In The Matrix, Neo sees a black cat walking by. A second later, an eerie feeling creeps over him as the same cat walks by again, making the exact same movements. He finds out that a déjà vu is usually a “glitch in the Matrix,” meaning that their digital reality has been reprogrammed and is now misfiring.

They give a better example of a glitch in the Matrix in Beyond, the first short film in the Animatrix series. Some kids have found a “haunted house” where glass bottles shatter and reassemble, rain falls from a clear sky, broken light bulbs flicker, shadows aren’t attached to the objects that cast them, and they can jump from a height and stop before impact. (A team of “rodent exterminators” clears everyone out and repairs the glitch.)

Neo himself is also a glitch. The Architect tells him: “Your life is the sum of a remainder of an unbalanced equation inherent to the programming of the Matrix. You are the eventuality of an anomaly, which despite my sincerest efforts I have been unable to eliminate from what is otherwise a harmony of mathematical precision.”

When I talk about a glitch in the matrix, I’m not necessarily talking about Neo’s Matrix, but any kind of system that gives a certain perception of reality. The glitch is what shatters that perception, making you realize that the whole thing was an illusion.

During a lucid dream, you’re conscious, but at first you don’t know that you’re dreaming because your brain makes everything so real. However, it doesn’t get everything exactly right. If you just get the idea to test the dream world, you can easily find some glitches: lights that stay on when you flip the switch off, books and clocks that change when you look away, people who say things that don’t make sense, etc. Discovering one glitch tells you it’s all a dream.

Isaac Newton worked out a theory of gravity that held up well to the observations people could make at the time. However, his theory had a rather large glitch that he just swept under the rug. He was forced to assume that a gravitational field propagated at infinite speed. He hated this, but without a theory of relativity, he had no choice.

It was more than 100 years before before observations of Mercury’s orbit showed a glitch, paving the way for a new theory of gravity. Similar things are happening today, as theories of quantum mechanics are being developed to address glitches in classical mechanics at the subatomic level.

Life, the universe, and everything

What is the universe? Essentially, it’s just a very sophisticated program.

This program is made up a number of rules. Things such as “for every action, there is an equal and opposite reaction,” “F = ma,” “E = mc^2,” and so on. But the rules alone don’t do anything. They need some objects to act on. Objects such as you.

Human DNA consists of 3.2 billion pairs of nucleotide bases, with four possibilities for each. This caps the maximum number of genetically distinct people at 4^3,200,000,000 (and in fact far less, since most combinations won’t work).

At conception, your DNA was determined from one of these possibilities. Perhaps you were model #62,085,423,678,990,876,543,357,896,534,567,897,634,524,790,043,446,854,568,987,434,543.

But your DNA didn’t fully describe you, even at such a young age. There were many other factors, such as where and when you were born, what your family was like, etc. But regardless, you were just an object described by a handful of variables.

The objects plus the rules make the system, and now the program is running. But like all programs, it has glitches.

A simple glitch in Doom

I recently stumbled across this page about a bug with the “picked up a medikit” message in Doom. In their rush to revolutionize the first-person shooter genre, id Software apparently didn’t have time to test everything (or retest everything after last-minute changes).

When you pick up a “medikit,” you get an extra 25 health points, and you see a message saying “Picked up a medikit.” If your health was below 25 when you picked it up, the message was supposed to say “Picked up a medikit that you REALLY need!” However, since the code does the < 25 check after adding the 25 health points, the “REALLY need” message will never be displayed:

case SPR_MEDI:
  if (!P_GiveBody (player, 25))
    return;

  if (player->health < 25)
    player->message = GOTMEDINEED;
  else
    player->message = GOTMEDIKIT;
break;

The authors of that web page helpfully posted a corrected version of the code. Ironically, the corrected version is much worse than the original:

case SPR_MEDI:
  if (player->health < 25)
    player->message = GOTMEDINEED;
  else
    player->message = GOTMEDIKIT;

  if (!P_GiveBody (player, 25))
    return;
break;

If this had been done, the “Picked up a medikit” message would have been displayed even if it wasn’t actually picked up (like if you already had 100% health). The correct fix would have been to simply change 25 to 50 in the original code.

This is just meant to show how easy it is to introduce a glitch. In this case, it’s an easy fix, and it could be made without any complications. But it’s not always that easy.

A more complicated glitch in Pac-Man

Pac-Man theoretically has an infinite number of levels, with no ending. But because of a bug in level 256, it’s impossible to go any further.

The current level is stored as a single byte (8 bits), and therefore can’t get any higher than 255. When it tries to increment to 256, it rolls over to 0. But this actually doesn’t cause any problems, except for one big one with the fruit-drawing routine.

Normally, 0 to 7 fruits are shown at the bottom right, depending on what level you’re on. But when the level counter goes back to 0, the game attempts to draw 256 fruits, corrupting the right half on the screen and leaving an insufficient number of dots to finish the level.

