Alan Turing

The Turing Machine (TM) presents itself as the very landmark and initial design of digital automata present in all modern general-purpose digital computers and whose design on computable numbers implies deeply ontological as well as epistemological foundations for today's computers. These lines of work attempt to briefly analyze the fundamental epistemological problem that rose in the late 19th and early 20th century whereby "machine cognition" emerges. The epistemological roots addressed in the TM and notably in its "Halting Problem" uncovers the tension between determinism and uncertainty, regarded here as the primal and inherent features of machine cognition.

Integrating media studies, science and technology studies, and social psychology, Deceitful Media examines the rise of artificial intelligence throughout history and exposes the very human fallacies behind this technology. Focusing specifically on communicative AIs, Natale argues that what we call "AI" is not a form of intelligence but rather a reflection of the human user. Using the term "banal deception," he reveals that deception forms the basis of all human-computer interactions rooted in AI technologies, as technologies like voice assistants utilize the dynamics of projection and stereotyping as a means for aligning with our existing habits and social conventions. By exploiting the human instinct to connect, AI reveals our collective vulnerabilities to deception, showing that what machines are primarily changing is not other technology but ourselves as humans.

How does our brain give rise to the rich world of our conscious experience?  Each lecture in this course approaches this fundamental philosophical question by focusing on a specific issue regarding thinking, minds, and brains.  Topics include little-known facts about consciousness, what it is like to be a bat, the 'inner theater' view of consciousness, Charles Babbage's steam-driven computing engines, and how optical illusions work.  Philosophical theories of mind are at the core of the course, from Plato and Aristotle through Descartes and Locke to contemporary work by Daniel Dennett, David Chalmers, and Paul and Patricia Smith Churchland.  In addition, the course draws from psychology, neurophysiology, linguistics, and the amazing history of attempts at building thinking machines.

Resumen: El estudio de las máquinas es un campo que puede rastrearse hasta el Renacimiento. Sin embargo, en la Modernidad adquiere cierto perfil teórico. Se produce una transformación desde los tratados sobre la instalación y uso que conciben a las máquinas como una síntesis entre las estructuras materiales y el trabajo humano, hacia estudios analíticos que las consideran como entidades modulares, como ensamblaje de piezas y mecanismos susceptibles de ser analizados matemáticamente. A su vez, los primeros se caracterizan por un enfoque antropométrico que no ven mayores inconvenientes en usar el lenguaje coloquial e ilustraciones para transmitir el conocimiento acerca de las máquinas; los segundos, en cambio, plantean problemas cognitivos y proponen lenguajes simbólicos específicos para representar el diseño y funcionamiento de las máquinas. Comenzaremos por el siglo XVIII, al ilustrar algunos conceptos provenientes de L’Encyclopédie y de Adam Smith. Posteriormente, nos enfocaremos en los matemáticos del siglo XIX que promovieron las transformaciones mencionadas. En primer lugar, Charles Babbage y su notación mecánica; luego Robert Willis y Franz Reuleaux y sus “mecanismos puros”; y de nuevo Babbage para analizar la complementariedad de la economía política a sus estudios matemáticos sobre las máquinas. Finalmente, mostraremos algunos debates actuales sobre el vínculo entre máquinas, realidad, matemática y lenguaje que, explícita o implícitamente, retoman aquellos problemas y pueden encontrar en el pensamiento de los matemáticos del siglo XIX un antecedente.

Abstract: The study of machines is a field whose origin can be traced back to the Renaissance. However, it is in Modernity when it attains a certain theoretical outline. There was a transformation from the treatises on installation and use - which conceive of machines as a synthesis between material structures and human work - towards analytical studies that consider them as modular entities, as the assembly of parts and mechanisms that can be analyzed mathematically. In turn, the former are characterized by an anthropometric approach that sees no major inconvenience in using colloquial language and illustrations to transmit knowledge about machines; the latter, on the other hand, pose cognitive problems and propose specific symbolic languages for representing the design and operation of machines. We will start in the 18th century, illustrating some concepts from L’Encyclopédie and Adam Smith. Later, we will focus on the 19th century mathematicians who promoted the aforementioned transformations. First, Charles Babbage and his mechanical notation. Later Robert Willis and Franz Reuleaux and their “pure mechanisms”; We will then return to Babbage to discuss the complementarity of political economy to his mathematical studies of machines. Finally, we will show some current debates on the link between machines, reality, mathematics and language that, explicitly or implicitly, take up those problems and can find an antecedent in the thought of 19th century mathematicians.

