Category Archive

If religious knowledge, like any other form of knowledge, develops through a process of falsification, then an immutable religious tradition is unattainable. Cultural continuity, on the other hand, allows a given religion to develop with time–and even acknowledge past mistakes–without forsaking the tradition at hand.

To put it bluntly, classical Christianity is itself now our Old Testament… We have to use traditional Christianity in the same way as Christianity itself has always used the Old Testament. In both cases there is a great gulf but there is also continuity of spirit and religious values… When a Christian sings a psalm he knows there is a religion-gap and a culture-gap, but it does not worry him because he believes his faith to be the legitimate successor of the faith of the psalmist. Similarly, since the Enlightenment there has developed a religion-gap and a culture-gap between us and traditional Christianity, but we may still be justified in using the old words if we can plausibly argue that our present faith and spiritual values are the legitimate heirs of the old.

–Don Cupitt, Taking Leave of God (HT: Exploring Our Matrix)

Present-day fundamentalist theology makes a position of cultural continuity nearly impossible, though strangely enough almost no fundamentalist position (perhaps none at all) has remained constant with time.

Truth is not manifest. Positive empirical knowledge is impossible, learning proceeds through a process of falsification, and knowledge by induction is illogical.

Revealed religion maintains that Truth is knowable. As knowledge of this Truth cannot be obtained empirically, faith is required to identify ultimate sources of Truth. Revealed religion may persist, but it can never assert itself as logically valid because Truth is still logically unattainable.

Now faith is the assurance of things hoped for, the conviction of things not seen. (Hebrews 11:1)

If all knowledge is negative–that is, we can never arrive at a proven fact–then revealed religion cannot make demands of the so-called secular world to demonstrate knowledge of physical Truth. Logic, it seems, lends itself to negative knowledge (i.e., the best idea so far) while faith generates positive knowledge (i.e., we know this to be True).

Faith cannot be systematically taught, though, requiring evangelists instead of teachers–persuasion over information. This seems to me sufficient reason for the exclusion of dogma from science curricula (read: ID/creationism), for though we cannot fault those who choose faith, the insistence on positive accumulation of knowledge ultimately stems from a faith-based worldview for which no logical justification exists.

My friend Shawn just started a new blog, Models for Life, where he will discuss sports, climate, politics, and life from a scientific modeler’s point of view. Shawn and I also share many similar research interests, so I’m sure he’ll have some interesting astrobiological tidbits to share.

The falsificationist notion of scientific method states that no theory can be purported to be absolutely true, for science advances in knowledge by demonstrating theories to be false. Under this paradigm we cannot gain positive knowledge but instead learn through the falsification of ideas. This form of learning by trial and error (as opposed to induction) applies not only to science but also to any form of human knowledge.

When developing a computer program, for example, there is (almost) no way to prove the correctness of your code. Mathematical functions (and to a lesser extent, certain algorithms) can sometimes be proven equivalent to one another–for example, the functions f(x) = 2x and g(x) = 3x - 1x are equivalent–but the same procedure cannot be easily applied to code. Even if I write a simple procedure that takes as input any integer and returns an integer as output, I will never be able to prove the correctness of my program when compared to another method of calculating the same values. Instead, computer code improves by falsification: whenever your code behaves incorrectly you know that something must be wrong. Fixing the problem does not guarantee that the code is now perfect, but at least you can (hopefully) say the code is more correct (or less false) than the previous version.

(As a caveat to the above discussion, there are a small class of programs that can be shown to produce identical output when the limits of machine precision are taken into account. On any architecture there is a finite limit to the range of integers and real numbers, so for two procedures that output integers a case-by-case comparison could eventually determine agreement. This still does not prove correctness, though, as you can always get the right result for the wrong reason.)

Development of defect-free code, then, is a process, not a destination. Mediocre programmers think they’ve got things right, but good programmers have the ability to cleverly break their code in new ways and approach the limit of unattainable perfection.

I acquired a lot of good books over the past few months. Here’s some of what I’ll be reading over the next few snow-barren winter months. Feel free to share your own lists, too.

• Douglas Adams (1986) The Ultimate Hitchhiker’s Guide to the Galaxy - That’s right, I never got around to reading this until now. I’m at the beginning of the 4th book now, and I can’t put it down!
• Søren Kierkegaard (1843) Fear and Trembling - “If there were no eternal consciousness in a man, if at the bottom of everything there were only a wild ferment, a power that twisting in dark passions produced everything great or inconsequential; if an unfathomable, insatiable emptiness lay hid beneath everything, what would life be but despair?” Upon recommendation by my brother.
• Joseph Campbell (1959) The Masks of God: Primitive Mythology - In his four book Masks of God collection, Campbell examines the history and development of world mythology, stretching back as far as written and oral tradition reach in the first volume and continuing on to the East, West, and modernity in the following three. And, of course, I’m rarely disappointed by Campbell.
• Frans de Waal (2006) Primates and Philosophers: How Morality Evolved - Religion is often argued to be necessary because atheism is without morals, yet most atheists are decent people. People often think of morality as a cultural invention, but de Wall develops an evolutionary argument for a strong continuity between human and animal behaviors.
• John Gillespie (2004) Population Genetics: A Concise Guide - I just began a population genetics course this semester, which looks like it will consume a lot of time. I’m enjoying it so far, though; it’s a good combination of probability mathematics and genetic logic, and my genetic vocabulary is slowly returning to me.

Today’s guest entry comes to you courtesy of Brent Miller.

Who is the author of this essay?

Jacob asked me to write about my experiences learning how to write software and computer codes.  I got a B.S. in Computer Science from the University of Minnesota; managing to cram the “4 year” computer science part of the program in to 6 semesters.  I was 100% ignorant of how to program computers on the first day of college.  6.3 years later, a degree, 2 scientific programming internships (AHPCRC, and ARL), and 2.25 years of enterprise business software development, I’m 95% ignorant of how to program computers.  If it were up to me, anybody who’ll eventually program a computer should have at least 10 semesters worth of classes to take.

What is programming?

Read the Wikipedia article if you want to learn a definition without understanding it.  I’ll focus on scientific programming in this essay.  Your predecessors think programming is nothing but a tool to compute values.  They are correct.  Programming is a nothing but a tool to do work more efficiently.  This is not as simple as others make it sound.

Programming is not the language you use.  I repeat: the language you use does not matter at the highest level.  Please note I said level.  Programming is about levels of complexity and layers of abstraction.  Computing basic math is suitable in any language on modern hardware.  Computing huge simulations is not suitable in many languages.  The fundamental lesson to learn from these two statements is for programming to be a tool, you have to know what the right tool is for the current task.

How do I determine the right tools?

Answer: You need a thorough understanding of the multiple levels of computing.  Programming requires understanding more levels of complexity¹ than a mathematician, physicist, chemist, or structural engineer.

