We Can Worry About That Later

As I've been in the middle of purchasing a house, my life has been extremely hectic. The past several weeks have been filled with housing inspections, mortgage application, reviewing and signing legal documents. And naturally every detail has someone who wants to renegotiate it. After closing, we'll still have painting, moving, decoration, and furniture purchases to handle. My life is really stressful.

A few days ago my wife and I were reviewing the upcoming task list and for one item I mentioned, "we can worry about that later". I then realized I use that phrase an awful lot, and to me it means "we can handle that later". In other words, to me worrying is synonymous with work. Put negatively, I don't stop worrying until the work has been completed.

It was an excellent moment of self reflection. I simultaneously realized why I'm so driven, intense, productive, and stressed. This life attitude has its benefits, but it's certainly not healthy in the long term. A few weeks ago I wrote about how the secret of a successful marriage is reducing stress. Perhaps it's better to say:

The secret of happiness is reducing stress.

After all, what else is stress but discontent about the possible future? It's a tricky balance though. If you live life in the future, you'll solve all these potential problems but always be tense as a result, never enjoying the present. If you live in the present you'll enjoy it only until something happens that you should have dealt with. How do you focus on the present while building your future? Personally, I feel like I don't balance these constraints very well.

So I've been thinking about different ways to manage stress. Some common things people try include:

• Eat and/or drink
• Have Sex
• Exercise or spend time outside
• Sleep or practice deep breathing
• Read a book, watch a movie, or play a game
• Daydream or imagine good things
• Procrastinate by doing less important work
• Remove the source of stress

Personally I spend a bit too much time on the last two items. Classifying these options, they seem to fall into one of three categories:

• Solve the issue
• Ignore the issue
• Accept the issue

Unfortunately if the only mechanism for relieving stress is to solve the problem, you're in for a rough life-- there's always something else you can worry about, and many things you can't fix Ignoring issues seems fine for small problems. And acceptance is the only option available for problems too large to be solved or ignored.

I believe the real secret to happiness is properly identifying which problems should be accepted and which should be solved. And then realizing that most problems are of the former type. It's easy to get caught up in trying to fix everything, especially as a perfectionist. But the more you genuinely accept misfortunes as Not A Big Deal, the more you can enjoy the truly good things in your life.

If that's true, then the real secret to happiness is forgiveness.

Imitating Life

As a hobbyist game designer, I quickly learned a simple truth about game players:

Players always know whether they are having fun but rarely know why.

If you don't believe this, read the fan forums for literally any game you can think of. You'll see all kinds of suggestions, 80% of which would clearly make the game less fun. But when someone says, "I don't like it when this happens," then they're stating a personal opinion. It's not a debatable point-- they are certainly not having fun. This is a bit ironic. How can they know what they like but not why they like it?

The answer is that while humans are capable of self reflection, they aren't actually that good at it, nor do they usually enjoy it. Humans excel at pattern matching and general recognition problems. Not coincidentally, the vast majority of fun in games comes from what could be abstractly labeled "pattern matching".

Pattern matching is more than just seeing the Virgin Mary in a grilled cheese sandwich. In an abstract strategy game like Chess, it involves recognizing a board position as similar to one you've played before, so you have an idea for how to approach it. In Poker, you learn to recognize how a player's bids correspond with their potential hands. Basketball and football players need to recognize openings in the opposing team's defense. Party games like Charades and Apples to Apples are nothing but pattern matching: can you connect the dots between someone's gestures and a previously known phrase?

So I was intrigued after reading an article by Mark Rosewater on resonance in Magic: The Gathering. A game mechanic has "resonance" if it reminds the players of already known concepts. If you see that a dragon has the special power "flying" or giant has "trample", you don't even need to know how the game works to appreciate the design. We know that dragons fly, giants are large, zombies are slow, merfolk swim, and so on. It's just fun when a game does a good job of embodying its theme! And game mechanics with resonance are also easier to learn, since you can apply your standard intuition to situations they apply in. You would assume that a creature with flying is harder to attack and has an easier time attacking other people. A trampling giant probably causes more damage on average than another creature would. Zombies are probably weak overall, but easier to use as minions than something like a Dragon.

I confess this lesson took me a long time to learn as a game designer. The first games presented players with interesting decisions but totally lacked theme and resonance. This made them as entertaining as sudoku, which puts them the same realm as, "things I'm willing to do while in a train." Not exactly a rousing endorsement. But just because of how our brains are wired, humans just like seeing connections between abstract actions and known concepts. When kids play cops and robbers, "house", or pretend a doll is a real baby, they are taking enjoyment from resonance. Since even the very young participate in imaginary play, it suggests this enjoyment is somehow wired into our brains. Some people think of games as escape from real life, but it's specifically when games are connected to life that they are most enjoyed. A game doesn't need dragons, ninjas, aliens, or Roman emperors to be fun, but these things help a lot. Especially the ninjas.

Mental Filters

I write about a pretty wide variety of topics. Some common ones are:

• Algorithm Design
• Ethics and Morality
• Philosophy
• Politics
• Learning

While these topics seem somewhat distinct, they all intersect on one question:

What things are good and how can I best do them?

The site's tag line is "Genuine and Artificial Intelligence", but the purpose of intelligence is to do good things, rather than bad things, inefficient things, or evil things. I think of each post as an opportunity to learn how to learn.

So it shouldn't surprise you that I love well reasoned and articulated explanations, especially things explaining how we think. For example, this essay on Lies We Tell Children was worth a blog post. I found one last week on the subject of open-mindedness. It's well worth the 10 minutes, so I highly recommend watching it:



There's a lot of good stuff in there, but I particularly liked the point about mental filters, starting at roughly 7:23. A lot of people think that being open-minded means you are willing to consider any idea, which essentially means your brain lacks a mental filter. They define being open-minded as accepting any idea you are told, no how far-fetched the circumstantial evidence is. What this really means is, "you should agree with me no matter what." And ironically, that's the exact definition of being close-minded, as it means they aren't open to other ideas.

The main point is that being open-minded means considering the evidence and logic for different explanations, then believing the explanations that best fit the evidence and throwing out the ones that don't. In other words, being open-minded is predicated on having an evidence filter.

Everyone is confronted with both true and false concepts all the time, and it's impossible to have a world view that only believes the "correct" things. Statistically speaking, you are virtually guaranteed to believe some ideas that are flat out wrong. But the objective is to minimize the percent of incorrect ideas you believe. That means the key word is "filter". If you have a brick wall for your mind, you are unable to accept any new ideas, so the incorrect premises you have will only turn into more incorrect conclusions. And if you have no mental protection at all, you'll believe all kinds of crazy things.

I believe many people who are truly close minded mistake the mental filters other people have for brick walls. They falsely conclude that because someone rejected their idea, that person must not be open to any opposing ideas, rather than simply having a good reason for rejecting theirs.

