In our project of understanding the world, we have created physics, biology, psychology, and a number of other disciplines. Now we want to turn our project into a rational one, a science that does not only find good hypotheses about the world, but that does so effectively. This requires that we first understand science itself: How do different sciences relate to each other? Why are there different sciences in the first place? And, within a single science, why does it look like we can distinguish ordinary science from scientific revolutions?

One way to ask how biology, psychology and elementary physics relate is the following: Given enough time and space to write, could we translate each biological statement into a statement in terms of physics that is true if and only if the biological statement is true? Likewise, can we translate psychological statements into statements about the physical states and processes of a system? Can psychological statements be translated into statements about biology?

Here is the gist of my thoughts:

The laws of a correct theory of elementary physics must be able to compress complete descriptions of system without loss. A complete description is a description that, in principle, would allow you to recreate the system exactly. It contains all the information there is in the system.

In contrast, inexact physics and special sciences like biology and psychology are lossy compressors of a system’s complete description. Given a lossy description, you might be able to restore certain features of the system, but never recover it completely (except for some degenerate systems).

To make an analogy:

A .png file compresses an image without loss — given the file, you can recreate the original image on the screen perfectly. The price you pay for this ability is that your file is relatively large.

In contrast, lossy image formats like .gif and .jpg create smaller files and they allow you recreate certain features of the original image, but usually do not recover it completely. For example, .jpg is more faithful to the original colors, .gif preserves edges and structural details better, but neither can restore the initial image completely.

Consequently, taking a .jpg image and saving it to .gif will result in loss both of the information that .jpg does not preserve and of the information that .gif does not preserve.

As may already be clear from the analogy, there are implications of the compression view for the relation between the sciences:

Statements from the special sciences and approximate physics can be translated into (possibly very long, disjunctive) statements about elementary physics without additional loss. However, such a translation will not make the statements more exact — information that is not there in a statement from one of the special sciences won’t be there in its translation, so what you get might be something like “it looks like physical situation A, or like physical situation B, or like physical situation C, or …”.

Statements from the special sciences can be translated into each other, but this will result in additional loss of information. In effect, what we need to do here is to first translate into exact physics (without additional loss) and then recompress into the target special science (with loss). Since different special sciences usually keep different structural details of the state intact, such a translation will usually throw away information. The more different the features that the two special sciences keep, the more loss we suffer.

I say usually since it is conceivable that statements formulated within a certain special science contain strictly more information than the translations within another science, just like statements in a correct elementary physics contain strictly more information than any of the special sciences, and just like a .gif format with 8 bits of color information (256 colors) contains strictly more information than a .gif format with 4 bits of information (16 colors). If you’re a philosopher, you might say that the latter, more coarse theory/format supervenes on the former, and if you’re a daring philosopher of mind, you might hypothesize that the relation between biology and psychology is just like this.

Why are there different special sciences? Why do we need special sciences at all?

The analogous question can be asked about image, audio and movie compression algorithms, and here the answer is clear: We don’t have enough space for lossless compression and in the end all we care about are certain features (and in different situations, we care about different features). In the case of audio compression, we only care about sounds within the range of 20 Hz to 20,000 Hz since everything else isn’t perceivable by the human ear.

Similarly, when we want to describe a phenomenon using scientific theories, we cannot use elementary physics as it takes too much time and space (although that would give us the most accurate predictions) and in the end, we do not care about all aspects of the phenomenon equally anyway. In thinking about how the brain works, neuroscientists do not look at the brain as an arbitrary physical system whose behavior is to be predicted, but instead it is certain aspects of this system that they try to explain. The aspects we care about tell us how to compress our observations into theories, and since we are not always interested in the same aspects, we need different ways of compression: different special sciences.

The compression view also gives us a way to think of the difference between ordinary science that discovers new facts within a framework and scientific revolutions that bring conceptual change:

Ordinary science is the process of finding out how the compressed version of interesting situations look like and how lossy the compression is when we apply existing compression algorithms — theories — to different situations. We smooth out small bugs in the compression algorithm, but fundamentally, we don’t change our framework: we use the existing compression algorithm.

Scientific revolutions change how we compress our observations: Every reasonable revolution either improves how strongly we can compress (e.g. by showing that what we thought of as different phenomena can be explained by the same principle) or makes our compressions less lossy (e.g. by replacing a black box term like elan vital with a structured theory). Since compression and prediction are two sides of the same coin, another interpretation of scientific revolutions is that they change the prediction algorithm whereas ordinary science mainly makes and checks predictions.

Now you be the judge how lossy this view on science really is.

Jeder Moment stellt die Frage “Was willst du tun?” und die Summe unserer Antworten ist, wer wir sind. Jede Minute, die wir vor uns selbst rechtfertigen, entfremdet uns von uns selbst. Die Welt hat einen unerschöpflichen Vorrat an Zeitfüllern, dringenden Verpflichtungen und ganz wichtigen Dingen und wir suchen uns aus, wie groß der Anteil unserer Zeit ist, den wir ihr überlassen. Jede Antwort ist okay, so lange sie für uns okay ist und so lange wir nicht glauben, wir hätten keine Wahl. Wir erschaffen uns selbst, erfinden die Erzählung unseres Lebens und die Welt passt sich an. Die Welt hat keine Wahl.

So, weiter.