Was ist es, was eine Maschine, einen Computer oder einen
Menschen überhaupt intelligent macht? Diese Frage ist weitaus schwerer zu beantworten, als man zunächst erwarten würde. Man kann hier etwa auf die Fähigkeit des Denkens oder auf eine bestimmte Art des Handelns abstellen. Der Ansatz, dass sich Künstliche Intelligenz daran festmachen ließe, ob eine Maschine den gleichen Output wie ein Mensch nachahmen kann, ist seit den Anfangstagen der Fachdisziplin der Künstlichen Intelligenz bis heute stark verbreitet. Doch ebenso lange wird auch darüber diskutiert, ob damit der Wesensbereich menschlicher Intelligenz
überhaupt erfasst wird. Seit den 50er Jahren wird darüber
debattiert, ob die Simulation eines intelligenten Outputs ausreichend
sein kann für die Frage, ob ein System intelligent ist oder nicht
und ob zur menschlichen Intelligenz nicht gerade etwas hinzuzuzählen
ist, das über einen entsprechenden Abgleich von Outputs gar nicht zu
erfassen ist. Mit dieser Kritik an der Verifikationsmethode ist eine Kritik
an der Idee, dass Maschinen prinzipiell das Niveau menschlicher Intelligenz überhaupt erreichen könnten, eng verbunden. Die Idee, dass
dies prinzipiell erreichbar ist, wird einem Vorschlag von John Searle
folgend auch als starke KI-These bezeichnet, während die Kritiker nur
einer schwachen KI-These zustimmen können, wonach artifizielle Systeme lediglich Teilaspekte menschlicher Intelligenz erfolgreich simulieren können, aber eben nie im vollen Umfang als intelligent zu bezeichnen wären.
Zwei Kritikpunkte an der Vorstellung einer starken KI sind bis heute extrem wirkmächtig:
1.: Künstliche Intelligenz könne niemals ‚wirklich‘ intelligent sein,
weil das selbstbewusste ‚Ich‘ fehle, an dem nicht nur das Denken i.e.S.,
sondern wahlweise auch Intentionalität, Emotionen, phänomenale Bewusstseinszustände, freier Wille, Intuition und Ähnliches mehr hingen.
2.: Künstlicher Intelligenz fehle es an einer Verkörperung und
menschlichen Sozialisation, sie sei nicht eingebettet in eine Welt, in der
sie lernen und mit der sie reziprok und kontinuierlich in Verbindung
steht, was aber konstitutiv für die menschliche Art von Intelligenz sei.
Diese Art der Kritik und der Gegenentwurf von Befürwortern der
starken KI-These zeigen, dass die philosophische Debatte
um Künstliche Intelligenz untrennbar mit den ganz zentralen Fragen
der Philosophie des Geistes der letzten Jahrhunderte verbunden ist. Die
KI-Debatte wirft Fragen auf, deren Antworten von den Grundpositionen
abhängen, die in ihr eingenommen werden. Drei große kartographieren das Feld: Die erste Frage lautet, was das Selbst eigentlich ist, das sich in unserem Geist seiner selbst bewusst wird: Ist es eine Seele, also eine vom Körper zu trennende Substanz, oder handelt es sich um ein stark emergentes Phänomen, das jenseits physikalischer Gesetzmäßigkeiten aus dem Zusammenspiel unseres Gehirns entsteht oder aber ist es schlicht ein besonders kluger Trick der Evolution, der im Rahmen darwinistischer Prozesse entstanden und in keiner Weise mysteriös ist, sondern auch physikalisch erklärt werden kann. Die zweite ist die metaphysische Frage nach unserer Realität, ob das Sein also monistisch oder dualistisch zu verstehen ist, ob es letztlich aus den physikalischen Elementarteilchen und den zwischen ihnen wirkenden Kräften besteht, ober ob im Falle des menschlichen Geistes etwas Spirituelles hinzukommt. Und die dritte schließlich ist die anthropologische Frage nach dem, was der Mensch ist: Ist er etwas Herausgestelltes und Besonderes, das etwa kraft seiner Vernunft allem anderen Leben enthoben ist, oder ist er ein evolutionär besonders weit entwickeltes Tier oder aber ist er letztlich auch nur eine Maschine? Je nach Position zu diesen drei Fragen ergibt sich ein anderer Raum von Optionen, wie die starke KI-These zu beurteilen ist.