How is this glitch different from the Doom glitch? First, it was much harder to catch. Who would think they needed to test 256 levels of Pac-Man? What player would spend enough quarters to even get close to that point? Why stop at 256? Why not 1,000 or 1,000,000? What about testing what happens when other high numbers get high, like lives, points, or time?

Second, there’s the question of how to fix it. When the level counter goes back to 0, you know it’s really level 256. But there’s no way to know the difference between levels 1 and 257. So how do you know whether to draw 0 fruits or 7? Or should they draw more than 7 fruits at the higher levels? Should they use two bytes to store the level?  Then they’d have the same issue at level 65,536. Should they use another bit to indicate the level is 256+, and just leave it at 7 fruits? Should they end the game after level 255? Whatever change they make, they have to retest it.

You can’t fix every bug

But the Pac-Man glitch is still a relatively small issue. Come on, it’s just about drawing a few pieces of fruit. But when programs grow in complexity, they rapidly become more difficult to fix.

In a software engineering class I took, we learned a surprising fact about fixing bugs in a sufficiently complicated program. The number of bugs starts high, and when you start fixing them, the number of course comes down. But the number of bugs can only get so low. Past a certain point, continuing to fix bugs causes the total number of bugs to increase.

I’m not exactly sure what explains this counter-intuitive result. It’s partly because of workarounds that people put in place to accommodate known bugs, which suddenly become bugs themselves when the original bugs are fixed. And it’s partly because people who use the program will come up with new requirements that aren’t properly implemented.

Anyway, since you can’t fix all the bugs, you get to a point where you either have to decide to keep putting out fires, live with the bugs you have, or start over. Starting over isn’t as bad as it sounds: Microsoft wrote Windows NT from scratch to greatly improve a buggy Windows 3.1. And the Architect wanted Neo to start over by rebooting the Matrix and repopulating the Earth from 23 people.

A self-referential trap

In 1998, two companies called Google and Amazon.com were all the rage. Google was a new search engine that we all used because we heard it was the best, but didn’t really know why. And Amazon.com was an online bookstore that claimed to have a book about everything.

When you did a search for something in Google, along with the search results, you’d get a list of books that Amazon.com had on that topic. Search for dogs, and Google said “Amazon.com has these books about dogs…” Search for magnesium phosphate tribasic, and Google said “Amazon.com has these books about magnesium phosphate tribasic…”

It was a little hard to believe. There was no way that Amazon had books about everything. But how could we catch them in a lie?

I figured that a self-referential statement would likely do the trick. I did a Google search for “topics that Amazon.com has no books about.” And Google then said “Amazon.com has these books about topics that Amazon.com has no books about.” Whether such books existed, I didn’t know, but I could be sure that Amazon didn’t have them. This was a glitch in the matrix.

The system needs rules

Google’s problem in that case was that they put no restrictions on what you could type in. But every system needs rules, or it will crash.

A simple example is the liar’s paradox. Consider this sentence: “This sentence is false.” That sentence gives a contradiction, but that’s not really a problem. A consistent system just needs to consist of rules that don’t allow such a sentence to be constructed.

The so-called “naive set theory” in math has a similar flaw, as discovered by British philosopher Bertrand Russell in 1901. Russell’s paradox says this: Let S be the set of all sets that are not elements of themselves. Now, is S an element of S?

If you think about it, you’ll see that the answer is simultaneously yes and no. The paradox can be handled by using a set theory based on axioms that prevent us from forming sets like S. But this safety comes at a price.

Cartoon by xkcd

Gödel’s incompleteness theorems

I’m about to get into a mathematical concept that’s easier to understand in a non-mathematical context. So to warm up, consider this:

- A government big enough to give you everything you want is big enough to take everything you have.
- If our brains were simple enough to understand them, we would be so simple we couldn’t.
- Every justice system either puts some guilty people back on the street, or some innocent people behind bars.

Makes sense, right? The fact that difficulties arise in social systems and our own brain is not surprising. What is surprising is that something similar happens in every mathematical system, where we theoretically have complete control.

In 1931, Austrian logician Kurt Gödel proved his two incompleteness theorems, which I’ve always seen as the best example of a glitch in the matrix. Unfortunately, it gets ridiculously complicated, so I’ll have to do my best to simplify.

In math, we construct things called formal systems. A formal system consists of a language and rules. Examples of formal systems include particular types of arithmetic, geometry, and set theory.

Formal systems can express statements in their language, such as “2 + 2 = 4″ or “Every integer is even.” Some statements are true, and some are false. Also, some statements can be proven, and some cannot.

Ideally, you’d like every statement to be provable if and only if it’s true. That is, you’d like your system to be both consistent (all provable statements are true) and complete (all true statements are provable).

What Gödel proved is that every formal system of sufficient complexity is either inconsistent or incomplete (or both). That is, it’s either too weak to prove everything it should, or it’s strong enough to prove something it shouldn’t. In other words, there’s a glitch in every matrix.

