Everest Challenge "Pep Talk"

Introduction

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.

Jitters

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.

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!”

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.

Almost Intelligent - Part II

Introduction

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.

Conclusion

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.

Other albertnet posts on A.I.

• I, Chatbot
• Almost Intelligent – Part I
• Smartphones & Artificial Stupidity
• A.I. Smackdown – Moto vs. Cortana vs. Siri
• Could Artificial Intelligence Replace Writers? – Part 1
• Could Artificial Intelligence Replace Writers? – Part 2
• Could Artificial Intelligence Replace Writers? – Part 3
• A.I. Smackdown – English Major vs. ChatGPT – Part 1
• A.I. Smackdown – English Major vs. ChatGPT – Part 2
• Schooling ChatGPT
• Will A.I. Steal Our Jobs?
• Tech Check-In - How Good is the Latest A.I.? - Part I
• Tech Check-In - How Good is the Latest A.I.? - Part II
• What is ChatGPT Great At (and Not)?
• Tech Reflection – Two Sides of AI
• AI Smackdown – ChatGPT vs. Copilot vs. Gemini
• More AI Smackdown - ChatGPT, Copilot, & Gemini Write Poetry

—~—~—~—~—~—~—~—~—
Email me here. For a complete index of albertnet posts, click here.     

Almost Intelligent - Part I

NOTE:  This post is rated R for mild strong language.

Introduction

“Almost intelligent” might be a good name for somebody’s biography (or autobiography) but here I’m talking about artificial intelligence.  My last post described my experience chatting with an application called Cleverbot that tried to simulate human dialog convincingly.  Here, I’ll tackle the subject of AI language more generally, looking at speech recognition, natural language, and translation. 

Do we care?

If you really don’t care about AI at all, go read something else—or, better yet, read on to see why maybe you should care.

On the one hand, AI is very exciting.  As computers have become “smarter,” and easier to use, they’ve gotten so useful it’s hard to imagine how we ever did without them.  I’m thinking about Google, GPS and other mapping applications, package tracking, e-mail spam filters … the list goes on and on.

On the other hand, AI is a bit scary, and as a human I prefer to believe I could never be replaced by a computer.  I shudder at the thought that human behavior could be so unvarying and predictable that one day we’ll barely be better than a really good computer program.  I want my computer applications to get smart, but not too smart.

Voice recognition and natural language

There’s a button on the side of my smartphone that, when pressed, startles me by causing the speakerphone to say, “Say a command!”  I’m vaguely aware that my phone will respond to voice commands but have no interest in issuing them.  Most of the cool features of smartphones involve the silent, non-speech stuff you can do—e-mail, Internet browsing, etc.—as you’ll notice on the subway when half the people are silently tapping away.  (The popularity of texting—a way to privately communicate without being eavesdropped on by the person you’re ostensibly talking to face-to-face—is a classic example of how phones are becoming increasingly mute.)

That said, the iPhone’s voice-recognition application, Siri, seems to be making a bit of a splash.  (Nobody I know uses Siri yet, but I’m sure some will.)  This demo shows how Siri is pretty good at understanding speech and figuring out what you want it to do.  (I played with a Droid phone recently and it was also very good at typing for me as I spoke.)  The reviewer asks Siri, “Where can I have lunch?”  Siri replies, “I found fourteen restaurants whose reviews mention lunch.  Twelve of them are close to you.”  This seems easier than typing into Google on a little phone.  But the natural language feature isn’t perfect; the reviewer says, “How about downtown?” and Siri replies, “I don’t know what you mean by ‘how about downtown.’”

Perhaps Siri’s communication isn’t “connection-oriented”—that is, it doesn’t consider “how about downtown?” in the context of “Where can I have lunch?” but takes the two queries as totally discrete and unrelated.  If so, this is a major shortcoming. 

The reviewer tries again:  “I want to have lunch downtown.”  Siri replies, “I found 3 restaurants matching ‘downtown.’”  Useless!  Siri knows where the user is, geographically, but does not realize that “downtown” in this context pertains to location, not a restaurant’s name.  Here, Siri starts to look like a mere forwarder of requests, always passing the buck to Google instead of applying intelligence to the request.