All of this is well and good for understanding "the other guy". But how do you make sure that you aren't "the other guy"? Remember, the most close-minded people think they are open-minded, in the same way that arrogant people think they are humble. There's no firm method to conclude whether you are actually open-minded, but keep this in mind: Truly open-minded people occasionally change their beliefs. Ask yourself this question:

When was the last time I philosophically disagreed with someone, and after we expressed our beliefs, they changed my mind?

If you think you are open-minded but can't remember the last time someone else changed your opinion on something substantive, you are probably more close-minded than you think. Recognizing when you are wrong and having the humility to admit it is the true mark of an adult mind.

Ride With Hitler

"Hitler ruined that mustache for the rest of us."


It's true too. Culturally, western society so strongly associates Hitler's mustache with a genocidal dictator that even seeing it conjures up an emotional connection. I find it fascinating that a man has been so thoroughly associated with his mustache that more than 60 years after his death, people would face social stigma if they wore it. This is a testament to the effectiveness of the the World War 2 propaganda. Hitler's mustache and right-to-left hair part became icons that were easy to caricature:


That meant American propaganda wasn't America versus Germany; it was America versus Hitler. Mentally it's much easier to view a person as evil than an entire nation, so Hitler became the face of evil. Here's one of my favorite propaganda posters:


Yes that's right. When you ride alone, you ride with Hitler! The poster's message was that you waste gasoline when you don't car pool, and this in turn hurts the war effort. Technically stated that wasn't true. Commodities like gasoline, bread, and meat were strictly rationed on a nationwide level, so that the war effort would always have enough of these things. From an economic perspective, if the general public consumed more gasoline, then the price of gasoline that the public had access to would increase, but it would have no effect on the quantity of gasoline the soldiers could use. The rationing merely makes publicly accessible gasoline more price sensitive. Put another way, you don't even have access to gasoline until the army says it has enough, so buying more gasoline only affects what you and your neighbors will pay.

Of course, the poster's message is still in the viewer's best interest even if the logic is incorrect. Somehow "When you ride alone, you pay more for gasoline" just isn't as compelling as "When you ride alone, you ride with Hitler." Propaganda works because most people don't think rationally. The emotional connection of "Hitler is a bad guy so I won't help him" is easy to reinforce through caricatures. But the logic of "Because gas prices are high, I should save myself money by using less of it" is too subtle to convey through a simple poster with a one line slogan.

The propaganda is a lie that encourages people to do things in their own self interests. Do the ends justify the means in this situation? I don't think so, but then again I'm a purist. I can certainly understand why people would disagree with me.

The Year In Review

Today marks one year since I publicly released the source code for BrainWorks, the artificial intelligence rewrite for Quake 3. I've written slightly more than once per week, giving roughly equal time to the technology behind BrainWorks and general philosophical topics related to AI. While I have more things to write about, I feel like I've covered the basics of BrainWorks in sufficient detail. If people want to hear more about a specific algorithmic part of the code, please let me know and I'll write about it. But for this next year I'm going to focus on more abstract topics likes philosophy, ethics, religion, and the meaning of intelligence. And of course there will be computer science related topics like programming style, structure, game design and so on.

I confess I'm been a bit disdainful of blogs. This is primarily because they are so many of them, and they always focus on the same thing: whatever the author finds interesting. The problem is that so few people have interesting thoughts. And now internet fads like Twitter have made it even easier to share information no one cares about. That's the reason I rarely link to other blogs: It's only when they have something profound to say. If I can only regurgitate someone else's ideas rather than writing new thoughts myself, those ideas had better be very meaningful. The only things worth writing are those things worth reading, and 99% of blogs break this rule.

With that in mind, I plan on doing posts once every two weeks rather than once a week. Quality is far more important than quantity, and good posts can take several days (or weeks) of thinking to best articulate. Here's a rundown of some topics I'd like to cover:

• Reading between the lines (extracting truth from lies)
• Living as a non-Christian who still has Christian ethics
• What does it mean to have feelings?
• Designing games that invoke emotional responses
• Intellectual consistency (why I'm not offended when people pray for me)
• Learning how to learn

If you want to hear my opinions on other topics, now is your chance to make your voice heard.

The Meaning of Truth

I recently had a conversation with my mother where she was bemoaning the current bias in media. She is very conservative, so hearing about the liberal media bias is something I've heard about from her for over a decade. But this time her point was different. For the first time in my memory, she called the conservative media sources untrustworthy as well. And if you can't trust Fox News, how do you find out what's really true? Everyone's message is tampered with their own personal biases.

I explained to her how facts ("truth") can and must be extracted from the biased information we receive from others. Nothing can be accepted at face value, even one's own first impressions. In other words, I believe there's no such thing as unbiased information. In contrast, my mother's contention is that people can and do recognize absolute truth, but immoral people twist it to suit their own purposes. I spent a good amount of time thinking about this dichotomy, and I believe what side you fall on depends on how you answer the following two questions:

1) Can absolute truth be perfectly understood?
2) Can absolute truth be communicated without distorting it?

My mother believes that the answer to both of these questions is yes for the vast majority of truths. My personal belief is that absolute truth cannot be perfectly understood, but you can get a really close approximation most of the time. And it's possible but extremely hard to communicate absolute truth without distortion. That's why learning to extract truth from imperfect information is such a crucial skill for a free thinker.

The problem is that statements must fit within the confines of language to be expressed to others, but not everyone has the same underlying definitions. Much of language depends on context. For example, consider this claim by Alcoholics Anonymous:

Alcoholism is a disease.

The majority of lay people believe this is true, while most medical professionals disagree. Whether you agree with this statement depends on how you define a disease. And that in turn depends on what conclusions you want to apply. The impression I get from Alcoholics Anonymous is that it's important to classify alcoholics as diseased so they can receive sympathy for their troubles in life. If alcoholism were caused by personal choices, then friends and family would be more inclined to chastise the alcoholic than rally around and support them in their addiction.

Meanwhile, medical professionals think about diseases differently. When someone has a disease, that means specific kinds of medication and treatment may be effective. From a medical perspective, diseases tend not to require psychotherapy to treat. So calling alcoholism a disease is misleading for the purposes of treatment. But it's a reasonable statement if you define a disease as, "a malady that requires outside assistance to treat."

The question is which definition of disease are people thinking of when you use the word, and I tend to think the medical professional definition is the more common usage. After all, if all ailments that required psychiatric help were considered diseases, that would include depression and schizophrenia, and no one is claiming that is the case. That's a sign the statement "alcoholism is a disease" lacks internal logical consistency. Nevertheless, that statement is still true within the context of a rather contrived set of definitions.

Perhaps its best to think about statements as belonging to one of two categories: scientific or sociological. A scientific statement is any statement that uses precise terms that are universally understood and agreed on. For example, "the sum of 0 and 1 is 1." Scientific statements, being the basis of science, must be both well defined and measurable.