Here are the slides for the presentation I held in Tuesday’s philosophy class, in the hope that they may be of some use, even if part of it is incomprehensible for anyone who did not read the paper or listen to the talk:

Wir Menschen unterscheiden uns nicht großartig in unseren Wünschen. Wir wollen Glück, Freude, Freiheit, Unabhängigkeit, Sicherheit, Wissen, Kreativität, Individualität, Sexualität, Freundschaft und Liebe (nun ja, Männer zumindest). Wir schätzen unser Leben, das unserer Freunde, unserer Familie und das unserer sechs Milliarden Mitmenschen. Trotzdem ziehen wir in verschiedene Richtungen, konkurrieren, intrigieren und machen generell den Eindruck, als ob wir es darauf anlegen, paradox zu handeln.

Wenn wir verstehen, welches Ausmaß die Zukunft hat, die auf dem Spiel steht, und wenn wir uns im Großen und Ganzen einig sind, was uns jetzt und für diese Zukunft wichtig ist, warum funktioniert es dann nicht besser^TM?

Warum leben wir nicht längst in Utopia, wenigstens asymptotisch?

Die Erklärung, die ich nicht glaube: Es geht nicht besser. Würde man jeden Menschen fragen, wie sehr diese Welt seinen Vorstellungen entspricht, und so zu einem Gesamtbild kommen, so gäbe es nichts, was dieses Bild dauerhaft besser machen könnte. Für diese Erklärung spricht die Anpassungsfähigkeit unseres Gehirns, die daran schuld ist, dass die meisten Änderungen unsere Gesamtzufriedenheit nicht dauerhaft verbessern. Glück ist die erste Ableitung positiver Veränderung. Aber, erstens: Lasst uns die offensichtlichen Unmenschlichkeiten dieser Welt beheben, dann können wir noch einmal darüber reden, ob es nicht besser geht. Zweitens: Manche Leute scheinen immer ein bisschen glücklicher zu sein als andere. Gene und Umwelteinflüsse legen die Biochemie unseres Gehirns fest und wir sind dabei, beides zu verstehen.

Die Erklärung, die ich gerne glauben würde: Die Probleme unserer Welt sind komplex. Wir sind auf dem Weg zu Lösungen, aber die erfordern ein gewisses Mindestmaß an Zeit und Technologie. Es wäre falsch, sich an neue Technologien zu klammern, weil diese beinahe immer zu polaren Zwecken eingesetzt werden können, aber ein Blick auf die Geschichte macht klar, dass neue Technologien Einfluss haben. Die Kombination aus omnipräsentem mobilem Web für die Massen und Suchmaschinen, die natürliche Sprache verstehen, könnte die Wissensverteilung weiter demokratisieren. Prognosemärkte (die von Google, Microsoft, HP und Intel bereits intern eingesetzt werden) könnten Teile der Politik rationaler gestalten, der Anfang der vollständigen Aufzeichnung der Menschheitsgeschichte alle kollektiven Entscheidungen.

Die Erklärung, die immer nur andere betrifft: Das sind alles egoistische Nichtsnutze, denen die Menschheit egal ist, so lange sie Familie, Job und ein halbwegs interessantes Leben haben. Unterstützt werden sie in ihrer Haltung von Wissenschaft und Wirtschaft, die Gedanken über den Lauf der Welt zugunsten kurzfristiger und handfester Resultate bestrafen. Andererseits werden gesellschaftliche Fragen gerne mal eben beim Mittagessen gelöst (wenn gerade keine Fußball-WM stattfindet) und mit zufriedenem “Tja, so müsste man’s machen” abgehakt. Zu Handlungen kommt es natürlich nicht, denn dafür bräuchte man Lösungen, die tatsächlich funktionieren, müsste herausfinden, wie man als einzelner zur Umsetzung beitragen kann, und müsste die Lösungen finden, von denen man selbst profitiert. Wozu die Menschheit retten, wenn es nicht entweder Geld, Sex oder Status bringt oder sowieso auf dem Weg zur Rettung des eigenen Lebens liegt?

Die Erklärung, die mich (und dich!) betrifft: Wir arbeiten auf Teilziele hin, die nicht direkt dem entsprechen, was wir wirklich wollen. Weil das fast jeder tut, weil verschiedene Teilziele oft gegensätzliche Aktionen erfordern und weil die Ziele selbst dann oft nicht erreicht werden, heben sich unsere Bemühungen mehr oder weniger auf. Unser Tun führt so zwar zu neuen Methoden und zu neuen Erkenntnissen über unsere Welt, die indirekt zur Realisierung unserer Wünsche beitragen können, ist aber ineffektiv und potentiell schädlich. In dem Moment, in dem wir uns einer Ideologie verschreiben, weil wir glauben, dass die Durchsetzung von deren Axiomen den Menschen das geben wird, was sie wirklich wollen, arbeiten wir an der Verbreitung der Ideologie und nicht mehr an den eigentlichen Problemen.

Glücklicherweise ist die Lösung einfach: Wir wählen in jedem Moment die Handlung, die für sich genommen am ehesten unseren Werten entspricht, anstatt uns auf eine Ideologie oder auf ein langfristiges Ziel festzulegen und darauf hinzuarbeiten.

Dummerweise funktioniert sie nicht in jedem Fall, insbesondere dann nicht, wenn wir existentielle Risiken — Katastrophen, die das Ende der Menschheit bedeuten können — in Betracht ziehen und uns der Fortbestand der Menschheit doch ein bisschen kümmert.