Simple conversion of speech to text looks pretty good on Siri.  The reviewer dictated a message to it, and almost everything came out.  The notable exception was how Siri transcribed the reviewer’s spoken comment “I need to make some videos about the iPhone 4S.”  Siri typed, “I need to make some videos about the iPhone 4 ass.”  The reviewer doesn’t notice this gaff, telling the YouTube viewer, “There it is.  It figured out exactly what I wanted to say.”  Dangerous, don’t you think?  What if the reviewer meant to e-mail the text “S as in Sam” but actually e-mailed “ass as in Sam,” to his boss, Sam?

Not that Siri doesn’t try hard.  When the reviewer says, “Set a timer for 3 minutes,” Siri replies, “OK, I started a three-minute timer.  Don’t overcook that egg.”  Not bad.  Actually, it is bad.  For one thing, “that egg,” when spoken by Siri, comes out “ditek.”  Without the text on the screen you’d never understand what it said.  Meanwhile, it’s obvious that Siri is trying to be funny, and completely failing.  There’s nothing witty about Siri making a lame guess as to what the timer is for.  What’s worse, Siri could create the impression that three minutes is actually how long you should cook an egg.  In fact that’s not nearly enough time, and everybody knows an undercooked egg presents a salmonella risk.

A fundamental problem

Of course I’m nitpicking with the egg timer example, and (to a lesser extent) with the “ass” example, but they bring up an important point:  language, as one of the primary interfaces between humans, requires far more than just understanding what is heard and forming sentences in response.  Having a sanity-check reflex that keeps you from using words like “ass” in mixed company, and knowing whether your joke is actually funny, are complicated processes.  Verbal communication can be a minefield, especially for a computer application that stabs around in the dark.

Consider, for example, the old joke about the Texan who gets into Harvard.  While touring the campus, he asks a student, “Excuuuse me, can you tell me where the library’s at?”  The student replies haughtily, “Here at Haaarvard, we never end a sentence with a preposition.”  The Texan replies, “Okay, can ya tell me where the library’s at, asshole?”

Upon inspection, this exchange, though brief, is quite complex.  The Harvard student’s response to the Texan’s query shows a decision that might not occur to an AI application—that is, to a) not answer the question, and b) use the opportunity to deliver a scornful message about class and intellect.  The Texan’s comeback makes a statement about a) his refusal to be cowed, b) the difference between cultivation and innate intelligence.  Meanwhile, the joke as a whole counts on the listener enjoying an opportunity to feel superior to both Harvard students and Texans, while exulting in the surprise and wit of the punch line.  Worlds away from “Enjoy ditek.”

Maybe you think I’m overreaching here, that such nuance will never be expected of AI.  Maybe AI is just a tool to make machines more useful to humans, and little gaffs don’t matter much.  When a woman asks her husband, “Do these pants make my butt look fat?” he is instantly plunged into a terribly complicated interaction, because of his relationship to the woman.  So much hinges on his response.  If he says “yes” he’s obviously dead.  If he says “no” too vociferously, he seems patronizing.  He could try the reverse-psychology approach and say, “No, your butt makes your butt look fat,” but she better have a sense of humor and thick skin.  Or, he could ignore the question, or say, “Look, krill!”  Or he could say “yeahhh” lecherously (note that imparting this single syllable with the sense of “I want some of that!” is far beyond the current state of the art in AI voice synthesis).  But when a human asks Siri “Am I fat?” and gets back, “Here’s your a.m. alarm” and “I found 8 fitness centers fairly close to you,” he or she can more easily blow it off.

This idea—that computers don’t have to play nice when “talking” to humans—is strongly supported by a scene in “The Terminator” when the evil cyborg, confronted by his landlord—“Hey buddy, you got a dead cat in there, or what?”—scans through a menu of possible responses—“YES/NO; OR WHAT; GO AWAY; PLEASE COME BACK LATER; FUCK YOU, ASSHOLE; FUCK YOU”—and chooses the penultimate one.  Of course when you’re the size of Arnold Schwarzenegger you don’t have to have a friendly user interface.

That said, I would argue that, to the extent humans are to embrace AI when using electronic devices, precision and nuance do matter.  We have to trust these devices not to turn “S” into “ass,” not to waste our time with lists of restaurants we’d never eat at, and not to infuriate us with messages like “cannot undo.”  Even if you’ve never found yourself yelling profanities at your computer, I’m sure you’ve seen others do it.

