Fundamental Science and Myths about Men: Edge 2013 Questions, II

Every year John Brockman tries to reach the Edge of human knowledge by asking lots of the world’s best minds the same question and then compiling their answers.  This year the question is ‘what should really worry us’, and I’m reading through them, writing summaries as I go.  Here are essays 17- 32.

17) Nicholas Carr notes that technology is becoming faster and faster and that humans are consequently becoming accustomed to less and less time waiting — especially now that our phones are powerful computers as well.  He speculates that this could have a negative impact on both the cultural and social level, because many deep human experiences require time to cultivate, understand, and appreciate.  A deficit in patience could rob many of us of some of the most profound aspects of life.


Psychology, Technology, Culture

18) Kevin Kelly  says that we are not worried enough about what will happen when underpopulation becomes a problem.  Everyone has heard about the dangers of having too many people and not enough resources, but the prospect of not having enough people to drive an economy and help the aging is also a scary, and less well-understood, problem.  Statistics from many countries, both in the developing and developed worlds, shows that fertility rates are dropping, and we should be concerned.

Fertility, Economics

17) Lisa Randall is worried that we’ll stop doing the kinds of experiments which require long-term planning and big funds but which also probe the deepest mysteries of the universe.  She passionately argues that knowing how things really are is a worthwhile goal in and of itself.

Physics, Science

18) Evgeny Morozov thinks that we may craft technology that’s too smart for our own good.  He reminds us that not all problems need a “smart” solution, and asks us to consider what might be lost in a world where smartphone apps and games ensured that everyone recycled, but at the expense of reaching these same people through the moral force of arguments.

Psychology, Technology

19) J. Craig Venter believes that a combination of poor sanitation, improper use of antibiotics, and growing numbers of unvaccinated people (among other things) could lead to an outbreak of novel strains of infectious diseases against which there are no current defenses.

Medicine, Biology

20) Adrian Kreye thinks that a desire for catharsis — hardwired into us and at the foundation of our enjoyment of art, music, literature — is also antithetical to rational thought.  While we probably can’t change something as deeply wired as the near-ecstatic release of tension, if we understand what’s happening, we can avoid falling into a trap of escapism.

Art, Culture, Literature, Psychology

21) Terry Gilliam claims to have stopped asking questions, choosing instead to ‘marvel stupidly’ at the world.

Uncategorized

22) Jennifer Jacquet fears what she calls the “anthropocebo affect”.  Two seemingly unrelated facts are drawn upon for insight: one, that pain and illness can sometimes be relieved when the patient merely believes they’ve been given medicine; and two, humans have recently become a global geologic force.  She believes that knowledge of human influence in global climate will engender apathy, a perception of the destruction we cause as somehow inevitable.

Psychology, Climate Change, Culture

24) Hans Ulrich Obrist is concerned about extinction on two different levels.  The first is more well-known, and is simply the good-’ol-fashioned annihilation of humans.  The second is more subtle cultural extinction resulting from global homogenization.  He discusses a number of different artists who are fighting against these two types of extinction through their work.

Art, Culture

25) Robert Sapolsky believes that free will is a myth and that some combination of genetics, environment, randomness, and unknown-but-not-free-will variables completely decide what a person is and does.  But it’s really hard to feel like free will doesn’t exist, even armed with this knowledge, and thus our attitudes are still subtly out of alignment with the facts.  It is this difficulty that should worry us.

Neuroscience, Philosophy

26) Katy Jeffrey is not as excited about the prospect of ending death as many others are.  She believes that death is there for a reason and that it has served us well.  It is an integral part of the process by which humans improve from one generation to the next, and she asks us to consider a world in which ten generations (or more) of humans were competing for the same resources, and in which attitudes/mindsets might be half a millennia old.

Death, Aging, Science,

27) Lawrence Krauss discusses two possible reasons we may never be able to answer certain fundamental questions in physics.  The first is that modern physical theory predicts an infinite number of universes beyond our ability to explore.  Should the laws of physics be probabilistic, we stand little chance of finding this out if we can only explore one universe (our own) out of the many.  The second is that, because the universe is expanding away from us, the amount of universe we can explore is shrinking all the time.  The longer we wait, the less of nature’s secrets we’ll be able to discover.  He isn’t too worried though; there’s plenty to keep us chewing on for a long while.

Physics, Science

28) Tim O’reilly draws on his classics background to apply lessons from the fall of Rome to the modern world.  He sees in certain parts of American religion and politics the same sort of superstitious thinking that lead to the dark ages.  Should anti-science win the day, what might the consequences be?  Today’s world is different in a key respect from the world in Roman times, in that we very nearly have a global civilization.  If one falls, we all fall.