You took the required intro to programming course(s), right?  In my experience at UMN, these classes for non-Computer Science majors suck and the students retain nothing.  So, how do you regain that knowledge now that you have to write scientific codes for your primary research field?  What if the professor that taught it doesn’t even like programming and therefore didn’t teach you everything you should know?  What if you find yourself needing to read and write codes to do your graduate level work?

Well, you’re screwed if you don’t take the initiative to correct the mistakes of the current system.  I could discuss the inadequacies of the university system in another essay, but that won’t help us here.  universities do not teach students how to program well.  They simply use programming to help demonstrate computer related principles.  Computer Science is not the same as Software Engineering/Development.  Computer Scientists are not educated to be computer programmers; they are educated to be professional problem solvers with computers.

Let’s assume you are vaguely familiar with C++ or Java.  You know that programming requires a language (which consists of syntax and a grammar at minimum, but often has useful code libraries for repeated tasks).  You also know things have to be written in the right order to get the right results.  Great.  You can now write complex mathematical programs.  Wrong. 

What do I do now?

Do not buy a book that claims you can learn how to program well after reading it.  You cannot.  Programming well comes only from years of experience and the self motivation to continuously improve your craft.  Since programming is mostly art and partly math, individual skill determines the sustainability and quality of the codes you write.  Why should you care about the long term quality of codes?  Because it is more expensive to revise code later than to do it correctly from the start.  You must want to do things Right from the start.

Since you need to learn how to read and write code in a short amount of time, you’ll have to make sacrifices. 

You’ll need to learn the following concepts just so you can begin to understand scientific codes:

1. Computability theory
2. Data structures and how they influence the codes you write
3. Algorithms and their effects
4. Computers aren’t perfect
5. Computers do exactly what you tell them to do, unless physics affects the hardware level, or #4 shows up again from someone else’s code.
6. Abstraction
7. Reading computer codes is more important than writing computer codes.
8. Ugly code is often bad code.  Codes that look simple are often well written.
9. Stop thinking in Boolean terms.  Most things in computer science depend on other things, so yes/no or true/false logic often misses the big picture, even though computers function on Boolean logic.  Abstraction hides this from you.

Once your brain stops spinning from dense computer science theory, read Steve McConnell’s superb book, Code Complete (2nd Edition) from cover to cover.  This book is geared towards business programming, but every bit of advice is completely applicable to scientific programming.  It’s that good of a book.  This book should teach you the majority of practical knowledge you’ll need for your scientific codes.

Now what?

If you now see that programming computers well is not as trivial as arrogant non-computer scientists think it is, then you’re on the right path.  To help prove this point, I wrote a series of small programs that all compute a single number.  The screenshot in that link shows 1 computation implemented 11 different ways.  A couple of them are trivially different, but result in huge performance differences.  I needed all 6.3 years of my computer science career to be able to write, measure, and understand the impact of naive codes.

Your intro to programming course(s) were probably a waste of time and taught you the wrong things.  Once you grasp the basics as I enumerated above, try some or all of the following help correct your mistake of not getting a B.S. in Computer Science and then deciding on a scientific specialty in which to conduct computational research:

1. Ask an experienced programmer for help and take everything s/he says with a grain of salt.  Most of us spew opinions masked as gospel.
2. Read as many of the classic computer science texts as you can.
3. Read and participate in helpful programmer communities.
4. Take as many undergraduate computer science courses as you can.  Start with algorithms, numerical methods, or any other topic immediately relevant to your specialty.
5. Write simple programs.  When I took a Math department cryptography course, I did my homework by hand then wrote small programs to do the exact same computations for me.  This teaches you two things at once.
6. Write more simple programs.  The more times you start over on a new program or a rewrite, you should find yourself making improvements.  If you don’t, you’re not learning or doing something wrong.  This is key.  If you don’t see your old code as crap, then you’re failing to improve. Failing to improve is what separates the horrid programmers from the good/great programmers.
7. Try a new computer language.  You’ll no longer see programming as a language.  This is an important step in the growth process.
8. Learn what not to do.

Closing thoughts

If you treat programming as a simple tool, then you’ll likely end up with Escherian codes.  Don’t write a line of code until you’ve mentally solved the problem.  If you write the same code over and over, you’re doing it wrong.  Remember abstraction.  Simplicity is often an indicator of correctness.  If you don’t know the best way of doing something, then ask someone else.  Asking questions in good programmer communities will often yield more knowledge than you originally requested.  We like to share experiences so we can advance the discipline.

I participate in the Ars OpenForum Programmer’s Symposium because I truly believe in helping others become better programmers.  To further prove this, any of Jacob’s students or peers are welcome to email me for help or advice.  I’ll do my best to give answers or point you in the right direction.  Please get my email address from Jacob.

A good programmer knows s/he will be better tomorrow.

¹Famous quote from an E.W. Dijkstra talk (italics mine):
“The town is made up from neighbourhoods, which are structured by streets, which contain buildings, which are made from walls and floors, that are built from bricks, etc. eventually down to the elementary particles. And we have all our specialists along the line, from the town planner, via the architect to the solid state physicist and further. Because, in a sense, the whole is “bigger” than its parts, the depth of a hierarchical decomposition is some sort of logarithm of the ratio of the “sizes” of the whole and the ultimate smallest parts. From a bit to a few hundred megabytes, from a microsecond to a half an hour of computing confronts us with completely baffling ratio of 10⁹! The programmer is in the unique position that his is the only discipline and profession in which such a gigantic ratio, which totally baffles our imagination, has to be bridged by a single technology. He has to be able to think in terms of conceptual hierarchies that are much deeper than a single mind ever needed to face before. Compared to that number of semantic levels, the average mathematical theory is almost flat. By evoking the need for deep conceptual hierarchies, the automatic computer confronts us with a radically new intellectual challenge that has no precedent in our history.”

We can learn music mathematically, but only when we reach creative understanding do we fully appreciate it. I think other mathematically described systems are no different.

Off to Minnesota (and still trying to make physical intuition of climate dynamics).

Creationists and science skeptics often claim that science, like any belief system, is based on faith. When scientific and religious ideas conflict, then, there is ultimately no objective basis for choosing one over the other; faith in science is really no different than faith in religion.

This type of argument actually has merit–but only as a critique of the verificationist paradigm of scientific method. Verificationism itself is logically flawed, though (falling prey to the problem of induction), so it must ultimately rest on tenets of faith. Verificationism looks for positive proof of existence and accompanies the belief that Truth is manifest; in a sense, religion and verificationism both rest on the premise that Truth, whether by divine revelation or proven theory, is knowable.

The falsificationist description of scientific inquiry, on the other hand, is not concerned with whether or not we can know Truth–even if we stumble upon a theory that is True, we could never recognize it as such! Falsificationism itself is a logical process, not one based on the faith-based belief in the attainability of Truth, and thus can be reasonably preferred when comparing with certain religious interpretation.

