‘Beginning to reason is like stepping onto an escalator that leads upward and out of sight. Once we take the first step, the distance to be travelled is independent of our will and we cannot know in advance where we shall end.’

— Peter Singer (1982)

‘You seriously believe a universe in which billions of sentient beings on this planet alone die horrible deaths in war, famine and plauge [sic], a universe in which people have a fleetingly short time to live before their health and strength drain away, a universe in which misunderstanding is endemic and barriers between minds are unbreechable, a universe in which most sentients are forced to serve others to obtain the basic necessities of life and in which a great many live under fear and repression, a universe where the only known planet with sentient life is under constant threat of being wiped clean by a whole range of disasters, where the entire population is under some degree of harmful delusion, doesn’t suck?’

— Michael Wilson

There are many situations where we lack knowledge to move the world from an undesired state to a more desired state. We call these situations problems. In the following, I describe what I see as one of the major challenges that a technical solution to the problem of problem solving faces.

If we cannot ignore a problem, what we do is this: We take our limited knowledge about the world, our limited knowledge about what we want, and use our limited reasoning capabilities to find out what we should do to move the world closer to how we want it to be. Given that we believe the world to be like this, and given that we would like the world to be like that, we infer what action we should take.

In general, inference denotes the process of assuming that certain statements about the world are true and deriving what follows for the truth of other statements. Machines are potentially much better at inference than the human mind is. Programs have the potential to encode much more and much more precise knowledge than any one of us could learn in a lifetime. The laws of probability theory and approximations thereof can be used to infer precise knowledge about the world from data and to reason using this knowledge.

The fact that we can use machine inference to solve problems that are too difficult to be solved using human reasoning makes research into inference methods important. When we use machine inference to figure out what the dynamics of protein folding are in order to solve diseases like Alzheimer’s, we do so because, given all our data and our wish to cure the disease, our human minds still cannot figure out the cure on their own. A prerequisite for the use of machine inference is to have the problem statement available formally, as a program, a mathematical object. By singling out a small, well-defined problem, we can formally write down knowledge about the problem domain (or a program for inferring such knowledge from data) and a program that uses this knowledge to solve a particular reasoning task, i.e. to help us in determining how to change things for the better.

Any such small, formal problem statement captures only very little about what we want things to be like more generally. What do we want things to be like? We know that there are some things we want because they lead to other good things — these we call instrumental values — and there are some we want for their own sake — terminal values. We can make guesses about what our terminal values are, what we value for its own sake — joy, freedom, discovery, beauty, kindness — but ultimately, what we want the world to be like is not summed up well by any (or all) of the individual concepts behind our guesses. The name for that which does capture all about what we want is preference.

Because preference is stored opaquely in our brains, we cannot directly access its content, we can only use it to some extent. Similarly, we have little access to how our minds represent concepts like “tree”, “word”, or “spring”. This does not mean that there is no precise structure behind any given concept. On the contrary, the theories that best predict human concept learning and reasoning in recent psychological experiments are those that assume that concepts are represented as probabilistic programs.

Likewise in the case of preference: Our meager introspective abilities obscure the fact that the term ‘preference’ denotes a precise informational structure. This is easy to see where limitations of reasoning make us more uncertain than dictated by what can be deduced from the information that we know is stored in our brains, but is likely to be true more generally.

Take our search for a cure for Alzheimer’s: We have an intuitive idea what results are good and what results are bad — those that actually cure the disease and that do so without side-effects are the good ones. Nonetheless, we are uncertain about how our preferences rank different states of the world; without using machine inference to figure out the dynamics of protein folding, we do not know how preference orders possible states of the world because we cannot tell which state corresponds to a cure and which to a useless substance. By using machine inference to determine which state corresponds to a cure and which does not, we factor out a small part of our preferences that we assume to be independent from the rest (although it is not!). We thus hope to improve our understanding of what this part of our preferences says about the world.

When we — or our machines — work towards the solution of any particular subproblem without taking into account our preferences as a whole, we commit what could be called a mistaken factorization of preference. Preference as a whole makes a statement about what is the best choice at any given point in time, and if we look at only a small part of this statement, we lose value. On the other hand, if we can access the formal statement that our preferences make as a whole, then we may be able to use the reasoning capabilities of machines to determine much more precisely what the best choice looks like than would be possible through introspection.