Turing solves Nature’s “enigma” post mortem!!!

https://en.m.wikipedia.org/wiki/Bayesian_statistics

According to Bayesian statistics the true believer is always successful in a "quantum" way, as historically shown with Columbus and Pizarro, although they were completely wrong about the size of the earth or the Aztec Inca similarity and incredibly outnumbered by natives, and the same is with the Bayesian way  of Turing to decipher Enigma: He used any hypothesis about the possible meaning based on previous decipherments or regardless of what seems appropriate.

Why don’t we do the same with all nature’s secrets hypothesis with our powerful computer now?!

Here what I mean: “Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. ... Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data” That is mathematically compare relativity with strings theory and with the whole bunch of crazy hypothesis that in my opinion do not stand a chance against the quantum mechanics “objective” facts of CORRESPONDENCE ENTANGLEMENT SUPERPOSITION TUNNELING (CEST) only as a start …

As stated by Wikipedia with the advent if powerful computers, very much like with the mathematical monsters of the Mandelbrot set, we can much easily reconcile our physics hypothesis about reality that until now have being considered unrelated due to poor human hability of quickly managing huge amount  of data without be bored to death finally seen the whole movie instead of the static and meaningless picture … planes fly the same way than ideas: if you do not have enough speed you stall … or, as Einstein said more generally about our life which is like riding a bicycle, that if you stop you fall ...

Turing risolve l'enigma della natura post mortem !!!

Statistiche Bayesiane

https://en.m.wikipedia.org/wiki/Bayesian_statistics

Secondo le statistiche bayesiane il vero credente ha sempre successo in modo "quantistico", come storicamente dimostrato con Colombo e Pizarro, sebbene si sbagliassero completamente sulle dimensioni della terra o sulla somiglianza Aztechi Inca e incredibilmente superati in numero dai nativi, e lo stesso è con il modo bayesiano di Turing per decifrare Enigma: ha usato qualsiasi ipotesi sul possibile significato basata su decifrazioni precedenti o, anche  indipendentemente da ciò che fosse effettivamente fosse appropriato.

Perché non facciamo lo stesso con tutte le ipotesi dei segreti della natura con il nostro potente computer adesso ?!

Ecco cosa intendo: “La statistica bayesiana è una teoria nel campo della statistica basata sull'interpretazione bayesiana della probabilità in cui la probabilità esprime un grado di credenza in un evento.  ... I metodi statistici bayesiani usano il teorema di Bayes per calcolare e aggiornare le probabilità dopo aver ottenuto nuovi dati "Cioè confrontare matematicamente la relatività con la teoria delle stringhe e con tutta una serie di folli ipotesi che secondo me non hanno alcuna possibilità contro la meccanica quantistica"  fatti "oggettivi di CORRESPONDENCE ENTANGLEMENT SUPERPOSITION TUNNELING (CEST) solo come inizio ...

Come affermato da Wikipedia con l'avvento di computer potenti, molto simili ai mostri matematici del set di Mandelbrot, possiamo conciliare molto facilmente le nostre ipotesi fisiche sulla realtà che fino ad ora sono state considerate estranee a causa della scarsa capacità umana di gestire rapidamente enormi quantità  di dati senza annoiarsi a morte finalmente visto tutto il film invece dell'immagine statica e priva di significato ... gli aerei volano allo stesso modo delle idee: se non hai abbastanza velocità stalli …o, come diceva più in generale Einstein a proposito della nostra vita che è come andare in bicicletta, che se ti fermi cadi ...