A sociological statement is anything that's not scientific and therefore not well defined. For example, "Pablo Picasso was a great artist." Not all sociological statements are obviously opinions, however. The statement, "Rappers are musicians" depends on your definition of musician. I personally think of rap stars as poetry artists and not musicians, because their work lacks both melody and harmony.

Even statements that are thought of as facts can be disputed. Consider the following statement:

Senator Obama won a decisive victory over McCain in the past presidential election.

Most but not all people believe this statement is true. The disagreement centers around the definition of decisive. It's true that Obama had over double the electoral votes of McCain, but Obama only had 13% more votes than McCain had. Some people claim that a victory is not decisive when 46% of the country voted for the loser. (I disagree for a variety of reasons, but that's not really the matter at hand.)

The further statements get from scientific statements things like "0 + 1 = 1" and the closer they get to sociological statements like "Rappers are musicians", the harder they are to agree upon and therefore test. And as a result, their absolute truth becomes impossible to verify and bias works its way in. Unfortunately, it is sociological statements that interest most of humanity, not scientific ones. Almost every statement you've read in this post has been a sociological one-- its truth can only be approximated, not scientifically tested and verified. The vast majority of "facts" you interact with on a daily basis are untestable. That is why its so important to analyze whatever you hear rather than accepting it as fact. It's also why artificial intelligence is the most difficult area of computer science. AI is the area that deals with answering sociological questions like "what is best" or "what do you want".

Boot Camp

An old friend of mine was a former army drill sergeant. I was surprised to learn this, as he didn't fit the stereotypical personality at all. Thoughtful and friendly are not the first words that come to mind when you hear the phrase, "Drill Instructor". This former job came up when someone mentioned they were leaving for basic training (aka. "Boot Camp") in six weeks, and my friend immediately told the guy, "Start doing push-ups now." Apparently you do so many push-ups in boot camp that it's never to early to get in shape, and the better physical shape you are in, the easier it goes. Not that boot camp is ever easy.

Intrigued by learning that my friend had been a drill sergeant, I asked him about basic training from the sergeant's perspective. According to him, the most important thing you can do to survive boot camp is to be practically invisible. You need to blend into the crowd and never even be noticed by the drill instructors. The instructor's job is to spot recruits that don't fix the mix and make them fit in.

"The instructors don't hate you," he said. "It's just that nothing you've learned in civilian life is of any use in the army, and they need to beat that crap out of you."

The typical civilian doesn't take order very well. Generally orders are considered "loose guidelines". They're likely to ignore orders outright, only do the parts they want, or do things differently based on their preferences. That kind of attitude is acceptable (although not optimal) in real world business settings. In the army though, the lives of thousands if not millions of people are at stake, and defying orders can have large consequences that the enlisted units can't possibly be aware of.

If your army company controls a bridge, but your officer tells you to march one mile down a river and swim across at midnight, then that's exactly what you do. The option of taking the bridge across and walking down the other shore shouldn't even enter your mind, even though your officer never told you why he gave you your orders. Soldiers who disobey orders either get killed or get other people killed. That's why the number one objective of a drill instructor is to teach privates to follow orders.

There's a similar issue for AI in team play video games. If your team contains some bots or possibly "side kicks", the game designers generally give you some way to give them orders. No game designer considers making your minions sometimes ignore orders, of course. Rather, the problem is this: Just as most people aren't used to following order precisely, they also aren't used to giving them precisely. There's a whole set of problems determining what the player even intends when they say something as simple as, "go back through the door". Which door? How far back? So much of language depends on context and it's just hard to determining meaning from a small snippet of words.

The second issue is that no matter what order a player gives, they almost never want the bot to perform that task indefinitely and precisely. When you say "go guard the red armor", it's just until they spot an enemy. When the bot spots an enemy, you don't want it to let them get away if that enemy leaves the red armor spot. The bot should stop guarding and start attacking in that case. Similarly, if no one goes by the red armor for a while, the bot should stop guarding and find something useful to do. When the player says, "Guard the red armor", the bot needs to understand that as "Guard the red armor until you see an enemy or until you've been there for three minutes".

This is the basic strategy BrainWorks uses for its message processing, which is largely unchanged from the original Quake 3 message code. It does the best job to determine what the player means and treats that instruction as a relatively important high level task. But it's still just a guideline. Stuff like combat takes precedence, and the bot will forget about the order after a few minutes. Ironically the bots need to function like civilians rather than soldiers, or the person playing the game wouldn't be happy with how the bots would follow orders.

Ignorance Is Bliss

Having written twice about the dangers of believing everything you are told, I'd like to give some face time to the opposing argument:

Ignorance is bliss.

It's all well and good to say, "You should understand things, not just follow blindly." But to be pragmatic, there is only one time this is a serious improvement: when the blind belief is wrong. What about the times that blind belief is right? Humans survived for centuries without understanding how gravity truly worked and that didn't stop them from creating some awesome things. They were even able to use the fact that "things fall towards the ground" to great effect, such as building water clocks, without ever learning Kepler's laws of motion.

Even if someone did want to totally understand the world today, it wouldn't be possible. There is such a vast corpus of information that learning even 1% of it is literally impossible in the span of a human life. Mathematicians who are experts in their field rarely have the chance to keep up on other branches of mathematics, to say nothing of physics, chemistry, or medicine.

The fact of the matter is that most of the information we've been told is correct. If I get a bus that says "Downtown" as its destination, it really is going there. The driver could take it anywhere but I'm very certain that the destination is downtown. When I order a meal at a restaurant, I take it for granted that the cook can actually make the things on the menu and that I'll be served food that's reasonable close to the description provided. The waitress serves me food assuming that I will pay the price listed. I suspect that less than 1 in 10,000 people really understands what a computer does when you turn it on, but hundreds of millions of people use a computer every day. Understanding is a luxury, not a necessity.

Like it or not, our lives as humans are anchored in faith, not reason and understanding, and this is the cornerstone of the religion well all share: Causality. If we do something ten times in a row and got the same result, we expect that the eleventh time will produce the same result. And it does, even though we rarely know why. Understanding everything is impossible, and the whole purpose of culture is to provide structure so that everyone can use the discoveries other people made.

If that's the case, why bother thinking about anything at all? Why not let someone else do your thinking for you? The primary purpose of education isn't to give people information, but to teach them how to think. And most importantly, to teach them when to think. Thinking is really important in uncharted waters. In any situation that doesn't match your typical life experience, thinking will give you a much better idea of what to do than trying something at random.