KI in zwei Sätzen: Die Annahme, dass wir in absehbarer Zeit auf einen relativ allgemeinen Mustererkennungsalgorithmus stoßen, der mit genügend Rechenpower die Mustererkennungs- und Vorhersagefähigkeiten des menschlichen Gehirns übertrifft, ist (für diese Art von Annahmen) weit verbreitet. Deutlich kontroverser ist die Idee, dass Algorithmen praktisch möglich sein könnten, die Veränderungen an sich selbst vornehmen, um so große Klassen von formalisierbaren Probleme bestmöglich zu lösen — unabhängig davon, wie anspruchsvoll diese Probleme sind, d.h. wie viel Intelligenz zu deren Lösung nötig ist.

Die formale Analyse der Approximierbarkeit theoretischer Modelle von Superintelligenz in unserer physikalischen Welt benötigt unsere Aufmerksamkeit, wenn wir wissen wollen, wo auf unserer Liste existentieller Risiken und Chancen maschinelles Lernen steht. Forschung auf dem Gebiet ist ein langfristiges Vorhaben, eines, das jahrelanges Lernen voraussetzt und das mit signifikanter Wahrscheinlichkeit fehlschlägt. Das ändert nichts daran, dass solche Forschung wirklich, wirklich wichtig ist.

Letzte Woche, bei Pasta und Pizza, hat Jürgen die Frage in die Runde geworfen, wie groß denn der Anteil unserer Zeit sei, den wir für das Jetzt leben, und wie groß der, den wir für die Zukunft leben. Zunächst allgemeine Übereinkunft, dass man seine Zeit wohl kaum so klar kategorisieren könne. Dann, von dem, dessen theoretische Grundlagenforschung auch in 100 Jahren noch relevant sein wird (mehr als jetzt): I don’t care about the future.

I do. Aber vielleicht macht das keinen Unterschied.

Auf der Suche nach Wahrheit sollten wir Welt- und Menschenbilder in dem Moment verwerfen, in dem wir merken, dass es uns schwer fällt, Hinweise dafür zu finden, dass sie die Welt besser beschreiben als konkurrierende Ideen. Weil das, was wir momentan glauben, beeinflusst, mit wem wir zu tun haben, welche Informationen wir an uns heran lassen und wie wir Nachrichten interpretieren, sollten wir dem unguten Gefühl im großen Denkknäuel umso mehr Aufmerksamkeit schenken.

Die Realität ist, wie sie ist, unabhängig davon, was wir glauben (und das ändert sich auch nicht dadurch, dass die Autorin eines der populärsten amerikanischen Bücher der letzten Monate das Gegenteil behauptet). Die meisten von uns können nicht vermeiden, im Laufe ihres Lebens der Wahrheit näher zu kommen, sei es in Bezug auf Wissenschaft, Beziehungen oder Menschen im Allgemeinen. Insofern ist es sinnvoller, wenn wir falsche Einstellungen nicht zunächst verteidigen und uns langsam zurückziehen, sondern mit jedem gegenteiligen Indiz die Wahrscheinlichkeit unserer Ideen nach unten korrigieren und unplausible Theorien so früh wie möglich verwerfen. Wenn das bedeutet, zu verstehen, dass Zynismus in manch ungemütlicher Hinsicht Realismus ist, müssen wir auch das akzeptieren (oder private Inseln schaffen, auf denen andere Regeln gelten — aber dem Willen von Welt und Evolution widersetzt man sich nicht leicht und selten auf Dauer).

Menschen sind zu anpassungsfähig, als dass uns kurzfristiges Unglück abschrecken sollte, insbesondere nicht dann, wenn es auf längere Sicht zu mehr Wahrheit und damit zu einer höheren Chance darauf führt, unsere Ziele zu erreichen. Wir gewöhnen uns an praktisch jede Änderung so weit, dass unsere Lebenszufriedenheit nach einiger Zeit der vor der Veränderung entspricht. Wir gewöhnen uns an Klassen von Veränderungen, egal ob Tod (nun ja, zumindest an den anderer Leute — beim eigenen bleibt wenig Gewöhnungszeit), Trennung oder andere Traumata, indem wir abrufbare Verhaltensmuster entwickeln und vermutlich gewöhnen wir uns auch an den Gesamtpegel an Veränderungen in unserem Leben.

In den Augenblicken, in denen wir die Götter nicht dafür verfluchen, dass gerade diese Anpassungsfähigkeit uns gegenüber den Tragödien dieser Welt blind macht, sollten wir ihnen danken, denn manche Veränderungen sind endgültig. Manche Dinge kann man nur einmal sagen und so meinen. Das rettet unser Handeln vor Bedeutungslosigkeit; der Wert dessen, was wir tun, liegt in den Dingen, auf die wir dafür verzichten. Dinge, für die man nichts aufgeben würde, sind nichts wert. Es ist nicht die Hochzeitszeremonie, die dem “Ja, ich will” so viel Wert verleiht, sondern das Wissen, dass wir mit den Worten manche Freiheiten für jemand anderen und für immer aufgeben (oder, wenn wir sie wiedererlangen wollen, das nur unter mittelschweren gesellschaftlichen Strafen tun können).

Was wahr ist ändert sich nicht dadurch, dass wir anderer Meinung sind oder dadurch, dass wir es ignorieren. Das, was wir tun, wird durch das bedeutungsvoll, was wir nicht tun. Glück braucht Bedeutung um nicht leer zu sein, Bedeutung braucht Wahrheit um überhaupt zu existieren. Sowohl Glück als auch Unglück sind Teil unserer Welt und je besser wir diese Welt verstehen, desto mehr Einfluss haben wir auf sie.

