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.)

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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.

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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!

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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?

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Yes, this guy looks really creepy. But don’t judge him because he’s different. Perhaps we just don’t know beauty when we see it.

Notice the unmistakable look of utter serenity on his face. His left and right sides are brought together in perfect harmony, forming a new being that knows no limits, united with the oneness of the universe.

OK, that’s about all I can say with a straight face. Anyway…

After my initial post on mixed handedness got some attention, I began a deep exploration into the mysteries of handedness: left, right, and especially mixed. I wasn’t just looking for trivia; I wanted to know how we can use both sides of our brain and body to our best advantage.

Seven months later, that search now culminates with the release of my new ebook, which comes in two versions:

Ambidextrous: A Right-Hander’s Guide to Developing Two Dominant Hands
Ambisinistrous: A Left-Hander’s Guide to Developing Two Dominant Hands

The word “ambidextrous” sounds balanced but it’s actually a very biased term, being Latin for “right on both sides.” Its counterpart, “ambisinistrous,” is Latin for “left on both sides.”

I’m using these terms to refer to a right-hander or left-hander who has made their weak hand into a strong hand. These people are more formally called Penwald ambidextrous, and I’m now one of them.

Why two versions of the ebook? Because I’m so sick of books that explain how to do something right-handed, and expect left-handers to just reverse everything! They deserve something specifically for them.

Also because these are really two different audiences. For example, left-handers and right-handers learned to write differently and they have different motivations for writing with the other hand, so you can’t treat them exactly the same.

(To keep things simple, both versions are included together in one download for one price. Read the version for you, and ignore the other.)

After a lot of research and personal experimentation, I’ve learned that:

1. You can change your natural handedness. I learned to do many things with the other hand, and even went from -0.2 (mildly left-handed) to +0.2 (mildly right-handed) on the Edinburgh Handedness Inventory (whose scale goes from -1.0 to +1.0).

2. There are cases where it’s very practical and beneficial to switch hands, but also cases where it’s not. That’s why I explain how to know when it’s worth it, and also how to practice efficiently and effectively.

3. Two hands are way better than one.

If you want to boost your physical prowess, balance your brain and body, and awaken your sleeping half, check out the right-handed sales page or the left-handed sales page. Any questions? Ask away.

Photo by striatic.

Hunter Recommends:
Greatness Without Genies: The Law of Attraction for Realists
How to Be Rich And Happy: Whatever You Want, Whenever You Want
How to Finally Find What You Love to Do And Get Paid For Doing It

“What resemblance more perfect than that between the hands, and yet what a striking difference there is!”

- Michael Herz

The release of my new ebook on ambidexterity is just around the corner. More on that later, but I thought this would be a good time to answer some of the questions I’ve received about being ambidextrous or mixed-handed.

1. How many people are ambidextrous?

It depends on your definition. Almost nobody on earth can do everything equally well with either hand. But about 40% of right-handers and 75% of left-handers are considered mixed-handed, meaning that they prefer their non-dominant hand for at least one task on an official handedness test.

In between are an unknown number of people who might be called ambidextrous – those who get significant use out of both hands, even if they’re not perfectly equal.

2. What famous people are ambidextrous?

It’s hard enough just coming up with a reliable list of famous left-handers, because so many people circulate rumors without consulting a primary source.

And reliably determining what famous people are ambidextrous is a million times more difficult for the same reason, and also because we’ve always thought in terms of left and right while ignoring everything in between.

However, there are lots of people who were known to be at least very mixed-handed, and possibly ambidextrous. The list includes James Garfield, Gerald Ford, Ronald Reagan, Robert Baden-Powell, Plato, Hippocrates, Leonardo da Vinci, Michelangelo, Nikola Tessla, and Ludwig van Beethoven.

Not surprisingly, there are mixed-handed athletes from just about every sport, including LeBron James, Maria Sharapova, Mickey Mantle, Eli Manning, Pelé, and Gordie Howe.

