Wednesday, September 26, 2012

Everest Challenge "Pep Talk"


The Everest Challenge is this weekend.  This fills me with a combination of excitement and dread.  Mostly dread.  It’s kind of like that back-to-school feeling all the kiddies went through this month, except EC is more intense.  Imagine packing all the trials and difficulties of a school year into one weekend.

I had my best week of training recently:  29,000 feet of vertical gain.  Well, EC has that much climbing in just two days.  Cyclists are, by and large, an analytical bunch, and I could easily find all kinds of statistics to support the notion that I’m doomed.  And yet, having finished this race three times already (click here, here, here, and here for details), I’m actually pretty confident.  (Not entirely, though—more on that later.)

My friend John is coming out from upstate New York to do this race for the first time.  He’s a bit nervous because there aren’t that many hills to train on there.  He lived in Berkeley for awhile, so he knows how good the riding is here.  (Mount Diablo, a 10-mile climb reaching 3,800 feet, is particularly good EC preparation, as is Lomas Cantadas, a two mile climb with an average grade of 11%.)  John e-mailed me with some misgivings, and I replied with a little pep talk which I’ve decided to embellish a bit and post here, for two reasons:  a) some of my readers may be doing EC or something similar at some point, and b) the gist of my pep talk could apply to all kinds of difficult undertakings, not just sport.

Here is a photo of John from the last time we did an epic ride, which was the Markleeville Death Ride in 2000.  He’s on the right; my brother Bryan is in the middle.  We’re wearing garbage bags because it was chilly and we didn’t bring warm clothing for the final descent.


I’m more nervous about this race than I’ve been before, probably because I’m coming back from a major injury.  For me, more is at stake this year:  this isn’t just a race, but proof that I’m back (or that I’m not).  I’ve spent this week in an elevated state, something like a continuous fight-or-flight reflex.  It feels like I have a cold as well, though I felt the same way last year and a few other times before major events.  Perhaps my body is faking illness to make sure its master (i.e., my brain) gives it all the rest it needs.  Meanwhile, I’ve become obsessive about my bike, wanting to fix everything but not touch anything. 

The truth is, everything could go perfectly and still it would be absolutely grueling.  My friend Craig (who’s also doing EC) remarked, “Perhaps it’s a curse that your specialty is being good after 100 miles and 10k of climbing—you wouldn’t have to suffer so much if your specialty was a 12-second track event.”  I’m also anxious about some mishap (illness, mechanical failure) stymieing me.  Devoting so many weeks of hard training to a single event really leverages you emotionally.

John, on the other hand, is a bit worried about finishing (despite winning a race this year and getting third in another).  He’s studied the course profile carefully, and said in his e-mail, “I can’t stop thinking about EC.  I climb a lot of hills around here, but they’re all a lot shorter….  You love Lomas Contadas.  Looking at Strava, I can’t fathom why anyone would love that hill—it looks ridiculous. It looks pretty equivalent to Blakeslee Road here in Ithaca (if you want to look it up on Strava), and I frickin’ hate that climb. I’ve only done it twice:  it totally kicked my ass the first time, so I swore off it. Then I rode it on Sunday just to prove to myself that it didn’t own me. Well, it did (own me, that is).  If there are sections [of EC climbs] that get over 12% for any length of time, that will totally mess with my rhythm and my head.  And my legs.”

On the face of it, both John and I are over-thinking this thing.  But there’s no point commanding ourselves to stop; it wouldn’t work.  There’s no daytime equivalent to counting sheep until our preoccupations fall away.  What’s needed is to rethink this thing, to keep from falling into the same well-worn ruts of thought that make us worry.  A shift in perspective, away from the analytical, is in order.

The limitations of analysis

We can analyze the EC climbs, Strava data, our own training experience, etc. all we want but it won’t really make much difference at this point.  The fact is, the EC is fricking hard, and there’s no way to totally prepare yourself.  My own training hasn’t approximated either stage of EC, but then it hasn’t any of the other times I’ve done the race.  Nobody ever said the training has to be as hard as the race.  Training never is.  The human body always holds something in reserve to make up the difference.  Plus, for once we’ll be rested and fresh, instead of doing a big ride when our legs are already tired.