The infinite possibilities each day holds should stagger the mind. The sheer number of experiences I could have is uncountable, breathtaking. And I’m sitting here refreshing my inbox. We live trapped in loops. reliving a few days over and over, and we envision only a handful of paths laid out ahead of us. We see the same things each day, we respond the same way, we think the same thoughts, each day a slight variation on the last, every moment smoothly following the gentle curves of societal norms. We act like if we just get through today, tomororrow our dreams will come back to us. — xkcd

In einer Welt, die ohne vorgegebenen Sinn einfach existiert, erfinden wir uns und das, was wir als sinnvoll ansehen, auf dem Weg in die Zukunft. Jegliche Gründe, Ziele und Zwecke jenseits der biologischen erschaffen wir selbst. Aus “Wie soll ich handeln?” wird “Wer will ich sein?” und “In welcher Welt will ich leben?”.

Mit jeder Handlung machen wir aus dem aktuellen Zustand unserer Welt einen anderen. Der Raum aller möglichen Zustände ist die Menge der Zustände, die keine physikalischen Gesetze verletzen. Denkbar ist eine astronomische Zahl, wünschenswert sind die wenigsten davon. Die Menge der Zustände, die bewusstes Leben enthalten, macht nur eine winzige Ecke im Raum aller möglichen Zustände aus. Unsere Welt ist ein Punkt irgendwo in dieser Ecke.

Manche Zustände unterscheiden sich stärker voneinander als andere. Der Zustand der Welt, in der die Tasse Tee neben mir zwei Zentimeter weiter links steht, liegt näher am aktuellen als der, in dem sich die Tasse in einen feuerspeienden Drachen verwandelt hat. Ein mögliches Maß für den Abstand von zwei Zuständen wäre eine Art Informationsdistanz: Die Länge oder Laufzeit des kürzesten Algorithmus, der aus einer vollständigen Beschreibung von Zustand A die entsprechende Beschreibung von Zustand B berechnet.

Indem ich mich für eine Handlung entscheide, wähle ich einen unserer Nachbarn im im Raum aller möglichen Zustände. Wie sieht die Teilmenge der Zustände aus, die vom jetzigen Zustand der Welt aus durch mein Handeln oder Nicht-Handeln erreicht werden können? Das bestimmt, welchen Einfluss ich mit meinen Entscheidungen als einzelner auf die Welt habe.

Die Frage, was festlegt, welche Zustände ich erreichen kann und welche nicht, führt unmittelbar zu der Frage danach, was unsere Position im Raum aller möglichen Zustände bis jetzt am stärksten verändert hat. Die Antwort definiert, was Optimierungsprozesse sind: Systeme, die den Zustand unserer Welt auf einen kleinen Zielraum mit bestimmten Eigenschaften hin bewegen.

• Evolution ist ein Optimierungsprozess, der Replikatoren — Bakterien, Tiere, Menschen und die Gene dahinter — durch Mutation, Rekombination und Selektion auf effektivere Vermehrung hin optimiert.
• Ein Schachcomputer ist ein Optimierungsprozess, der aus der Vielzahl möglicher Kombinationen von Schachzügen die auswählt, die die Position der Figuren auf einem Schachbrett so verändern, dass sich die Welt in einen Zielraum mit der Eigenschaft “Der Schachcomputer gewinnt.” bewegt.
• Menschliche Intelligenz ist ein mächtiger Optimierungsprozess, der für verschiedenste Ziele eingesetzt werden kann. Rationalität erreicht klar definierte Ziele, nonlineares Handeln die unbewussten.

Cognitive Science ist die Lehre von den Optimierungsprozessen. In Psychologie und Neurobiologie wird der effektivste bekannte Optimierungsprozess, das menschliche Gehirn, analysiert, in Mathe, Informatik, Statistik und Logik werden die methodische Grundlagen für den Bau von künstlichen Optimierungsprozessen unterrichtet.

Optimierung ist ein Vorhersageproblem. Jeder Maschine steht eine festgelegte Menge an Aktionen zur Verfügung. Um ein Ziel zu erreichen, muss die Maschine vorhersagen, welche Kombination aus Aktionen die Welt dem Zielzustand am nächsten bringt. Dass wir Menschen die Auswirkungen unserer Handlungen vorhersagen können, zeigt, dass Quanten- und Chaoseffekte bei Vorhersagen umgangen werden können, wenn man Abstriche bei der Genauigkeit der Prognosen macht.

Intelligenz ist die Fähigkeit, akkurate Vorhersagen zu treffen um Aktionsfolgen zu finden, die unsere Zukunft auf kleine, weit entfernte Regionen im Raum des Möglichen hinsteuern. Die Frage, ob künstliche Intelligenz möglich ist, lautet eigentlich: “Wie weit werden wir uns übertreffen? Wo liegen die Grenzen der Berechenbarkeit?”

Weil die Grenzen, denen wir unterliegen, universell sind, sehen wir sie nicht. Algorithmen, die Information optimal extrahieren, unterliegen keinen kognitiven Fehlern. Die unvoreingenommene Instrumentalisierung aller verfügbaren Mittel stellt einen enormen Machtzuwachs dar; als Menschen schaffen wir es oft nicht, funktionaler Fixiertheit zu entrinnen, sobald wir einmal gelernt haben, wozu etwas gut ist.

Im nächsten und letzten Schritt, der genauso unvermeidbar und unintuitiv ist wie die davor, schreiben wir Optimierungsprozesse, die den Teil ihrer selbst restrukturieren, der für das Optimieren zuständig ist. Algorithmen, die vorhersagen, welche Veränderungen es braucht, um bessere Vorhersagen zu treffen. Prozesse, die Welt auf Zielregionen hin bewegen, von denen wir nicht gedacht hätten, dass sie in unserer unmittelbaren Nachbarschaft liegen.

