Learn Hebrew in only 10,000 easy steps!!

Hasn't it already become kitsch that 10,000 of something can get you mastery over something else, or something?  Well, I'm no linguist (and my friends will tell you how slow I am at learning languages), but it seems that quite less than that number of words can give you a significant chunk of a language.  How many less and how much of a chunk?  Read on.

I looked into this because I wanted to boost my Hebrew skills, but i didn't know which words were the most important to learn, and in what order.  I was memorizing words randomly and quite arbitrarily, picked up from books, conversations, and the like, and often would never hear the words in usage again.  This felt frustrating and futile.  It is often said (and makes perfect sense) that the most beneficial words to study are those that are used the most often in speech.  This led me to a quandary, because I couldn't find any ready resource with a list of the most common spoken words in modern Hebrew.

I was discussing this with a friend over lunch one day and we came upon an idea: text subtitles in movies are nearly 100% dialogue, so movie subtitle files are perfect candidates for looking at frequency of spoken word usage.  Luckily, my friend is an avid movie hoarder, and offered me a large supply of movie subtitle files on the spot.  By aggregating enough such files together, we figured it should be possible to parse out individual words and get a pretty good idea of how often different words are used in bone-fide, modern, spoken Hebrew.  So that's what I went ahead and did.  

Aside from being extremely useful for my own studies, I found the results of this little experiment to be rather fascinating.  To see why, take a look at the most common Hebrew words that came up on my list, in order from 1st to 36th:

hebrew word		meaning (google translate)	pronunciation	cumulative % of word usage covered
לא		Not / no	loh	2.8
את		You (f) / *	at / eht	5.4
אני		I	anee	7.9
זה		It / that	zeh	9.8
אתה		You (m)	atah	11.1
מה		What	mah	12.4
הוא		He	hoo	13.2
לי		To me	lee	14.1
על		About / On	al	14.9
לך		To you	lekhah / lakh (m/f)	15.5
כן		Yes	ken	16.1
של		Of	shel	16.7
יש		There is	yaysh	17.2
רוצה		Want	rotzeh / rotzah (m/f)	17.6
טוב		Good	tov	18.1
כל		All	kol	18.5
אבל		But	a-vahl	19.0
בסדר		OK	beseder	19.4
אם		If	eem	19.8
שלי		My	shel-ee	20.2
עם		With	eem	20.6
יודע		Knows	yo-dey-ah	21.0
היא		She	he	21.4
היה		Was	hayah	21.7
שלך		Your	shelkhah / shelakh (m/f)	22.1
הם		They	hehm	22.4
אותך		You	oh-takh	22.8
אז		Then / so	ahz	23.1
אותו		Same	oh-toh	23.4
רק		Only	rahk	23.7
אנחנו		We	ah-nakh-noo	23.9
יותר		More	yoh-tair	24.2
יכול		Might / Can	yah-kol	24.5
אותי		Me	oh-tee	24.7
למה		Why	lamah	25.0

(the * is for the 2nd meaning of the word 'eht,' which is as an article for direct definite objects.  For example, to say "I ate the banana" in Hebrew, you would say something like: "I ate eht the banana"..).

Note the last column in the table, which marks the cumulative percentage of total word usages (out of >600,000) that are accounted for by each individual word plus all the words preceding it on the list.  The amazing thing is that with only 36 words, we have covered 25% of total word usages in the language!

That's 36 words.  How much comprehension will we have if we dutifully study and learn, say, 10,000 words?  The answer is: around 80%.  If you learn words in the order they appear on my list, here's how much more Hebrew you will have gained from your effort in learning each individual new word (i.e., the marginal amount of Hebrew gained per word):

It looks pretty good up until around 100 words, at which point you're already drowning in an abyss of seemingly futile vocabulary.  Of course, this is somewhat misleading, since multiple forms of the same verb will come up separately on this list, and there's also much more to learn in a language than just vocabulary.  Despite usage frequencies, not all words are equal.  For example, if you're visiting Israel, you'll probably be most interested in knowing how to say 'bathroom' (sheh-roo-teem) and 'food' (okhel), among many others.  But even so, if you want to put in minimal effort to get maximal lingual return, you're probably best off with my list. 