If preference is a mathematical structure — even if it is currently implemented in our brains in a distributed and implicit way — then what kind of structure could it be? I do not know the answer to this question, but there are situations that are similar in the sense that they also take an intuitive idea and reify it into a mathematical object.

In computer science, there is the notion of the future of a computation. For example, at point # in the program (* 3 (+ 4 #)), what the future holds is that whatever value we hand it, it will add 4, multiply the result by 3, and then do whatever it does to the return value of a program that has finished, e.g. print the result to the screen. The notion of a continuation captures the idea of taking the future of a computation and storing it in an object. If we capture the continuation at #, the future starting from this point becomes a mathematical object, a value that we can pass around just like any other object and that we can reason about formally.

Analogously, we would like to take the diffuse notion of the preferences of a decision-making system (like you and me) and reify it into a formal object. And analogously, we expect this object to be a computational structure that contains information about what will — or, in the case of preference, should — be done in the future, but it may take lots of computation to determine what exactly this information says.

The project of formalizing preference has two parts: understanding the structure of preference (i.e. what kind of object are preferences, how do they compose) and getting at the actual content of human preference (i.e. extracting or pointing to the preferences of a given agent).

There are proposals for what the structure of preference could look like (e.g. preference logics, utility theory), but they seem insufficient in non-trivial situations. Two examples of such situations are (1) that we want our preferences not to lose meaning when it turns out that we have been mistaken about some of the things our preferences talk about (the so-called ontology problem), and (2) that we may have preferences about how we want preferences to interact. Different people appear to want different things, and even within a single mind, seemingly contradicting wishes exist. For example, there are things we want to do and there are things we actually enjoy doing, and these are often not the same. How do we figure out the statement that such a system of preferences makes about what should be done? This is called preference aggregation across agents and could be called compositionality of preference within a single agent.

Formalizing the content of our preferences, i.e. pointing to preference in a precise, machine-readable way, poses similarly challenging problems. The strongest illustration of this that I can currently think of is the following: If our preferences determine which method of formalizing their content is the correct one (namely the one that results in our actual preferences), and if we cannot know or use our preferences with precision until they are available as mathematical objects, then how can we find the correct formalization method? I can imagine that knowing the structure of preference would clarify what properties a method needs to have that allows us to formally refer to the preference content of any given agent, but to what extent this is the case is an open question.

To summarize, the fundamental problem is this: We have only limited access to what we want, and we cannot really figure out what follows from that which we do know about what we want. Machines are potentially much better at reasoning about what follows if we can give a formal description of what we want. However, if we formalize only a few small problems, we lose value due to our limited reasoning about the remaining part of our preferences and due to assuming independence between preferences when in reality they are intertwined. We need to understand preference as a formal object if we want to use machine inference to figure out what should be done to make this world a nicer place.

I thank Vladimir Nesov for useful discussion and for originating many of the ideas mentioned here.

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.

As you grow up, your perceptions gain depth: In your head, you have built up an elaborate model of the world and its structure and behavior. When you perceive, you perceive more than the immediate — you see context. You look at the thing in front of you and you see a computer, a keyboard, its functioning and, below the surface of perception, you know how it relates to other things, people and ideas.

As you grow old, your internal representation of the world gains more and more detail, albeit the rate of incremental updating slows down. Simultaneously, the world outside keeps on changing as rapidly as before. At some point, there you are, trying to interpret data from a world that has changed using a model that is no longer accurate. What can be expressed succinctly in the terminology that your outdated conceptual framework uses is different from that which is simple for younger people, and your framework is no longer an efficient representation of what is out there in the world.

What you see has always been an interpretation imposed on the data your eyes provide, but now your interpretation mechanism is tuned to a world from 30 years ago. When you talk to people and perceive the meaning of what they say, you round to the nearest simple interpretation in your model and reply to that; the actual intended meaning may not be easily expressible within the conceptual language you use to organize your world. You see and hear that which is in terms of what has been. You are growing blind.

Das letzte Semester habe ich als Visiting Student im Brain and Cognitive Sciences Department des MIT verbracht. Was folgt ist der Versuch einer Zusammenfassung dessen, was ich in Cambridge getan und gelernt habe. Besonders wenn ich von dem berichte, was ich gelernt habe, wird vieles skizzenhaft bleiben und vieles andere werde ich gar nicht erwähnen, weil ich nur begrenzt Zugriff auf die Veränderungen habe, die in meinem Kopf stattgefunden haben. Ich weiß jetzt mehr über das Denken und wie ich es erforschen will, über meine Zukunft und darüber, was für ein Mensch ich sein will. Im Folgenden will ich ein Bisschen davon vermitteln.

