My Father’s Things ... Looking For a Home

Introduction

This post is about getting rid of my late father’s stuff. Not the valuable stuff, like his house and his car (which have new owners), but all the rest of the stuff that meant something to him, and possibly even to somebody else (given that there are over 7 billion people in this world).

You’ll get to see some of this stuff showcased below, in case you want any of it. But I’m also going to proselytize a bit about not accumulating stuff. You might find that amusing and/or a needed wake-up call.

A soldier’s things

There aren’t many activities sadder, I think, than going through a dead person’s stuff. The poor guy … his stuff outlasted him, and now he can’t use it anymore! In case the sorting activity itself isn’t enough to make you cry, it helps to get the Tom Waits song “A Soldier’s Things” in your head. Although I have just discovered there is some debate about the meaning of this song, I take it to be about a yard sale for all the stuff a soldier left behind when he didn’t make it home.

Some time ago I went through a similar process with the bike gear left behind by a friend of mine who drowned. That was almost unfathomably tragic but at least I had the satisfaction of finding good homes for the vast majority of the stuff. Many friends bought his old bike components (with the proceeds going to Mark’s widow) simply because they wanted something to remember him by. I myself am enjoying Mark’s old rollers, and I suspect I’m using them more than he even did.

But with my dad’s belongings … well, it’s all just such weird, arcane stuff. My dad realized well ahead of his death that he ought to do something with all of it, since it’s not really anything you could offer up at an estate sale. But by that time he was so old and sick, I found myself suggesting that he not spend his last months or years on this Earth getting rid of it all. I suppose it was reasonable advice but now I’m dealing with the aftermath.

A bit of advice

If you’re the hoarding sort, as I’ll confess to (somewhat) being, it sure doesn’t hurt to pair up with somebody with the opposite approach. My wife has a strong aversion to almost all physical objects. A running joke in my household is that I have to keep moving, or she’ll drag me out to the curb. As her most recent birthday was approaching, I suggested that I find some object in the house, gift-wrap it, and announce—as she opens it—that I’ll get rid of it before the end of the day.  She was delighted by the idea—and not just as a joke. She’d have actually been thrilled (though I couldn’t bring myself to actually do it).

But even if you aren’t lucky enough to be mated to an anti-hoarder, here’s a suggestion: as you approach retirement age (but while you’re still energetic and have your wits about you), go through all of your belongings and ask the following questions about each object:

• Is this making me happy?
• Is this making me money?
• Would my kid(s) want to inherit this?

If the answer to all three questions is any version of “no,” get rid of the object. If the answer to the third question is “yes,” ask yourself a follow-on question: would my kid(s) want this now? If so, offer it up. If your offer is declined, maybe you answered the question wrong (i.e., you should jettison the object).

My dad’s stuff … you want it?

Okay, let’s get into the stuff itself. Some of it my brothers and I are keeping, like an assortment of beautiful, expensive-looking prisms. (There are dozens of them, weirdly enough, so if you have a thing for prisms, let me know.) Obviously some stuff has sentimental value, like the homemade coat rack with our initials carved in the base, and thousands of photo slides, most of which nobody has ever laid eyes on before (since slide projectors take so long to load up). But the vast majority of the stuff had value only to him (and perhaps not even much value, as most of it spent the last few decades out in the garage or moldering away in boxes).

Exhibit A is his recumbent tricycle. Five of the six members of our family find the original, classic bicycle design to be a rare example of perfect functional elegance. The recumbent makes about as much sense as microwave popcorn (i.e., none at all). When I see a person riding a recumbent I feel terribly sorry for him. He looks like a tortoise flipped over on its back. Oh, these recumbent riders try to look like they’re enjoying themselves, but they’re not. There is actually no logical way to explain this recumbent-riding behavior except a nerdy person held prisoner by his own iconoclastic yearnings. When my dad purchased this tricycle, to replace the recumbent bicycle he never quite managed to learn to ride, I was dead set against the idea (but I kept my mouth shut … there’s simply no point arguing with an iconoclast). He never ended up riding it much because it hurt his neck.

But anyway, perhaps you’re a weirdo yourself and think a recumbent trike is a fine idea (the product does, after all exist), so here’s a movie showcasing it. I will confess that my narration of this video flies in the face of everything I’ve just typed.