If you want this list for the top 10,000 words in Hebrew (actually more), you can download it as an excel file by clicking HERE.

Enjoy!

Fool's wisdom

It’s okay to fail they said.  Failure is the key to success.  ‘It’s the key to success!’ they screamed.  Your ears are still ringing.  That’s what they told you then, because they didn’t understand.  You hadn’t moved anywhere in months.  Do they know what it’s like?  You didn’t even have an idea where to go.  Not only did you fail, but the mistakes you kept repeating were moronic.  They weren’t ‘smart’ mistakes.  You didn’t learn from them the first time – or the second or the third, or the fourth, or the fifth. And this wasn’t a grand struggle in the face of ultimate mysteries, or something.  The things that caught you up were trivial things, which nobody else even struggles with.

Now that you know how you could have gotten out of the rut the whole time, it’s even worse.  You had all the skills to get there from day one if you’d only been thinking straight.  In the end, you solved the problem over the duration of a coffee.  Now you’re in flow again.  It’s insane to you that you could have gotten so stuck for so long that way.  Look at how efficient you are when you’re flowing.  It washes that stuck feeling away like a bad aftertaste.  In the end, that stuckness was only temporary, and it was just a step to get here, wasn’t it?  Think of the perspective you've now gained, all that wisdom.  Failure is the key to success, you realize.  You tell all of them.  

Why are they covering their ears?

Shutting the Dawn Wall

Here’s Autumn again, carrying upon its still breeze a very special flavor of stagnation.  With an ineffectiveness that has become emblematic, the US government has shut down.  The world stands perplexed.  Although large political events can often go unnoticed in daily life, the government shutdown does not seem to fall in that category.  It has caused quite a few very visible ripple effects.  

Aside from the fact that government-employed friends of mine are furloughed, my brother Andrew was kicked out of Yosemite this month.  Rangers announced the government shutdown and gave everyone 2 days to vacate the park, which led to a scramble of many rock climbers to get final routes in before the park closed (or, for the more anarchistic ones, to make sure they were up on a wall and thus unable to be kicked out when the shutdown commenced).  The always-booked-to-the-brim famous climber campsite, Camp 4, became eerily empty.  Andrew told me a few bummer stories, like about a New Zealander he met who was just in the beginning of a month long US national parks tour.  Not a great way for our country to treat guests. 

On a more peripheral but personally upsetting note, the shutdown has prevented my all-time favorite rockclimber, Chris Sharma, from joining an all-star team of climbers on a ridiculously hard new route in Yosemite called the Dawn Wall.  Since the Yosemite climbing season is short, this will likely throw off attempts at the Dawn Wall for an entire year. 

Come on, US government!

To be fair, these issues are mere inconveniences compared to cancer patients put on hold for clinical trials, scientists waiting for grants to come through, and federal employees unable to pay bills or legally seek other work while their paychecks are withheld.  The shutdown has been a major inconvenience and burden for a great number of people. 

With all this in the news, I became curious about what kind of measurable effect a government shutdown might have on the economy.  There seems to be a lot of supposition and back-of-the-envelope calculations about this in blogs and the news, but little data.  My question was simple: did past government shutdowns affect any indicators of US economic health in a length-of-shutdown dependent way?  While I’m not an economist, I did a naïve set of calculations and found quite a surprising signal -- in short, I found that yes, the economy does seem to suffer after government shutdowns, in the form of inflation.  The longer the shutdown, the more inflation is seen.  

The data I analyzed is the consumer price index (CPI), which gives the cost of a battery of goods across the US at a given moment in time.  The CPI is available on a monthly basis since at least 1914, which easily covers the period from 1976 to 1995, during which there have been 17 government shutdowns.  (That's right, I said Seventeen!  I could barely believe it when I first read it.)  I lumped together two shutdowns that occurred back-to-back in September-October 1984.  The dates of the shutdowns can be found here.