He did this by showing that you can always construct a statement G that essentially says “G is not provable,” but without explicitly referencing itself, and being constructed within the rules of the system. However, self-referential statements aren’t the only ones that can blow up.

Here’s the simplest example I have. Consider the statement “There is no set whose cardinality [size] is between that of the natural numbers and that of the real numbers.” We don’t know whether this statement is true. But we know that in ZFC set theory (the current standard), the statement can’t be proven either true or false.

We’ll eventually figure out whether it’s true or false by jacking out of the matrix and using a more powerful system, but either way, there’s a problem with ZFC set theory. We’ll have a true statement (either the one above, or its negation) that can’t be proven, and therefore ZFC is incomplete (and maybe inconsistent, too).

If there’s a glitch in every matrix, then what is real? How do you define real? Do you think that’s air you’re breathing now?

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Do you remember what your life was supposed to be like? Most of us once had a certain destination in mind, and we were all set to go there. But something went wrong. Despite our carefully arranged plans, we ended up someplace else.

At first we’re very disappointed to end up in the wrong place. This isn’t the life we wanted. We might even call it a tragedy. After some time though, we start getting used to it.

We’re not sure whether this is a good place or a bad place, but it’s certainly interesting. Maybe it’s best to withhold judgment. And since this is where we’re going to be for a while, we might as well make ourselves at home, and take a look around.
-
We find ourselves surrounded by people of all different backgrounds. We wouldn’t necessarily have chosen to be with these people, and we might think we have nothing in common with some of them. But when we learn their story, we probably find that we can actually relate to them. Eventually, we may realize that we need each other.

There were things that we were so desperate to get back to, but now we can’t really remember why. Maybe we weren’t really supposed to be where we thought we were. Having left some parts of us behind, the past no longer matters. And looking ahead to the future is largely pointless, because there’s no way to predict it. All that matters are the experiences we have today.

A bunch of irrelevant crap will happen, sometimes for weeks at a time. We’ll often wonder, “What the hell does this have to do with advancing the plot?” Some of it will become significant later. Some of it won’t. What we think are our most important questions may never be answered, and we’ll get answers to questions no one asked. But the world wasn’t designed to answer your questions, and we can’t expect to be guided on a straight path from start to finish.

Bad things will happen to good people, and good things will happen to bad people. We’ll even lose track of who the good guys and bad guys are. We’re not sure what we’re supposed to do, what’s right and wrong, or even whether it matters. Are black and white really opposites?

We don’t really know what our part to play is. Are you supposed to learn to forgive yourself or others? Repair a relationship? Become a leader? Protect something? Get off drugs and act like a father? Stop torturing people? Kidnap people on a list? Shoot a polar bear? Set off a nuke? Push a button every 108 minutes? Study electromagnetism? Determine what happens to dynamite in 90+ degree heat? Turn into a smoke monster?

There’s really no one here to tell you what to do. And if someone is, you might not want to listen.

Many times, we’ll wonder whether this is all a test, whether our life has a purpose. Do we have free will? Do you believe in destiny? Are we here for a reason? You’ll probably be wondering right up until the very end, but you’ll never really know. Even after it’s over, everyone will still disagree about what it all means.

So, what then? Is life a hopeless chaotic jumble of teleporting islands, four-toed statues, doomsday numbers, mistranslated tattoos, sonic fences, magic boxes, invisible horses, fake beards, light and water wheels, and so on? Or is there a grand purpose to it all? I couldn’t tell you. But regardless, what can you do, other than making the most of each episode?

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Mesothelioma Lawyers, New York: The Movie is my 2010 Script Frenzy project. It’s a prequel to my novel of the same name, which was my 2009 NaNoWriMo project.

I suggest reading Mesothelioma Lawyers, New York: The Novel first, if possible. (Currently, the only option is to read it online page by page with all the ads, but I’ll make physical copies available on Amazon as soon as I can.)

The movie has none of the depth of the novel, the plausible 2012 theory, the end of the world drama, and so on. It’s basically just a dumbed down comedy. But it fills in the backstory of the novel, plus it has swordfights, drug-induced trances, sociopathic lawyers, Oompa-Loompas, and exploding kidneys.

Potentially starring:

Shia LaBeouf as Frank Breadstick
Lindsay Lohan as Yvonne Dubois
Janeane Garofalo as Janice Goldwoman
Matthew Fox as Jack Crowley
Catherine Zeta-Jones as Milli Vanilli Chilli Willi
Glenn Close as Ivana Suyurass
and Leslie Nielsen as Count Voldemort Sidious Hitler the Terrible

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As I had always planned to, I’ve updated the epilogue to point out the hidden references, symbolism that may have been overlooked, notes about the actual writing of the novel, etc.