Mein Verständnis davon, wie das menschliche Denken funktioniert, hat Fortschritte gemacht. Grob lassen sich diese Fortschritte in drei Kategorien einteilen: Modelldenken, Wahrscheinlichkeitsdenken und Entwicklungsforschung. Zusammen haben sie dazu geführt, dass ich eine Vorstellung davon habe, wie ein Pfad hin zu einem naturwissenschaftlichen Verständnis des menschlichen Geistes aussehen könnte und, fundamentaler, dass ich mir überhaupt vorstellen kann, wirklich zu verstehen, wie das menschliche Denken funktioniert. Letzteres ist schwer zu vermitteln, und doch ist es das, was mir lange Zeit gefehlt hat (auch wenn mir das nicht klar war) und was zentrale Teile der Kognitionswissenschaft für mich weg von der Philosophie und in die Naturwissenschaftsecke rückt.

Der erste Punkt, das Modelldenken, ist der, der am meisten Auswirkungen auf meinen Alltag hat. Wissenschaftlich meine ich damit die repräsentationale Theorie des Geistes, insbesondere wie von Fodor in seiner Theorie einer Sprache des Geistes beschrieben. Im Alltag äußert sich das darin, dass ich häufiger zwischen meinem (und anderer Leute) geistigem Modell der Welt und der dahinter liegenden, wirklichen Welt unterscheide. Wenn ich eine E-Mail bekomme, die auf den ersten Blick schwer nachvollziehbare Dinge sagt, dann hilft es, wenn ich mir überlege, wie das Modell der Welt und das Modell von mir im Kopf des Schreibers aussehen könnte. Erst dann kann ich mir überlegen, was ich schreiben muss, um in diesem Modell das zu rekonstruieren, was ich beschreiben will. Wenn ich dagegen direkt das schreibe, was mich überzeugen würde, weil es in mein Modell der Welt passt, dann würde oft keine Kommunikation zustande kommen.

Dass man nicht um Wahrscheinlichkeitstheorie herumkommt, wenn man das Denken verstehen will, war auch schon vor der Zeit hier meine Einstellung. “I am interested in the theoretical foundations of inductive learning” hatte ich in der E-Mail geschrieben, mit der ich mich um das Praktikum hier am MIT beworben hatte, und bei induktivem Lernen hatte ich an probabilistisches Generalisieren gedacht. Und auch daran, dass für das Denken und Handeln in der echten Welt Annäherungen an das exakte Berechnen von Wahrscheinlichkeiten (z.B. von zukünftigen Ereignissen) wohl nötig sind, weil die exakte Lösung prinzipiell zu rechenaufwändig sein könnte, hatte ich schon damals wenige Zweifel. In einem Philosophie-Aufsatz schrieb ich: “Besides using approximate reasoning, prior beliefs that are tuned to the statistics of our world and access to a large amount of real-world data are two other strategies that are likely to be used in any solution to the AI problem.” Wovon ich wenig wusste, war, wie solche Annäherungen aussehen könnten und, allgemeiner, wie Wahrscheinlichkeitstheorie und die oben genannten — oft sehr komplexen — mentalen Modelle zusammen passen. Durch meine Arbeit an der probabilistischen Programmiersprache Church und an probabilistischen Modellen von schlussfolgerndem Denken, von Pragmatik und Konzeptlernen ist mir das klarer geworden.

Bevor ich am MIT-Harvard Seminar “Computational Models and Cognitive Development” teilgenommen hatte, wusste ich nicht zu schätzen, wie geeignet das Denken von (Klein-)Kindern als Studienobjekt ist, wenn man mehr über das Denken im Allgemeinen lernen will. Drei Fragen, die mich in meiner Forschung interessieren, sind: Was sind Konzepte? Wie lernen wir Konzepte? Was läuft ab, wenn wir in unserem Denken Konzepte manipulieren? Insbesondere wenn man annimmt, dass Konzepte aufeinander aufbauen, wird klar, warum man von Kindern besonders viel über das Denken mit Konzepten lernen kann: Die vorhandenen Konzepte sind weniger komplex und es kann praktisch live beobachtet werden, wie sich die Konzepte verändern. Ein Beispiel dafür ist das phasenweise Verstehen von Zahlen (no-knower, one-knower, two-knower, three-knower, number-knower), das in praktisch allen Kindern der zivilisierten Welt gleich abläuft.