I first looked at whether there is a correlation between the number of days of a government shutdown, and the % change in the CPI afterwards.  I found that in fact yes, there is such a correlation.  Below I've plotted % change in CPI from the day before each shutdown to a day 4-months after each shutdown (y-axis), versus the number of days of each shutdown (x-axis).  For the statistically inclined, the Spearman's correlation is: rho=0.77, p=4e-4.  For the non-statistically inclined, this is considered a strong and non-random correlation.  

A few outliers are marked by the dates of the shutdowns, and each point is labeled R(epublican) or D(emocrat) depending on the party of the president during that shutdown.  The correlation here means that the longer a government shutdown, the bigger the increase in CPI afterwards, i.e., the bigger the increase in inflation.  

Also, curiously, shutdowns during democratic presidencies have generally been longer than those during Republican presidencies.  Hmm...

It is possible that the correlation between length of shutdown and % increase in CPI is simply there because the CPI nearly always rises over time, and thus will rise more during the period of a long shutdown than the period of a short one.  To eliminate this confounding factor (or other, potentially more hidden ones), I scrambled the dates of the shutdowns, keeping them the exact same lengths, to see if scrambled dates would show the same correlations as the real ones.  In short, the answer is no -- the actual shutdown dates showed a much more significant correlation than scrambled ones.  The results of this test are plotted here:      

What you see above on the y-axis is the Spearman correlation coefficient, which represents how well the length of each government shutdown correlates with the % change in CPI (i.e., what i just showed in the first plot), for the actual dates of shutdowns (blue bars) and for the dates scrambled in various ways (lines with errorbars, which represent means & standard deviations for multiple random trials).  I kept the scrambled dates within the period of 1976 to 1995, i.e., between the first and the last of the shutdowns being analyzed.  The x-axis here represents the number of months after the shutdown that we're checking.  

The important thing is that for the first few months, the blue bars (correlations for actual shutdown dates) are higher than any of the errorbars for randomized shutdown dates.  I circled the datapoints of most interest in red.  This eliminates a lot of the chance that the shutdown-length dependency of CPI increase is merely an artifact.

You can also see on this plot that the correlation between shutdown length and % change in CPI is the highest when checked 4 months after the end of the shutdowns.  This implies that it takes about 4 months for the inflationary effects of a shutdown to become most apparent.  If anybody reading this blog is an economist, I would be curious if it is common to see a 4-month lag in changes to CPI (or other economic indicators) after a causative economic event. 
  
Aside from looking at the % change in CPI, I was also curious if the CPI increases following long shutdowns were more likely than after short shutdowns to exceed extrapolations based on pre-shutdown data.  Suffice to say, this is another test that the CPI tends to rise faster after long shutdowns than after short ones.  

To check this, I looked at the % difference after each shutdown between the actual CPI and the CPI expected based on a linear fit of data from before the shutdown.  I checked this various numbers of months after each shutdown (using an equal amount of pre-shutdown time for the projection), and found that indeed, the CPI increase exceeds projections by more after long shutdowns than after short ones.  You can see it in the below plot, as, again, the blue bars (correlations for actual shutdown dates) are higher than any of the errorbars for random shuffles (with the effect being most dominant 4 months out).  Again, I circled the most interesting datapoints:

What all this means is that yes, government shutdowns do appear to affect the economy, and they do it more the longer they are.  My analysis indicates that the effects will likely peak around 4 months after the end of the shutdown.  

By the way, I did not see similar trends in the S&P500, the NASDAQ composite, or the GDP.  I’m guessing that the stock indicators equilibrate to the expected slump after a shutdown, and the GDP is simply too rough of an economic estimate to be useful since it’s posted only once per year.  It's also possible I just didn't look hard enough. 

One note -- even with my paltry knowledge of economics, I'm aware that an increase in inflation is often a sign of economic growth.  Despite this, I would wager that the post-shutdown inflation is not a sign of economic health, but rather a sign of economic illness.  I'm sure there's a way to tell between helpful and harmful inflation.  Maybe a nice follow-up study for an enterprising economist?