This overstates the case a bit, of course, for falsification assumes we can recognize when an idea is disproved; in a sense, falsification takes for granted that we can trust our own experiences. Perhaps we should take this on faith as well, but it seems to me that continual distrust of our own experiences would create dysfunction–total chaos or total apathy.

The classic scientific method taught in most schools–and even believed by many scientists–goes something like this: a scientist makes a hypothesis and then carries out an experiment to see if this hypothesis is confirmed; if so, the hypothesis has gained support and, with enough support, eventually becomes a scientific theory. This view that scientific ideas can be empirically verified was popular among the logical positivists but cannot be placed on a logical framework. Karl Popper, one of the biggest critics of logical positivism, proposed an alternative view of the advancement of scientific knowledge in terms of falsifiability rather than verifiability.

If I make a hypothesis that water boils at one hundred degrees Celsius, I will have no problem finding an infinite number of confirming experiments. After a thousand successes in which I experimentally verify that water boils at this temperature, will the thousand and first experiment make me any more confident in my initial hypothesis? The verificationist school of thought will view this repeated act of confirmation as the gain of knowledge, but this reasoning falls prey to Hume’s problem of induction. Logically, an increasing number of confirming cases does not–and cannot–prove the validity of a statement.

Instead, suppose I were to try and disprove my initial hypothesis by ascending a mountain and finding that the water actually boils at a different temperature. Rather than perform a thousand experiments that all tell me the same thing, in this case I will actually have learned something: there are some situations where water does not boil at one hundred degrees Celsius. I can re-state my initial hypothesis as “water boils at one hundred degrees Celsius at sea level”, for example, and proceed to experimentally seek additional ways of critiquing and disproving my hypothesis.

We can never say for certain, then, that a given scientific theory is true, for we can never logically verify any theory. Falsification, though, provides a mechanism by which we can gain knowledge and develop better and better ways of looking at the world. Newtonian mechanics provided an excellent (and still useful) way of viewing the Universe, yet this idea ultimately falls short in relativistic realms–and even Einstein’s theory is far from a complete physical law of the Universe. Even if such physical laws exist, we will never know we have arrived at such a law, for our current theories are the ones that have withstood the greatest attempts at falsification with success. We don’t know which theories are true; we simply know which ones have not yet been proven false.

Popper delves into these ideas much more in his books (see, for example The Logic of Scientific Discovery or Conjectures and Refutations), but this basic idea of almost trial and error is certainly more gratifying than the (illogical) paradigm of verification. Science is not a quest for Truth, then, for even if Truth exists it can never be recognized as such; science, instead, improves upon itself by systematically rejecting old ideas in favor of more complete ones–which themselves will eventually be falsified.

My NaNoWriMo novel is finished, and I am quite pleased with the result. The story Inhabitants of Eternity is a journey of philosophical and spiritual ideas set in the context of near-future scientific discoveries. In particular, the Terrestrial Planet Finder mission will be capable of observing terrestrial planets around other stars, which includes the ability to determine the gases present in the atmosphere. It is conceivable, then, that in the next century we will observe a planet that, at a distance, looks nearly identical to our own life-filled world. Though we have continually been pushed into mediocrity by the discoveries of astronomy and cosmology, Earth is still the only inhabited planet that we know of, and so the uniqueness of Earth’s phenomenon remains a steadfast truth for many people. Set in the near future when this mission first starts surveying the skies, Inhabitants of Eternity explores the implications of such a discovery on both religious and secular thought.

The story takes place through a series of dialogues that explore our conception of knowledge, the implications of biological evolution, the likelihood of extraterrestrials, and the relationship between religion and science. The main character is also a dreamer, which provides a unique setting for his own exploration of these personal and profound ideas. I have written several essays that touch on some of the themes in this story, but I until now I had not outlined the totality of these thoughts into a single work of discussion. I certainly learned a lot from this process, and I am looking forward to what the eventual finished work will look like. I like to give my writings time to ripen, so I will likely not start revisions until January or February; revision seems most beneficial with a fresh look at the manuscript.

For thus says the high and lofty one
     who inhabits eternity, whose name is Holy:
I dwell in the high and holy place,
     and also with those who are contrite and humble in spirit,
to revive the spirit of the humble,
     and to revive the heart of the contrite. (Isaiah 57:15)

Scientific models get a lot of press in the climate change arena, but models for all sorts of processes are commonly used every day, usually without question or concern.

At a baseball game, for example, the distance of a home run is not directly measured but instead is calculated from a computational model based on the ball trajectory and other variables. (Indeed, there have been cases when an out-of-the-ballpark home run bounced off a pillar, causing the reported distance to be much larger than the actual distance.)

For that matter, any mathematical description of a system is a model. Mathematics do not define reality; they are simply a tool for description. Our electronics, economics, politics, and science are all based on models at some level. Climate change skeptics are sometimes quick to dismiss the use of models in understanding the climate system, but the basic use of models to understand physical (or social) systems is so prevalent that most people take it for granted.

Our thoughts, perceptions, and interpretations–that which forms the core of our being–are only accessible to ourselves. Others view us solely through our words and actions, which provides a mechanism for diverse persona.

Back in State College after attending an interracial wedding directly across the street from Bob Jones University (really).

Communities in our society are more nebulous than tribes, yet we can still gain communal knowledge from these associations. This knowledge is more than descriptions of simple facts, processes, or techniques; instead, knowledge gained from communal membership applies to the way in which we should live.

Within each community, we are all continual teachers and continual learners.

A continuing inquiry

If we are able to find and ask the right questions, there is still no guarantee that we will expose a delusion.

It is not sufficient to ask the right questions; we must also probe the proper sources of knowledge. Delusory sources of knowledge can explain away even the right questions, giving answers that circle upon itself and perpetuate the delusional tautology.

If our experiences are our only certainty, then our perceptions cannot be delusional.

Delusion can only exist if we adhere to a view or belief that contradicts one or more of our experiences.

By continually assessing our own experiences with our perceived worldview, then, we can prevent delusion–provided we can come up with the right questions.

Given this dependence on the proper line of reasoning, though, can we ever assume that we have reached a non-deluded state of mind? There is no mechanism to generate the right questions, so there is no guarantee that a delusional worldview will be shattered.

If an idea or establishment is fundamentally flawed, then it is dangerous to ask too many unfriendly questions. Tautological dogma can be discussed endlessly but broken by a certain path of questioning.

Sometimes we get stuck in circles because we are unable to come up with the right questions.

My contribution to the 85th issue of Tangled Bank hosted at Migrations.

Many of the basic sciences can be viewed in terms of fundamental discrete units, at least on certain levels. Particle physics, cellular and molecular biology, and chemistry all have simpler discrete units that comprise larger, more complex systems. What about the discrete units of astrobiology, though? It is even reasonable to think of astrobiology in reductionist terms?