It is true that I love Lomas Cantadas.  The very reason I love it is that over time it has helped to forge in me a rare and useful trait:  grace under pressure.  I don’t have gobs of this trait, but more than I used to.  This is probably the only part of cycling that I have gotten better at with age. 

When I was dusting off my old Odyssey ‘91 story for this blog, I was struck by how much of the difficulty of that ride was just me panicking every time the road went uphill.  Recently I did several of these climbs again, during my “Non-Death-Ride Non-Warmup,” a point-to-point ride to a place I’d only seen on a map.  I had no idea what climbing was in store, or even how long the ride would be; plus, I wasn’t even very strong yet.  And yet, it wasn’t a disaster. 

The difference between Odyssey ‘91 and the Non-Warmup is that this time I was “tranquillo,” as the Italians say.  I was riding on about 10% physical ability and 90% resignation.  Resignation is totally underrated.  Sometimes I think I have a talent for resignation, but actually I think I’ve merely developed it by riding over Lomas Cantadas more than 500 times over the last seven years.

Climbing stupid

In that many trips up Lomas, I’ve never made it over without a struggle.  Sometimes the struggle is completely absurd—and yet I’ve never actually tipped over, or ground to a halt, or had to walk my bike.  Yes, I’ve occasionally yelled, “Spock!  Help me, Spock!” but I’ve never failed to make it over the hill.  I’ve tackled that climb several times when I was already shattered.  The trick is to pretend you have no choice and to take one pedal stroke at a time, riding like a robot.  Climbing stupid, you might say.  Not “climbing stupidly,” which I would never recommend, but “climbing as though you were stupid.”  Sometimes the brain just needs to be shut off (though actually I usually leave a few processes running, like the event logger that watches each half-pedal-revolution with astonishment and keeps track of the implausible ongoing progress). 

Eventually, even an 11-percent grade ceases to seem like a crisis, and starts to feel normal.  Not easy, mind you, but normal ... as in, “the new reality is that my life involves a lot of suffering on this hill, and there’s no way around it.”  Fear is replaced by fatalism.  This helps because suffering itself is never the real problem in sport; fear of suffering is the problem.  Suffering is inevitable, but fear doesn’t need to be.

The point isn’t that you need to ride Lomas 500 times to be ready for EC.  The point is, during a hard climb, once you stop thinking things like “what if I can’t?” and “is this too much?” and “oh, no!” and switch to thinking either a) nothing at all, or b) “I will do this until it’s done,” then the ride—any ride—is doable.  If you pace yourself, and keep panic and despair at bay, and ride as though you could not fail, you will succeed.  It may take a very long time, and you may find yourself mired in misery, but that’s okay.  Suffering and misery will not stop you from succeeding.  Only fear and doubt and despair can stop you.  

Here’s my brother Bryan lying in the road on the second trip over Ebbetts Pass during the 2002 Death Ride.  Sure, he’d had the stuffing knocked out of him, but that’s nothing a little rest can’t help, eh?

The psychological factor

Even without routinely climbing a hill like Lomas, a rider can have faith that, once the physical preparation for EC is complete (or as complete as it’s going to get), the rest of the race is mental.  Not as in intellectual, but as in psychological.  “It’s all psychological” is of course a cliché, and I’ve been struck by how untrue it seems in the context of racing.  You can’t (or at least can’t reliably) beat somebody who’s stronger just because you pretend you can, or try harder, or whatever.  But you can certainly silence the wimp in your brain that starts internally whining and casting doubt on your operation.  The notion that “I might not be able to do this” is just a psychological trick your brain is playing on you, to get you to quit.  Framing your progress as a “can vs. can’t” question is just weakness. 

(Obviously there are exceptions to this.  If you blow up completely, and literally cannot turn your lowest gear, and even lying down for awhile doesn’t help, then it’s no longer a psychological matter.  Or, if it’s 100 degrees and you’re not handling the heat well and you get goose bumps or something, then you need to quit to avoid heat stroke.  But these are very rare scenarios, in my experience.  It’s far more common for somebody to decide he can’t hack it, and either quit well ahead of total exhaustion, or sabotage his efforts by refusing to eat or drink enough, which reliably leads to total exhaustion and the mythical conclusion that failure was inevitable.)