It's a shame to me that our government can't do such an elementary task as passing a budget.  This hurts Americans on many levels, including, I would argue, broad economic ones.  It also makes us look like damn fools.  I only hope that in future shutdowns, congress will be so kind as to consider my family, my friends, and the entire US economy.  Not to mention, I would like them to also consider the Dawn Wall.

Getting to somewhere

Past cafes and restaurants and sandwich shops, dodging couples and bikestands, skipping over the piles of sewage at the rear of the shuk, and onto the beachside tayellet, I ran.  Green and red and orange fluorescent lights flickered across women with strollers, bike riders, and slick torsos.  Before long, the rainbow-colored hotel loomed before me.  It’s where I usually turn around.  But today, for some reason, I kept running.  

Israeli folk dancers and peddlers of cheap perfume slipped past me.  Intermittent crowds became clusters of dining families and teenagers out shopping.  I was entering the port.  A street angel held a child, and delighted parents took pictures.  Kids played on scooters.  I skipped down to a low dock to avoid a mass of people congesting the slim walkway bordering the marina, and, jogging on past all the crowds, came to where the waves boom like cannons against the walls of the dock and then erupt into saltwater sprays, a place beyond the pedestrians.  Just past that is the bridge that marks the end of the port.  For the first time I hesitated, for I’d never run past here before.  But, feeling spry enough, I took but an instant to decide, and went on.  

Somehow I lost sight of the beach.  I passed alongside a factory guarded by serious-faced men with machine guns, followed the road inland, and then continued along a highway while cars swam languidly by.  My attempts to get back to the beach were continually stymied.  Beyond a small airport, I found myself on a dark dirt road, unsure if it would lead anywhere, but hoping it would provide a shortcut between highways and building complexes to the beach.  Kitchy Israeli pop music trickled in on the breeze.  It became louder around this bend and softer as I went behind a high mound of dirt, but progressively closer until I realized that my meandering path was leading me towards it.  Then I burst out of the darkness into a set of young and middle aged revelers dancing under too-bright lights, too bright for a party but fit for an exhibition, as if their festival had been planned on a stage for my benefit.  To what I owed this, I don’t know.  I was tired, my knees ached, and I didn’t belong there, so I ran past them, and as I climbed the dune of shoveled dirt beyond the party, I slowed to a walk.  What was on my mind now?  Not that which had set me to run.  Peace had swept in, with only disconnected thought snippets interpolating between the sounds of the night.  A bird chose that moment to fly over me and squawk shrilly.

I had arrived at a vista from which I could see the staged party behind me, a smokestack from the factory jutting up from the south, and what I thought was the rim of the sea straight ahead.  Fields and low enclaves inhabited the darkness, followed by a wide trench cutting off two long archipelagos of office buildings and apartment complexes, and beyond that, an even, grassy hillock atop the dunes lining the ocean.  I glanced once more at the party and, with dirt cascading into my running shoes, slid down the far side of the dune.  The night filled with crickets.  I wondered, then, if I could see the stars, and was surprised to find that the night was half shrouded in clouds, a rarity for summer in Israel.  I kept on.  The way was straightforward, and in not too long I was back on another beachside tayellet, passing quiet conversers on park benches, nodding to an old couple planted down by the entrance to the beachfront, and then, rounding a tall dune, making it back where I was aiming -- to the beach.  

The beach was dark, quiet, and empty.  Only occasionally did I pass solitary walkers, mostly lost in their own reveries, faces featureless in the shadows.  Hot plumes of ocean air rolled over me and swept up the dunes, which were as tall as houses, and the stir of the waves over the low natural jetty obscured all other noises.  I passed a couple having wine by the light of a headlamp and a fisherman whose head swiveled the whole way around to keep tabs on me, as if he were suspicious I might contaminate the night’s stillness.  I looked backward and forward.  Cities stood on both horizons, equidistant: the smokestack, high-rises, and lights of Tel Aviv to the south, and a mirrored collection of lights and buildings to the North, which I suppose were Natanya.  Once more, I hesitated.  My mind was now tuned into the night’s sensual offerings, with my initial thoughts dissolved into the ocean’s roll and the natural solitude of the duned, darkened landscape.  I hadn’t thought about getting back, and now I started to wonder -- how would I?  But the thought washed away, and my body said “forward,” and I obeyed.  