Astrobiology by definition is an interdisciplinary field, consisting of geologists, biologists, chemists, meteorologists, astronomers, and others who collective seek an understanding of the context of life in the Universe. To this end, there is no singular reductionist unit, because the participating scientists come from widely varying academic disciplines; in effect, the disciplines themselves become the building blocks. The knowledge of evolutionary biology (based on the study of genes) is as foundational to astrobiology as the knowledge of high energy physics (based on increasingly elusive subatomic particles).

The reductionist units of astrobiology, then, are not concrete physical observables, but are scientific disciplines themselves! Scientists in various disciplines no doubt find immense satisfaction in breaking apart a problem to reach the most fundamental–and perhaps meaningful–level. But in an interdisciplinary field, such as astrobiology, we begin with the fundamentals to build up a multi-faceted picture that can bring greater meaning to the questions we ask.

Where have we come from?
What is our future?
Are we alone?

These questions are as old as the dawn of humanity, yet it is only recently that they have entered the domain of science. And every day that we gain a better understanding of fundamental science at a reductionist level is a day closer to answering some of the most profound questions of existence.

Statistical studies based on the random sampling of a population have wide degrees of usefulness, often dependent on the population in question. When human behavior (such as consumer tendencies, voting paterns, and social perception) is evaluated in an academic setting, this is often accomplished by soliciting participants to form the random sample. Solicitation may be random (within the constraints of the experiments), providing an adequate sample from which something meaningful can be inferred.

Although there are statistical techniques that attempt to correct for various biases in the sample, I wonder if there is any tendency among people who tend not to respond to such inquiries. That is, I would like to see a study that evaluates similarities between people who routinely reject invitations to participate in studies. I realize that undertaking such a study might be self-defeating (since you would get no voluntary participants), but the results of such a study could prove invaluable in defining the applicability of a random sample in many cases.

Why is it that many (most?) people prefer to categorize themselves whenever possible? Categories are a useful construct for discussion, but given the emphasis our culture places on individuality, it is interesting how often people apply a broad category in their own description. These may be ideologies (republican, democrat, libertarian, anarchist), philosophies (nihilist, materialist, Hindu, Christian), or qualities (race, gender, nationality)–nearly everyone (at least in our culture) puts themselves in such categories.

There is a language benefit in referring to a population by a category, but often an individual will cling to the identity of a category. Is it really useful to have a binary construct of politics (republican or democrat only), gender (masculine or feminine only), or religion (theist or athiest only)? When such binaries are constructed, most of us tend to fall somewhere on a spectrum between the two (sometimes arbitrary) opposites; yet we find a need to identify with a single side than to exist along the spectrum.

Perhaps categories create and enhance a sense of community, but they might also act to impede creativity.

I like this story because it is an interesting example of how a scientific idea forms. Scientific theories may have gaps, but it is rarely prudent to discard an entire theory. Sometimes a better idea comes along, but sometimes you just learn to think in new ways.

Astronomers have long known that “the Sun is a mass of incandescent gas”, but until relatively recent times it was not known to be “a gigantic nuclear furnace”. Early views of the Sun assumed it was chemically powered–but this could only last for ~1000 years. Gravitational potential energy is sufficient to sustain the Sun for ~30 million years; but once it became apparent that the Solar System was billions of years old, astronomers realized they had a problem.

Nuclear fusion of hydrogen into helium was a good solution, but there was one flaw in this theory. Any elementary school student who has played with magnets can tell you that like charges repel and opposite charges attract. The same is true for protons. But if protons repel each other, then how can they ever fuse together to make heavier elements?

Astronomers sat on this question until quantum mechanics provided a neat little solution: quantum tunneling. If I throw a tennis ball against a concrete wall, Newtonian mechanics tells me the ball will bounce back every single time. In the quantum world, however, there is a nonzero probability that a particle will tunnel through a barrier. The potential energy barrier between two protons is one such barrier. Almost all of the interactions between protons in the Sun follow the classical pattern of like charges repelling; but a small number of these interactions result in collisions from this quantum tunneling effect, resulting in successful fusion and a Sun that shines.

Some of the difficulties in teaching math and science come from the way we grasp knowledge in general. A textbook or lecturer can present many observations, theories, and conjectures, but a novice student (even one who is interested in the material) will initially have difficulty in creating a coherent framework. Even if students are able to remember everything they are taught, application to real-world problems will still be difficult because they have not fully synthesized the material. A student might know the mathematical and physical definition of a concept, such as divergence, but this does not ensure the ability to identify or process similar information in a textbook-deviant setting. Mathematics is a feeling, and true application only follows when practice and repetition lead to a deeper understanding.

Two observations after much discussion:

1) It is convenient to assume democratic reality–that is, collective reality is defined by the experiences of the majority.

2) Each person should walk the path of maximum experience. This is beneficial for the community at large.

A follow-up to Faith Without Words.

Books are to memes what chromosomes are to genes.

One advantage of the written word is that it allows inconsistencies in thought to persist longer, perhaps indefinitely. If a certain line of thinking (a religious tradition, for example) leads to self-contradiction, oral tradition will eventually identify and rectify the conflict–perhaps by shifting to a new paradigm. This is because there is no tangible authoritative truth statement in oral tradition; the exploration of an idea proceeds easier and converges toward a useful, working solution. This does not mean oral tradition is easily changed; it simply has a better response in reaching functional and consistent thought.

With a written authority, this exploratory process is more difficult. When a self-contradiction is identified, there is not an easy way to bring about change–because the written authority itself is being challenged. Discussion always begins with the written source, often assuming the writings have absolute authority. Exploration of new ideas to resolve self-contradiction are more difficult, then, as the challenged memes are inscribed in an immutable source. If the ideas in the written source are the best possible solution, then there is no problem. But for a self-contradictory worldview, the presence of a written authority impedes progress toward a consistent outlook.

Spawned from comments in yesterday’s post.

I wonder if modern Christianity or Islam really could succeed in an illiterate society.

Consider an isolated and illiterate people group. A single external missionary is allowed to evangelize to this group with any resources available (including holy texts for reference), but the missionary is not allowed to teach the people to read. The missionary continues until all the population (or a sufficient percentage) become converts, after which the missionary departs and the people remain in isolation.

As time progressed, would oral tradition be sufficient to sustain the religious tradition so that it remains doctrinally consistent with the missionary’s teachings? If a religion is genuinely a fundamental truth, then there should be a low risk of contamination to the point of contradiction. But at the same time, without a holy text as reference it would be difficult to measure any doctrinal drift.

Many religions of the world use one or more holy texts as the source or confirmation of a divine message. The Bible and Qur’an, according to many traditions, are divinely inspired and contain the necessary information for attaining salvation.

If a crucial divine message is contained in a book, then literacy is a prerequisite for salvation. Independent development of a writing system has occurred only three times in human history (Cuneiform, Maya hieroglyphs, and Linear B), and these developments took place late in human history, even after the Agricultural Revolution.