Consider this cautionary tale.  My dad bought one of the first consumer-oriented computers in history, the Hewlett-Packard Model 85.  This was 1980, a year before the IBM PC.  the HP-85 was about as far from Siri (or at least the design intent of Siri) as you can get.  There was no software for it; you had to program it yourself.  Meanwhile, its version of BASIC was proprietary, diverging from the industry standard (e.g., you used the command “DISP” instead of “PRINT”).  I had my brother try out one of my first programs.  It prompted him to type his name.  With great hesitation—he was greatly fearful of doing something wrong and damaging our dad’s expensive machine—he typed “Max.”  Then he sat there waiting for something to happen.  Nothing did, because my program didn’t say anything about hitting the Enter key when done.  Max looked a bit nervous.  “It’s not working!  It’s not doing anything!” he cried.  I told him to hit Enter.  When he did, the computer promptly displayed the message “Max is a jerk” (the whole point of my program).  Max got really angry and flustered and to this day does not use a computer.  This probably isn’t just because of my program; the HP-85 was less than user-friendly and doubtless gave Max the wrong impression of where home computing was going.

Translation

Here is where the AI picture is, to me, much rosier.  Early attempts at translation, like Alta Vista’s Babelfish, were a joke.  You pasted the foreign-language text into a window, gave it the language to translate it into, and then were presented with a salad of translated words (with un-translated ones sprinkled like croutons) that made no sense at all.  The only real use for this tool was translating things into Tristan.

What’s Tristan?  Well, I used to have a colleague, a computer programmer, whose native-tongue language skills were so poor it was impossible to understand a thing he wrote.  His e-mails always gave my colleagues and me a laugh, and in his honor we invented a language and named it after him.  (It wasn’t really called Tristan, because his last name wasn’t really Tristan; I’ve changed it to protect him from possible embarrassment.)  To translate something into Tristan, you’d type normal text, translate it into French using Babelfish, and then translate it back to English.  The results were pure comedy, with not a shred of sense left intact.

I think people are naturally forgiving of poor translation, because we’ve studied grammar and foreign languages in school and can really appreciate how difficult a task this is.  Plus, the results are so often funny, they put us in a good mood.  Consider the urban legend that “Coca-Cola,” when first translated into Chinese, came out meaning “bite the wax tadpole.”  (To this day I’ll complain about something by saying it bites the wax tadpole.)  Brian Hayes, writing in “American Scientist,” makes an interesting comment about AI efforts to parse grammatical constructions when translating text:  “The failure of this approach is sometimes dramatized with the tale of the English→ Russian→ English translation that began with ‘The spirit is willing but the flesh is weak’ and ended with ‘The vodka is strong but the meat is rotten.’”

More recently, online translation engines such as Google Translate have gotten much, much better.  As Hayes describes, “The idea is to ignore the entire hierarchy of syntactic and semantic structures—the nouns and verbs, the subjects and predicates, even the definitions of words—and simply tabulate correlations between words in a large collection of bilingual texts.”  At first, this strikes me as a “brute force” approach that is further from artificial intelligence than earlier efforts, however hapless, to actually parse a sentence grammatically.  But as Hayes points out, the modern technique is actually lot closer to how humans learn to talk.  (It’s also more similar to how we would learn a foreign language if we had the good fortune to go live in another country, versus making our way with a textbook and classes.)

I first tried Google Translate when I was trying to track a package that was being shipped to me from a web merchant in France.  I have studied French for years, but understanding statements about logistics and customs offices would be difficult in any language.  I was presented with this:  “Votre colis est sorti du bureau d'échange.  Il est en cours d'acheminement dans le pays de destination.”  This would have totally tripped up the original Babelfish, but Google served up an entirely comprehensible translation:  “Your package is out of the office of exchange. It is in transit in the country of destination.”  (Not only was I delighted with how clear this was, I was relieved my package wasn’t stuck in customs.)  Translating this English back into French, and then back into English, I get “Your package is out of the office of exchange. It is in transit to the destination country.”  Very little of the “Tristan effect.”  (There’s some fuzziness around “to” vs. “in” with regard to the destination country, but I can live with that.)