Nonetheless I do not believe in demons. The concept of demons is lousy at explaining what goes on in ill minds and has been replaced by psychological theories that, albeit less colorful, have much greater explanatory power. Nothing that exists in the real world has been shown to inhabit the causal position that was attributed to demons with regard to mental “misbehavior”.

Eliminative materialists deny that beliefs are any more real than demons. According to Paul Churchland, beliefs and other propositional attitudes don’t refer to anything in the real world. Nothing has the causal and semantic properties we attribute to beliefs, therefore these concepts will eventually be replaced by a theory of mind that explains our actions, thoughts and sensations a lot better than folk psychology and that is based on empiricism rather than introspection.

In order to examine whether eliminative materialism makes sense, I am first going to look at what folk psychology, the theory eliminative materialism intends to replace, actually looks like and, especially, what the building blocks of folk psychology — propositional attitudes like beliefs and desires — really are. In the next step, I evaluate Churchland’s arguments in favor of eliminative materialism and contrast his theory with an approach to the theory of mind that does not question the validity of propositional attitudes.

Folk Psychology

A proposition is the content of a statement that can be true or false. When we talk about the world, we usually use propositions. “It is raining outside” is a simple example. A propositional attitude is the attitude of a person towards a proposition. I can have different attitudes towards one proposition, for instance, I can believe that it is raining outside, I can desire rain or fear rain. Intentionality is often described as the “aboutness” of mental states. Mental states have the unique property of being directed at objects. Since propositional states, e.g. the hope that it is raining outside, are about something, in my example the rain outside, they are said to be intentional mental states.

When Churchland uses the term “folk psychology”, he refers to our habit of relating different propositional attitudes towards each other in law-like ways. Two propositional attitudes may be equivalent, mutually inconsistent or one attitude may entail the other. When we talk about other people and we mention that someone fears that x happens, we usually infer that this person does not desire that x happens. We are experts at predicting the behavior of other persons by attributing certain attitudes to them and thinking about the actions these attitudes result in.

We are similarly good at predicting the physical behavior of middle-sized, common objects because doing so was just as helpful in our evolutionary past as being able to predict the behavior of our friends and foes. Nonetheless, our common-sense beliefs about physics are as intuitive as they are fundamentally flawed. If you did not take physics in high school, you might even think that there is such as thing as objective time. It turns out that this is not true. For some observers, A happens before B, for others B before A — a fact that is hard to digest even years after hearing about it the first time.

Scientific Thinking

When we ponder how to decide the question whether we have reason to think that our intuitive notion of beliefs and similar attitudes is not as far from reality as our intuitive notion of physics, it is helpful to remember how we decide analogous questions in physics. When we have two competing theories and none of them is obviously false, we let nature decide and favor the theory that is most exact in its predictions. If one theory works well for one area and a different theory works better for others — think of general relativity and quantum mechanics — we choose predictive success over consistency and use each theory where it works best.

Before starting to take apart folk psychology in scientific terms, we first need to establish whether it actually is a scientific theory. In science, a theory is an explanation of a natural phenomenon that is capable of predicting future observations of the same kind, and capable of being falsified through empirical observation. Our network of common-sense psychological concepts enables us to explain and predict the behavior of other persons with remarkable success. Since these predictions are predictions over behavior, they can be falsified, and, if they are falsified, we need to doubt the folk-psychological hypotheses folk-psychology relies on.

According to Churchland, folk psychology is inadequate in its explanations, has not improved significantly over the last 2000 years, limits its explanatory power by focusing on propositional elements and does not even have the normative qualities that are sometimes attributed to it.

It is true that folk psychology cannot explain lots of important phenomena. Mental illness, creativity, differences in intelligence, the psychological function of sleep, perceptual illusions, nonlinguistic learning processes — the list of those aspects of mind that are not accounted for by our everyday thinking in terms of propositional attitudes could be almost arbitrarily long. This does not necessarily imply that folk psychology is false, but it does suggest that, as a scientific theory, it is by far more shallow than an elaborate theory of mind should be. A theory that makes no predictions at all about lots of the phenomena we are interested in is only barely better than a theory that makes wrong predictions.

Folk psychology has remained largely unchanged for the last 2000 years. Looking at the list of unexplained phenomena, one could expect a steady improvement of folk psychology in order for it to catch up with the advances of neuroscience. Nothing like this is happening. For all practical purposes, the folk psychology of today is identical to the folk psychology of the Greeks two or three thousand years ago.

Folk psychology is limited by the fact that its elements are modeled on the elements of human language. This is a subtle point. We live in a world where nearly all the talk about thoughts is talk about propositions, therefore the constraints that are imposed upon us are not apparent to us. Nonetheless they do exist and keep us from describing processes like the large-scale non-linguistic conceptual changes that go on during the first months of a new-born in any meaningful way.

Even as a normative theory, a theory that describes how we should behave, folk psychology is far from perfect. The normative dimension of folk psychology depends on how we value the propositions it deals with, its rationality is not ideal (since we do not know how such a rationality would look like) and it is questionable whether a framework of cognitive virtue would really work on the level of propositional attitudes since this presupposes the use of language.