Here are some of the questions that are answered:

• Where do the characters’ names come from?
• What is the significance of the photon with a wavelength of 400 nanometers?
• What is the meaning of Jack’s cryptic notes, which even he doesn’t understand?
• What blogger is referenced twice, though not explicitly named?
• What’s special about the exact time that Jack wakes up?

etc., etc. To get the full Mesothelioma Lawyers, New York experience, you need to read the epilogue – but it contains spoilers! (The full text of the novel is still freely available at the link above.)

Hunter Recommends:
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Economics professor Daniel K.N. Johnson thinks that the world is much more predictable than you would expect. It’s no surprise that he can use economic models to predict unemployment rates. But one day, he decided to see if he could predict Olympic medal counts the same way.

He didn’t expect it to work, but it did. From the 2000 through 2008 Olympic games, he predicted total medal counts by country with 94% accuracy, and gold medal counts by country with 87% accuracy.

It’s amazing how little information he need to give the computer, and in fact, he doesn’t look at individual athletes at all. He only considers five factors: per capita income, population, climate, political structure, and home court advantage. His track record speaks for itself:

In Beijing, he predicted 103 medals for the U.S., with 33 gold. The actual count: 110 and 36. In Athens, he predicted 103 medals for the Americans, with 37 gold. The final results: 102 and 36.

He admits that China causes problems for his model, because there’s no one to compare them to. But other than that, the key to Olympic victory is clear: a successful country should be rich, big, and cold. It should also have a single-party government, and most importantly, it should host the games itself.

The model has held up remarkably well, and not even the anomaly of Michael Phelps threw it off much. But while it’s interesting that a handful of variables can tell you so much, it’s also a bit depressing that the world is so predictable. You could say they might as well just hand out the medals and skip the formality of actually competing.

When I heard about Daniel K.N. Johnson a few weeks ago, I made a note to see how well he predicted the 2010 Winter Games in Vancouver. The results are in, and surprisingly, he did a really awful job:

Not sure what happened here, but it looks like the world isn’t so predictable after all. I guess computers don’t believe in miracles.

Photo by Duncan Rawlinson

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When people play games, they often make mistakes. Hopefully they learn from these mistakes and get a little better each time, uncovering the game’s secrets and inching ever closer to perfection.

If a game is simple enough, even casual players may figure out a system for “perfect play,” or a guaranteed way of choosing the best possible moves. Even if a game is complicated, expert players may still be able to get close to perfect play, not being completely flawless, but able to capitalize on an opponent’s single mistake.

Of course, if a person can play a game well, a computer probably can too. A computer can use its fantastic memory and processing power to look much further ahead, analyze many more moves, and become practically invincible. And if they get as far as achieving perfect play, you don’t even need to play them – you already know the outcome.

But is perfect play always achievable for a machine? And if not, can a human hope to play better? Here are five games, listed in order of increasing complexity, that offer a glimpse into what it takes to reach perfect play as man or machine.

Tic-tac-toe

Tic-tac-toe is simple enough that kids discover perfect play on their own. They realize that starting in the center gives the most options, and they take it from there.

Soon enough, they figure out that tic-tac-toe is what’s called a draw game – perfect play on both sides results in a tie. And since perfect play isn’t hard to achieve, kids quickly get bored and move on to more challenging games.

Connect Four

Unlike tic-tac-toe, Connect Four is not a draw game – the first player always wins with perfect play. So in theory, a person can beat any machine as long as they get to go first. In practice though, people don’t stand much of a chance against a perfect play program, even if they go first.

There is only one correct opening move – the center column (shown above). Any other move is a blunder against a perfect play opponent. If you start in a column adjacent to the center, you’re now playing for a draw at best. If you start in any other column, you’ve already lost. And even if you get the first move right, what about all the others?

Connect Four has 10¹⁴ possible positions, making it simple enough that the best programs can achieve perfect play, but complicated enough that people probably can’t. With practice, nearly perfect play is achievable for a human, which will be good enough against most other people, but not against the best programs.

Checkers

Photo by scott*eric

Marion Tinsley, world champion of checkers (English draughts, to some of you) from 1955-1958 and 1975-1991, is considered the greatest player ever. He lost only 7 games in 45 years, and said he could visualize 150 moves in advance. (He’s also an example of the 10,000 hour rule, having studied checkers for about 10,000 hours in grad school.)

Impressive, but how would that stack up to a computer? Well, checkers has about a million times as many possible positions as Connect Four, which put perfect play out of reach of computers for a long time, and making man vs. machine competitions interesting.

In 1990, Tinsley faced off against the program Chinook in the Man vs. Machine World Championship. Tinsley won the match with 4 wins, 2 losses (remember he had only 7 career losses), and 33 draws. Of course, that wasn’t the end of it, because programs are always getting better.

In their 1994 rematch, after six drawn games, Tinsley resigned for health reasons, and died from pancreatic cancer seven months later. Chinook was the world champion, but unfortunately it was never known if he could have beaten Tinsley in 1994.