Writing is clearly a human invention (and a recent one, at that), yet it would seem that certain divinely inspired messages require the ability to read. Books are certainly a convenient means of sharing information, and there is no reason books should not spread religious ideas as well as secular ones. But the idea of a holy text that forms the basis for a faith or religion is questionable, since it requires a human invention in order to access a divine message.

Language reflects the way we think, but it does not necessarily constrain us from thinking in new ways. There is a problem, though, when trying to express a foreign idea in an unsuitable language. Languages evolve quite rapidly, so they are well-suited to express the most commonly held current worldview; implicit assumptions in a language thus make it difficult to formulate a new worldview into words.

This does not mean that new ideas and new worldviews are never formed, of course, for paradigm shift has certainly occurred in the past. Part of the challenge of introducing new ideas is transcending the implicit language barrier of any human culture. Metaphor, simile, and storytelling are excellent vehicles for communicating ideas that are not easily framed in a culturally-dependent language. Conveying ideas via story is more difficult than direct expression, but sometimes direct expression is not possible.

For some reason, I just can’t get excited about rocks. I am very interested in what we can learn about Earth’s history from the geologic record, but I have so far failed in my attempts at generating interest in actually studying rocks.

At first I thought it might be an issue of timescales (since rocks don’t “do” anything in an observable timescale), but this couldn’t be it–after all, I did my undergraduate work in astrophysics, where the timescales are so large they sometimes become incomprehensible. Perhaps familiarity adds to this; I see rocks almost every day, but I only see a planetary nebula if I look through a telescope or find pictures other astronomers have already taken. Whatever the reason, I want to care about rocks, but I haven’t been able to yet.

Maybe the Death Valley field trip in two weeks will help change this. Or maybe I just don’t have a geologist’s heart.

Information is teachable. It is simple enough to teach informational detail of an event: the date, the location, the number of people, the outcome–all of these things can be put in textbooks and reformulated as exam questions in a classroom setting. Certain thought processes are also teachable. Mathematics is a good example of this, as is writing technique and general critical thinking.

A new worldview is not easily teachable, however. In Herman Hesse’s famous novel, Siddhartha points out that the Buddha could never truly teach what he knew to be true, nor could he make people accept and understand them. This does not mean that no one ever reached the same truths as the Buddha, but this point cannot be reached by learning alone. No teacher, no book, no explanation is sufficient to arrive at a different worldview–a completely different way of looking at life.

Teaching a new worldview is not a futile attempt, but success likely requires additional factors that may be beyond the teacher’s control.

The world will not be saved by old minds with new programs. If the world is saved, it will be saved by new minds–with no programs. –Daniel Quinn, The Story of B

Recycling is a program. It is well-intentioned, and it has positive outcomes; but recycling will not change the world. The world is changed with new minds. The Agricultural Revolution had no programs nor did the Industrial Revolution, but these changes of worldview were immensely successful in our culture. The open source movement is an interesting example of new minds with no programs.

The principles of the open source movement are simple: free exchange of information, no monopolizing an idea, encouraged cooperation, and so on. What is interesting is that this was not a dogma at the time the movement started (nor has it ever had a single discernible leader). The open source movement began with changed minds. When enough minds changed, larger projects were possible. The open source movement never reverted to evangelism–because a program would be useless. People might choose to change their minds when they understand what the movement means (although they may understand and choose not to agree). In this way the movement grew, developing in ideas and even producing products. But the products are the fruit of a more fundamental energy: the movement of changed minds.

I created a database of my books using Books for MacOS X and have now exported this database to the web. My book collection is available online at library.gravityatwork.com.

The online version of the database does not show all the information contained in Books for MacOS X. In addition to keeping track of lending history, I can also give each book a rating on a 1-5 scale. This allows me to keep track of which books I have not read, since there are inevitably books that always seem to fall to the bottom of the pile. Currently, I have read 70.8% of the books in my collection. I’d like to get this number up to around 80%; I’ll post on my progress at significant milestones.

There are various causes and beliefs that people try to sway others to accept. Often this can take the form of a simple conversation between friends: a political discussion, a religious debate, a socio-economic comparison, or a countless number of other issues. Often the goal of such an endeavor is to change a person’s mind–not a simple feat, but something that does occasionally happen.

One method used in changing minds is evangelism. The classic example of evangelism is the big-tent-revival characterizing certain religious movements, but evangelism is used in many secular arenas as well (political campaign rallies, for example). A problem with evangelism, however, is that it gives the false impression that it is easy to change minds. Using religious evangelism as an example, consider the “call to salvation” that occurs at the end of a powerful sermon. Many people many step forward to embrace a new religious ideology, to change their former style of life. This is often cited as an example of rapid conversion; namely, the people only need to hear a message with an open mind in order to accept it.

Evangelism would be incredibly powerful if this were true, but a person’s life journey plays a much larger role in determining their acceptance of an idea. The drug addict who turns to Jesus is not making an immediate rejection of their former lifestyle in favor of a new faith. Rather, their experiences up to this point have led to an undesirable result, and the evangelical message reached them at a proper moment. AFter all, addicts can certainly be aware of their addiction (or at least the consequences), even if they take no action to change the situation. And, of course, not all converts had a large obstacle to overcome: some of these new converts have simply been on a personal philosophical journey that led to acceptance of the evangelist’s message.

Leaving the religious example, the concern over global climate change is another issue that requires a change in worldview. Evangelism to raise concern over our culture’s impact on the climate system is useful, but it is useful in the sense that it reaches out to people who are at the point where they are ready to accept this paradigm shift. These people are not making a 180-degree shift in worldview, but their journey in life has led them to the point where this message makes sense.

How are minds effectively changed, then? Evangelism should not be completely abandoned, but perhaps the one-on-one coffee shop conversations are more effective–not only because the message can be tailored to the individual, but also because these conversations contain an important personal element that is lost when addressing a crowd.

In argument, falsifying someone’s premises or assumptions does not falsify their conclusions.

I could make the (incorrect) assumption that today is Saturday. From this, however, I can draw many correct conclusions: today is a weekend day; today starts with an ‘S’; yesterday was not Wednesday; or any number of other (correct) conclusions. Falsifying my claim that today is Saturday will have no effect on the validity of my conclusions.

Rewind back to the time of the Agricultural Revolution. There are humans living all across the globe in a variety of climates. One particular culture in Mesopotamia, however, decided to live differently than all the other global human cultures. This group of people did not simply practice agriculture–rather, they practiced totalitarian agriculture. This came from (and reinforced) the mindset that this particular group of humans were above the world of Nature and need not be subjected to Nature’s Law.

This radical transformation in worldview from the rest of humanity led to many unique (and sometimes dangerous) developments by this culture. Among these are the development of philosophy and modern religion. Philosophical questions arose because of the assumption that humans are above Nature; this led to discussion of cognition, self-awareness, afterlife, ethics, morality, and other such questions.