Eliminative Materialism

The conclusion from all these deficits is called eliminative materialism and can be summed up as follows: Folk psychology describes our internal activities in a way that is a radically inadequate. As a scientific theory, it is probably too confused and too incomplete than that it could ever be fixed by small adjustments and, therefore, it will be replaced by a better theory sooner or later. Even if it is hard to imagine a world that does completely without current folk psychology since this theory is involved with every aspect of our culture, it does not have any features that justify its predominant status.

Quite to the contrary: If we accept that our introspective judgement does not have any special status or any guarantee for being right, we might realize that the conviction that there has to exist something that has the causal and semantic properties we attribute to propositional attitudes has long been one of the obstacles to a unified theory of mind. It might feel as if our thoughts were about something, but this is less important for the issue at hand than one might think. How something feels to us does not say much about how the physical reality of our thought processes looks like. The subjective feeling itself is to be explained by qualia, not by intentionality, and Churchland does not claim that qualia can be eliminated. The concept of intentionality, however, will turn out to be empty quickly as soon as we switch to the framework of eliminative materialism.

The central claim of eliminative materialism might be one of those facts that are hard to digest even years after hearing about them the first time: Beliefs will not be explained by neuroscience as the result of some low-level brain functions. Beliefs simply don’t exist.

Still I won’t be able to stop thinking in terms of beliefs and desires, fears and hopes, mostly because it is the only way that works from a pragmatic point of view. I have to admit that I could not give up my Newtonian intuitions on everyday physics after I learned about general relativity and quantum physics either. The human mind has not been shaped by evolution to match reality where a less expensive and less accurate hack suffices to keep our genes in circulation. The best we can currently hope for is to realize that it is not the world that is bizarre — it is our intuitions.

It is amazing that, after almost 30 years of philosophical discussions, John Searle’s argument against the possibility of programming a robot in a way that makes it really think is still alive. I am now going to analyze what it means for an entity to have intentionality, then give a short account of the strongest version of Searle’s thought experiment and finally argue that the only way to deny intentionality to robots on the grounds of Searle’s thought experiment is to assume a priori that intentionality is tied to biochemical processes.

Intentionality is the difference between a human being that answers questions about a story and a machine that uses a lookup table that includes all possible questions and appropriate answers. The difference between arriving at an answer by understanding the question compared to arriving at an answer by purely mechanical means is to be found not in different behavior but in the mental states that are present in the process of answering the question.

Mental states are conditions like being in pain, having the experience of seeing red, feeling love and remembering that the Statue of Liberty played an important role in a story. A mental state cannot be present without an entity that experiences the state, therefore there can be no understanding without some entity that understands. Since experience cannot be present without consciousness, the entity needs to be conscious. By definition, entities that are not conscious don’t have subjective experiences.

For very simple systems like thermostats, it is safe to assume that no understanding is involved. Even for systems that have or exceed human capacities in a specific realm, e.g. chess computers, we can assume that these systems do not have any intentionality.

Suppose we design a program that does not just represent information by means of symbols that can be identified with real-world counterparts but simulates the actual sequence of neuron firings at the synapses of a human brain. Suppose further that this program is part of brain-shaped computer that is contained within a robot that receives sensory input, e.g. through cameras, and communicates with the world by motor output in a way that is analogous to the motor capabilities we have. If the behavior that this robot shows is indistinguishable from the behavior of real human beings, should we attribute intentional states to the robot?

According to Searle, such a robot cannot have intentionality. A little man inside the robot could be receiving uninterpreted, purely syntactical symbols from the robot’s sensory receptors, he could follow fixed rules that tell him how the input symbols relate to manipulating a system of water pipes that are modeled similar to the neuronal connections of a human brain and how the result of the pipe manipulation is connected to returning other uninterpreted formal symbols to the motor mechanisms of the robot. If the man knew nothing about what the purpose of manipulating the water pipe system was, we would certainly not attribute understanding to the man.

Therefore, Searle argues, we cannot conclude that something has intentionality on the grounds that it has a certain sort of input and output and a program in between. Furthermore, we don’t have any reason to assume that intentionality has anything to do with computer programs, i.e. computational operations on purely formally specified elements, since for any program it is possible for something to instantiate that program and still not have any mental states.

Although the claim that behavior is not sufficient to make attributions of intentionality is true, it is not sufficient to conclude that no program can possibly exist that, when instantiated, has mental states. The systems view of Searle’s robot — even if the man himself has no understanding, the conjunction of man and water pipes does have understanding — sounds less absurd if you take a moment to think about how such a system would look like. In contrast to what Searle claims, it is not possible for the man operating the water pipes to internalize the formal structure of the water pipes since no system can perfectly simulate a system of comparable complexity within itself.

Even if the man could internalize and simulate a system analogous to the 10 trillion neurons of our brain with on average 7,000 synaptic connections to other neurons, it is not clear that the simulation of all the chemical and physical interactions going within the quadrillions of interconnections will not give rise to emergent phenomena, one of which may be actual understanding. It makes a lot of sense to point out, as Searle does, that there is a significant difference between physical phenomena and the simulation of physical phenomena. A simulated rainstorm does not make you wet, a simulated fire does not burn down anything. Why should we suppose that a simulation of understanding, be it executed by a computer or by another brain, actually understands anything? Because mind may be fundamentally different from other phenomena in that it is not so much the physical constituents that make up the phenomenon but the pattern of the constituents. Patterns don’t get lost when they are simulated. We cannot be sure that it is the patterns that matter, but we cannot be sure that this is not the case either.