Politics, Religion, Science

29) Eduardo Salcedo-Albaran draws on philosophy and specific examples to argue that crime is more fundamental to the operation of many governments than is commonly realized, and that the ways in which political scientists examine modern governments is outdated.

Politics, Crime

30) Bart Kosko fears that, like the drunk looking for his keys in the lamplight because that’s where he can see, we restrict ourselves to just five probabilistic models because they are easier to teach and calculate.  The result is that we’re not modeling the world as well as we could be, and the negative effects may especially hamper the Bayesian revolution in probabilistic computing.

Statistics, computing

31) Timo Hannay believes that consciousness is the origin of suffering and joy, but that we have absolutely no way of telling what things are conscious and what things are not.  Though we know much about the brain as a physical system, and though we have powerful intuitions about the degrees of consciousness which different animals possess, neither is a very strong basis for analyzing subjectivity.  That we are so in the dark about such an important matter should worry us.

Consciousness, Philosophy,

32) Helen Fisher
thinks that not enough time has been spent dispelling myths about men.  After conducting a staggeringly comprehensive survey of 5,000 single people, she found that men compared to women were just as eager to marry, faster to fall in love, less picky in their mate choice, more likely to have very personal conversations with their partner, and much more likely to kill themselves following a break-up.  This differs markedly from the typical stereotypes American culture associates with men.

Sex, Psychology

Data Discrimination and Monsters In Our Brains: Edge 2013 Questions, I

It’s that time of the year again, when John Brockman attempts to “arrive at the edge of human knowledge” by asking some of the world’s most unique thinkers a single big-picture question.  Reading the Edge contributions is always a pleasure, but this year’s question is especially pertinent to me, because I’ve become very interested in a relatively new field called existential risk.  Scholars in xrisk, as it’s also called, try to bring all the modern tools of philosophy, mathematics, and science to bear on addressing issues which threaten the survival of humanity.  Extinction-level scenarios range from thermonuclear war to alien invasion to runaway-AI to out-of-control climate change.  I hardly think I need to stress this research’s importance; it comforts me a great deal that minds like Nick Bostrom’s are devoted full-time to making sure that we don’t all die.

I’m working my way through these essays one-by-one, and writing short summaries in the process. Here is the first round:

1) Geoffrey Miller worries that the West will have a poor response to what he sees as effective eugenics on the part of China.  To him it seems plausible that a combination of public policy, culture, and research will see the average IQ of Chinese people creeping up from generation to generation, leaving the West behind.  He endorses the sort effort that would allow us to follow suit (though, of course, he never sanctions anything like the murder of ‘genetically unfit’ people).

Science, genetics, culture

2) Nassim Nicholas Taleb builds on his previous work by making the case that many of our statistical tools are pretty well useless, especially when applied to finance and economics.  He concludes that the only thing which can prevent further financial disasters like the one the world is still reeling from is for investors to have ‘skin in the game’.  People make very different decisions when they will be directly harmed by misunderstanding risk.

Economics, Risk

3) William McEwan’s essay is extremely specific and centers on virus mutation rates.  He fears that we will not learn how to push viruses over the “error catastrophe threshold”.  Remember that DNA replication is not error-free, a fact which is part of what allows evolution and diversity.  Compared to mammals viruses have many more errors in replication which, coupled with their short life cycles, enables them to evolve much more quickly than humans.  But at the error catastrophe threshold there are enough errors that an organism simply can’t replicate.  We need to design drugs which get viruses there without harming us in the process.

Biology, Evolution, Medicine

4) Helena Cronin is troubled by the public reception of evolutionary psychology, particularly when it has ‘politically incorrect’ things to say about sex differences between men and women.  She writes with force that humans have an evolved nature but that, in the public marketplace of ideas, dubious opinions to the contrary are juxtaposed with empirical facts and little distinction is made between them.  Science is science and fact is fact, she reminds us, and these are not matters to be decided by public opinion.

Culture, Science, Evolutionary Psychology

5) Dan Sperber begins by pointing out that worrying is an investment of emotional and cognitive resources.  There are therefore good investments of worry (where the next meal will come from) and bad ones (whether or not the gods will kill you for wearing your hair a certain way).  In a fast-changing world of staggering complexity, he fears that humans will get worse and worse at picking the right things to worry about.