In this light, it makes sense that people not originating from this Mesopotamian culture would not have developed the field of philosophy–they did not assume that humanity is above Nature, so these questions would never have been relevant. What about science? Why did non-Mesopotamian cultures never develop science? This question bothered me for awhile until I realized that science is historically a branch of philosophy (natural philosophy, to be specific). In other words, although the scientific framework deals with understanding the natural world, separated from speculations of meaning and purpose, it has its origins in philosophy. This scientific view developed out of the questions of meaning and purpose, so even though this is no longer the case, we can see why non-Mesopotamian cultures did not develop science. Without the worldview that humans are above Nature, there is no obvious path leading to science. There may exist such a path, but unfortunately the prevailing Mesopotamian culture expanded with such rapidity that we may never know.

It is nearly impossible to change the mind of an individual. You can present whatever evidence or reason you have for your view and argue until you’re blue in the face, but your opponent will not change their mind unless they want to. Even if your opponent understands everything you say, this does not automatically imply that they will change their worldview to facilitate your ideas. This is simply not how individuals work.

People, however, operate differently. As a population, there will likely be some people who are ready to listen and change their minds. The Agricultural Revolution, Enlightenment, and Industrial Revolution are all examples of radical ideas that eventually propagated through the population to become part of a collective worldview. Of course, for every successful revolutionary idea there are countless failed ideas. Nevertheless, the fact remains that radical change in worldview is possible even on a global population level. Although certain (and perhaps many) individuals may not be interested in changing their minds, the effects of a revolutionary idea on a population can be (and have been) the catalyst for a global change in cultural perspective.

Religion and philosophy provide a lens through which people view the world. Especially where organized religion is concerned, there is a fundamental order to the Universe that is expressed through a particular spiritual revelation (whether this be a holy text, a prophet, enlightenment, continuity of tradition, or any number of additional contributors). Furthermore, because this worldview follows from this spiritual/religious revelation, and because the spiritual revelation is assumed to be completely accurate (since it came from God/the gods/a higher power), it follows that a religious system should be able to address any and all questions posed it. After all, a complete worldview will be able to address any question of the world.

Are there any dangerous questions?

Yes, I believe there are. A dangerous question is one that has an attainable answer, but following the answer to its logical conclusion results in the collapse of the worldview in question. The existence of dangerous questions does not mean religious systems will immediately fall; instead, it predicts strong reactions, turmoil, war, anger, and resentment, followed by patchwork attempts and sometimes ignorance. Of course, any religion will claim there is no such thing as a dangerous question. After all, the religious worldview is sufficient to address any issue imaginable. But such questions do in fact exist.

So what questions are dangerous, then? For now, I’ll leave that as something to ponder…

Induction is a useful process by which we learn many things in life. However, the process of induction is not a strictly logical one, as successful as it may be. Consider the following example:

1) A young turkey wakes up in the morning and eats breakfast while watching the sunrise.
2) The next day, the turkey wakes up again to breakfast and the sunrise.
3) The next day, the turkey predicts that breakfast and the sunrise will be waiting in the morning. The turkey is correct.
4) The next day, and several days after this, the turkey becomes increasingly certain that breakfast and the sunrise occur every morning, since past experience has shown this to be true.
5) One day, the turkey wakes up to find this is not true. It is Thanksgiving Day, and the turkey’s knowledge based on induction has failed.

This does not mean the turkey’s inductive reasoning was worthless, but the example illustrates that induction is a fallible process.

Today, I discuss one of my favorite topics in astronomy: nucleosynthesis!

All matter came into existence at the Big Bang; however, this matter was mostly hydrogen, with some helium and trace amounts of lithium. Elements heavier than this did not exist, though. Due to a nonuniform distribution of matter, gravitational forces led to clumps of matter that eventually formed stars. These stars fused hydrogen into helium, and helium into some of the heavier elements. Large stars formed nearly all of the elements up to iron late in their life (as Red Giants). Iron is the most stable element on a curve of binding energy, though, so normal fusion does not allow the combination if iron nuclei. (Fusion occurs when elements lighter than iron are combined to release energy. Fission occurs when elements heavier than iron are split to release energy.)

Toward the end of a large star’s life, there comes a point at which the inward force of gravity overcomes the outward pressure forces, leading to core collapse. This event is a supernova, and it is sufficient to fuse iron into higher elements–all of the elements up to uranium. The supernova explosion has the added effect of distributing matter in a large vicinity, where it can be transported away from the generation site to form new stars. Smaller mass stars also contribute to this interstellar matter when they shed their outer layers later in life (a planetary nebula). This matter then clumps and forms gas clouds that become new stars–but this time they are already enriched in heavy elements.

Our Sun is a third generation star, which permitted planets, moons, asteroids, and comets to condense from the metal-rich solar nebula (astronomers consider anything heavier than helium to be a metal). We can observe many of these heavy elements in the Sun with a spectrometer, but the best evidence of this is simply to look around. All matter we interact with has its origins in the fusion process of stars long past.

We truly are stardust.

This post could work with a variety of creatures, but I seem to enjoy talking about dolphins.

Many human civilizations that developed counting systems used a base of ten–and obviously the most prominent modern mathematical convention is to use base ten. It is simple enough to consider a reason for choosing to base a counting system on ten: we have ten fingers. Perhaps other factors came into play and perhaps not, but it is a reasonable enough guess.

Now let’s apply this same conclusion to dolphins. In a few million years, say that dolphins evolve to a higher level of intelligence and develop their own counting systems. Assuming dolphins do not develop fingers or any additional appendages they would have two front flippers, a dorsal fin, and a tail. Following the human convention for selecting a counting base, we might expect dolphins to use a binary (base two) counting system (counting with each flipper). Computer systems presently operate with a binary system, as a zero and one can be designated easily with low and high voltage, respectively. If binary computing is an advantage, then perhaps a dolphin society that primarily uses a binary counting system will have a technological advantage at some point in their development.

I’m telling you, watch out for the dolphins–but at least they’re friendly.

Since humans have had the ability to think, they have sought to uncover the “deeper” meanings of life: questions of existence and purpose. The most fundamental questions of the Universe were (and are) discussed, pondered, answered, and re-thought over and over again.

Shhh…listen carefully. Everything quiet now? Good. Listen really hard. If you listen closely enough, you can hear them–the questions of the Universe. You can hear them, right? No, that’s just your computer fan, try again. Concentrate. Still nothing? Well now, I guess that shows something: the Universe does not ask questions. People ask questions.

Either that, or you’re not listening properly.

At the Pale Blue Dot III workshop in Chicago

When discussing my research and scientific interests, every now and then I encounter questions such as “what’s the point?”, “why do we care?”, and–perhaps most importantly–”why is this worth spending money on?”. I have my own set of responses to questions such as these, but a talk by Michigan State University’s Jon Miller gave me a bit of a new perspective on this issue.