I well remember my first EC.  I was good and scared about every climb, including the first one, and when I reached the top of it and still had good legs, I felt ecstatic.  The second climb, another out-and-back, was a lot harder than it looked.  When I saw my pals coming down it I assumed they were just really, really far ahead of me, but to my pleasant surprise the climb was over before I expected.  The last climb seemed endless and really beat the crap out of me, and I was worrying the whole time about the really steep pitches at the top, but when I finally reached them, they were more manageable than I’d expected.  I remember thinking to myself, “Is that it, mountain? Is that all you got?!”

The best part is, if you’ve paced yourself carefully and kept it in your pants, sometimes you (or at least I) feel strong like bull on the last climb, and it’s exhilarating!  On the flip side, if everything goes wrong and you suffer like never before, to where you want to curl up in the gutter in the fetal position, well, that also has value.  That’s vision-quest territory, and I’ve been there, too.  Just last year, in fact, while racing the Everest Challenge.

Friday, September 21, 2012

What Shouldn't Cyclists Eat?

I recently blogged about what cyclists eat when training for long races.  What I didn’t get into was whether any food or drink is off-limits to cyclists.  So, here goes.

First of all, the pros eat differently from amateurs.  Pro cyclists really watch their weight, because being anything other than freakishly emaciated disrupts the absurd power/weight ratio that makes them competitive.  That said, their diet isn’t exactly restrictive.  As reported in cyclingnews, world champion Mark Cavendish decided this year to trim down for the Olympics:   “I’ve stopped candy, soft drinks and ready meals.” 

It’s also worth pointing out differences between the Europeans and Americans.  I understand they eat a lot of horse meat over there.  Much of the Euro approach strikes Americans as unscientific, being based on tradition and mythology.  For example, in the 1980s the French castigated Greg LeMond for having the audacity to eat ice cream—which was obviously terribly detrimental to his fitness—during the Tour de France, especially while he was in the lead.  To the French, LeMond’s decadence was an insult to the yellow jersey.  At the time, I figured, “Heck, LeMond is burning a lot of calories, he can get away with it.”  After all, I’d watched plenty of Coors Classic racers in Boulder strolling along the outdoor mall eating ice cream.

As it turns out, LeMond’s ice cream wasn’t just a treat.  I found this out in summer of 2000 when I happened to encounter him at the Nevada City Bicycle Classic, where we were both spectators.  It was either my birthday or his—we’re off by just a day—so he bought me an ice cream.  I reminded him of the flak he’d taken during that Tour for his ice cream indulgence, and he said, very seriously, “That wasn’t an indulgence, that was crucial.  When you’re racing at that level you’ve got to replace the calories.”  It turns out ice cream is an excellent sports recovery food:  plenty of protein and much-needed calcium.

My friend John, who stepped up his training this year in anticipation of the Everest Challenge, told me recently, “These days I eat massive amounts of ice cream.  If I do a good ride, I’ll eat a pint of Ben and Jerry’s (about 1200 calories), guilt free!  Heck, I eat that much ice cream even on days when I don't ride!  But I weigh less now than I have in decades.” 

So ... is diet really “anything goes” for us freedom-loving American cyclists?  Well, as I was reminded quite recently:  no. 

Last Friday, I did a double-Diablo (digging myself into a deep calorie deficit), and then spent Saturday picnicking at a winery in Sonoma with a bunch of old cycling friends from SoCal.  While training for the Everest Challenge I don’t drink any alcohol—I figure my liver is working hard enough as it is restoring muscle glycogen—but I made an exception and had a teensy bit of wine.  More problematic were the lack of water (they were selling it, bottled, and I’m cheap) and the big bag of chips and other junk I ate all day.  My plan was to meet up with other friends in the area and go out for a giant Italian dinner to true up my stomach.  Instead, my friends served dinner at their house.  It was take-out dim sum.

Now, I don’t want to complain.  I love dim sum and my hosts bought tons of it, being familiar with my oversized appetite.  Meanwhile, I teach my kids to eat whatever they’re served and I have to lead by example.  Plus, it was delicious.  It must be said, however, that dim sum is no way to carbo-load.  By the time you’ve eaten three or four thousand calories of it, you’ve taken on a ton of salt and grease.   By the time we’d driven home from the wine country, my normally invincible stomach was in turmoil.