Fireworks suddenly rose one after the other like phosphorescent palm trees across the northern skyline, their low thuds blowing in softly, long moments after the palm trees themselves had evaporated.  I kept on, not able to move faster than a walk for the last hour probably, thirst beginning to harden my throat, yet determined.  After a long while, high dunes and low jetties finally fell away, and I emerged at the other end of the beach into a Disneyland tayellet, with manicured grass on multiple clean terraces broken neatly apart by curvilinear rock walls.  There was a water fountain.  I drank and then sat, watching the waves break apart over the jetties.  

I was here, I was -- where?  Somewhere.  

The city lights to the North were still a ways off, but not nearly as distant as the Tel Aviv lights, which shimmered like a tiny bundle of lightning bugs.  I had gone far past the halfway point, far past the point from which my body could carry me comfortably back.  I hadn’t budgeted for the return; I had only blazed forward, carried first by my legs and then by my spirit, and now I had gotten to this somewhere, and I felt lost.  Thirst abated, I felt a deep strain in my leg ligaments, long unaccustomed as they were to running.  I looked back and forth and then out to the ocean.  The waves rolled in endlessly.  For a while I stayed, soaked in sweat, sand carpeting my insoles, the tayellet's floodlights overpowering the dark night I had journeyed through to get there.  Wherever I was going, no matter how weary I felt, I couldn’t stay here. I would have to keep moving.  Somehow, I would have to get home.


Related posts:

Elderly, shmelderly

Tastes fresh, the way a cucumber raw in my salad

Breaking the frame

Categorizing people is only natural.  We decide that this one could be a friend and that one could be a lover, and the one over there should be avoided, because he’s toxic.  We list in our heads the attributes that we like, and then tick items off the list when we meet someone.  But it’s the ones who don’t fold neatly into our boxes who intrigue us.  We can’t keep our minds off of them.  Those are the ones we become obsessed with, or fall in love with, become elated and then crushed by.

We also categorize things, situations, life events, and indeed even reality itself.  On a bad day, the world can appear filled up with demons, and on a good day, with angels.  We frame the way it should all be interpreted.  But these frames are illusions.  Any frame misses something real, and the deeper we fall into a frame, the more inflated and distorted become its edges, until they are ready to devour us.  This is our shadow world -- the world that lies outside of our frames -- and if we are too rigid, the shadow will eat us.  Look no further than Ted Haggard.

Our frames go way beyond where we think they do.  We see, we feel, we hear and touch and smell and intuit things, but are those things actually real?  Usually not.  We experience a physical presence to objects like wooden tables, but it turns out that at the atomic level, tables are mostly empty space, and it’s electrical force fields that make them seem solid.  Quantum mechanics makes us wonder if any of the components of the table have independent existences at all.  Human consciousness seems to be actually a grand experiment in framing, because the reality we experience is certainly not much like the reality which actually exists.  Our animal past has forced us to categorize.  We are evolutionarily tuned for survival, and we must understand our surroundings in a way that allows us to wield it, whether or not that understanding is accurate.  

If our frames are too feeble, we’ll wash about in the frames of others, like plankton.  If our frames are too strong, they’ll force the world to our image, and we’ll be blind to the world’s actual shape.  What we do not think a person capable of, if our frame is strong enough, he actually won’t do, and what we decide a person ought to do, he or she will.  The stronger our frames the less we are ever surprised, because our world is so tightly controlled.  

But ever so occasionally, an encounter can slap the frames away from our eyes.  For that split second, we can see the world naked.  These are the events that have the power to change us.  We are left shocked, lost, and with edges of frames jutting everywhere, exposing all of the monsters that were hidden.  We are suddenly aware of our frames, and of the fact that they’re frames, and of all the truths that, by operating within them, we’ve been missing.  We see our old world as a dance of illusions.  And we experience a choice -- to fit the frames back upon ourselves ever tighter, or to transcend them.  