Introducción a las ideas matemáticas que subyacen a los asistentes virtuales, con indicaciones también a sus aspectos filosóficos, técnicos y comerciales. Cuatro capítulos: (1) inteligencia artificial, test de Turing y procesamiento del lenguaje natural, (2) paradigma simbólico, (3) paradigma estadístico, (4) paradigma conexionista. Hay edición en español e italiano.

Will we be able to escape extinction like the Neandertals by using quantum conscious ability to settle between chaos and order as among everyday life between attractors and fractals  Not even Wikipedia is “conscious”: The Neanderthal’s tailor sir Roger Penrose: An "exocentric" perspective EVOLUTIVELY TOTALLY IN CAPITAL LETTERS is "urgently needed".

The “mind” of the Neanderthal emperor - "clothed" in academic and non-academic arrogance - represents evolutionary homeostasis, individually lethal, just as the tale of the Naked Emperor shows, BUT NOT EVEN Wikipedia UNDERSTANDS IT ...

THE PENROSE’S TITLE ALLUSION to a humble attitude is completely missed in the subsequent introduction that should have to the list have to be approached reversely (what it could possibly mean “understanding without consciousness”?!) with a specular perspective by which to face the relationship between quantum mechanics and the Newtonian world, that is, that of collective "evolutionary" consciousness or general cultural. Instead, it does exactly the opposite: IT IS OBVIOUSLY CONSCIENCE PLAYING AN ESSENTIAL ROLE IN UNDERSTANDING QUANTUM MECHANICS AND NOT THE OPPOSITE, right?! IT IS OBVIOUSLY CONSCIOUSNESS WHICH PLAYS AN ESSENTIAL ROLE IN UNDERSTANDING QUANTUM MECHANICS AND NOT THE OTHER WAY AROUND, isn’t

Le théâtre contemporain accueille aujourd'hui des projections d'images d'une manière particulièrement diversifiée et cette démarche créative semble ne plus étonner personne. La surface de projection qui, au départ, constituait principalement une solution scénographique, implique aujourd'hui une étonnante complexité interprétative autant chez les pionniers de cette esthétique comme Robert Lepage et Elizabeth LeCompte, que dans la génération plus récente, avec Milo Rau ou Krzysztof Garbaczewski. On oublie parfois que le cheminement de l'image projetée dans le spectacle vivant s'ancre dans l'histoire du théâtre, ainsi que dans les zones de rencontre avec d'autres arts dont le cinéma s'avère probablement le plus fécond. Ce parcours est parfois tortueux, car expérimental aussi bien que spectaculaire, marquant à la fois des pas esthétiques majeurs chez les uns et un ludisme excessif chez les autres. Enfin, ce procédé esthétique avance main dans la main avec le progrès technologique et par conséquent suit le développement des différents dispositifs : cinématographique, vidéo, numérique. De l'intermédialité aux humanités numériques, l'analyse de cette démarche prend aujourd'hui encore plusieurs voies théoriques pour pouvoir comprendre ses enjeux et ses parentés.

(ENGLISH)
Contemporary theatre today hosts image projections in a particularly diverse way and this creative approach no longer seems to surprise anyone. The projection surface, which at the beginning was mainly a scenographic solution, nowadays implies an astonishing interpretative complexity both in the pioneers of this aesthetic such as Robert Lepage and Elizabeth LeCompte, and in the more recent generation, with Milo Rau or Krzysztof Garbaczewski. We sometimes forget that the path of the image projected in the performing arts is rooted in the history of theatre, as well as in the areas of encounter with other arts, of which cinema probably proves to be the most fertile. This path is sometimes tortuous, because it is experimental as well as spectacular, marking both major aesthetic steps for some and excessive playfulness for others. Finally, this aesthetic process advances hand in hand with technological progress and consequently follows the development of different devices: cinematographic, video, digital. From intermediality to digital humanities, the analysis of this approach still today takes several theoretical paths in order to be able to understand its stakes and its relatives.