Unsurprisingly, the same problem comes up in artificial intelligence. There's only so much processor time to go around. So if seven bots all need the same piece of information and they will all come to roughly the same conclusion, it's much faster to do a rough computation once and let them all use those results. This leads to an information dichotomy, where there's general "AI Information" and "Bot specific information". Each bot has its own specific information, but shares from the same pool of general information. In BrainWorks, all bots share things like physics predictions and the basic estimation of an item's value. If a player has jumped off a ledge, there's no reason for every bot to figure out when that player will hit the bottom. It's much faster to work it out once (the first time a bot needs to know) and if another bot needs to know, it can share the result.

These are the kinds of optimizations that can create massive speed improvements, making an otherwise difficult computation fast. If you think about it, it's not that different from one person researching the effects of some new medicine and then sharing the results with the entire world.

Social Mentality

I cannot help but comment on the results of the recent American presidential election, in which Barack Obama became the first non-white to be elected president. As a jaded American, I recognize that America has its share of both wonders and problems. But I have never been more proud of America than I was on this past election night. To me, the election of Barack Obama is symbolically a major victory in America's war against racism. And were Obama the Republican candidate and McCain the Democratic one, I would be no less overjoyed. Some things in life are more important than what percent of a candidate's positions we agree with.

I want to stress that this election is a victory over racism, not slavery. Most cultures in pre-modern times practiced slavery, but it was enslavement of people from the same ethnic background. Slavery in America and the Caribbean isles differed from other forms in slavery in that the belief was also coupled with a sentiment of racial superiority. So even after the abolition of slavery in 1863, the underlying tone of racism permeated much of American life. In contrast, the Britain empire outlawed slavery in 1833, but their enslavement wasn't particularly racially biased, so their past two centuries haven't been filled with racial tension. Abraham Lincoln won the war on slavery in 1865, but completely decimating the southern American states couldn't change the racist opinions that much of the country still retained. The election of Barack Obama to the office of President is proof that a large percent of America is now racially blind-- the ethnic background of a candidate is not a reason to select against them.

Of course, not all of America feels that way. That's why this election is a sign of major progress on the issue of racial discrimination, but it doesn't represent the end of it. Looking at the final electoral vote distribution, this election was heavily slanted towards Barack Obama but unanimous. For example, Alabama and Mississippi again voted for the conservative candidate as they've done for decades. But Virginia and North Carolina both voted for Barack Obama, two states that split away from America during the American Civil War because they wanted the right to keep slavery legal (among other things).

This might seem like a trite point, but each major population center in America has its own local way of thinking. People in Los Angeles are more liberal than people in Salt Lake City, for example. The southern states tend to be more conservative than New England states. And the way they vote is a reflection of their local societal beliefs. If this were not the case, every city and state would vote exactly the same way. There's nothing magical about the geography that makes people think in certain ways. The social mentality is a purely contained in the minds of all people in that local society, and if you think about it, that is an incredible thing.

For North Carolina to vote for a black man 147 years after it tried to secede from the United States, the entire cultural mentality had to change. It does not change quickly either. It wasn't until all the adults of that generation died, along their children and grandchildren, that the majority of the state decided that maybe blacks and whites were equals. That should be a sign of how easy it is to believe the first thing you're told and how hard it is to consider outside opinions.

More often than not, people belong to the political party and religion of their parents, quite apart from the actual merits of those positions. Over 90% of America is Christian and well under 10% of India is Christian, despite extensive missionary work. Even if there were strong, logical reasons to believe in the Christian religion, it's clear that those reasons are not why America is a Christian nation. If those reasons were so logically compelling, then India would be a Christian nation too.

Like it or not, if your political and religious views closely match your family's and city's views, the odds are high that you haven't thought very hard about them. That doesn't mean your beliefs are wrong, but if they are right, you probably don't know why they are. It means you accept the basic beliefs of society's "hive mind", and you've ceded some of your thinking.

While this can be dangerous, accepting society's beliefs without too many questions can be really helpful. I'm not advocating, "question everything", but, "question everything important". You might be wondering why that is, and what this has to do with Artificial Intelligence. Next week I'll explain what I mean.

Groupthink

There is a psychological concept known as groupthink, where members of a group discussing a topic gravitate towards a consensus based on a desire to minimize conflict rather than rationally discussing the subject. The play and film 12 Angry Men is a famous view of groupthink in action. A jury must decide whether a man is guilty or innocent, and the general group consensus is that he is guilty. But one juror refuses to ignore his intuition, even though it differs from the other jurors. The drama of the film is primarily based on how the other 11 jurors come to change their minds and eventually find the man innocent. Groupthink is a dangerous phenomenon because it can cause rational people to make irrational choices purely because it is the most common choice made by other group members. Just because a decision is right for everyone else in the group doesn't mean it's right for you.

Surprisingly, groupthink can be a problem in video game AI as well. This seems counterintuitive, as a bot must be programmed to follow logical rules. How can following logical rules produce illogical results? The issue is one of scope. In a teamplay game variant like capture the flag, each bot might be doing the best thing without knowing what the other players are doing, but not coordinating as a team. Suppose one bot does some logical deductions and concludes the optimal strategy is for him to guard the flag. Since all other bots have roughly the same understanding of the game state, some poorly designed AI code could cause all of them to defend the flag. It's optimal for more than zero players to defend, but having no players attack is clearly the wrong choice.

So bots that don't take into account the overall strategy of the team are likely to fall into a groupthink scenario. This results in a poor strategic choice even if each individual choice is "optimal" when consider without knowledge of the other choices. The solution is for bots to make teamplay decisions as, well, a team. Each bot's decision needs to take into account the likely decisions of other players, bots and humans alike.

The good news is that if bots are good at general combat tactics, the amount of additional logic to handle a teamplay scenario is minimal. It's not like players have better or worse aim when they're fighting in a team, or suddenly some weapons are sigificantly worse when you're playing capture the flag. None of that has to be rewritten to handle play in a group. Teamplay logic is just a question of priorities: when an enemy player has your flag, it's a lot more important to kill that player than any other opponent, and guarding your own base is a lot less important than attacking theirs. And in almost all situations, the choice is between defending and attacking. How the bot attacks and defends doesn't really change.

So BrainWorks can get away with some extremely simple logic to handle team games and still produces reasonable results. One bot becomes the defacto leader for choosing the strategy the bots will take-- exactly how offensive or defensive it is. Some number of players are slotted for defense and the rest for offense, but never 0% in either category. Then the bots are sorted based on how close they are to the two bases. If there are 5 bots on a team and the leader decides on a 2-3 split of defense to offense, then the two bots closest to the home base are assigned with "defense" and the three bots furthest from home are the "offense". Any human players in the game have no assignments, obviously.

It's worth noting that "offense" and "defense" depend on the context of the game. In capture the flag, for example, the defenders guard your team's flag when it's in the base, but they also track down the enemy flag carrier when it's been taken. Attackers try to take the opposing flag, but once someone has the other flag, the attackers need to escort their flag carrier. Once the concept of whether to defend is separated from how to defend, the actual algorithm to defeat groupthink is simple.