Searle seems to be sure that this is not the case when he states that, as long as we simulate only the formal structure of the sequence of neuronal firings at the synapses, we are still missing what matters about the brain, that is, its ability to produce intentional states, since even such a low-level simulation is no more than syntactic manipulation that relates symbols to each other but not to the external world. It is obvious that, if you subscribe to the view that only certain biochemical processes can cause experiental states, there is no way to convince you that other physical arrangements may result in the same experiential states. However, it is important to note that Searle’s claim that “all the computer has is more symbols” is not sufficient to justify this view. The information that is received by our sensory receptors and converted into electrical signals is no less symbolic than the information that is processed by the program of the robot. The information is no more grounded in reality than the signals from the light that hits the camera of the robot.

Therefore, to say that no purely formal model will ever be sufficient for intentionality because what matters about brain operation is not the formal shadow cast by the sequence of synapses but rather the actual properties of the sequences can only justified by an a priori assumption that intentional states can be caused by biological mechanisms exclusively. If you take into account the fact that biology, which follows the rules of physics, is no more semantic than computational models which follow purely syntactical rules and that physical events involving complex, large-scale patterns may result in phenomena that are qualitatively different from their low-level consituents, you don’t have much ground for the claim that robots can’t show understanding just because they are implemented as a computer with the right sort of program.

In 1667, when the chemistry behind fire was still a great mystery, Johann Becher theorized that all flammable materials contain phlogiston, a substance without color, odor, taste, or weight, that is liberated in burning. Similarly, two hundred years later, Lord Kelvin went on record saying that “[life's] power of directing the motions of moving particles, in the demonstrated daily miracle of our human free-will, and in the growth of generation after generation of plants from a single seed, are infinitely different from any possible result of the fortuitous concurrence of atoms… Modern biologists were coming once more to the acceptance of something and that was a vital principle.”

Nowadays we know that combustion requires oxygen, a substance that was well known in Becher’s time, and that life is not caused by a mystical essence that makes inanimate substances animate but by cell processes that can be explained without any physics that were not known in Kelvin’s time. We cannot exclude that Searle’s suggestion that understanding can only be caused by a hitherto unknown mechanism in the brain’s biochemistry is true, but it nonetheless strikes me as improbable and may well be a repetition of the common error of making up new laws of physics instead of trying to see where a phenomenon fits into the current framework of the natural sciences.

[1] The entity may not necessarily be physical, but the occurrence of a mental state implies that something experiences the state.

[2] This implies that either animals have some degree of consciousness or that animals are no more than machines without actual intentionality. Cf. Jean-Paul Sartre, Being and Nothingness.

[3] Which, to our knowledge, could be computable.

Dennett introduces three levels of abstraction we can use to describe intentional systems: Descriptions on the level of physics, a functional perspective and the intentional stance. In order to determine in how far these levels presuppose optimal design and rational behavior, I am first going to explain what Dennett means when he talks about intentional systems, then describe each of the three different levels of abstraction and finally analyze what role the notions of optimality and rationality play for each of them.

When Dennett talks about intentionality, what he refers to is a property of linguistic entities. An entity is intentional if it is not possible to replace part of its description with terms that describe the same physical object and not to lose part of what the entity means. Intentional systems are systems with states that “can be directed on something”, systems that have states that are “about something”.

Obviously, the most basic perspective of looking at a system in the physical world, be it intentional or not, is the physical stance. We base our description of a system solely on the actual physical state of a system and predict its behavior exclusively by applying our knowledge about the laws of nature to this state. Such a low-level stance makes it possible to achieve a level of perfection in our predictions that no other stance allows — only the physical point of view allows us to know beforehand when a system will break down and stop functioning as intended. There is no need to know anything about how close the behavior of the system is to optimality or whether it makes sense to even think about the system in terms of rationality. On the other hand, such a stance can’t tell us much about systems that are not really simple or simplified. Even in predicting breakdown, the causes have to be easily locatable, otherwise the sheer complexity of the system we want to explain will make it impossible to predict anything from a physical point of view.

As systems get more complex, it makes more and more sense to adapt the design stance. Thinking about how a system is designed and which functions it fulfills enables us to predict behavior in a way that is computationally much more economical. There is no need to know how the electrons within the central processing unit of my computer move in order for me to tell that typing a letter will make it appear on the screen. There is just one condition: My computer must not break down this very second, the system we are looking at needs to work as designed. Just like any other predictions on a non-physical level, design stance predictions can’t foresee malfunctions since everything we take into account when thinking about the behavior of the system is the functional design — there is no need to assume that the actual physical condition is part of our knowledge about the system. Every system can be divided into different smaller or larger functional parts. In order to make functional predictions, the first thing one needs to do is to establish the level of abstraction of the individual functional parts. How small are the functional elements that we need to look at in order to make meaningful predictions? For really complex systems, there is no clear-cut answer to this question. Since the human brain is said to be the most complex system in the known universe, we might need to adapt a different, more coarse stance.

Looking at a system from the intentional stance implies that we ascribe to the system the possession of certain information, assume that the system acts in the direction of certain goals and that it always follows the most reasonable action relative to the information and goals the system has. Call the information possessed by the system “beliefs”, call the goals “desires” and the level of abstraction of the intentional stance matches the level we use when we talk about behavior in our everyday life. There is no need to assume that a system that possesses information really believes, but it is nonetheless striking how well these two notions match. The intentional stance not only assumes that a system will function as designed as does the design stance, but also that the design is optimal and that the system therefore always chooses the most rational action relative to its goals. Therefore, whenever we can suppose that it is valid to assume that a system has optimal design, we are justified in assuming the intentional stance as it may well be impossible to apply either the design or the physical stance in a computationally feasible way. Natural selection is an optimization process that molds systems in the direction of optimal design. When we try to find out how close a system is to being optimally designed, we need to take into account the extent of the information the system has and how its actions are constrained.