S U M M A R Y : The alleged proof of the non-mechanical, or non-computational, character of the human mind based on Gödel's incompleteness theorem is revisited. Its history is reviewed. The proof, also known as the Lucas argument and the Penrose argument, is refuted. It is claimed, following Gödel himself and other leading logicians, that anti-mechanism is not implied by Gödel's theorems alone. The present paper sets out this refutation in its strongest form, demonstrating general theorems implying the inconsistency of Lucas's arithmetic and the semantic inadequacy of Penrose's arithmetic. On the other hand, the limitations to our capacity for mechanizing or programming the mind are also indicated, together with two other corollaries of Gödel's theorems: that we cannot prove that we are consistent (Gödel's Unknowability Thesis), and that we cannot fully describe our notion of a natural number.

NOTE: This paper is slightly improved compared to the penultimate version  available here  (at the author's academia page).

Se tiene como objetivo revisar algunos de los planteamientos hegelianos a la luz de un problema como la inteligencia artificial. De esta forma, se hará una lectura de la “dialéctica Amo y Esclavo” en la clave de un problema contemporáneo como lo es la inteligencia artificial. Para ello se utilizarán, además de la fenomenología del espíritu, dos posiciones encontradas en el tema de las IA como es el caso de Turing y de Searle.  Esto se hará con el fin de obtener una reflexión acerca de las repercusiones de la máquina dentro de la conformación tanto de nuestras vidas, como del uso contemporáneo de la información. Finalmente, esto nos servirá de base para abrir preguntas sobre las cuales profundizar en un futuro.

La Filosofia e l'Intelligenza Artificiale sono due discipline che, nel corso del tempo , hanno interagito e si sono impegnate in un dialogo fruttuoso: è innegabile, ad esempio, che l'IA abbia plasmato il modo in cui i filosofi concepiscono la mente umana e, viceversa, le diverse conce-zioni della mente hanno avuto un impatto in merito alla ricerca nel campo dell'IA. Siffatte interazioni hanno avuto luogo in quanto gli obiettivi che l'IA si pone l'hanno resa una disciplina di notevole interesse per molti filosofi.

A tal proposito, in quest'elaborato verranno analizzati due esperimenti mentali molto famosi, cioè il "Turing Test" ed il "Chinese Room Argument", formulati rispettivamente da Alan Turing e John R. Searle. Entrambe queste prove mirano a rispondere, in maniera più o meno diretta, all'interrogativo "Le macchine possono pensare?": Turing afferma di essere molto fiducioso, mentre Searle si dichiara assai scettico a riguardo.

Tale domanda non è ovviamente stata dibattuta solo da Turing e Searle, poiché ha dato vita ad una smisurata molteplicità di rivoli interpretativi: esistono infatti approcci anche radicalmente differenti alla disciplina dell'IA. Tramite questo paper non si ambisce ad una trattazione approfondita e rigorosa della domanda poc'anzi citata e neppure di tutti gli interrogativi suscitati dai testi presi in esame. Purtuttavia, l'elaborato mira a fornire alcuni spunti di riflessione limitatamente ad alcuni aspetti relativi all'intelligenza artificiale e alle implicazioni più immediate dei due esperimenti mentali che son stati esaminati.

Как узнать, есть ли сознание у бота? Это тот вопрос, на который будет дан ответ в этой статье. Предлагаемая автором гипотеза сознания (см. другие статьи автора) не может дать однозначного ответа на вопрос о наличии сознания у бота, но по мнению автора значительно приближает к нему. В статье предпринимается попытка определить квалиа как признак сознания, а также очерчиваются специфичные и проверяемые свойства сознания. Это позволяет описать условия проведения теста на наличие сознания и субъективности. Abstract How to find out if a bot has consciousness? This is the question that will be answered in this article. The hypothesis of consciousness proposed by the author (see other articles by the author) cannot give an unambiguous answer to the question of whether the bot has consciousness, but according to the author, it brings it much closer. The article attempts to define qualia as a sign of consciousness, as well as outlines specific and verifiable properties of consciousness. This allows us to describe the conditions for conducting a test for the presence of consciousness and subjectivity. English translation is given at the end.