Related posts:

The Daemon's logic

Tangoing life

Science Communication's Future

The journal Science put out a call for short essays on how science communication will look in 50 years, and my response was chosen for print. Check it out here (it's the 2nd one on the 1st page):

http://www.sciencemag.org/content/340/6128/28.full.pdf

The revolution is inside you

She was a dignified beast.  Stuffed full of coils of wire and disk drives and mysterious metal-laced innards, she occasionally emitted whirring sounds or high pitched beeps from within her thick plastic carapace.  Her husk was beige and somewhat rough, because those were the textures of that day, kind of like black and white photographs.  We had to feed her a floppy disk to get her to boot.  She only had about a thousandth of the storage space of a modern thumb drive, and the same computing power as a modern throwaway flip-phone, but we didn’t care -- she crunched our numbers for us and had a word processor.  What else could we ask of one?  My brother was one of the few, the elite, who understood how to even use her.  He was fluent in DOS.  He even taught himself back then a few of the mystical languages of programming.  Yes, my family’s first computer was a Brontosaur.  But she was a Brontosaur with some megs, and it made all the difference.  

All that wasn’t so long ago.  Who would have thought we would have smartphones and tablets with apps for every bit of our lives in our hands by the year 2013?  Computers have exploded in speed, functionality, and ubiquity since those early days.  While this explosion involved billions of programmer-hours, its most convenient metric is Moore’s law, which states that computers tend to double in computing power approximately every two years.  The trend has held since the mid 1960’s.  Bootstrapping off of those advances, we have reached an era of computerization that a generation ago, few could have possibly predicted.  

But this blog post isn’t about computers.  You see, there’s another technological explosion happening right in front of our faces, an explosion every bit as far reaching and powerful as the computer one, but one about which tremendously fewer people are aware.  I mentioned it in my last blog, but I didn’t get too deeply into it.  What I’m talking about is the explosion in Genomics.   

What is most striking is that over the last decade, genomics has been advancing at a rate even faster than Moore’s law.  How much faster?  Well, if we measure the progress of genomics by the cost at which we can determine the sequence of DNA in an organism, then nearly four times faster.  This means that while sequencing the length of DNA in a bacterium in 2001 would have cost around $20,000, today it would cost something less than a dollar.  The cost to sequence is not the whole story -- to actually assemble the genome of a whole bacterium you need to do quite a bit more sequencing and other processing, and interpreting genomes is one of the biggest challenges we face in science today -- but you can imagine what this reduction in cost has enabled.  What this all means is that what took ~3 billion dollars, 13 years, and millions of man-hours to achieve in 2001 at the pinnacle of the Human Genome Project -- sequencing a human’s genome -- is now possible in a few weeks for several thousand dollars, and will be possible for less than a $1000 price-tag within only a few years.  

The $1000-genome milestone matters, because around that price, genome sequencing starts to become relevant on an individualized basis in medicine.  To put this more clearly, this means that when you go to the doctor not too long from now, she will be able to send off a drop of your blood or skin and have your genome sequenced as a routine procedure.  In fact, even now, companies like 23andme will sequence parts of your DNA that indicate susceptibility to a plethora of diseases for just a few hundred bucks.  

Just as with the computer revolution, by the time that the genomics revolution is done, we may barely even recognize the world that we live in.    

If you were to ask a person on the street what genomics will do for them in their lifetime, they probably wouldn’t even know what you’re talking about.  They might respond like a 1950’s housewife may have responded to a question about computers, or a pre-industrial farmer to a question about gasoline.  Or perhaps they’ll think about embryonic stem cell research or human cloning and have a gut negative reaction.  Most wouldn’t think of designer bacteria that emit wonderful perfumes, genetically modified algae that may solve the majority of our energy needs, cures for pretty much any genetic disorder, or totally personalized medicine (like a more advanced version of this).  But these things are precisely what scientists think, talk, and dream about.  The field is so rife with potential and is expanding so rapidly that how it will reshape us in the future is extremely hard to predict.  But just as computers have changed our lives in ways we couldn’t have fathomed, so may genomics.  And just as with computers, although there are certainly negative consequences, the potential of genomics for our lives is one of vast improvement in quality of life and happiness.