Culture, Technology

6) Martin Rees believes that people are simply not worried enough by existential risk; that is, but extremely low probability, high impact events.  These include things like nanotechnology, global warming, system-wide internet failures, and the like.  He notes that most people didn’t realize until after the fact how close nuclear war came to erupting during the Cold War, and that a host of new threats just as dangerous faces us in the next century.

Technology, Risk

7) Barbara Strauch is worried about a great — and growing — disconnect in culture surrounding the communication of science.  There is simultaneously a decrease in science coverage in general-interest newspapers, an increase in scientific ignorance in many places (most conspicuously in political figures), mountains of misinformation, but also an insatiable appetite for science news from a significant number of people.  The ramifications for science funding, science education, and public understanding of science is cause for worry.

Science, Culture

8) John Tooby’s sweeping contribution covers two vast sources of worry: the world outside our heads and the world inside it.  On the one hand, civilization has flourished within a wildly improbable bubble of safety, which could at any moment be popped by a gamma ray burst or a coronal mass ejection.  Meeting these threats will require forging a far more technologically advanced civilization that the one we have now.  Unfortunately, standing in the way of this project are the myriad evolved biases –groupthink, status-seeking, etc — which plague human thinking.  We must deepen our understanding of the human mind’s many failure modes before it is too late.

Evolutionary Psychology, Evolution, Physics

9) David Gelertner thinks that the internet has devalued the written word.  Because so many people can write and publish their opinions, the market has essentially been flooded with words, with each one getting less attention from both writers and readers.  Consequently the quality of much writing has dropped.

Culture, Writing, The Internet

10) Brian Eno notes that lots of smart people steer clear of politics, and as a result less intelligent people end up being the ones who do politics.  The result is the geopolitical mess which we see today.

Politics

11) Seth Lloyd compares huge financial institutions to stars and the fiscal meltdown to the formation of a black hole.  We should be worried anytime we see enormous institutions leveraging themselves to the hilt.

Physics, Finance, Economics

12) Danny Hillis says that more sophisticated search engines should worry us because they will increasingly be not a reflection of statistics but rather a conceptual model of the world.  Searching for ‘dictators’ will return a list which reflects the assumptions built into the search engine about what constitutes a ‘dictator’.  This could be very powerful, but it puts a greater share of the the task of deciding what’s true into the hands of machines; this could increase the insulation of echo chambers or serve to expose us to unfamiliar points of view.

Internet, Technology

13) David Buss thinks we should be more aware of the evolutionary logic behind the people we choose to pair up with.  In his view, a significant chunk of infidelity, marital dissatisfaction, stalking, and domestic violence can be explained by looking at the dynamics of dating up and dating down.  Men are more likely to stalk women when they sense that they may not be able to attract as high-caliber a mate again.  Extremely attractive women are beset on all sides by ‘mate poachers’ seeking to lure them away from current relationships.  None of this excuses lying, cheating, or abuse, but with understanding comes the ability to modify our own behavior accordingly.

Evolutionary Psychology, Biology

14)David Bodanis is worried about the confluence of two things: the long-term consequences of extreme wealth inequality and the emotional wiring that make fascism an attractive choice for the oppressed.  He invites us to imagine what might happen when only the rich can afford the kind of medical treatments that make them young, beautiful, and long-lived while the vast majority of people go on suffering illness and death.  The result might be Fascism-fueled retribution, but that would be disastrous for technological progress.

Politics, Psychology

15) Benjamin Bergen thinks everyone should calm the fuck down over the use of bad words, particularly when ‘protecting children’ is used as a justification.  There is nothing in the sound or idea behind words which makes them inherently bad; indeed, swear words derive their power exclusively from out reaction to them.  We should not be so worried about the possibility of children hearing foul language.   Language, Culture, Censorship 16) David Rowan acknowledges the power of Big Data, but worries that a ‘data underclass’ will not benefit from it.  Those without access to massive stores of information are at a disadvantage in a variety of markets.  Further, many of us are in danger of being unable to escape the crude portrait drawn by our online data, and this portrait is fast becoming what matters in deciding our prospects, even when it’s mistaken or outdated.  Something should be done to empower individuals in the yottabyte age.

Technology, Data, The Internet  

16) David Rowan acknowledges the power of Big Data, but worries that a ‘data underclass’ will not benefit from it.  Those without access to massive stores of information are at a disadvantage in a variety of markets.  Further, many of us are in danger of being unable to escape the crude portrait drawn by our online data, and this portrait is fast becoming what matters in deciding our prospects, even when it’s mistaken or outdated.  Something should be done to empower individuals in the yottabyte age.

Technology, Data, The Internet

Preceding The Meatless

“To realize dream of announcer I am preceding the meatless.”