Wenn ich sage, dass ich jetzt besser verstehe, was für eine Art von Mensch ich sein will und was für ein Leben ich leben will, dann ist das wahr, aber auch irreführend. Auch vor meiner Zeit hier war klar, dass, besonders wenn man nach gesellschaftlichem Urteil “alles richtig macht”, die Gefahr groß ist, sich und die großen Fragen, die man einst hatte, in institutionalisierten Systemen zu verlieren. Auch ohne groß zu suchen findet man in solchen Systemen zu jedem beliebigen Zeitpunkt im Leben Ausreden, warum jetzt gerade nicht die richtige Zeit ist für die großen Fragen. Systeme sind wie Lückentexte — sie machen es einfach, weil sie Struktur vorgeben, und aus dem gleichen Grund machen sie es schwierig, wenn man Wert auf die Freiheit legt, seinem Handeln selbst Struktur zu verleihen. Vor meinem inneren Auge sehe ich mich als Postdoc rechtfertigen, warum ich unbedingt an einem Projekt arbeiten muss, das zwar nicht wirklich spannend ist, aber die Konferenz-Deadline ist nahe, so langsam wird es Zeit für eine Professorenstelle und irgendetwas muss beim Vorstellungsgespräch ja erzählt werden — nur dummerweise wurde die PhD-Arbeit schon zu oft wiedergekäut, darum muss jetzt was Neues her, aber die großen Fragen sind dafür ungeeignet, die brauchen viel mehr Zeit, die kann ich mir jetzt nicht leisten. Die Gefahr sah und sehe ich, aber es macht einen Unterschied, ob man sich ihrer abstrakt bewusst ist oder ob man sich regelmäßig mit PhD-Studenten und Postdocs unterhält, die einen ähnlichen Hintergrund haben und sich ähnliche Fragen stellen und sieht, wie sie die in den Institutionen der Wissenschaft verwirklichen und nicht verwirklichen. “Ist das deine Zukunft?” frage ich mich regelmäßig und in verschiedenen Situationen habe ich diese Frage unterschiedlich beantwortet. Konkret stellt sich die Frage jetzt, wenn ich mir überlege, ob ich nach dem Bachelor einen PhD machen will, und bis jetzt ist meine Antwort “ja, aber”.

Auch außerhalb meines Labs habe ich interessante Menschen getroffen, und vielleicht war die Hauptmoral, die sich mir dabei eingeprägt hat, dass wir alle nur Menschen sind, ohne Ausnahmen. Vom Abendessen mit dem Physik-Nobelpreisträger Wolfgang Ketterle sind mir die karriereorientierten Fragen in Erinnerung geblieben, die die anderen Anwesenden gestellt haben, außerdem Ketterles Erzählung davon, wie er zeitweise seine Arbeit für seine Familie und zeitweise seine Familie für seine Arbeit vernachlässigt hat. Und die Erkenntnis, dass auch Handeln, das zu allgemeiner Anerkennung führt, oft aus Zielen folgt, die nur von innen einsichtig sind und unter Reflektion vielleicht nicht in sich konsistent wären. Vom Abendessen mit Garrett Lisi (unabhängig arbeitender Physiker und Extremsportler) blieb mir ein Wortwechsel besonders in Erinnerung. Ich habe ihn gefragt, ob er jetzt (da er gerade viel mit anderen Wissenschaftlern zu tun hat) glücklicher ist oder eher früher (als er hauptsächlich für sich allein gearbeitet hat), und er meinte daraufhin, dass er bis jetzt sagen würde, dass es früher schöner war. Dass es schwierig sei, die Vorstellungen verschiedener Menschen zu koordinieren. Worauf ich gefragt habe, ob es ihm denn lieber wäre, wenn jeder genau das täte, was er ihnen zu tun gäbe, und er wurde fast ein bisschen wütend, jedenfalls lauter als im Gespräch davor: “NO! No! Everyone should do what they want to do!” Seitdem denke ich ab und an daran, wie er das gesagt hat, und wie sehr ich das auch so sehe.

Es gibt zahlreiche andere Dinge, die ich letztes Semester gelernt habe — dass ich aus wissenschaftsphilosophischer Sicht struktureller Realist bin, dass ich klassische Musik hören kann, dass ich Hermann Hesse mag — und einige andere Dinge, die ich getan habe — der Lab-Ausflug nach New Hampshire, das Treffen mit Lena in New York, Neujahrsklettern in Dresden — aber was ich oben beschrieben habe ist das, was mir aus akademischer Sicht und aus dem aktuellen Moment heraus am bedeutendsten scheint.