3. Can you learn to be ambidextrous, or at least a little more ambidextrous than most people?

Yup! People who are born perfectly ambidextrous are extremely rare, and possibly nonexistent. But many people have learned to become ambidextrous or mixed-handed to boost athletic performance, improve left brain/right brain integration, do things more efficiently, reduce the risk of carpal tunnel syndrome, and more.

4. Instead of practicing with both hands, doesn’t it make more sense to get really good with one hand?

That’s like saying you shouldn’t learn a foreign language because it takes time away from perfecting your native language. But there’s a point of diminishing returns, where continuing to work on your native language gives little benefit, while learning a new language lets you make progress much faster.

5. But doesn’t it take twice as long to learn something with both hands?

No, because you’re not starting from scratch. Put it this way – if you’re right-handed, you can probably write in the sand just fine with your right foot. Even if you’ve never tried it before, your foot has learned by osmosis. To a lesser extent, your left hand has learned by osmosis as well.

6. Are there advantages to being mixed-handed, other than the physical ones?

Yes, though we’re not entirely sure what they are yet. They may include the ability to read people better, see both sides of an issue, and recall details of an event and their context.

For a long time, scientists thought that left-handers and right-handers had certain psychological differences, but the findings weren’t always consistent. For example, they say that lefties are more creative and emotional, but there are plenty of counterexamples to that.

Only recently have they discovered that the important differences are not between left-handers and right-handers, but between strong-handers and mixed-handers.

7. If left-handers are more likely to be mixed-handed than right-handers are, then why are most switch hitters in baseball right-handed?

Because (1) most baseball players overall are right-handed, and (2) since left-handers have a big advantage when batting, they have less of an incentive to switch.

8. What is the biggest myth about handedness?

That left-handers are right-brained and creative, while right-handers are left-brained and analytical.

It’s true that each side of the body is mostly controlled by the opposite side of the brain, but motor control is just one of the many things our brain does. People of either handedness can be either left-brained or right-brained.

9. What is the weirdest fact you know about handedness?

The hand clasping thing. Clasp your hands, and see which thumb is on top. It’s almost a sure thing you always do it the same way (and it’s not correlated with handedness).

About 60% of people in England put their left thumb on top. Now, the weird thing is that this percentage drops steadily as you go east across Europe and Asia, reaching a low of 30% in the Solomon Islands. If anyone has a theory about how this evolved, I’d love to hear it.

10. When right-handers see somebody writing with their left hand, why do they so often say “Oh my God, you’re left-handed?”

I’m not sure. There are 700 million of us, you know.

For more mixed-handed goodness, check out my ebooks Ambidextrous (for right-handers) and Ambisinistrous (for left-handers).

Hunter Recommends:
Greatness Without Genies: The Law of Attraction for Realists
How to Be Rich And Happy: Whatever You Want, Whenever You Want
How to Finally Find What You Love to Do And Get Paid For Doing It

Let’s say your goal is to reduce gasoline consumption.

And let’s say there are only two kinds of cars in the world. Half of them are Suburbans that get 10 miles to the gallon and half are Priuses that get 50.

If we assume that all the cars drive the same number of miles, which would be a better investment:

• Get new tires for all the Suburbans and increase their mileage a bit to 13 miles per gallon.
• Replace all the Priuses and rewire them to get 100 miles per gallon (doubling their average!)

Would you believe that you save more gas by putting new tires on the Suburbans? Because that’s the right answer.

What’s great about this problem is that it seems so simple, but the result is so astonishing. Even after you know the answer, it’s still hard to get your head around it.

Seth’s point was that we’re not wired for arithmetic. True, but I think what this problem really shows is that we’re wired for making faulty assumptions about numbers. It’s not our arithmetic that fails us in this case. It’s our logic and intuition that do.