You may be scratching your head at that, I know I was when I read it.  As an English teacher in Korea, I come across a fair bit of total gibberish when I’m grading essays, but this sentence doesn’t fit that pattern.  Instead there is something almost comprehensible about it.  The student is a bit advanced, so it’s unlikely that she just completely missed the mark, and from context clues it was obvious that she was trying to communicate that she wanted to be a T.V. personality.  But when it came to this line, nobody in the office could make heads or tails of it.

The confusion rests almost entirely on “meatless”.  What the hell does that mean?  We asked some of the native Korean speakers if there was a construction in Korean that would shed light on it, maybe a turn of phrase or a word that had several meanings that might get mistranslated.  Alas, to no avail.

The interesting thing is what my response to this sentence was, as it’s something I’ve been more aware of doing since I got to Korea.  After a few minutes of puzzling I began trying to model the student’s state of mind, asking myself questions like “what might she have been trying to say here?”  I was running a simulation in my head of a non-native English speaker with limited knowledge of the vocabulary she was trying to use.  What thought was in her head which, when translated into a language she didn’t know well, would’ve come out as “preceding the meatless”?

Humans perform this sort of mind reading all the time, there is a whole fascinating psychology behind it.  That I was doing it isn’t noteworthy, but that I’ve become so much more aware of it is.   Experiences like reading this student’s essay are further supplemented by my day-to-day encounters outside the classroom.  My Korean is passable but I’m far from fluent, so in many of my interactions I’m forced to piece the message together from both nonverbal and environmental clues.

As a fairly articulate person who has been surrounded by other articulate people for most of my life, this is a skill that needed to be sharpened a little bit.  These days I feel like my awareness of subtext has heightened and I have a richer vocabulary for modeling other people’s mental states.  The trick will be holding on to what I’ve learned when I’m in a situation where everyone speaks English, especially if they are also articulate.  Bonus points if I can smoothly integrate it with my preexisting verbal agility.

Leaving aside the linguistic consideration, there is also the more general point that teaching requires a lot of metacognitive sensitivity.  That my students are non-native English speakers adds an interesting twist, but I think all teachers have to do this to a certain extent.

If you’re a math genius turned engineer, your job is to take your high-octane math knowledge and build a bridge or a space shuttle.  But if you’re a math teacher, your job is to take knowledge structures in your brain and transfer them to another brain.  Not only do you have to understand math, you need a secondary knowledge of where the sticking points might be for someone else.  It isn’t enough to know what an integral is, you need to know why someone else might not be able to make sense of it.  This is something about the profession I didn’t appreciate until I became a teacher.

While I don’t have any empirical evidence for it, I’d be astounded if teaching didn’t beef up a person’s empathy and ability to model mental states just a wee bit.  At any rate it’s taught me a lot about how my own mind works.

Moving Out Of The Pre-Practice Stage

A day or so ago something encouraging happened. I was working my way through a book on coding and I managed to solve an exercise in about 15 seconds, counting debugging. Now, granted, the problem was extremely easy:

Write a function that will return the number of digits in an integer.

Here is my solution:


#python 3.3
def length_digit(num):
    length = len(str(num))
    return length

If you’ve done any programming at all you’ll probably scoff at how trivial this problem is, but I’m not bragging about it’s difficulty.  Rather, I think it means I’m beginning to get to the stage where I can actually start learning how to program.

Having gotten pretty decent at a number of things in my life, I’ve had time to observe the arc of skill acquisition.  There’s a very uncomfortable stage right when you begin learning how to do something which is sufficiently distinct from learning at later stages that I’m calling it “pre-practice”.

Take chess as an analogy.  When you’re still working on remembering how the pieces move and what their names are, you’re not practicing chess.  Sure, we generally use that verb, but until you have fully internalized piece movement, you can’t think in any real sense about deeper concepts like controlling the center of the board, to say nothing of the abyss that is chess opening theory.

Or guitar.  In the weeks when you first pick up the instrument your fingers are going to hurt when you press down on the fretboard and just holding it is going to feel awkward;  you can tell it’s going to be a while before Stairway.

Up until now I’ve been pre-practicing programming.  I’m sure I’m not done with this stage yet, especially since my programming has all been in python, but I’m getting closer.  In solving the problem named above I was above to conceive a solution immediately because I’ve become familiar with a lot of basic programming concepts, implement it quickly because I’ve developed a pretty good muscle memory for typing in python, and debug it because I’ve written and read enough code that I can spot a missing semicolon pretty quickly.

