obschon dieses bekannte Argument gegen die mechanistische These in der Literatur beträchtliche Beachtung gefunden hat, ist es bisher noch nie eigentlich widerlegt worden, obgleich eine mechanistische Betrachtungsweise allgemein als die einzig plausible gilt. In dieser Arbeit biete ich eine Analyse von Lucas Argument an, die zeigen soil, dass seine Überzeugungskraft auf einer subtilen Konfusion beruht. Im besonderen untersuche ich Benacerrafs Rekonstruktion des Arguments, was rnirerlaubt, meine eigene Analyse in genauer Weise zu formulieren und meine Schlussfolgerung bezüglich des von Lucas gemachten Fehlers zu erharten. Diese Untersuchung von Benacerrafs Rekonstruktion ermöglicht es, gewisse radikale und im höchsten Grad paradoxe Schlüsse, die Benacerraf hinsichtlich der empirischen Psychologie zieht, zu beurteilen. Das wiederum erlaubt uns, gewisse den Geist betreffende Implikationen zu erörtern, die tatsächlich aus Gödels Theorem folgen kUnnten. (shrink)

A critique of algorithmic rationalisation offers at best some initial reasons and preliminary ideas. Critique is understood as a reflection on validity. It is limited to an “epistemological investigation” of the limits of the calculable or of what appears “knowable” in the mode of the algorithmic. The argumentation aims at the mathematical foundations of computer science and goes back to the so-called “foundational crisis of mathematics” at the beginning of the 20th century with the attempt to formalise concepts such as (...) calculability, decidability and provability. The Gödel theorems and Turing’s halting problem prove to be essential for any critical approach to “algorithmic rationalisation”. Both, however, do not provide unambiguous results, at best they run towards what later became known as “Gödel’s disjunction”. The chosen path here, however, suggests the opposite way, insofar as, on the one hand, the topos of creativity appear constitutive for what can be regarded as cognitive “algorithmic rationalisation” and which encounters systematic difficulties in the evaluation of non-trivial results. On the other hand, the investigations lead to a comparison between the “mediality” of formally generated structures, which have to distinguish between object-and metalanguages, and the “volatile” differentiality of human thought, which calls for syntactically non-simulatable sense structures. (shrink)

PROFESSOR LEWIS 1 and Professor Coder 2 criticize my use of Gödel's theorem to refute Mechanism. 3 Their criticisms are valuable. In order to meet them I need to show more clearly both what the tactic of my argument is at one crucial point and the general aim of the whole manoeuvre.

J. R. Lucas argues in “Minds, Machines, and Gödel”, that his potential output of truths of arithmetic cannot be duplicated by any Turing machine, and a fortiori cannot be duplicated by any machine. Given any Turing machine that generates a sequence of truths of arithmetic, Lucas can produce as true some sentence of arithmetic that the machine will never generate. Therefore Lucas is no machine.

J. R. Lucas serves warning that he stands ready to refute any sufficiently specific accusation that he is a machine. let any mechanist say, to his face, that he is some particular machine M; Lucas will respond by producing forthwith a suitable Gödel sentence ϕM. Having produced ϕM, he will then argue that — given certain credible premises about himself — he could not have done so if the accusation that he was M had been true. let the mechanist try (...) again; Lucas will counter him again in the same way. It is not possible to accuse Lucas truly of being a machine. (shrink)

This paper presents, from a historical and logical-philosophical perspective, the Gödelian arguments of two Oxford scholars, John Lucas and Roger Penrose. Both have been based on Gödel's Theorem to refute mechanism, computationalism and the possibility of creating an AI capable of simulating or duplicating the human mind. In the conclusions, the growing application of empirical methods in mathematics is mentioned and a possible path that would support Lucas and Penrose's arguments is speculated.

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes (...) “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)