Yes, I’ll write about other things soon.

Each dot is at least one person. If more than one person gave the same answer, the dot is bigger. Click on the graph for a better version, or take a look at the complete dataset.

I don’t know what to do with the graph, but lots of people wrote short comments explaining their choices which I really enjoyed reading. Here are some I liked, each with the age of the author and the age at which he/she expects to be the happiest:

An 18-year-old: 25
Because that’s when i’ll have a stable job, good income and my own house (hopefully).

A 25-year-old: 18
I wish I could stay 18 or ever.

A 33-year-old: 53
Will have met financial freedom and retirement goals.

A 46-year-old: 53
By the time I’m 53, I feel like I will be old enough to truly know myself and young enough to be physically fit.

A 53-year-old: 53
I was diagnosed with cancer around 2.5 years ago, went through the chemo and radiation. I’m finally at a point where I am grateful and happy that it was found in time to do something about it, and not worrying every second that it will come back. Not exactly a near-death experience, but as close as I want to come. Makes you REAL happy to be alive and to try and appreciate even the small things.

A 24-year-old: 40
I have already had a lot of happiness in my life and am very grateful for said happiness but I noticed that as my father ages, he seems to find more and more joy in the smaller things in life. Even in tough times he seems to maintain a more positive attitude – maybe it’s because he’s retired or he’s realized that worrying does’t accomplish much. I hope to achieve his level of wisdom someday and look forward to more happiness and fulfillment later in life.

A 53-year-old: 40
This was the point in my life that I had gotten through college, was married, had children and a career. I spent many hours at my childrens games (football, softball, etc) and and loved every minute if it! Financially, things were getting easier as my husband and I advanced in our careers so we could do more things such as travel and not have to worry so much about being able to afford it. It seemed that the hard work of college and “paying my dues” as I began my career were finally beginning to pay off.

A 21-year-old: 30
At 30, still young but old enough to be really developing my career.

A 30-year-old: 21
Life at my fingertips…

A 26-year-old: 35
I love the family life and independence; by the age mentioned I hope to have less day-to-day worries and more kids, but still be in a really good shape to enjoy it. Plus, I hope to be much more stable professionally.

A 34-year-old: 25
It’s amazing what experience, debt, and growing older can do to your outlook on life. I thought a decade ago I would be happer a decade later, and I am finding out that’s not really the case. I’m not unhappy by any means, but the more responsibilities we accrue in life, the easier it is to rate our happiness by different things.

A 23-year-old: 21
In college – so far it was the best time of my life. Hopefully it won’t be!

A 35-year-old: 21
College years were the best

An 18-year-old: 28
Done with college, can settle down, new job, etc

A 28-year-old: 22
I was a college student then. That were happiest years in my life, because my character shaped and tempered. Though any college assignment felt hard, I can enjoyed the hardship. Sincerely, I dreamed several times about my college years when I slept. I really missed that moment of life.

A 26-year-old: 15
15 was a great year. I was still to young to care and honestly thought the world was at my feet. I spent the school year hanging out with friends and of course school. Summer I spent most of it at my uncles enjoying time with my younger cousins and the cute boy down the street. Ahh… life before a I ever had a job.

Another 26-year-old: 26
As someone who has spent most of her short life daydreaming, I have learned not to waste my time measuring happiness or planning how to create it. I try and make the best of the present time and hope that I continue to do that for the rest of my life. Interesting question!

Yet another 26-year-old: 32
I feel by that time I will have finished grad school and be working in a field that I love. I will be more comfortable with who I am and my place in the world by that time.

A 26-year-old: 30
The day i get married will be the happiest day of my life.

A 41-year-old: 26
I met and married my husband at 26 years old. It was the best time of my life.

A 70-year-old: 30
We had a very happy marriage and two beautiful daughters. Although we are still married, things often got complicated and stressful but never hopeless.

A 23-year-old: 8
Childhood was a time of innocence; no worries, no bills, no thought as to cause and effect… You walk around with your fingers in your nose picking wedgies and thinking about the playground never wondering about world hunger war terrorism or even when companies may go bankrupt and cancel your favorite television show.