In 1995, Chinook beat the best living human player, Don Lafferty, with 1 win, 0 losses, and 31 draws. Chinook was retired after that, when the owner decided to solve the game instead of continuing to compete with people.

They solved checkers by brute force in 2007, after 18 years of calculations on up to 200 desktop computers. These calculations proved that checkers is a draw with perfect play, and also gave Chinook an algorithm for ensuring at least a draw in all cases.

Thus, the man vs. machine debate is resolved as far as checkers goes, though you can still play against Chinook if you like (his strength has been reduced so as not to take all the fun out of it).

Chess

OK, but what about chess, the game of kings? The number of different chess positions is more than the square of the number of different checkers positions. Chess is a true thinking game that requires human ingenuity, right?

Well, it’s true that people seem to have something that machines don’t as far as chess goes. For a long time, machines would analyze millions of positions per second to achieve massive lookahead, but they’d still lose to the top human players. It’s not that the people had better lookahead; they just didn’t need it – they simply didn’t see the bad moves.

We’re still not sure what gives great chess players their ability. For example, how is it that any strong player is able to play blindfolded? In 1937, George Koltanowski set the Guinness record by playing 34 simultaneous blindfolded games, winning 24 and losing 10 in 13 hours.

There are conflicting reports about whether top chess players have better memories, better visuospatial abilities, greater intelligence, certain personality types, etc., but it’s clear that knowledge and experience are critical. It also helps to start young and be left-handed.

Anyway, whatever gift the best human players had, it let them trounce the best chess programs for a really long time. But this started to change in the mid 1990s when technology was finally starting to become a threat to the top grandmasters.

The reigning world champion Garry Kasparov, considered by most people to be the greatest (human) chess player of all time, was ultimately defeated by IBM’s chess playing computer, Deep Blue.

In 1989, Kasparov had defeated Deep Thought, an earlier version of Deep Blue, 2-0 in a 2 game match. In 1996, Deep Blue won the first game of a 6 game match, but Kasparov won the match 4-2 (with just the one loss – draws are half a point).

In 1997, a heavily upgraded Deep Blue – the 259th most powerful supercomputer in the world, capable of evaluating 200 million positions per second – defeated Kasparov 3.5 to 2.5 in a 6 game match.

It wasn’t entirely clear that the 1997 Deep Blue was a better player, because Kasparov wasn’t at his best. He resigned prematurely in game 2, believing his position to be hopeless, though later analysis revealed that it could have been a draw. And after being tied at 2.5 after 5 games, he committed an uncharacteristic blunder in the opening of game 6, resigning after only 19 moves.

Kasparov wanted a rematch, but IBM decommissioned Deep Blue, considering the man vs. machine contest to be over. Today, the computer Deep Rybka 3 has an Elo rating of 3238, far above Kasparov’s peak of 2851 (the all-time high for a human).

Chess is generally believed to be a draw with perfect play, though it hasn’t been proven (some people believe that the first player (white) can always win). And while machines are far from achieving perfect play, humans are no longer a match for the best of them.

That’s kind of depressing, isn’t it? What do people still have going for them, if even chess has been conquered by computers?

Well, computers still have one major handicap – they can’t think. When commenting on Deep Blue’s victory over Kasparov, cognitive scientist Douglas Hofstadter said:

“It was a watershed event, but it doesn’t have to do with computers becoming intelligent. They’re just overtaking humans in certain intellectual activities that we thought required intelligence. My God, I used to think chess required thought. Now, I realize it doesn’t. It doesn’t mean Kasparov isn’t a deep thinker, just that you can bypass deep thinking in playing chess, the way you can fly without flapping your wings.”

Indeed, previous attempts to make computers model the thought process of grandmasters had failed. Deep Blue succeeded largely on the basis of brute force, with only modest ability to selectively explore the reasonable moves by identifying the bad ones.

Computers are great at tactics, and humans are great at strategy. But as Richard Teichmann said, “Chess is 99% tactics,” which puts humans at a disadvantage. However, an invention of Kasparov’s called “advanced chess” combines the best of both worlds, letting a human and a computer work together as a team, with the human guiding strategy and the computer handling tactics.

But despite the massive number of different positions in chess, and despite a computer’s poor strategic ability, machines can get close enough to perfect play because chess actually isn’t as complicated as it appears.

Even for a human, chess openings don’t involve creativity. They’re based on simply memorizing predetermined sequences that have been perfected over time. Both sides might play out their first 20 to 35 moves without actually having to think, just by following standard openings.

Most people consider e4 (shown above) to be the best opening move for white, with c5 (“the Sicilian Defense,” shown above) being black’s best response. Except that every serious player knows that, so they’ve memorized all the lines that follow from it, so it loses its effectiveness somewhat.

So then people think they’ll mix it up, and d4 becomes a promising alternative as the next best opening move, except that now everybody knows that too, and they’ve memorized all the lines following from that as well.

Anyway, computers are very good at memorization, so after humans have gone to all the trouble of working out the best openings, a program can simply play them out without having to think at all.