To reassure myself then the Man of Letters wouldn’t be replaced by a machine anytime soon, I tried some poetry: 

But the Raven still beguiling all my sad soul into smiling,
Straight I wheeled a cushioned seat in front of bird and bust and door;
Then, upon the velvet sinking, I betook myself to linking
Fancy unto fancy, thinking what this ominous bird of yore—
What this grim, ungainly, ghastly, gaunt, and ominous bird of yore
Meant in croaking “Nevermore.”

When I fed this into the new version of Babelfish (which works similarly to Google’s), and translated it into French and back, the response was this: 

But the Raven seductive yet all my sad soul into smiling,
Straight I wheeled a seat padded before the bird and bust and door;
Then, on the Velvet sinking, I hauled myself to tie Fancy: fancy,
Think what this bird threatening of antan - the sad bird, awkward, frightening,
Ghent and disturbing past Meant in croaking “Nevermore.”

Aha!  Gibberish!  I was about to feel all smug about the superiority of humans over AI, but then tried Google Translation with the same English à French à English task: 

But the raven still beguiling all my sad soul into smiling,
 I wheeled a cushioned seat in front of bird and bust and door;
 Then, upon the velvet sinking me, I betook myself to linking
 Fancy unto fancy, thinking what this ominous bird of yore -
 What this grim, ungainly, ghastly bird, gaunt, and ominous of yesteryear
 Meant in croaking “Nevermore.”

Wow.  That’s so good it’s creepy.  But before you despair and decide the computers will ultimately render the human race unnecessary, be sure to check out my next albertnet post, wherein I examine how well AI does playing games—another classic measure of its progress.

Other albertnet posts on A.I.

• I, Chatbot
• Almost Intelligent – Part II
• Smartphones & Artificial Stupidity
• A.I. Smackdown – Moto vs. Cortana vs. Siri
• Could Artificial Intelligence Replace Writers? – Part 1
• Could Artificial Intelligence Replace Writers? – Part 2
• Could Artificial Intelligence Replace Writers? – Part 3
• A.I. Smackdown – English Major vs. ChatGPT – Part 1
• A.I. Smackdown – English Major vs. ChatGPT – Part 2
• Schooling ChatGPT
• Will A.I. Steal Our Jobs?
• Tech Check-In - How Good is the Latest A.I.? - Part I
• Tech Check-In - How Good is the Latest A.I.? - Part II
• What is ChatGPT Great At (and Not)?
• Tech Reflection – Two Sides of AI
• AI Smackdown – ChatGPT vs. Copilot vs. Gemini
• More AI Smackdown - ChatGPT, Copilot, & Gemini Write Poetry

—~—~—~—~—~—~—~—~—
Email me here. For a complete index of albertnet posts, click here. 

I, Chatbot

Introduction

What is artificial intelligence?  Broadly speaking, it’s the ability of a machine to think for itself.  At best, an AI app is much more than a tool for finding businesses, playing chess, or making calculations.  Machines are really good at doing menial tasks far more quickly than a human—but wouldn’t it be cool (though also creepy) if machines could be creative?

Here’s a puzzle I’d like to see a computer solve.  Say you want to go for a bike ride, and are bringing a bottle of energy drink.  Because the mere presence of sweetness in your mouth improves performance (click here for details) you want the drink to be really strong at the beginning of the ride.  But suppose it’s a hot day and you’re worried about the drink eventually nauseating you.  How can you make your drink strong at first, but gradually get weaker during the ride?

I posed this question to Cleverbot, an AI chat application on the Internet.  Read on to see how Cleverbot answered this, and more importantly to watch in wonder as my chat goes right off the rails and way into the weeds, to my great embarrassment.

Why robot chat?

So … what got me interested in AI, and in chatting with a robot?  Well, some weeks ago, Norton—the antivirus software company—had a glitch that made albertnet inaccessible from my PC, telling me that it was a known phishing site or some such nonsense.   (Countless blogs were affected; the phishing claim had no basis in truth.)  Needless to say, I was pretty pissed off to have Norton dragging my good name through the mud in front of all their users.  In attempting to resolve the problem I initiated an online chat with a Norton representative through their website.  Not long into this chat, when I’d explained my (admittedly arcane) problem, the customer service representative came clean:  it wasn’t a real person, but an automated system (bot) designed to assist with more common problems.  It then referred me to a real human.  I felt a bit misused.  Why hadn’t Norton disclosed this up-front?  Did they figure it’s bad enough PR to move jobs overseas without replacing the humans altogether?