And now, on to the telescopes. Why don’t I want these for myself, you ask? Well, the Bay Area isn’t exactly a great place for astronomy. If you want to see anything cool, you have to take a trip to the middle of nowhere. I’m a true family man, so I don’t fancy the idea of a solo astronomy expedition. And I can’t bring my family on such a trip, because I’m married to The Woman Who Ruined Astronomy.

Here’s what happened. Like twenty years ago, before we had kids, my wife and I did a mountain biking vacation in Moab, Utah. My dad drove down and met us. We were camping in Arches or Canyonlands, and the astronomy was superb. Thus, my dad—a fanatic stargazer—gave us a free (if unsolicited) astronomy lesson that lasted, it seemed, for at least a couple of hours. I have to confess, when I peer through the eyepiece of a telescope I seldom see anything interesting. Often I think I’m only seeing the reflection of my own eye. Sometimes there are little pinpricks of light. It all just seems so pointless and arbitrary. The constellations? I have no use for them. I can recognize Orion’s Belt, and the Big Dipper, but all the rest? Forget it. Anyway, at some point my wife heard a noise. We were at a totally deserted campsite pretty much in the middle nowhere, and that sound could have been anything. A wolf, a puma, maybe even a Sleestak! So my wife turned on a flashlight and shone it around. This, of course, spoiled everybody’s hard-won night vision. My dad snapped, “Thanks a lot. You just ruined astronomy.” I guess it didn’t occur to him that she valued her own safety more than the wonders of the celestial heavens. My dad’s utterance instantly became a classic quote, trotted out regularly to this day, pretty much whenever somebody notices the night sky.

I wish I could have seen my dad in his element, holding forth at a star party to people who actually cared about astronomy. Surely he was everybody’s hero in that exalted realm. His telescopes are amazing. One of them in particular seems to be a crazy hybrid of state-of-the-art commercial technology and his own carpentry. My dad actually worked on the Hubble Space Telescope, fixing this giant flaw that originally plagued it. He was the real deal when it came to aerospace and optical engineering. It’s really a crying shame that although my dad had four sons, not one of us appreciate astronomy enough to want this stuff, but that there’s what it is. Anyway, here are a couple of videos showcasing the amazing telescopes themselves along with the crazy menagerie of tripods that supported them. If you are serious about astronomy, give me a shout and claim one or more of these for yourself. (Note: if you’re thinking of using one of these to spy on your sexy neighbor, consider that you would probably end up getting a very good look at his or her pores.)

Now, scopes ain’t all that he had … all kinda crazy shit was in this baller’s pad. (I’m paraphrasing Ice-T because that’s what my dad would’ve wanted. No he wouldn’t.) He also had all manner of useful, high-tech instrument. Unfortunately I can’t identify much of anything. I know that he built a number of interferometers, unmatched perhaps by anything ever produced by anyone, but he apparently didn’t save any of those. Suffice to say, the instruments in his collection were all top-of-the-line, made in America because nobody else knew how to build this stuff. Check it out:

Hey, wait! The video did not capture everything. Here are yet more instruments:

Here is a close-up of the two grooviest instruments:

Wow, what a lot of cool stuff. Probably the Smithsonian museum would love this stuff. Or maybe the Antiques Roadshow would pay a mint. Or some mad scientist would give anything to get his or her hands on it (either to put it to good use, or to store it in his or her house for a decade or two). Anyway, it’s free to a good home.

And now for one more thing: the Hewlett Packard 85 computer. As described here, this bad boy cost $3,250 back in January of 1980, which equates to over $10,000 in today’s dollars.  It sported 8K of RAM. (To put that in perspective, a decent modern laptop has 1.5 million times as much memory.) The HP-85 was a good investment: my brother Bryan and I both learned how to program on that machine, and one of us makes his living writing code. (Hint: it’s not me.) Here’s a print ad for the HP-85, followed by a video:

Some years ago, my dad offered me the computer. I politely declined, because I just couldn’t imagine myself actually making the time to mess about with it, and my kids would be even less interested. So it would just sit around taking up space. Well, I should have accepted, because that would have made my dad happy, and now here I am storing the damn thing anyway. (In a storage locker, no less, because I couldn’t bring myself to take it to the Goodwill or a disposal center.)

So, if you want any of this stuff, let me know—and if you don’t, forward this post (via this link) to everybody you know, and hell, even a lot of people you don’t know, and have them all do the same. Threaten them with bad luck if they break the chain—whatever it takes to find the right home for all these things! You’d be doing me and my father’s memory a huge solid.

--~--~--~--~--~--~--~---~--

For a complete index of albertnet posts, click here.

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.