The Theory Of Fun

Last week I ended with a simple question: Does making a game more balanced make it more fun? And like so many other things in life, the answer is both simple and complicated:

It depends.

To put it briefly, it depends on the context of the game and the reasons people are playing it. While on the surface, the original Doom appears similar to Quake 3, they are polar opposite games from a game design perspective. Doom is all about mowing downs armies of generally stupid demons with your increasingly diverse stockpile of weapons. Quake 3 is about fighting a skilled opponent with access to the same weapons and powerups you can use. Doom is a visceral game while Quake 3 is tactical. Rather than saying Doom is fun, it's more accurate to say that visceral experiences are fun, and Doom happens to fill that role. But a game like Serious Sam is just as fun, and in the same way.

If games are candy for the mind, then whether or not you like a particular flavor of game is a question of personal preference. What does your mind enjoy? Each kind of thinking corresponds to a different style of game.

• Analysis
• Action
• Discovery
• Creativity
• Socialization
• Identity
  

As best I can tell, these are the six basic thought elements that make games fun. Allow me to explain them in more detail:

Analysis

Examples: Chess, Go
Requires: Discrete set of Options

People who like analytical games approach them as a puzzle to be solved. The game needs to provide decisions for the player to make, any of which could be helpful in the right situation. The fun is in selecting your decision and finding out if it was a good choice. Winning the game usually means you were right, or at least more right than your opponents. Analytical games can also be single player games, such as a Rubix Cube. They need not include perfect information to all players like Chess and Go do, of course. A game like Poker contains a lot of analysis. But the analysis is in large part about probabilities, risks, and rewards. You can win a game of poker by making the wrong play, or lose making the right play, but people who deduce the right plays more often will win more often, and that's what makes the game analytical.

Action

Examples: Golf, Football
Requires: Physical Interaction

An action game is anything that involves a physical component. If there is ever a game situation where you know what you need to do but your muscles might not be able to do it, it's an action game. The joy in an action game is seeing how well you can do what you tried to do. It's really satisfying to shoot a three point shot in basketball, score a goal in football, or hit a home run in baseball. Every physical sport is an action game by this definition. But other games have action components as well. All first person shooter games are "action games" because they require you to aim at your target and shoot them. You know what you want to do, but your muscles might fail at moving the mouse to the right place. Even a game like Jenga is an action game, because it depends on precise muscle control.

Discovery

Examples: Final Fantasy VII, World of Warcraft
Requires: Artistry

Discovery games are fun because the player experience new things. Their joy is the joy of appreciation. A common theme in these games is a story line. As a result, discovery games generally have lower replayability. They are highly engaging the first time through, however. What the player experiences doesn't have to be the story, of course. It could be enjoying the look and feel of a new World of Warcraft zone. What all discovery games have in common is artistically designed content that the player enjoys experiencing.

Creativity

Examples: Charades, Magic: The Gathering
Requires: Very large set of Options

Some people really enjoy thinking outside the box. To become a medium for expressing creativity, the rules of the game must be extremely flexible, allowing for a wide variety of possible options. The number of options is far larger than that of an analytical game: it needs to be so large that players cannot exhaustively try all of the alternatives. This gives creative people the option to express themselves by making choices other people might not even have considered. Creative games often have a verbal component to them, but this is not necessary. For example, deck design in Magic: The Gathering is a very creative endeavor. There are more legal Magic decks than atoms in the Universe! And a number of Magic players enjoy making decks more than playing with them.

Socialization

Examples: Mafia, Shadows over Camelot
Requires: Cooperation

While it's true that games are only as fun as the people you play them with, some people explicitly enjoy games because of the social interactions. In their opinion, a game's purpose is to generate social interactions, and whether you win or lose isn't as important as how you got there. These games categorically encourage players to work as a team towards a common goal. Victories and losses are shared. The fun is about being part of a group and, for some games, deducing who isn't really a member of the team from their actions.

Identity

Examples: Counterstrike, Team Fortress 2
Requires: Winners and Losers

There are some gamers who play games to win. Obviously the typical sore loser falls into this category, but that's not the only type of person who fits this description. This type of gamer considers winning an expression of who they are. The sore loser is someone who considers that expression to be a validation of identity-- they are distressed when they lose because the loss injures their ego. By definition, every game has winners and losers. But in almost all cases, gamers who want to win prefer games against human opponents, as it makes winning more meaningful. Note that these gamers need not be good players. Often bad players who want to win will gravitate towards teamplay games like Counterstrike. No matter how bad you are, you will win roughly half of your games if the teams are large enough. When a gamer wants to express themselves through winning, all they really need is a human opponent and the plausibility to claim they won through skill. Actual skill could have been involved, but it's not required.

Anatomy Of The Brain

Last week I wrote in brief about the differences between conscious and non-conscious thought, and how experiments show humans use both. Even the most primitive animal brain is an extremely complicated organ, and the human brain is obviously the most advanced brain we've encountered. Conceptually there are three main sections of the human brain, corresponding to the evolutionary processes that produced the brain.

Pardon the aside, but I wanted to say a word to the Christian readers who believe in Intelligent Design instead of evolution. Feel free to interpret "evolution" with "how God chose to create life". Your personal belief about how humans came to exist won't change your interpretation of what I have to say, so don't let my choice of language get in the way. Remember, I was a Christian for 30 years. I was taught creationism by my parents. In high school I believed the theory of evolution, and just said "God used evolution to create humans". Now I just think "humans were created through evolution". I still think people who believe in Intelligent Design are wrong given the data, but I see no reason to be condescending or judgmental about it.

As I was saying, there are three main sections of the brain. At the core is the so called "Reptilian Brain", or more formally the brain stem. This section handles basic reflexive responses such as "fight or flight", mating instincts, and the fear of other species. It also handles exactly one emotion: rage. Next is the Mammalian Brain, or Limbic system. This is the area of the brain that handles all other emotions, as well as concepts like family, culture, and attachments. Some aspects of conscious thought and self-identity are handled by the mammalian brain as well. Last is the Neo-cortex, which is responsible for higher level thought such as speech, reasoning, imagination, and speculation.

While there's a clear physical boundary between the reptilian and mammalian brains, the division between mammalian (limbic) and neo-cortex is not as clear, and there seems to be a stronger bleed between the functions. For example, some conscious thoughts are handled by the mammalian brain while others are handled by the neo-cortex.

Humans have all three brain sections, as do higher mammals such as other primates and larger mammals. Small primates such as rodents do not have a neo-cortex, although they do have the limbic system and reptilian brain. And as the name implies, all reptiles have the reptilian brain, but lack the mammalian brain and neo-cortex. So the brain sections correspond to different evolutionary forks. The primary feature that separates mammals from reptiles is not hair or internal gestation, but a more advanced brain.