I looked in the bathroom mirror:  a disturbing, and yet fascinating, sight.  I could see my ribs (being a cyclist, after all) but also this crazy sphere of bloat pushing out over my waistband, like I was a boa constrictor who swallowed a soccer ball.  In all my years of overeating I’ve never seen anything like it.  All night I was up chugging water, tossing, turning, roiling.  At 4 o’clock on Sunday morning I couldn’t be in bed any longer.  I got up and prepared for my ride:  another double-Diablo.

Guess what?  I didn’t feel so hot.  Actually, I was miserable from the first pedal stroke and it got worse from there.  I thought about just going home—but I’d feel lousy there, too, so what was the point?  I met up with my friends and clung desperately to the back of the group all the way to the base of Mount Diablo.  Once the climb started, I got dropped so abruptly I didn’t even have a chance to tell them not to bother waiting at the top.  My stomach—a stalwart ally, the linchpin of my cycling ability—was livid to have been so abused.  As the climb dragged on, my system exorcised most of the evil, but my energy stores were empty, closed, all boarded up.

I ended up doing both Diablo assaults alone.  Many times I thought of cutting the ride short.  After all, each climb was optional.  Heck, the whole ride was optional—in fact, the whole sport is optional!  But I didn’t quit, because here was an opportunity to turn this poor dietary choice into a positive.  If I could stave off despair and continue pedaling under such duress, for no good reason, I’d give my psychological mettle a real test in advance of the big race.  The Everest Challenge is grueling, sure, but at least I won’t be riding it on rice flour and fatty pork alone.

After the ride I spent a good while lying on the living room floor.  I slept really well that night.  Once again, I dreamed of food:  I’m at my daughter’s soccer game coveting the snack somebody’s mom brought, which is a large bowl of cooked lentils.  “Could I have some now?” I beg.  “No!” the soccer mom replies.  “You can’t have any now or later!  It’s for the kids!”

Friday, September 14, 2012

Nutrition for Endurance Cycling

What does an ultra-endurance athlete eat?

I can speak for the cyclist who does really long training rides.  How long?  Well, lately I’ve been training for a two-day stage race, the Everest Challenge.  My favorite weekend ride these days is the “double-Diablo,” which is close to a hundred miles long with 11,000 feet of climbing.

It’s tough to convey, to the layman, and even to the typical cyclist, what “11,000 feet of climbing” really means.  Consider this:  a staircase rising 11,000 feet would have around 18,000 steps, would be about four miles long, and would take you to the top of a 900-story building.

I have it on good authority that the stomach of a well-trained athlete can handle about 200 calories an hour during intense exercise.  By calories I mean carbohydrates—protein and fat aren’t nearly as useful.  Any sugar will do:  energy bars, energy drink, energy gels, fruit.  I find fruit hard to carry and bars hard to eat.  So I try to drink a bottle of energy drink (160-180 calories) every hour, and eat a gel (110-120 calories) about every 90 minutes.  (I bring drink mix in baggies.)  The gels have caffeine, which speeds the metabolism and aids in fat burning.

So, during a 6½-hour double-Diablo, I’ll have five or six large bottles of energy drink and four gels.  That’s pretty disgusting:  1,300 to 1,500 calories of pure sugar.  (Don’t let anybody tell you there’s nutrition in energy drink.  Sure, these drinks contain electrolytes, but that’s not really nutrition.  There are only two electrolytes:  potassium and sodium.  Six bottles of Gatorade—about a gallon—provides, in total, just 270 mg of potassium, a mere 6% of a person’s daily requirement.  You can get that much potassium from four ounces of orange juice, or half a banana.  Four gels provides a total of 80 mg potassium—about as much as a mouthful of V-8 juice.  As for sodium, I don’t think that’s an elusive part of any American’s diet.)

The worst part of this forced gluttony?  It’s that I don’t even have a sweet tooth.  My kids are envious that I get to have so much sweet stuff, but I really don’t enjoy it.  The good news is, my stomach tolerates it pretty well—which puts me at an advantage over lots of riders, especially during a six- or seven-hour road race.  An envious teammate joked to me recently, “You have a Protour-caliber stomach.”  It’s true—my stomach is the one part of my body suitable for the Tour de France.