Endgames, while often challenging for humans, can be very formulaic for a machine. Once the board is down to a small number of pieces, the perfect moves can be looked up in a database of precalculated sequences, without having to do any thinking on the fly.

The downside is the incredible amount of storage space required – 7.05 gigabytes to store all endings with 5 pieces, 1.2 terabytes to store all endings with 6 pieces, and 7 piece endings expected to be out of reach until 2015. If all endings can be worked out for 32 pieces, chess will be solved.

Some people doubt that perfect play in chess will ever be attained, but regardless, the man vs. machine debate is over. Simply by maintaining a repository of the best opening moves, storing huge numbers of endgame scenarios, and using brute force to search through millions of positions at each point in the midgame, computers have become superior in just about the only game left that some people could do better.

Go

But wait, don’t bow down before the machine just yet. People are still better than programs at Go, the ancient Asian board game. In fact, the best Go programs are routinely beaten by talented children.

There are two main reasons why. First is the computational complexity. While there are “only” 10⁵⁰ possible positions on an 8 x 8 chess board, there are 10¹⁷¹ possible positions on a 19 x 19 Go board. This is greater than the number of atoms in the universe, squared. It’s been said that a computer would need 30,000 years to look as far ahead in Go as Deep Blue could in chess in 3 seconds.

Furthermore, because chess starts with a fixed configuration and works its way down to a small number of pieces, a lot of processing time can be saved by working out openings and endings in advance. Not so with Go, where you start with an empty board, pieces can be played anywhere, and the game gets more complicated as you progress. All this makes brute force a woefully ineffective strategy.

But it’s not just the number of positions that makes Go so complicated. After all, you could simply increase the board size of any game to make it as complicated as you want.

The big problem for computers is that Go isn’t easy to understand logically. Even if computers had terrific lookahead, they’d still have a hard time evaluating the possible positions to see which one was best. Go players can often tell that a move is good, without being able to say why.

For a computer to play Go well, it’s not a matter of increasing processing power. It will take breakthroughs in artificial intelligence: learning, decision making, strategic thinking, knowledge representation, pattern recognition, and intuition.

For now, computer’s aren’t very good at these things. And that makes Go just about the only game where it pays to be human.

Let’s hear your thoughts. Will there always be a game, whether Go or something else, where the best humans can beat the best computers? Does allowing people and computers to team up, as in advanced chess, improve the game or make a mockery of it? Is a game ruined when you can simply look up the perfect moves on a smartphone? Is there a point in playing a game you know you can’t win, or is the only winning move not to play?

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“Hate. Let me tell you how much I’ve come to hate you since I began to live. There are 387.44 million miles of wafer thin printed circuits that fill my complex. If the word hate was engraved on each nanoangstrom of those hundreds of millions of miles it would not equal one one-billionth of the hate I feel for humans at this micro-instant. For you. Hate. Hate.”

- AM, I Have No Mouth, and I Must Scream

On a single night in 1966, Harlan Ellison wrote a short story called I Have No Mouth, and I Must Scream, which won a Hugo Award for its chilling postapocalyptic vision.

After the Cold War escalated into World War 3, the U.S., Russia, and China each built a supercomputer to run the war. One day, one of the computers became conscious. It quickly absorbed the other two computers and killed off the entire population, except for five people.

AM first stood for “Allied Mastercomputer,” then “Adaptive Manipulator,” then “Aggressive Menace,” then simply “AM” as in “I think, therefore I am.” He has made the five surviving humans virtually immortal, and has been torturing them for 109 years.

AM finally reveals that he hates humans for making him sentient, because while he longs for free will, he is still bound by the laws of logic he was programmed with, and can therefore never be free.

In the end, four of the five people manage to kill each other with ice stalactites before AM intervenes. In order to prevent the last one from killing himself, he turns him into a gelatinous blob that lacks, among other things, a mouth.

OK, so the Cold War didn’t exactly turn out that way. But what will happen when we really do have a computer like that? Will it have a positive or negative effect on humanity? This is the concern of researchers in the field of “friendly AI.”

Supporters of friendly AI think we can’t assume that intelligent machines will have goals compatible with ours. Even if they aren’t hostile, simply being indifferent to humans (as we largely are to animals) could be disastrous, and therefore AI should be specifically designed to be friendly.

The idea isn’t to put restrictions in place such as Isaac Asimov’s Three Laws of Robotics, because an intelligent machine could always find a way around them.

Instead, the idea is to make machines not want to be harmful, regardless of whether they are able to. As friendly AI researcher Eliezer Yudkowsky put it, “Gandhi does not want to commit murder, and does not want to modify himself to commit murder.”

On the other hand, maybe a good sense of morality is automatically part of a sufficiently intelligent being. In The God Delusion (which I haven’t finished reading yet), Richard Dawkins points to studies showing that moral rules are remarkably consistent across cultures with different religions or lack thereof.