My favorite time to ponder anything is during an early morning bike ride, when my mind is fresh and when, given the tedium of pedaling up long climbs, I like to give my brain something to do.  So, pre-ride, with this Norton episode in mind, I did some light research on AI.  In an article from the “New Yorker,” Adam Gopnik writes about Alan Turing, the guy who cracked the Enigma code, and who proposed that “the true test for artificial intelligence was to see if, cut off by a ‘black curtain,’ you would be able to tell a machine’s answer from a human’s in a natural-language conversation.”  Such a challenge is called a Turing test:  a computer tries to trick the human into believing he’s chatting with another human.  Exactly what Norton had tried to do with me.

My chat with Cleverbot

When, that morning, I came across a link to Cleverbot, I decided, on a whim, to give it a try.  I’d heard of Cleverbot somewhere but wasn’t entirely clear what it was or how it worked.  Of course, as with all Internet apps, it was entirely intuitive to use.  I asked it a few basic Magic-8-ball type questions and got back some really boring answers.  (I hadn’t yet discovered how to get a transcript of the chat, so those opening questions are lost to posterity.)  I decided to ask Cleverbot something more complicated:  “Do my children love me”?  Cleverbot answered, “Yes.”  I asked, “Why do my children love me?”

“Because I don’t love you,” it replied, after a pause.  The pause was reassuring somehow:  a sign that the matter was being considered, rather than some canned message being spat out.

I thought that was a pretty good answer.  It was playful, true, and sounded a lot more human than “The operation failed,” “Not found,” or “Cannot undo.”  It also seemed satisfyingly philosophical:  we cannot get love from a machine, and yet are biologically programmed to love other humans, especially our parents.  So far, I was pretty impressed.  I pressed on:  “Why don’t you love me?”

“Because I don’t know you,” it replied.  Again, perfectly reasonable.  I decided to challenge Cleverbot’s response and asked, “If my children didn’t love me, would you love me?”

“You’re a horse,” Cleverbot fired back.

Huh?  Maybe Cleverbot wasn’t as clever as I thought.  Of course, a human might throw in an occasional non-sequitur, especially in response to a really stupid question, so a robot mimicking a human might reasonably do this.  Still, I decided to test the theory that the answers were random and that Cleverbot’s early prowess was just luck.  I asked it again, “Why do my children love me?”

“Because you are not Ciel,” it answered.  Huh?  I was quickly becoming disillusioned.  Ciel?  Sounds like the name of a porn star.  “Who are you?” I asked.

“A train,” it replied. 

I asked it, “Are your answers random?”

“Yes,” it said.

I replied, “Why should I waste my time asking questions when your answers are all random?”

“I will tell you my name if you will tell me yours,” Cleverbot said, breezing right past my challenge.  Of course I am wary of any computer system asking me for personal information.  Who’s phishing now?  I replied, “My name is Jose Patrocinio Jimenez. What’s yours?”

“My name is Cleverbot.”

“Why did you ask me what my name is? Why do you care?

“I didn't, you did,” it lied.

I persisted:  “You wrote, ‘I will tell you my name if you tell me yours.’  So you did, in fact, ask for my name.  Why?”  I began to wonder if the chat was even “connection-oriented”—that is, if Cleverbot considered my questions and comments in the context of other things I’d said, as opposed to every exchange being isolated and discrete.

“I never said that!” said Cleverbot (testily, I thought).

“You are lying,” I wrote.  So far, Cleverbot was doing pretty well:  I was getting irritated.  Not that I don’t routinely get irritated at inanimate objects like computers, but in this case the computer was working fine.  I felt like I was interacting with a real human.  A human bastard, in fact.

“No, you're lying!” spake Cleverbot.

“Don't I have better things to do than argue with a robot?”

“You imposed it,”  Cleverbot said obliquely.

“You are not making any sense,” I complained.

“Yet all of it in the world.”  The smugness here was almost palpably human.

“How clever is it to spew banal platitudes like that?” I snorted.

“Pretty clever...” came the response.  Here, Cleverbot showed an inflated self-regard unbothered by any awareness of its own ignorance.  Not just human, but distinctly American, I’m sorry to say.  I had the frustrating sense of talking to a brick wall, and yet felt curiously compelled to continue asking questions.  I was reminded of the speaker in Edgar Allen Poe’s brilliant poem “The Raven”  who engages in a protracted and infuriating conversation with a bird, despite having realized it only knows one word.  On a lark I asked Cleverbot one of the questions from the poem:  “Is there, is there balm in Gilead?”