What would happen if a cyclist drank only water during rides?  Well, on a short ride he’d be fine, though he might not go as fast.  (Recent studies—click here and here and especially here for details—show that a sweet drink increases power output, even if it’s spat out instead of swallowed.)  On a long ride, though, the sugar-free rider is doomed.  He’s a time bomb:  he can be hammering along just fine one moment but will suddenly crack, and then barely be able to turn the pedals.  It’s pretty spectacular, but also sad, to watch.

How you eat after a long ride is also important.  For about half an hour after hard exercise, sugar taken in goes directly into replacing muscle glycogen instead of being absorbed the normal way.  In other words, you’ll recover more quickly if you consume carbs during this “glycogen window.”  So, right after my ride, when I’ve already had a whole gallon of energy drink, guess what I get to do?  Have some juice, maybe some sweetened yogurt, a few Girl Scout cookies.  My kids flock to the scene like pigeons, looking for handouts. “Did you just ride six hours?” I snap.  (I do leverage the glycogen window as a parent.  My older daughter will ride for an hour on the indoor trainer just for a half-dozen jelly beans.)

Refueling doesn’t end there, though.  A cyclist can burn a thousand calories per hour on a hard ride, so it takes many meals to catch up.  After a really long ride I dream about food all night.  As with any dream involving appetites, satisfaction is never achieved.  On Saturday night I dreamed I’d locked myself out of my office:  no wallet, no keys, no train ticket … thus no food.  The dreamscape shifted:  now I was stuck at a boring lecture.  Just as I tried to sneak out, the speaker asked me to come onstage.  At that moment I discovered my hands were full of noodles.  I ran for the door but dropped the noodles.  Could I eat them off the floor?  Everybody was watching.

On Sunday night I dreamed I was at a barbecue and just before I got to eat, my brother showed up and needed a ride to the airport right away.  The next morning my wife said I was talking in my sleep about “some sauce, Florentine I think.”

A cyclist can’t eat right.  What I mean is, he can’t eat the same things that ideally healthy people eat—he needs more calories than that.  Sunday night I offered to make dinner:  “I can make gnocchi with gorgonzola, or tortellini.”  My wife replied, “How about neither?  I mean, we’ve got all this produce….”  I thought of a dog, starving after chasing a ball all day, hearing its master say, “I could give you this Alpo … but then, we’ve got this nice chew toy!”  If I eat “right,” meaning lots of vegetables and fiber, I’ll feel sated but never catch up on calories.  Distance athletes have to eat wrong.  They need massive plates of pasta.

Sugary drinks aside, do I enjoy this abnormal caloric need?  Well, sure!  I laugh when I see a food product labeled “guilt-free.”  The only guilt I feel, when I eat a fatty-starchy calorie bomb, is that I might be setting a bad example, or making other people jealous.

Friday, September 7, 2012

Almost Intelligent - Part II


In my previous post I explored modern efforts at artificial intelligence, evaluating them in terms of two common criteria:  how well AI devices can simulate human dialog, and how well they translate languages.  In this post, I will look at another classic measure of the progress of AI:  how well a computer can play a game.

It’s not hard to see why this criterion is a valid one.  So often, a computer (or other machine) simply does what we tell it do (or at least it tries).  With a game, the computer—far from accommodating you—is carrying out its own agenda, which is in direct opposition to you.  Also, whereas Siri or a chatbot may not be “connection-oriented”—that is, may not actually consider sequential inputs in the context of an ongoing conversation, a computer playing a game most certainly is.  Thus, if it does a really good job of beating us, all on its own, it’s both the most successful and (at least to me) creepiest manifestation of AI there is.

My early, early experience

I got a very early start with computer gaming.  Before personal computers were a common fixture in homes, my brother Bryan wrote a game for the Hewlett-Packard Model 85, a computer which my dad bought and let us kids use (which stands out as one of parenting’s finest moments, if you ask me).