If that's true, then reptiles don't actually have conscious thought. They simply respond to stimuli in the same fashion every time. But mammals, having a memory, can learn from past experiences and modify their behavior. Perhaps this is how mammals survived the dinosaurs after a natural disaster hit the earth. Dinosaur brains weren't programmed to handle the "meteor crashed into the earth and all your normal food dies" situation, whereas mammals could learn to find new food sources.

In computer science terms, the reptilian brain is analogous to a state machine, or a simple circuit board. It's pure hardware, and if you give it the same set of inputs, it produces the same set of outputs every time. It has no memory. The mammalian brain is more like a simple computer program, in that its responses are based both on sensory input and on past memories and experiences. Running the same computer program multiple times might produce different results. Mammals have the ability to learn throughout their life while reptiles do not. And since the neo-cortex handles imagination, reasoning, and "what if?" scenarios, it's closer in function to an operating system. An OS can run multiple programs in parallel, similar to the neo-cortex's ability to think about multiple thoughts at the same time, even conflicting thoughts.

This biological framework provides an interesting context from which to answer the questions "What are emotions?" and "Can a computer have emotions?" Some day I'll write on that, but there's a lot more to say than should be stuffed at the end of this column. In the meantime you'll just have to speculate.

Armchair Neuroscience

I'm really grateful for my undergraduate education at the University of Chicago. I believe it's the best liberal arts school in the country, in large part because it lacks the name recognition of Harvard or Yale while still placing academically in the top ten undergraduate institutions in America. No one goes to the University of Chicago for the fame or connections they might get at Harvard, and as a result the school attracts only those students who really want to learn. At the University of Chicago, they teach you how to think and analyze everything.

The joke in college was that a University of Chicago student didn't need to know anything about a subject to have an opinion on it. This is pretty much true. I distinctly remember getting an A on a paper about The Brother's Karamazov when I had only read the first quarter of the book. The trick was to relate the concepts discussed in class with the material I had actually read and with other texts read in that class. While I'm sure my professor would have been mortified to find this out, it speaks a lot about learning to analyze abstract concepts and come to conclusions that happen to be right.

In other words, The University of Chicago taught me to bullshit well.

Most of the time I talk about a topic, I really do understand the subject matter. But I'm not willing to let a lack of expertise or study prevent me from sharing my opinion, so today's topic is neuroscience. As a disclaimer, I've never studied this subject in an official capacity in my life. All the information I have is from personal reading into other studies, most of which are (I think) peer reviewed.

There! That's a more accurate disclaimer than you'll hear from any journalist, and yet they are often no better qualified than I am to write about these topics. In particular, I recently read an article on msnbc.com that hypothesized that your brain could be controlled by an inner zombie.

Yes, that's really their conclusion. And by "conclusion" I mean "hypothesis supported by circumstantial evidence that's phrased as a question so no one can refute your claim because you're not officially making one". Since there's no challenge in ridiculing a claim like this, let me instead give a fair and impartial summary of the article. Then I'll rip it to shreds and propose a hypothesis that better fits Occam's Razor by several orders of magnitude.

I'll admit the term "inner zombie" is just a term chosen to generate interest in the article. And in some sense it worked-- I'm linking to it. All they really mean by inner zombie is a thought process that's not conscious and not (always) connected to the rational decision making. The article cites some studies where people are blinded by deactivating the visual processing section of their brain, and then shown a word. The people have no conscious knowledge of the word, but when they are asked to "guess" what word might have been shown, they properly guess the word with much higher than expected accuracy. There's also a study that shows people who are temporary blinded can still reflexively react to visual information they can't see. And while the article doesn't mention it, there's also a study done on people who are blind because their brain doesn't process images even though their eyes are fine. Compared to other blind people, these people do a much better job than of dodging obstacles while walking down the street, like other people or sign posts.

I'd like to note that these results are all from real, peer reviewed studies. Scientists aren't questioning the validity of these results. But they also aren't jumping to the same conclusion that MSNBC did. Here's the basic argument the article makes:

1. People show the ability to reflexively process and react to visual information even when they consciously report blindness.
2. Therefore an unconscious process has control over their conscious minds.
3. Perhaps all consciousness is controlled by these unconscious processes and consciousness is a myth.

It's step 3 that doesn't make any sense. Sure, humans have unconscious processes that handle the same information conscious sections of the brain handle. But why does that mean the whole brain is controlled by the unconscious part? Given our daily experience, I'd suggest that the reverse is true. Our general actions are primarily controlled by conscious processes, and only when consciousness shuts off do the unconscious sections take control. There's no reason to suggest consciousness is a myth and a "zombie" is "controlling your brain".

But the brain is a really strange organ. The reasons the brain work this way are rooted in the differences between human brains, standard mammal brains, and the reptilian brain. Next week I'll talk more about these things and explain why I think dogs have consciousness but lizards do not.

Scouting for Trouble

One of the funny things about designing artificial intelligence is that you run into all kinds of problems you never even considered. For example, I spent a lot of time designing algorithms to handle realistic aiming. But what does the bot do when it's not trying to line up a shot? It has to look at something, even if there's no one else in the area. That's because the bot must either hear or see an enemy to become aware of it and start shooting back. Scouting around for likely areas an enemy might be is the best thing the bot can do when, well, it has nothing else to do.

The issue is that a bot doesn't visualize the game the same way a human does. Technically stated, bots don't visualize anything at all. While we as humans see a brightly rendered three dimensional area, that's too much information for the artificial intelligence to process in a short amount of time. A Quake 3 bot actually uses something similar to sonar to "visualize" the world. It sends out a instantaneous pulse in a perfectly straight line and figures out where that pulse hits something. It can even tell what kind of thing the pulse hits, like a wall or some fleshy target. However, there isn't enough time to send pulses in all directions, so the bot needs to carefully spend its allotment. If it spends too much time figuring out what's nearby, it won't have enough time to think about other important things (such as where to run).

Getting back to the problem at hand, think about how a human player would decide where to look for new targets. In a hallway the solution is obvious-- look in both directions the hallway runs, but not towards the walls. In a large room you look towards the other side of the room, in doorways, and archways. As humans we quickly process the two dimensional rendering of the area and mentally create a three dimensional depth map. Then we look into the areas with the furthest line of sight, for the most part. That's because new targets come from far away.

While it's very hard for a bot to identify which areas are doors and hallways using its "sonar", it can at least determine which areas of the room are far away. To do this, the bot just sends out eight pulses around itself and finds the three that have furthest line-of-sight. It does some additional checking to find the ground below that point, so when the bot is on a ramp or staircase, it actually looks down the direction of the stairs. And then the bot randomly picks one of the three furthest directions. It's a pretty simple solution, and it does a great job of making sure the bot scouts in the areas where opponents will appear.