If a system turns out to be unpredictable from an intentional stance, e.g. because it acts in a way that defies all rules of rationality and logic, we can still collect data about the response pattern of the system and adapt a design stance once we have collected enough data to draw some conclusions about the functional design of the system. Dennett himself makes clear that one can’t stay one the level of “belief” in order to explain the “actual, empirical” behavior of a believer — one needs to descend one level and look at the believer from a functional perspective.

According to Dennett, rationality is optimal design relative to a goal or to a hierarchy of goals and a set of constraints. When we predict intentional systems, what we are doing is no more than to predict what the most rational system would do, given the same goals, constraints and the same information about the state of affairs as the system we want to predict. The assumption of rationality entails a few other beliefs about the system that we suppose is rational. We can assume that, usually, it does not desire its own destruction and that it follows the truths of logic, since assuming that the system has some beliefs but acts contrary to what follows from these beliefs would violate basic rules of rationality. Systems in the real world are not perfectly rational, therefore not all logical truths appear in those systems, but, in order to make predictions from an intentional stance, we just assume perfect rationality and hope that the drawbacks are smaller than those associated with any other stance.

Dennett introduces the concept of an intentional system in order to connect the intentional domain to the non-intentional domain of the physical sciences. It is quite obvious that he achieves this goal. From a pragmatic point of view, our beliefs can be identified with the information we possess about the world, our desires are our goals and a large part of our behavior can be described by assuming that we approximate an optimal rational agent trying to achieve its goals within certain informational and physical constraints, a description that completely eliminates intentional wording. Dennett’s article does not explain intelligence, and neither does Dennett aim at a complete theory of mind with this article. What he does talk about is a way of thinking about systems, both intelligent and non-intelligent, that eliminates non-scientific vocabulary and that, in the end, might allow us to talk about and measure intelligence in a way that is both more formal and much more general than the Turing test or any known IQ test.

Wenn wir Nahrung zu uns nehmen, trinken oder Sex haben, wird in unserem Gehirn der Neurotransmitter Dopamin freigesetzt. Was auch immer wir dann gerade tun, sehen und fühlen, Dopamin verknüpft es mit einem Gefühl von Glück und Belohnung und führt so dazu, dass wir, wenn unser Dopaminspiegel wieder auf einem niedrigeren Level ist, diese Sinneseindrücke und Aktivitäten wiederholen wollen. Dopamin ist der Grund dafür, dass wir ein Verlangen danach haben, uns ein Stück Pizza in den Mind zu schieben, es zu kauen und zu schlucken.

Schon wenn wir die knusprig braune Schinken-Thunfisch-Pizza vor uns sehen, wird im Hypothalamus, dem Steuerzentrum für alles, was unbewusst in unserem Körper abläuft, etwas Serotonin freigesetzt. Mit den ersten Bissen und den damit aufgenommenen Kohlenhydraten steigt der Serotonin-Level im Hypothalamus weiter an, ein Gefühl der Zufriedenheit breitet sich aus. Wenn Schokolade glücklich macht, dann deswegen, weil sie Tryptophan enthält, eine Aminosäure, aus der im zentralen Nervensystem Serotonin aufgebaut wird. Serotonin führt dazu, dass es uns gefällt, zu essen.

Wenn wir selbst bestimmen könnten, wonach wir verlangen und was uns gefällt, beispielsweise indem wir regulieren, wann welche Neurotransmitter freigesetzt werden, hätten wir ein Maß an Kontrolle über unser Leben, das alles überschreitet, was man durch Selbstdisziplin erreichen kann. Wenn das, was wir wollen, weil es zu unseren Überlebenschancen im Sinne der Evolution beiträgt und deswegen als “gut” in unseren Genen verdrahtet ist, nicht im Einklang mit dem ist, was wir bewusst wollen, könnten wir es ändern.

Wir könnten das Verlangen nach sozialem Status und die Beeinflussbarkeit durch den sozialen Status anderer abschalten und unbeeinflusst von Autoritätsdenken objektiver denken und handeln. Wir könnten damit aufhören, Menschen unbewusst danach zu beurteilen, wie symmetrisch ihr Gesicht ist, wie sehr es dem Durchschnitt entspricht und wie sehr das Verhältnis zwischen Taillen- und Hüftumfang dem entspricht, das unsere Gene bei Menschen des anderen Geschlechts am liebsten sehen. Wir könnten uns dafür entscheiden, die romantische Liebe für einen Partner nie enden zu lassen (ein ausgeglichener Oxytocinspiegel wäre ein Anfang). Wir könnten Wissen zur aufregendsten Sache der Welt machen, zu etwas, wonach wir mehr Verlangen haben als nach allem anderen. Wir könnten dafür sorgen, dass wir in allem Sinn sehen — oder in nichts.

Unsere Gene legen die Konfiguration fest, die bestimmt, mit welchen Gefühlen wir auf welche Stimuli reagieren, wonach wir verlangen und was uns gefällt. Es ist unwahrscheinlich, dass diese Ausstattung dem entspricht, was wir als bewusste, nicht oder nicht ausschließlich an der Weiterverbreitung unserer Gene interessierte Wesen wählen würden, wenn wir die Wahl hätten.

Was von uns bleibt, wenn wir die Wahl haben, ist, wer wir wirklich sind.