A 44-year-old: 25
The age I married my beloved husband and set out on our new life together. We are still together and still very happy. The adventures we share and have shared have brought so much joy to my life.

A 46-year-old: 34
I was happiest when I was single and working at the beginning of my professional career.

A 27-year-old: 45
Having children and watching them grow will give me the greatest joy.

A 38-year-old: 45
at that age most of my kids will be grown and hopefully I will be able to quit my job by then and do some of the things i would like to be able to do in my life

A 29-year-old: 16
I desperately miss the imagined knowledge and unknown ignorance of being in high school

Another 29-year-old: 17
Got married at 16 have 4 kids…so life has been challenging, wouldn’t trade them for anything…but would like to go back and have less responsability even for a little while :)

A 29-year-old: 26
This is the age that I came to the realization that I had finally found what I wanted. Everything just seemed to be coming together.

A 50-year-old: 26
At 26 i had my only child…and i was going to college, met a man of my dreams and felt like i could do it all..then i got involved with my child and man everything i wanted went out the window with in the first year..do i regret it sometimes would i change it not on my life

A 54-year-old: 23
I know for sure that as I age I get unhappier. At this point I’m thinking about how much longer I have as compared to having my whole life in front of me.

A 31-year-old: 50
I said 50 is the age I would be the happiest because it seems like the older I get the happier I am. And I hope that when I’m 50 I will continue to get more happy with each passing year and I hope to still be in great health.

A 44-year-old: 32
I finally had my child after trying for 10 years and my life was complete then.

A 43-year-old: 61
My youngest child will be of age and hopefully off to college \u0026 then I will be free to do what I want with my life.

A 39-year-old: 40
We had always known we wanted to adopt 2 special children and our hearts goal was that it would be by the time I am 40 and my husband 45. Our second and final adoption will be finalized in 2009 and I will be 40 years old.

A 46-year-old: 65
I am looking forward to 65, so I can retire, and actually take some time out to enjoy life.

A 65-year-old: 34
I was well on my way with a career wide open, had purchased my first house and had a great social life. Since then I have been up and down, but never so enthusiastic about life as I was then.

A 53-year-old: 22
We don’t appreciate things at a young age, but as we grow older we always wish we knew then what we know now.

A 22-year-old: 22
Two young daughters make my life the happiest! :)

A 30-year-old: 25
I was earning, was healthy, carefree. Looked as if there is enough time in life

A 62-year-old: 19
I was young, gorgeous, full of life and in love

This last one made me stop and stare at the screen for some time, simultaneously not knowing anything about this person and yet so much.

Update: David Sturman did some statistical analysis of the data that is worth reading.

You know the situation. Someone is presenting his research, PowerPoint slides up, room slightly darkened, and what you understand best is what he communicates nonverbally: “I don’t care either. I know that the question my research answers is not the kind of question that keeps me from falling asleep at night, but hey, it’s not as if that’s what I’m living for.” — at the same time, he goes on talking about the effects of auditory priming on the calcium ion concentration in parvocellular neurons of the chimpanzee lateral geniculate nucleus. If you were thinking in words, your thoughts would be along these lines:

“I want to learn about the world, but this does not feel right. It’s not the fact that what’s presented is a minuscule piece of detail — I care about details. But the reason I care about details is because they are necessary to piece together the larger picture. I want to find answers to the big questions. To study, to travel, to get to know people and to exchange ideas sounds perfect, but then I see those who call themselves ‘scientists’ and, most of the time, I don’t want to live their lives.”

“I don’t want to spend two years working on a project where the result is a 2% improvement of efficiency in some manufacturing procedure and a journal article. At the same time, I don’t want to deceive myself by pretending to tackle the big questions while all I’m engaged in is philosophical word games. I don’t want to solve puzzles for the sake of puzzle-solving. Enjoyment from puzzle-solving has never been my primary motivation for doing science. It may be part of my motivation, but a necessary condition for me to enjoy what I do is to feel that it is significant. I want to believe in choosing science, but reality always gets in the way.”

So, do you choose an academic career, hoping that things will be different for you, or that, by then, you have changed enough not to notice anymore?

“Academia” is a name for a set of standard solutions to standard problems. You don’t have to accept all of them, or any of them, to do science. It’s just the most convenient way. It appears to me that, if you don’t want to, you do not need to make any choices in life — there is always a most convenient way. Once you start out (and you did not have a say in that decision), there is a default answer to almost every question life poses, conditioned on how well you perform at certain tests and on what you state as your interests.