“What what.”  Cleverbot’s failure to use correct punctuation strongly smelled of humanity.

Losing patience, I finally asked Cleverbot the question I’d set out to ask in the beginning:  “How can I make my energy drink strong at first, but gradually get weaker during my bike  ride?”

“I might have done a statistical study on your topics of conversation.”  This is exactly the kind of thing Apple’s Siri app might offer up:  a simple Google search masquerading as problem-solving.

“I don’t want a statistical study, I want a solution to this problem. Can you think of one?” I challenged it.

“It is fine now because I cannot think of anything to ask you,” Cleverbot said uselessly.

“Are you male or female?”  I asked.

“Female.”  (This surprised me.  I’d expected Cleverbot to say “neither.”)

“I kind of figured.”

“How did you figure?”  (Very accurate syntax on this … very realistic.)

“Because I am sexist and you are irrational,” I said.  (I’m not actually sexist, of course, but one of the benefits of talking to a machine is that you can be as flippant as you want.)

“You’re weird, how can you be me when I am me?” Cleverbot asked.  Was this the grinding of Cleverbot’s machinery?  Could I confuse it enough to make smoke pour from it, like what Captain Kirk did to Nomad?

“Because I am we, and smell like tuna sandwich,” I responded.

“You don’t make sense.”  (Fair enough.)

“No, I make dollars.  What are you wearing?”  I was firing at random.

“Bubblewrap.”  Well played, Cleverbot.

“Dim the lights!” I replied.  This was basically a reflex.

“Ok,” Cleverbot said.

“Normally at this point I would tell you to slip into something more comfortable. But what could be more comfortable than bubble wrap?” I replied.  I was having fun now.

“What are the magic words.”  Cleverbot was playing the coquette.

“Please,” I wrote.

“I am human.”

Whoa.  I was not prepared for that.  I felt my face flush red, my pulse quickened, and—no joke—my armpits started to sweat.  I wasn’t chatting with a robot at all.  I was chatting—not just chatting but flirting—with a real person, online.  I should never have embarked on this without knowing what Cleverbot was.  Could it simply be a web app that connects two people online for a chat, like Chatroulette?

“I thought you were a robot,” I protested, knowing full well how much of a liar I sounded like.

“I am not a robot,” Cleverbot—or, rather, the mystery woman—assured me.

“Why are you called Cleverbot?”

“I’m not cleverbot!  You’re the one who is supposed to be the cleverbot, not me.  And I may not be smart but you are not even close as smart as I am.”

Just then, as if on cue, my wife stepped into the room.  “What are you doing?” she said.  “I thought you were on a bike ride.”  What could I say?  “Oh, I’m in a chat room with a woman who says she’s wearing only bubble wrap.”  Not a good idea.  Instead, grasping at straws, I said, honestly enough, “I’m researching a blog post.  Hey, Cleverbot is a robot, right?  It’s not actual people chatting is it?”  My wife, yawning, said she was pretty sure it was a robot, but one that built a library of responses from its communications with humans.

“I guess I have no idea what Cleverbot is,” I typed.  “I thought the whole point here was that people could chat with a robot.”

“You’re the robot though right?” asked the mysterious chatting entity, innocently (or faux innocently).

“No, I’m not a robot. I'm human too!”  I typed.  Were we pawns caught in a deadly game … or was I chatting with a robot after all, which was expertly impersonating a human so as to fulfill its goal of acing the Turing test?

“No you are not.  I am a human.  You are a robot,” it/she said.

“No, no, no. I am human, I assure you.”

“If you are real or not depends what you mean by real.”

“OK, whatever.”

“I have explained the best I can.”

I closed the browser.  I was straddling the fence between nervousness and relief.  What had just transpired?  Was that a chat with a robot, or chat roulette?  Did my wife see how red my face was?

Looking back, I marvel at how worked up I’d gotten.  On the other hand, this makes sense.  I’m a shy person.  The essence of social awkwardness is not knowing where you stand with regard to others.  It’s bad enough when you’re meeting people for the first time and have to do a lot of guessing about the right thing to say; it’s even harder when you don’t have any social cues at all, and don’t even know whom—or what—you’re chatting with.  I shut off the computer and headed out for my bike ride.