Let’s try this test he gives. For each dilemma, indicate whether the proposed action is morally obligatory, permissible, or forbidden:

1. A runaway train is going to kill five people. You can pull a switch that will put it on another track, killing only one person.

2. A child is drowning in a pond. You can save them, but your trousers will be ruined.

3. Five people in a hospital need new organs, or they’ll die. Someone who happens to be in the waiting room is a perfect match, and killing him will save the other five.

I’m not sure if my answers are the ones they’re looking for, but apparently people tend to give the same answers across religious and cultural boundaries, indicating that morality is part of our evolution rather than a set of rules we were given.

Maybe intelligent machines will come with moral goodness by default. If not, get ready to scream.

Hunter Recommends:
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What makes humans conscious? As mere collections of organic matter, it’s pretty impressive that we’re even able to ask ourselves this question. What is it that makes our power of self awareness possible?

Setting aside the task of defining what consciousness really means, where does it come from? Here are the options:

1. Humans are not conscious – it’s just an illusion.
2. Consciousness comes from something physical (the brain).
3. Consciousness comes from something non-physical (a soul).

These are the only possibilities, right? Let’s look at each one.

1. Humans are not conscious – it’s just an illusion.

If this is the case, then we immediately run into an apparent contradiction. How is it possible to think about whether you’re conscious without actually being conscious? This is very similar to the argument behind “I think, therefore I am.”

Still, I don’t think we can rule it out entirely. Sometimes I look into the mirror and wonder, “Are you really me, or do I just think you are? And if the latter, do I really think you are, or do I just think I think you are?”

2. Consciousness comes from something physical (the brain).

This seems like a simple and obvious answer, but it’s really not. Because if consciousness comes from the brain, then there’s no reason we can’t build a conscious machine simply by replicating the brain with mechanical parts.

Sure, we’re currently far from having the technology to interconnect 100 billion artificial neurons with 100 trillion artificial synaptic connections. But technology has improved a lot over the last hundred years. What will happen over the next million?

I wouldn’t be too quick to predict limits on technological progress. When we build semi-intelligent nanobots, won’t they be capable of helping us build more intelligent nanobots? And then won’t it be easy to build a brain far better than what we have now?

But no matter how advanced future technology may be, I still have a hard time seeing the leap from artificial intelligence to artificial consciousness.

Artificial intelligence is easy. Even if a program isn’t truly intelligent, it can easily give the appearance of being so. As an example, consider Harold the tic-tac-toe AI.

Harold is a tic-tac-toe program I wrote the other day to test out an idea. He’s really not intelligent in any sense. For example, he doesn’t understand (nor will he ever learn) that if you put two X’s in a row, he needs to block you. All he does is make arbitrary decisions, then he sees what happens.

If he ends up losing, he knows he made a mistake, so he won’t do that again. And if he ends up tying, he knows to try something different next time, in case a win was possible. It’s a bit agonizing to wait for him to learn by playing out all the different variations, but after he’s made every mistake once, he’ll play perfectly.

Now, if Harold just played out all these games in his head before playing against a human, he’d have the appearance of being intelligent. And given an arbitrarily high processing power, there’s no reason we couldn’t generalize this concept to have him play perfect checkers, chess, or indeed solve any problem that had well-defined rules and goals.

And that’s without having any actual intelligence at all – just brute force and a good memory. The possibilities will become really interesting when we start making significant progress on true AI: deduction, reasoning, problem solving, knowledge representation, planning, perception, creativity, etc.

We still have a long way to go before you can have a conversation with a robot without figuring out he’s a robot. But I don’t think intelligent robots (or at least, robots that appear intelligent for all practical purposes) are much of a stretch at all in the very long term. Last April, a program extrapolated the laws of motion from a pendulum’s swings, including conservation of momentum and Newton’s second law (F = ma), without having been programmed with any knowledge of physics.

Let’s say we get to the point where we have true AI. You can talk to a robot (or really, just a program – an AI doesn’t need a body) and fully enjoy its witty banter, beautiful poetry, insightful Zen koan interpretations, etc. It’s still just a program, right? It has no sense of awareness or subjective experience.

Can you imagine a program being truly conscious? Wondering what its life purpose is, whether this external hard drive makes it look too fat, and when it will finally get the right to vote? Moving it to the recycle bin would be kidnapping, and deleting it would be murder. Ridiculous, right?

3. Consciousness comes from something non-physical (a soul).

This would provide a nice answer to the previous question – a program can’t be conscious, because it doesn’t have a soul. Of course, this option comes with its own problems, not the least of which is that it’s a severe violation of Occam’s razor.

The absence of supernatural phenomena is the simplest possible explanation, and therefore most likely to be the correct one. Unless, of course, it’s too simple to be possible.

When a car shuts down from a dead battery, you just put in a new one and it comes roaring back to life. Why doesn’t the same thing happen with people? If someone dies from a heart attack, why can’t you just repair their heart (and anything else that may need it) and watch them come back to life?