The game Bryan coded was Hexapawn, a simple variant of chess involving three pawns per player on a 3x3 board.  Wikipedia tells us that the game’s inventor, the famous mathematician and writer Martin Gardner, “specifically constructed it as a game with a small game tree, in order to demonstrate how it could be played by a heuristic AI implemented by a mechanical computer.”  I’m sure Gardner would be thrilled to learn that Bryan was inspired by his magazine article on the topic.  (I asked Bryan today if he can recall his precise motivation for that programming project, and he replied, “Well, I loved science, computers and futuristic stuff, not really sure why, heck, we all did, but there was just one problem, and that’s that the [HP-85] computer didn’t really do anything. There were a few primitive games and whatnot, but as you know, those got old pretty fast.”)

At first, the HP-85 and I were pretty well matched.  But once I got the hang of Hexapawn, I found I could beat the computer—but only for awhile.  The computer learned while playing:  it never made the same mistake twice.  Thus, after this learning period our games always ended in a draw.  But the HP-85 had one weakness:  when you exited the program, its memory was erased.  The next time you played, it had to learn all over again.

Deep Blue vs. Kasparov

I don’t need to say much about this because you surely know the story:  an IBM computer called Deep Blue beat Garry Kasparov, the world chess champion, at his own game.  I haven’t watched the matches (I’m not much into chess; in fact, I once lost to my four-year-old nephew) but I gather Kasparov got pretty heated.  He even accused the IBM team of cheating by helping Deep Blue out behind the scenes.  A documentary about the match, commenting on how visibly flustered Kasparov got, said he would be the worst poker player in the world.

In a sense, it wasn’t a fair matchup:  Deep Blue got Kasparov’s goat, but the computer had no goat.  An awareness of the significance of your activity is part of what it means to be intelligent, so to the extent that Deep Blue played mechanically, it wasn’t quite intelligent.  I cannot brood about Kasparov losing his temper against a soulless, ruthless computer without fantasizing about Kasparov grabbing a cheap knockoff peripheral device, unsupported by Deep Blue’s operating system, and jamming it into a USB port.  The machine’s calculations grind to a halt and eventually it blue-screens, thus losing by default to the human.  And the crowd goes wild!

My other  early experience

In 1984, a friend and I took on his Apple IIe computer in a game far more exciting than Hexapawn:  strip poker.  Needless to say, only our opponent would actually strip.  As I recall, we had three babes to choose from as our rival.  Now, before you get too excited (or offended), remember the quality of computer graphics in that era.  This was extremely low-resolution—the CRT equivalent of Pointillism.  Still, it was fun to play poker against, and strip, the babes.

We eventually discovered a huge weakness in the computer’s play, that has strong ramifications for AI in general:  you could easily win just by bluffing constantly.  So long as we bet big on every hand, no matter how lame our cards were, we’d have our opponent bare naked within minutes.  One babe was as gullible as the next—they never learned!  But then, how could they?  A smarter program could have noted the frequency of our bluffing, but this one didn’t.  Its creators could have implemented some sort of ratio-based “this guy bluffs” detector, but ultimately how smart can a computer get about human treachery?  Could it ever pick up on the hundreds of nonverbal cues that a human can?  Can it really learn the traits of its opponent?

Consider this anecdote.  I attended Poker Night (a fundraising event for my kids’ school) a few months back, and (not wishing to spend too much money) was very conservative with my betting.  When I finally got an obviously good hand (this was Texas Hold ‘em, a game unfamiliar to me, and I was hopeless at spotting opportunities), I finally bet big.  None of us had played one another before, so there was much conjecture about whether or not I was bluffing.  “He’s been betting low all night. He’s got something!” someone said.  “No, he might just have balls,” another guy said.  A third guy replied, “No, he’s in my wife’s book club, so I know he doesn’t have any balls!”  See?  Though he’d never played against me, that third guy had biographical information that came into play.  I’d like to see Deep Blue go up against a professional poker player.  It would get its CPU kicked!

What’s the point?

There are two main reasons I can think of for a computer to play a game.  One is so that a lone person can have somebody to play against.  The other is to prove that the computer can actually do it.  But what is the point of people playing games?  Why do we do it?  This question, I think, gets at the core difference between humans and AI.