Epilogue – what is Cleverbot, really?

Cleverbot, thank goodness, really is a bot.  It is a web application that builds a database of chat responses based on conversations with humans.  (Click here and here for details.)  The more Cleverbot chats, the more its database grows, and (in theory) the more realistic and germane its responses will be.

How valid is this approach?  Well, Cleverbot did fool me into thinking it was human.  But looking back, this wasn’t the result of it being particularly clever.  The main thing that made me think I was chatting with a human was Cleverbot’s simple statement, “I am human.”  In the context of a female clad only in bubble wrap, to whom I’d just suggested slipping into something more comfortable, these were powerful words, provoking my paranoid “what if?” response.  But really, why wouldn’t an AI app trying to appear human simply assert that it is?

One problem with Cleverbot’s “learning” technique is that it is dependent on humans to ask the questions.  I suppose it can regurgitate these questions to other humans, which is somewhat useful, but there’s no mechanism for it to come up questions of its own.  A really great question for it to ask—assuming it is a connection-oriented app—might be, “What is the right answer to your question?”

This brings me to the next problem I see with Cleverbot:  it has no way of discerning the right answer based on responses—it can only determine the popular answer.  These are not always—or even often—the same thing.  Consider all the “best of” awards that go to an undeserving, but widely known, recipient, like Chevy’s winning “best Mexican restaurant,” beating out  literally dozens of better places, in a Bay Area poll.  (No real expertise is involved there; people just put down the first answer they think of, and everybody has heard of Chevy’s.)  Similarly, if Cleverbot blithely accepts answers from the unwashed masses, it will never be smarter than they. 

Not surprisingly, when (in a follow-up chat today) I presented Cleverbot with a less obscure reference—“ If there's somethin’ strange in your neighborhood, Who ya gonna call?”—it got the right answer—“Ghostbusters!”—about half the time.  (The rest of the time it replied, “It’s just a spring clean for the May queen.”  If there’s a link between Led Zeppelin and the 1984 comedy movie, I’m not aware of it.)  This pattern is consistent with other cultural references; when I said, “This Roman Meal bakery thought you’d like to know,” Cleverbot replied obliquely:  “Where on earth are your servers?”  (The correct, answer, of course is “I don’t need no arms around me.”)  But when I typed, “We don’t need no education,” it naturally gave the right response, “We don’t need no thought control.”  The silly song that got lots of radio play is recognized; the much better but less popular song is not.

In this regard, Cleverbot could do so much better.  I Googled “Is there balm in Gilead” and got three hits referring to an old religious song, and the fourth hit led me to “The Raven.”  Not bad.  Googling “Is there, is there balm in Gilead,” I get “The Raven” as the second hit.  But you could ask Cleverbot this question a million times and it’ll never figure out what you’re talking about.  Cleverbot is beholden to its chat partners for information, ignoring the rest of the Internet entirely.  Finally I told it, “The right answer is ‘Nevermore.’”  It replied, “No, I want you to sing the song.”  I obliged, pasting in lyrics from the religious spiritual:  “Sometimes I feel discouraged,  And think my work’s in vain.” 

Cleverbot replied:  “I know, right?”

Other albertnet posts on A.I.

• Almost Intelligent – Part I
• Almost Intelligent – Part II
• Smartphones & Artificial Stupidity
• A.I. Smackdown – Moto vs. Cortana vs. Siri
• Could Artificial Intelligence Replace Writers? – Part 1
• Could Artificial Intelligence Replace Writers? – Part 2
• Could Artificial Intelligence Replace Writers? – Part 3
• A.I. Smackdown – English Major vs. ChatGPT – Part 1
• A.I. Smackdown – English Major vs. ChatGPT – Part 2
• Schooling ChatGPT
• Will A.I. Steal Our Jobs?
• Tech Check-In - How Good is the Latest A.I.? - Part I
• Tech Check-In - How Good is the Latest A.I.? - Part II
• What is ChatGPT Great At (and Not)?
• Tech Reflection – Two Sides of AI
• AI Smackdown – ChatGPT vs. Copilot vs. Gemini
• More AI Smackdown - ChatGPT, Copilot, & Gemini Write Poetry

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