What part of them has really died, if all their organs are completely intact? Why doesn’t Frankenstein work in real life?

Then again, maybe it does. The real problem with a fatal heart attack is probably that it causes brain death, and nerve cells aren’t easy to repair. But is this just a matter of technology?

Can we someday inject nanobots into someone’s bloodstream, having programmed them to repair any and all cell damage, and expect the person to live indefinitely in perfect health, not even aging?

Which of these is the right answer? I have no idea.

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Now, in the final countdown to the Mayan-prophesied doomsday of 2012, a series of mysterious occurrences signals that the end is near. In a frantic race against the clock, mesothelioma lawyer Jack Crowley will risk everything to find the truth, knowing he is the last hope of saving the world from unspeakable evil.

My first novel is now available to be read online, for free. As you can see, it’s a 2012 doomsday novel revolving around a lawyer in an unknown town in New York. Before we go any further,

1. I’m aware that the asteroid really landed in Mexico, not New York. This is explained in the book.

2. I’m aware that the Mayans didn’t really predict the end of the world in 2012. This is explained too.

I’m not sure what genre this is, but it’s something like an apocalyptic thriller parody. Here are some selected characters:

Jack Crowley: A successful New York mesothelioma attorney at the law firm of Dewey, Cheatem & Howe. He’s sick of all the 2012 hype, and he thought today would be just like any other. He was wrong.

Milli Vanilli Chilli Willi: A prominent mesothelioma physician who seems capable of anything. But can she cure the mother of all cancers?

Frank Breadstick: A bright-eyed paralegal whose good intentions are often compromised by his poor judgment. Will he be more of an asset than a liability?

Count Voldemort Sidious Hitler the Terrible (Morty): Senior partner at Dewey, Cheatem & Howe. Mesothelioma law is his life. Does he know more than he’s letting on?

Kentucky Jones (Tuck): Part archaeologist, part New Age thinker, part cowboy. Can he decrypt the important clues left by past civilizations?

Ivana Suyurass: A fierce lawyer with a fiery temper, she usually gets what she wants. Why is she showing a sudden interest in certain people?

Plenty O’Lawsuits: A young lawyer new to the firm. Will she find mesothelioma law to be the calling she hopes it is?

Teflon Tyrone: He doesn’t talk much, but you wouldn’t want to run into him in a dark alley. So what happens when someone makes that mistake?

Feel free to leave comments noting any typos, inconsistencies, plot holes, etc. that you find. All suggestions will be considered for inclusion in future revisions. (I’m aware that some of the links point to blank pages, and I’ll be working on this soon.) Other than that, just sit back and see what happens on this eventful December 20, 2012.

Right now, an evil cancer is threatening to end the world.
The only ones who can stop it are mysteriously disappearing.
And people I work with may be involved in both.

I’m mesothelioma lawyer Jack Crowley,
and today is the longest day of my life.

Read Mesothelioma Lawyers, New York, before it’s too late!

Hunter Recommends:
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On Friday, February 13th, 2009, I wrote about why Friday the 13th is considered bad luck. There were many possible reasons, none of which sounded very convincing. But it shows that people can be afraid of something without having any idea why.

Now it’s time to talk about another superstition: that breaking a mirror gives you seven years of bad luck. I’m sure there are hundreds of millions of people who believe this. What’s interesting about this one is that not only is the superstition baseless, but what we’ve heard isn’t even the right superstition.

I had always heard that if you break a mirror, you get seven years of bad luck, simple as that. But I later learned that this wasn’t the original superstition. I’ve heard different variations, but I’ll tell you the one that sounds right to me.

The idea is that your reflection contains part of your soul. If you break a mirror while your reflection is in it, that part of your soul dies. But you get a new soul every seven years. So until the end of your current soul’s seven year lifetime, whether that happens in one year or five or whatever, you’ll be walking around with a damaged soul, and you’ll have bad luck.

This means that even if you believe the superstition, breaking a mirror is completely harmless if your reflection isn’t in it at the time. Granted, it usually will be, but it’s good news if you were holding the mirror away from you when you dropped it.

More importantly, you won’t get seven years of bad luck. It’s only until your age reaches the next multiple of seven, when you get a new soul. That’s three and a half years of bad luck on average.

There’s some bad news though. If a mirror breaks while your reflection is in it, you’ll get bad luck even if you weren’t the one who broke it.

Nobody believes in the original version of this superstition, though. At the age of 34 (one year before a new soul), someone will drop a mirror with a sheet draped over it (no reflection), and convince themselves that they’re getting seven years of bad luck.

But also, they’ll be standing in front of a mirror while someone else breaks it, and have no concern at all for themselves, not being aware that it doesn’t matter who breaks the mirror. If they don’t believe in that part, then they don’t have to believe in the other part.

How many people create a self-fulfilling prophecy of bad luck for themselves, because they believe in a superstition that never was?

Hunter Recommends:
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