How to solve it logically

In 6 Ways To Improve Your Telecommunication, Zack Grossbart shows a simple way to solve the problem by using pictures and plugging in real numbers. He crunches the numbers for one Suburban, one Prius, and a specific number of miles for the commute.

That’s one way I might have done it. Another way I might have done it is by flipping the miles per gallon (1/mpg) to get gallons per mile. When you want to see how much gas you’re burning, the relevant metric is gallons.

As you can see, upgrading a Suburban saves 2.3 times as much gas.

How to solve it intuitively

OK, we know how to arrive at the answer. But how can we resolve the paradox? How can getting 30% more mpg possibly be better than getting 100% more mpg? Here are two ways to understand it intuitively.

1. Consider a more extreme version of the problem.

You own a Hummer that gets 5 miles per gallon. You also own a futuristic supercar that can drive across the country on a single drop of gas. Would you rather get 1% better mileage on your Hummer, or 1,000,000% better mileage on your futuristic supercar?

Don’t turn this into a problem of comparing one percentage against another. There’s no point in upgrading the supercar. The percentage you improve it by is irrelevant, because nanodrops of gas don’t matter. But any improvement on the Hummer is huge because it burns a lot of gas.

2. Consider a reworded version of the problem.

All the Suburbans in the world burn 83% of the gas. All the Priuses in the world burn 17% of the gas. Which model should you upgrade?

When it’s phrased this way, the answer is obvious. The wording of the original problem distracted you from what was really important. Of course, problems aren’t always nice enough to phrase themselves in the way that is most convenient for you.

Why doesn’t common sense work?

There are several reasons why it’s so easy to be led astray.

1. We fail to spell out our objective. The wording is critically important, because it’s easy to solve the wrong problem.

Are we trying to maximize the mpg of the average car? No. (If we were, we should upgrade the Prius.) Our goal is to minimize the total amount of gas burned by all cars. So focus on that.

2. It might seem strange that we’re not trying to maximize the average miles per gallon. Isn’t that the same as reducing the total amount of gas burned? Well, it would be, if there was only one car. But averages can be tricky.

If we upgrade the Suburbans, the average car would get (13 + 50) / 2 = 31.5 miles per gallon.

If we upgrade the Priuses, the average car would get (10 + 100) / 2 = 55 miles per gallon.

55 is more than 31.5, so upgrading the Prius means burning less gas, right? It might seem like it should work that way, but there is no mathematical law that says so.

Looking at the average isn’t enough – you need to look at the distribution. Here are three pairs of cars, each pair averaging 10 mpg. The greater the variance within each pair, the more gas is needed to drive a fixed distance.

That’s what we get when we keep the distance fixed and look at how much gas we need, which is what we have to do for this problem. But just for fun, let’s keep the amount of gas fixed and look at how far we can drive with the same cars.

Is that surprising?

3. Reciprocals (mpg vs. gpm) are confusing. We’re trained to think in terms of miles per gallon. But gallons per mile is actually a much more natural unit to work with when you’re looking at how much gas you’re burning.

The Suburban gets 10 mpg, and the Prius gets 50 mpg. The Prius gets 400% better mileage (mpg), but it burns 80% less gas (gpm), so you have to be really clear on what you’re talking about.

The differences between reciprocals get more pronounced when you approach a singularity. 0/1 is very different from 1/0, which is what caused the difference in the previous two charts.

4. We’re told that there are equal numbers of Suburbans and Priuses, and we subconsciously think they should therefore be treated equally. But we need to discriminate. The Suburbans are burning 83% of the gas in the world, so they need to be given more weight. It doesn’t matter how many there are, only how much gas they’re all burning.

Of course, what people should really be doing is trading in their Suburbans for Priuses.

Hunter Recommends:
Greatness Without Genies: The Law of Attraction for Realists
How to Be Rich And Happy: Whatever You Want, Whenever You Want
How to Finally Find What You Love to Do And Get Paid For Doing It