Now the interesting thing to note is that no human opponent ever actually sees the bot doing this for more than a fraction of a second. Once you're in the bot's field of view, it will start aiming at you rather than looking for more targets. But a human would notice the results of this code, in that the bots notice them and start responding in a realistic manner. An algorithm that no human actually sees is obviously less important than one they interact with all the time. But that's not the same thing as unimportant, and the relative simplicity of the scouting code reflects this.

A Simple Solution

As the story ended last week, I had uncovered a very strange bug in BrainWorks. There was up to a 10% accuracy difference between the first bot added to the game and the last bot. No one guessed the correct cause, but this one from cyrri was the closest:

bots occupy client slots in the same order as they are added.
in the very rare cases of two clients killing a third one simultaneously, it is allways the one with the lower slot id that gets the frag, becuase his usercommands are processed first. the other one gets a missed shot.

This actually does happen, but it only accounts for a 1% to 2% accuracy change between the first and last bots. Also, this value increases as bot accuracy increases, since it's more likely that two bots will be lined up for a good shot at the same time.

The real culprit was the mechanism through which the server processes client commands. The server processes each human player's inputs as soon as it receives them (as fast as 3 milliseconds for a human), but the inputs for bots are processed exactly once every 50 milliseconds. In turn, each bot handles its attacks and then moves. Then the next bot is processed, and they do this in the order they were added to the game. See the problem?

Every bot made its decisions based on where the other bots were currently located at the end of the last server frame, but only the first bot will actually attack against targets in those positions. By the time the last bot aims, every target had already spent 50 milliseconds moving. The first bot had 0 ms of latency and the last bot had 50 ms. Now 50 milliseconds isn't too bad, except the last bot was playing as if its latency was 0. That's why it missed around 10% more.

Since one of my original project constraints was that nothing would change in the server code, that meant that at least some bots had to play with 50 milliseconds of latency. There were no changes I could make to reduce this problem. So the solution was to add latency to all the bots. I wrote a feature into the AI core that tracked the positions of all players in the game for the last half second worth of updates or so, for all bots and all humans. Then if a bot needed to know where a target was, it looked up the properly lagged position and did basic extrapolation to guess where the target would be at the exact moment of attack.

Implementing this system gave all the bots very similar accuracies (within 1% due to the issue cyrri pointed out). But now the problem was that all bots the same accuracy as the worst bot, when they should have had the accuracy of the best bot. It turned out this "basic extrapolation" wasn't good enough. The original Quake 3 AI code used linear trajectories to estimate where a target would end up, nothing more sophisticated than that. So if a bot aimed at a target running to the bottom of a staircase, the bot would keep aiming up into the ground for a while, even though a human would know the target would move straight.

I tried some slightly more advanced solutions, doing basic collision checking against walls, but that didn't solve the problem of running down a ledge. I eventually concluded that humans have a learned sense of physics they take into account, and the bots would need that same sense if they were to play like humans. Solving this problem was both straightforward and time consuming.

I made an exact duplicate of the entire physics engine, modified it for prediction, and placed it in the AI code.

Every detail needed to be modeled-- friction, gravity, climbing up and down ledges, movement into and out of water. Even the force of gravity acting on a player on an inclined ledge. Everything had to be duplicated so that the bots could get that extra 10% accuracy in a human-like manner. It was not easy, and I learned far more than I wanted to know about modeling physics in a 3D environment. But in the end, it was worth it to see bots that could aim like humans.

Making Things Right

You do it thousands of times each day without even thinking about it. It happens when you put on your clothes, brush your teeth, eat your food, and open a door. Every time you walk down a hallway, drive down the street, or type at your computer, your brain is rapidly, subconsciously processing all kinds of data. Most of the information is uninteresting and gets discarded. But when the brain encounters little bits of unexpected data, for the most part it seamlessly reacts, performing minor adjustments to correct whatever mistakes were made in fine motor control. Someone might brush your shoulder as they pass you on the street, and without even thinking about it you step to the side. Or you see the car in front of you start breaking and you slow down as well. You don't swallow your food until the pieces feel sufficiently chewed in your mouth.

Somehow, the human brain performs thousands of minor error corrections while performing basic motor controls. Large motions are fast but they aren't precise. How exactly does this work? If you mean to move your hand 10 centimeters and actually move it 11, your brain compensates for the extra centimeter and has your hand move back. Well maybe it only moves 9 millimeters instead of 1 centimeter, but it gets close enough that it doesn't matter.

When I discussed how BrainWorks emulates human wrist motion, I explained this elegant mathematical model where errors accumulate based on muscle acceleration. But I brushed over the other half of this system, where the bot accounts for its error and readjusts. With error correction that's not good enough, a bot's aiming just looks erratic. And with error correction that's too accurate, the aiming looks robotic. There's a very fine balance to getting error correction that looks human, and to do that you need to understand how the human brain corrects muscle errors. There's just one problem with that...

I have no idea how it happens.

There have been numerous studies on this kind of thing, but I confess I haven't read them. Maybe they would have been useful; maybe they wouldn't. Instead, I tried a number of different methods for error correction (that number is 5), and only one of them produced realistic results. While I can't tell you how the human brain solves this problem, I can explain the one that worked.

From the bot's perspective, here's the problem. It thinks it's aiming at position X, but in reality it's aiming at X+e, where e is the error factor. As the bot corrects its error (ie. understands the mistake it has made), the error factor e approaches zero. An error factor of zero means the bot thinks it's aiming at X and is actually aiming at X+0 = X, meaning it perfectly understands where it is aiming. Most of my failed attempts used some form of exponential or linear decay. Ironically, the algorithm that worked is by far the simplest. It's just a canonical monte carlo algorithm:

1. Set the error value to a random number between -e and +e
2. There is no step 2

It's just that simple. You can read the full explanation behind this algorithm in the DataPerceiveCorrect() function in ai_view.c. It's a 100 line comment describing, quite literally, one line of actual code. Here's the body of the function:

float DataPerceiveCorrect(float estimate_offset)
{
 // Pick a new value in (0, +error) or (-error, 0)
 //
 // NOTE: It doesn't matter what the sign of error is; the random
 // function will preserve it.
 return random() * estimate_offset;
}

While I won't reproduce that entire 100 line description here, here's a portion of it that explains in more detail why this works:

Humans seem to use the following algorithm for refining estimates. For example, finding the dictionary page that contains a given word.

1) Make a reasonable estimate given the human's understanding of the situation. For example, even though W is the 23rd letter of the alphabet, humans don't look at the (23/26) * MAX_PAGES page of the dictionary when looking up W words, simply because they know the X-Y-Z sections are so short. This indexing is similar to the Interpolation Search algorithm. This result is compared to the actual value (ie. is the guess too high or too low?) and this value is fixed as either an upper or lower
bound. In other words, you mark this page with your finger.