Now comes the hard part…

This Is A Test

For the past month or so I’ve been spending most of my free time learning how to program.  It’s something I’ve wanted to do since college, especially after I came across this essay, but until now I’ve simply lacked the time to really buckle down and do the work.

Well no more.

I’m not sure to what extent I’ll blog my learning process; it’s actually surprisingly difficult to write about the steps taken when solving a programming problem, mainly because there are so many tiny changes made that it’s hard to remember what you did when you finally get the damn thing running.  Plus I hate stopping to write something down when I’m on a roll, and I wanted to wait until I have code worth showing.

All of this notwithstanding, I’m posting some code today.  I’m pulling the trigger because I know that I often over-think things and I want to avoid that this time.

This snippet is a solution to the infamous FizzBuzz problem, a fairly basic coding challenge which apparently stumps quite a few programming job applicants.  It took me about three minutes to write it:

# Python 3.3

for i in range (1, 101):
    if i % 5 == 0 and i % 3 == 0:
        print('fizzbuzz')
    elif i % 5 == 0:
        print('buzz')
    elif i % 3 == 0:
        print('fizz')
    else:
        print(i)

This isn’t anything special.  The for loop iterates over every integer from 1 to 100, meaning  i is first equal to 1, then 2, then 3, …, 100.  Keep in mind that range stops one integer shy of the upper bound, in this case 101 .  We want the output to be “fizzbuzz” if a given integer is a multiple of both 5 and 3, “buzz” if a given integer is a multiple of 5, and “fizz” if an integer is a multiple of 3.  If a number is not a multiple of 5 or 3, we just want it to output the number.

The ‘%’ operator simply means “remainder”, so i % 5 means “the remainder of i divided by 5”, and 5 % 3 would be 2.  This is handy because it gives us a way of expressing the concept “is a multiple of”; if 15 is a multiple of 5, then the remainder of 15/5 is zero.  Ergo if i % 5 == 0 (don’t forget to use double equals for equivalence), i is a multiple of five and the program should print “buzz”.

With that, I’ve accomplished my mission of putting some code out there. It isn’t much, but it’s something.  Mastery has to start somewhere, right?

And if my reader happens to be a prospective employer some months or years down the line, at least you can see I know how to solve the Fizzbuzz problem 🙂

UPDATE [5/22/2013]
While rereading this post, I discovered that I’d made a coding mistake. My original post featured two code snippets, the first of which was this one:

# Python 3.3

def fizzbuzz():
    for i in range (1, 101):
        if i % 5 == 0 and i % 3 == 0:
            print('fizzbuzz')
        if i % 5 == 0:
            print('buzz')
        if i % 3 == 0:
            print('fizz')
        else:
            print(i)

If you run this code, it almost works; the error is that on an integer like 5, it prints both ‘buzz’ and the number 5, which is not what I want. The reason is because I used ‘if’ in the two conditional statements:


        if i % 5 == 0:
            print('buzz')
        if i % 3 == 0:
            print('fizz')

rather than ‘elif’:


        elif i % 5 == 0:
            print('buzz')
        elif i % 3 == 0:
            print('fizz')

I also thought that there was no difference in output when this change was made, but obviously ‘elif’ is more exclusionary than ‘if’. In hindsight I should’ve been a lot more careful in double-checking my code’s output, but the point of the exercise was to learn, and that’s what I’ve done.

A Year In (Brief) Review.

This year was a big one for me. I moved to Asia, the first time I’ve been outside the States, only to find that dating across the language barrier is not that difficult. As it turns out, a lot of things are communicated adequately with almost no words. Teaching has been both the best paying and most difficult job I’ve ever had.  Each day, often each hour, is a fresh test of my patience, while financial independence has brought with it the challenge of investing and saving money responsibly.  I recently put my money where my mouth is, so to speak, making a few small charitable donations to the Center for Applied Rationality and the Singularity Institute, research organizations I like.

My positions on a number of very important philosophical topics has changed, including free will (I’m much more skeptical of it than I used to be), the Singularity (at the very least it’s worth thinking seriously about), and Transhumanism (I see no reason why technology shouldn’t radically augment us for the better). Not everything has changed; I’m still a moral realist, still an atheist, and still a libertarian.

Finally, I’ve vastly expanded my proficiency with computers and programming, a feat which on most days is almost as stressful as teaching.  This recent obsession with programming is a big part of the reason I’ve not written much lately.  I have essays planned on a variety of technical topics, but right now it is hard enough just staying motivated and understanding the basics of computing technology, to say nothing of communicating them.

All things considered, not a bad year. Here’s to hoping ’13 is even more momentous.