Genomics is the study of genomes.  A genome is the collection of all DNA in a person or organism.  DNA is a long, stringy molecule that dictates all of your genes, i.e., the traits passed on to you by your parents.  The most amazing thing about DNA is that it dictates our genes using a digital code with only four basic letters -- A, T, C, and G -- which act sort of like binary code in a computer.  This makes it extremely amenable to computerized analysis, an aspect that scientists have taken tremendous advantage of.

Every person on earth has a unique genome, and to sequence a genome means to use a combination of automated physical platforms and sophisticated computational methods (often run on a huge number of computer servers) to figure out the exact series of the A’s, T’s, C’s, and G’s that make a particular person genetically unique.  Although the genome doesn’t explain a person’s every trait, it explains a great majority of them.    In 2001 when researchers published the first draft human genome, it was actually an averaged genome of several people.  Now, our technology enables us to sequence individual genomes to near completion, which may be the key to truly personalized medicine.  

Each one of your cells has a couple copies of your own personal genome (except for a few weird cell types, like red blood cells, which contain no DNA).  The fact that there’s a copy in every cell is how, for example, scientists were able to create embryonic-like stem cells out of skin cells, a technology that may both bypass many ethical issues and allow for some amazing new therapies.  Imagine regrowing a damaged organ, and having a transplant from yourself.  As we get better at understanding and manipulating genomes, we will shine guidelights into many more areas than just that.  

This was amply demonstrated in 2010 when a team led by Craig Venter created the first ever synthetic lifeform.  To do this, they synthesized from scratch the entire genome of an organism based on a string of DNA code that had been planned on a computer (mostly following the DNA plan of a natural organism, Mycoplasma mycoides), and then implanted the synthesized genome into a cell whose DNA had been removed.  The “synthetic” cell proved to be viable, replicating billions of times.  While this synthetic cell was not so different from its natural parent, the process could be repeated for much more outlandish designed genomes.  

I saw Craig Venter speak about this in Tel Aviv last year when he accepted a science award called the Dan David Prize.  During a student Q&A session, he spoke about automated evolution: creating synthetic lifeforms and then mutating, evolving, re-sequencing, analyzing, and re-designing them, and thus closing the loop between computers and biology, enabling us to build and understand bugs that do anything.  He spoke about cells in a way I had never heard before from a biologist -- as computers running DNA software that is now, with Venter’s technology, easily exchangeable between silicon machines and biological hardware.  I asked him about Ray Kurzweil’s singularity, and whether he feels his technology is driving towards it.  He smiled like a man in the know.  

Of course, such power is not without dangers.  Who should be able to wield this technology, especially if synthesizing new genomes becomes cheap enough to be commoditized?  Because of the potential damage, it is almost inconceivable to release synthetic cell technology into lay hands.  Think atomic energy.  Good bioethicists and regulators must play a role, but even there we will face difficult dilemmas.  

And the dilemmas don’t just begin with synthetic lifeforms.  There are also basic ethical questions surrounding the mere sequencing, and not even getting into the manipulating, of genomes.  Sergey Brin, the co-founder of Google, knows this well.  When his wife founded the sequencing company 23andMe, Sergey Brin was one of the first to have parts of his genome sequenced.  It turns out he has a rare mutation putting him at high risk for Parkinson’s disease.  Brin has taken a pragmatic approach, and is now doing everything he knows of that will decrease his risk.  But the lesson is obvious.  Even if you can know about all the diseases you’re at risk for, do you really want to?  Do you want potential employers to?  Your insurance provider?   

That being said, to forsake such technology because of fear of its dangers seems foolhardy.  Last century saw the atomic age and the space age and the computer age, and I believe that when we look back, we may call nowadays the genomics age.  We should proceed with caution… but we should proceed.


See my related post:
Why a black swan named Brooke Greenberg might make you immortal -- or not