If ‘knowledge’ is high on your list of interests, here’s what to do: Finish high school, get a bachelor’s degree and don’t forget to take some jobs at your university (you want experience in teaching!), write your bachelor’s thesis about a topic that’s somewhat familiar to you (even if it’s not the thing you really want to do — after all, it’s only three months of your life) and get a bachelor’s degree. Next step, join a master’s program, internship included, during which you publish a few papers (research experience is crucial!). Your master’s thesis ends up using knowledge you already have from working on your bachelor’s thesis (because there is not enough time to start from scratch) and luckily you manage to suppress any thoughts about how your research is taking more and more directions that are not truly yours, for the sole reason that that’s what you’re an expert in. By the time you are working on your PhD thesis, you’re thinking that you are probably the only person that understands why one would spend years working on the problem you are trying to solve, and sometimes you are close to admitting that you do not understand it yourself, but rationalization goes a long way. By then, a significant portion of the possibility that once lay before you and that you didn’t appreciate at that time is already gone.

You can deviate from the most convenient way, of course, but only a small minority does. The sad thing about the whole situation is that there are people who want to do science but for whom the most convenient way is soul-crushing, while alternative choices are not an option (think money, acceptance, etc.). Therefore, they either don’t end up in science (despite their interest and motivation) or they do choose academia and suffer from the restrictions it imposes, fighting against the windmills of institutionalization that, like Dementors, suck out any sense of purpose until it’s just a job, fight over, next generation please.

(This is a gloomy way of seeing things, but to me it’s a real problem in search of a solution — not necessarily or primarily for personal reasons, but because, for some people, academia does not live up to its promise, the primacy of the pursuit of knowledge. I believe that it could and should, since they tend to be the kinds of people that would make good scientists.)

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.

Many people feel that they don’t have important things to care about. People like to feel important, and they like to talk about things that matter. Unfortunately, talking about things that matter tends to bring up a lot of thorny, difficult questions and issues. Often the answers are unpleasant, and people don’t like things that are unpleasant. Thus the subject of sports acts as an empty, meaningless alternative to the real issues that exist in the world.

Wir verschwenden unsere Möglichkeiten auf die denkbar bedeutungsloseste Weise.

RepRap ist ein Do-It-Yourself 3D-Drucker, der unter anderem Teile für die Konstruktion von 3D-Druckern herstellen kann. So soll der Drucker Kopien seiner selbst herstellen, jede der Kopien wiederum Kopien und so soll es weitergehen. Der erste funktionierende derartige Drucker würde demnach den Beginn eines exponenziellen Vervielfältigungsprozesses darstellen.

Auf der Website des Projekts steht:

RepRap achieved self-replication at 14:00 hours UTC on 29 May 2008 at Bath University in the UK.

Das bedeutet: Der Drucker hat vor einer Woche das erste Mal aus Rohmaterialien alle Plastikteile hergestellt, die für den Bau eines solchen Druckers benötigt werden. Für die vollständige Replikation werden zusätzlich Platinen, Motoren, Temperatursensoren, Cat5-Kabel, ein Computer (der den Prozess steuert) und ein Mensch (der die Einzelteile zusammensetzt) benötigt. Das macht die Ankündigung weniger eindrucksvoll.

Allerdings ist es leicht, Projekte in der Anfangsphase als “wenig eindrucksvoll” abzutun und sich trotzdem nicht davon abhalten zu lassen, die tatsächlich folgende, beeindruckende Entwicklung später als “unvermeidbar” zu bezeichnen. Ich würde darauf wetten, dass die Entwicklung von sich selbst replizierenden Maschinen — Katastrophen und ähnlich disruptive Ereignisse bei Seite gelassen — unvermeidbar ist und beeindruckend sein wird. Ein Grund dagegen, alles auf RepRap-ähnliche Makroreplikatoren zu setzen, ist der, den Caledonian hier erklärt:

It’s fundamentally harder to make a large, self-replicating machine than a small one. Individual molecules have far fewer degrees of freedom than macroscale objects do – much greater precision is needed when crafting a gear, even a microscopic one, than a protein.

Selbstreplikation bringt Gefahren mit sich und ist auf Nanoebene möglicherweise nicht sinnvoll. Brauchen wir einen Preis für den ersten Selbstreplikator, der ohne menschliches Zutun und ohne ungewöhnliches Rohmaterial auskommt, oder ein Verbot desselben?