The course of science policy and direction is determined largely by elected officials and the heads of certain organizations (NASA, NSF, NAS, etc.). The president of the United States is more or less elected based on a few broad high-profile issues and a general public perception of leadership potential, and seats in Congress vary only by ~5-10% each year–so it is important to lobby and inform elected officials. Likewise, scientists in decision-making positions may have expertise in a particular field while asked to make broad scientific policies. Informing and influencing these people to guide the future direction of science and funding is very important.

Moving on to the public, there is one important demographic. These are the people who are interested and aware of science research and progress. This group has a good general understanding of science and would write their own elected officials regarding scientific policy. Science-public relations are also important here in influence science policy. Another segment of the public is interested in science, but perhaps feels uninformed enough to act politically. This group visits museums, buys science books, and watches science television, but will never write their elected officials. The remaining group of the public has little to no interest in science, and will have little to no impact on science policy.

Now, public education is important for a number of reasons. But if we only consider the shaping of science policy–and the justification of money spent–the last two public groups are inconsequential. So when someone from one of these groups asks for cost justification, a reasonable response could be, “there is no reason you should care about this research, but you opinion is also of no consequence.” I do not suggest actually using this response, but the perspective of policy-making in the US is interesting in determining communication effectiveness.

Philosophy is great, don’t get me wrong.

But the study of pure philosophy can also be a dangerous pursuit. People have sought to decipher the so-called “meaning of life” since the beginning of history–the most popular form of this exploration being religion. Philosophy also deals with this fundamental question, but in a manner devoid of any of the symbolic framework in most religions. The advantage of the philosophical approach is that one does not have to operate within a specific religious context, allowing for a “pure” and direct study of this question.

The disadvantage, however, is the possibility of being easily overwhelmed. Historically, many philosophers have been affected by their state of constant deep-thinking, sometimes for the worse. Philosophy asks a lot of questions, and provides few answers in return. While religious expression can aim toward the same set of questions, it operates in such a way to minimize this type of reaction when faced with some of the most fundamental questions. Leaning too far toward religious tradition has the potential to greatly water down this journey, but leaning too far into philosophy carries the risk of being overwhelmed. Like most things in life, balance is the key.

A friend recently told me that “everyone needs a good hangover at least once”. Why would anyone consider this to be good advice, though? After all, just about everyone experiences physical illness at least once in their life, and most people do not enjoy the feeling of being ill.

The difference between these two situations is the process by which sub-optimal health is achieved. Typical illness may be developed by unsanitary hygiene, stress, improper nutrition, infection, or various other ways. Some of these sources may be more under our control than others (e.g. washing hands); but very few people purposefully avoid washing their hands in order to become sick. The hangover, on the other hand, is almost always a self-inflicted ailment. Although symptoms may be similar to other sicknesses, it is the knowledge that a series of conscious decisions previously led to the present health condition. That is, it is not a question of experiencing sickness, but rather the experience of negative consequences following a positive sensation.

Most of life involves weighing actions and consequences in an effort to maximize some factor (fun, profit, etc.). The hangover is simply an obvious illustration of this struggle in life–and ultimately a harmless one when contrasted against other situations in life. Still, there are some consequences that are best avoided whenever possible. I’ll drink to that.

Part of the fun of being a scientist is inventing reasonable and testable hypotheses. In any project, there are countless hypotheses that are developed and tested, from the broad over-arching questions of science to the small and detailed process of debugging an experiment. Since there are an infinite number of hypotheses for any given situation, developing reasonable and testable hypotheses can almost be as much art as science.

For better or worse, I have found that this type of analytical thinking has invaded the rest of my life. If I were watching an advertisement on television and someone in the room says, “I wonder why they…”, I immediately try and think of a viable hypothesis to explain the anomaly we just watched. The same thing happens when people watching at a train station, airport, or restaurant. Of course this hypothesis are never tested, but they are usually falsifiable and provide a reasonably explanation–whether or not it is actually correct. And sometimes in science that is what we have to go on, until we have the means for further testing.

Many of you are already well aware of this, but as a PSA I am posting a calculation to determine whether or not the Coriolis effect helps govern the draining of sinks and toilets. First, let’s start with the atmosphere:

The length scale of the atmosphere is on the order of L ~ 10⁵-10⁶ m, and the velocity scale is U ~ 1-10 m/s. The magnitude of the Coriolis frequency is Ω ~ 10^-5 s^-1. Thus, the ratio of the inertial force to the Coriolis force is (U/L)/Ω = 0.1-10. For some atmospheric phenomenon, the Coriolis force certainly is important (such as in the rotation of a hurricane).

Now on to the kitchen sink. For a sink or toilet, the length scale is L ~ 1 m and the velocity scale U ~ 1 m/s. Taking the same ratio of the inertial force to Coriolis, we get (U/L)/Ω = 10⁵. Even the slowest of drains would have a difficult time being affected by the rotation of the earth. But for some reason, people still assume that something magical happens to toilets when they cross the equator and head down under.

It’s a classic experiment to determine when “self-awareness” occurs: a monkey (or child, or other creature) is placed in front of a mirror. A hat, food item, or other object is placed on the subject’s head. If the monkey reaches toward the image in the mirror, it is assumed that the monkey thinks there is someone sitting in front of it. If, however, the monkey looks in the mirror and proceeds to reach on top of its own head to retrieve the object, then the monkey is said to be self-aware–because it knows that the image in the mirror is itself.

Although the experiment itself is interesting, I have always had a problem with the conclusion. Does this really test self-awareness, or is it simply a way of assessing a creature’s learning ability (i.e. when does a creature learn how a mirror works?). Just because a monkey/baby/whatever reaches for the images doesn’t mean they aren’t self aware; it doesn’t even mean they don’t recognize their own image. All it means is that they do not understand the reflective properties of a mirror (after all, why wouldn’t they assume that a duplicate was created in the image of the mirror?). Self-awareness is indeed a difficult thing to assess, but often it seems to me that the methods for quantifying this are vague at best.

“It would take more than a million Earths to make a ball as big as the sun. It would take about 109 Earths to make a straight line across the sun.”

One of my favorite gifts from my childhood is My First Book About Space (Western Publishing Company, 1982). In just 24 pages, the book led me through a tour of the solar system, with descriptions of gravity, comparisons between the planets, and photographs of the moon landing–with full color illustrations, of course. Looking back on it now I am impressed by the factual content of the book, as I would not expect many children’s books to contain too many numerical quantities. But thinking back to when I frequently read this book, I don’t think this distinction ever occurred to me. Rather, the combination of the book’s fun presentation and my fascination with “space science” kept me reading it again and again–not to memorize the figures, but to get that sense of excitement and discovery that drives us to explore.

Although most of my childhood books have since disappeared, My First Book About Space still sits on my shelf. I must admit, although my outlook and education is different now, I still enjoy reading through this book, and I still have much of the same excitement toward my scientific career as I did then. This feeling and outlook that has so far lasted through my life is perhaps best expressed by the last line of this book:

“Are there any space creatures? No one knows yet. Space is huge, and there is still a lot to learn about it. Exploring space is exciting. Maybe one day you will be a space explorer.”