Of course there are all kinds of reasons people play games, but a computer only plays a game because a human told it to.  And all a computer knows how to do is to try to win.  I play games to have fun (which a computer can’t do) and to teach my kids things. 

For example, my family loves to play Apples to Apples.  In this game, players take turns being the judge.  The judge turns over a green card that has an adjective on it (e.g., brave, difficult, scary).  Each of the other players has seven red cards, each with a noun printed on it (e.g., doorknob, t-shirt, egg).  Each player selects from his hand the card whose noun best exemplifies the adjective on the green card.  The judge chooses which player’s card matches the green card the best, and awards the green card to the player who provided it.  Although the Wikipedia article about it lists many variations for this game, none matches the way my family plays, which is that each player makes an argument for his choice, to persuade the judge.  (We assumed this was the whole point of the game; otherwise, the game seems pointless.)  These arguments are often elaborate, sometimes ingenious, and always funny.  I’m hoping this game will help my kids learn the art of rhetoric.  I cannot imagine that a computer will be able to even create a rhetorical argument, much less teach rhetoric to a human or learn it from a game, anytime soon.

My favorite game, Sorry!, exists in a computer version, and though I haven’t tried this version (why would I? I have kids!), I can imagine that a computer could do okay against humans if all parties took a similarly cutthroat approach to the game.  But for me, a cutthroat approach is out of the question.

Why?  Well, for one thing, I’ve been playing this game with my kids since they were very young and given to bursting into tears when they got bumped or Sorry’d.  (It’s natural to feel singled out when an opponent, faced with multiple options of how to play a card, chooses the option that hurts you, as opposed to another player.)  I don’t like to make my kids cry.  Also, I like to give a little help to my younger daughter to better her chances against her big sister.  And of course I want the game to be fun.  But most of all, I want to teach my kids about quid pro quo.  I want to teach them how to make deals.

“Okay, I’m going to show you mercy here,” I’ll declare.  “I could split this seven and knock your pawn back to home, but I won’t—I’ll just move seven spaces.  But I want you to remember this the next time you draw a ‘Sorry’ card.”  There’s no codified way of keeping track of these favors … they’re informal and involve approximations of justice.  Such deal-making is a crucial capability—not just in a game but in life.  I cannot play Sorry, in fact, without thinking about the epic failure of Flavr Savr genetically engineered tomatoes.

I read about these tomatoes in a 1993 “New Yorker” article, written a few months before the product hit the market.  The obviously creepy idea of genetically engineered food is not all that stuck with me from the article.  I was very impressed by the account of a Ed Agrisani, a Rolex-sporting, big-time tomato salesman interviewed for the article, who predicted (accurately, as it turned out) that Calgene’s $25 million experiment would be a complete failure.  To Agrisani, the quality of the new tomatoes was almost beside the point, because Calgene had no experience actually selling tomatoes: 
“What separates the men from the boys in this business is whether you can sell your tomatoes when nobody wants them, when you’ve got a whole field that’s just going to rot out there unless you can move ‘em out.  I’ve got customers who know that when the supply is tight they can call me and I’ll sell ‘em a load.  So when I get oversupplied I can call them and say, ‘Hey, I know you don’t need it, but how about buying a load?’  And they’ll say, ‘We’ll send the truck.’  It took me sixteen years to get to where I had the relationships to do that.  Now, maybe the folks at Calgene think they can come in and do it overnight—and, like I say, I wish ‘em the best—but it’s not a simple deal.”
I’ll let somebody else teach my kids chess.  For me, the speech-making involved in Apples to Apples and the deal-making in Sorry! are the better skills to learn, as they completely transcend the game itself.


A computer can play a mean game of chess.  But perhaps chess is unique among games in relying mainly on intellect, strategy, and computational ability.  When we consider games that use the full spectrum of human intelligence—interpreting facial expressions, ad hoc profiling of opponents, making arguments that appeal to quasi-rational humans, making deals, having fun—it starts to look like AI is still pretty far from the end zone.  And even if a computer gets good at a game, it will remain utterly powerless to take what it’s learned and apply it to real life.  (Of which, of course, it has none.)

This is all fine with me.  I’m all for improvements in AI to the extent this makes the machines into better slaves.  I’m much less excited about a computer defeating me at anything.