(This entry inspired by a question at the World’s Fair blog.)

Navigating in a foreign city can be a challenge–especially when there is a significant language barrier. Armed with the name of a famous duck restaurant written in Mandarin, we managed to hail a cab and arrive in the general vicinity of the restaurant. We still have no idea why the driver didn’t take us right to the restaurant, but no matter; we just harnessed skills that had been developed through a massive multiplayer online game–in my case, Dark Age of Camelot (DAoC).

When entering a city in DAoC, locating a particular non-player character (NPC) could be a bit daunting, as the 3-D cities were sometimes rather large and complex. Guards were very abundant, and evidently knowledgeable about the denizens of the city, and provided a means of deducing an NPC’s location. By asking a guard the location of a particular person, the guard would turn and point in the correct direction, as the crow flies. Sometimes this was through a wall or across an impassable barrier, but in all cases it was the correct direction. By speaking with multiple guards along the way, it was possible to deduce the location of any NPC in the city.

Back to Beijing, these DAoC skills came to be of use. We wandered from person to person, displaying our Mandarin paper along with a “lost tourist” expression. No one we met spoke English, but they could read our directions and point out the way. And eventually we made it, with a sense of accomplishment at having utilized our DAoC skills. Unfortunately, the journey itself was more enjoyable than the destination.

One of the great tragedies of the modern era is the loss of the genre of adventure games. You may remember such classics as Monkey Island, King’s Quest, Sam and Max, Space Quest, and others as being the source of both fun and frustration as you figured out exactly the right order in which to talk with people and use your items in order to complete a certain puzzle or task.

Perhaps these games do not provide an opportunity to showcase modern graphics hardware and algorithms (hence the flop of the 3-D Monkey Island 4), but the tactics involved in these adventure games has not been replaced with anything on the market. Yet the problem solving skills learned in this type of game are highly valuable in the “real world” when trying to locate information, objects, or people–especially in a large corporate or university setting.

Example: three years ago I was working as a research assistant in the UMN physics department. My advisor told me to get an electronic balance in order to make some measurements. I asked him where I could find one, and he replied, “Oh, somewhere in the building.” The ability to track down information in a vast dataset (such as a building or campus) is an invaluable tool, one which adventure games developed into something fun and entertaining. It is indeed sad that the children of today will grow up without a Guybrush Threepwood or Roger Wilco of their own.

A common (not necessarily incorrect) anthropocentric opinion is that humans are the sole “intelligent” creature on Earth, intelligence is a binary construct (either you’re self aware, or you aren’t), and behavior of other animals that may mimic certain human behavior is classified as “instinct”.

A less anthropocentric view (not necessarily correct) is that intelligence and self-awareness comprise a spectrum, with humans perhaps the most developed along this continuum. Observations of dolphins and whales indicate complex language and social behaviors that could be indicative of intelligence and/or awareness. But then again, how would one design an experiment to test this hypothesis? If intelligence is a binary construct, then it could perhaps be demonstrated with reasonable certainty that dolphins are unintelligent. If a spectrum exists, however, then we return to the curse of the astrobiologist: a sample size of one.

Philosophically I have an easier time believing that humans are not so unique that we operate based on intelligence while dolphins and blue whales (which have very high brain/body ratios) operate entirely on instinct. The Copernican principle is always present, especially in matters of life and our place in the Universe. At least in my mind, the distinction between human intelligence and animal instinct is a remnant of the geocentric view, even if the name no longer persists. There’s more than one way to say “potato”.

With every successful problem science is able to explain, a ple-THO-rah of new unanswered questions opens up. This is, of course, one of the exciting things about science–or any profession working on the “leading edge” of our knowledge.

There are also approximately 6.5 billion people in this world. Almost all people will contribute to the general body of human knowledge during their lifetime, although only a small fraction will do so in a publishable/archival format. But the knowledge contained in oral traditions and community practices can certainly be of equal or greater value than published material. With increasing globalization, we are starting to see the interplay between various knowledge repositories–such as Western pharmacists finding a new medicine based on the herbalists of a distant indigenous people group. This cross-fertilization is significant not only across cultural boundaries, but also between academic disciplines and professional arenas. A certain technique that has been used by engineers for decades may shed light on a modern environmental research topic, or a certain terrestrial geological discovery may have strong implications for an extrasolar astronomer. Interdisciplinary communication, whether across a physical, cultural, or academic boundary, is increasingly important in solving the growing number of mysteries of the world (and not just science either; this is true of business or any other area of knowledge).

Sadly, there are no collaborative filtering algorithms (yet) to foster collaboration between unlikely fields. Until then, I suppose the best solution is simply to become as diverse and well-rounded as possible. There is a small danger in falling too deeply into a sub-sub-field, in that the view of the big picture is sometimes lost. But such specialists are also a necessity, and staying mindful of this danger can be a sufficient remedy for the specialist.

(From Beijing, after having just completed a 10 km climb of the Great Wall.)

Patterns are everywhere. People seem to be very good at intuitively recognizing patterns in nature, behavior, and progressions–even when no true pattern exists! Indeed, although I support SETI in spirit, I am sometimes skeptical as to their ability to discover a true patterned message from extraterrestrials (if in fact such a message exists). I’ve been noticing patterns of several types recently, in travel and elsewhere. I suppose the only way to check if these are true patterns is to watch the progression in time–but even then, if we want to see patterns then we will see patterns.

For more on patterns, I recommend the movie Pi.

A few million years from now (give or take), let’s say humanity has annihilated itself and evolution proceeds in other species. Perhaps dolphins would continue to develop their intelligence and maybe even have a technological revolution of their own. Of course, there is no reason to expect that advanced dolphin intelligence would have a lot of outward similarities to human intelligence, but there would likely be some. What about the urge to explore beyond one’s own environment? I imagine that these evolved dolphins would eventually embark on discovery journeys onto land, similar to our exploration of the oceans. And then…a dolphin space program? Just try and picture what that would look like!

The problem with homework problems is that they have solutions.

When given a problem from a textbook or a professor, it isn’t too difficult to sit and work through it. True, it may be a painful problem that requires the better part of your weekend, but generally you don’t give up. Why? Implicit in assigning the problem is the knowledge that the problem is solvable. It may be tough, challenging, and/or frustrating, but the knowledge that a solution exists is sufficient to continue forward. In “real world” problems, though, this is not necessarily true. Problems are often ill-defined, and many times may not have a solution. Sometimes, it is actually necessary to give up and walk away or find a completely different approach. However, I’m guessing that if a teacher were to put an unsolvable problem on an assignment there would be talk of mutiny in the classroom.

A strange game. The only winning move is not to play.

I think I found one of my favorite numerical series. Bonus points to whoever can figure it out. (Possible Hint: array indices should always begin with 0.)

2, 3, 3, 5, 7, 13, 23, 43, 79, 149, 263, 463, …