2) Possibly make one or two more reasonable estimates to determine both an upper and lower bound that the data must reside in. At this point the human knows that lower < value < upper. He or she knows the precise values of "lower" and "upper" but NOT of "value".

3) Pick a random value in the interval (lower, upper) and compare it to "value". This selection replaces either the lower or upper bound as necessary. Repeat until the selection equals "value".

This might seem unintuitive, but humans don't actually use binary search to narrow down their errors when the range gets sufficiently small. Perhaps it takes too much time to roughly estimate the middle. In practice people will flip through maybe 10 pages at a time, or 1 page at a time, or just pick something and see. It will take more iterations to converge than binary search would but-- and this is crucial-- it takes less time overall than computing the midpoint at each iteration.

That is it say, a computer's most natural method of search is not the same as a human's. Computers usually operate best with binary search while humans "just try something". Only when I programmed the AI to do something counter-intuitive (for a computer) did the AI seem human.

Little Lies

I read an exceptionally good essay by Paul Graham entitled, "Lies We Tell Kids". It talks about white lies, the kind you tell to protect people rather than manipulate them. It's worth reading in full, but let me summarize what I took out of it:

People usually lie because the listener cannot emotionally or mentally handle the truth. While this is often the best thing for the listener at present, it has a cost: They do not understand reality as it truly is. They are unprepared to handle that truth when they encounter it in another fashion.

As I wrote earlier, I see strong parallels between designing AI and being a parent. There's a lot to be said for the "lies" that we as AI designers tell our AI: "No really, you can have perfect information about the entire world. And if you have to spend a long time thinking, that's okay. The entire world will stop and wait for you to decide." Whenever we let our AI cheat at the game, look up information it shouldn't know, or otherwise do things a player can't do, we are in some sense lying to our AI about what reality is like. And the price we pay is this:

We live in fear that our AI will encounter a game situation where the player will know the AI has cheated.

I spend a lot of time talking about good AI and bad AI, and why it's often worth the time to write good AI. But I'm not going to say we shouldn't lie to our bots! I recognize that "bad AI" has a place in AI design. Sometimes a corner must be cut because a problem is simply too difficult. And often these are not isolated exceptions. It's possible to write enjoyable AI that cuts an awful lot of corners as long as you know the right corners to cut.

For example, a bot can get away with almost anything if the player can't see the bot. The amount of cheating a bot can get away with dramatically decreases once the player actually watches the bot, in the same way that a magician's tricks only work when you watch the hand he wands you to watch.

The real key to writing good AI is learning which corners to cut and which problems must be explicitly solved in a human-like manner. In other words, it's about determining when to write bad AI and when to write good AI. In BrainWorks, I certainly erred on the side of too much good AI, but I think this is still a long term benefit. Once someone has solved the problem (and released the source code), everyone can reap the benefits and we can all move onto harder problems. But actually writing artificial intelligence that doesn't cheat about anything is an excruciatingly difficult problem. An AI that does everything like a human does would appear to observers to be a human. It could pass a Turing Test, the holy grail of Artificial Intelligence research.

When I say "Holy Grail", I mean it in more ways than one. Even the most optimistic estimates on when we'll be able to create a truly artificial mind say it won't be done for decades. My personal estimates are that it will be done in 300 to 1000 years. And some people claim that it is literally impossible to create an artificial mind, that it so complex that it can only be evolved or created by God (depending on your philosophical preference).

If you want an example of what roughly 700 years of research will give you, the atomic bomb was created only 500 years after the birth of Leonardo da Vinci. Think about how much science advanced in those 500 years. While that time was centuries long, it was punctuated by thousands of little scientific discoveries about how the universe worked. Einstein couldn't have had his flash of insight about how Newton was slightly wrong if Newton hadn't done work to come up with equations that were 99.9999% right.

I believe the next 700 years of AI Research (give or take a little) will be similarly punctuated. Humanity's journey to the creation of an artificial mind will be punctuated by little advancements. No one person could do all the work necessary to design everything needed for a true artificial mind, but every time an AI designer chooses to do a bit of "good AI" rather than cutting corners with "bad AI", we get one step closer to the final goal, even though the destination is light years away. I will not fault anyone for writing AI that cheats; I've done the same. But writing AI that cheats even a little less than usual is worthy of high praise.

The Man Behind the Curtain

Last week I wrote about the Christian philosophy I had. The basic conclusion was that if someone has sufficiently strong reason to believe in a religion, nothing external from that religion can ever make them lose faith. Rather you will see them modify the fringes of their beliefs to fit whatever counter-evidence they encounter, without changing the core beliefs.

For example, a Christian may start claiming that God created the world in seven days. When presented with scientific data demonstrating the world is older than 6000 years, they might claim Satan just planted the dinosaur bones so deceive people into believing God doesn't exist. (Yes, I've heard someone present argument in all seriousness.) The more rational believers can apply Occam's Razor to this situation and conclude that maybe "day" isn't the right translation, and perhaps end at the conclusion that God used evolution to create humans. But no amount of external (scientific) evidence will convince a believer that their religion is wrong. I was familiar with all of those arguments and they didn't make me lose my faith in God. Only an internal inconsistency could fracture my faith.

I moved to California in the middle of 2000, a year and a half before I started work on BrainWorks. I started the project by programming on evenings and weekends, but after almost a year I realized that writing truly good AI would take more time than that. My wife and I talked about the options, and after a lot of time spent in prayer, we came to a rather dangerous conclusion. I decided to quit my day job and work on BrainWorks full time as a way of building my portfolio, live off savings, and then look for a job in the game industry. Even though this was a crazy idea, we felt assured by God in our prayers of four things:

• God would provide for us
• God would vindicate this audacious decision to others upon completion
  
• I would truly meet God through the work I did
• I would finish the project before we left the state of California

In the years following, we would return in prayer during difficult times, and also ask for the prayers from others, and we were continually encouraged when we heard these promises repeated, both in our minds and from the words of others.

Concurrently, there were other issues we had lifted up in prayer. Most notably I had back problems for over a decade. Having tried multiple chiropractors, doctors, physical therapists, exercises, and received countless prayer on the issue, I was at a loss. Nothing seemed to work. But I believed in a God that could do real miracles, and healing my back was certainly not the hardest of them. After years of prayer, I kept hearing that God really was going to heal my back, at the proper time, and that receiving healing was about more than just freedom from physical pain. It was about encountering God in a deeper way, which is why the timing mattered and why God didn't just heal me immediately. We felt like God spoke to us that he would give me full healing for my back before we left California. In some sense, our sojourn in California was an opportunity to witness the miraculous works of God, in my work and in my life, and to encounter God.

So what happened? In case there is any doubt about the story's outcome, let me set the record straight. While we did have enough to live while I was unemployed, I did not "encounter God" through the work. I received a job offer in Boston in January of 2007, which my wife and I prayed about and decided I should take. I did not complete BrainWorks before I left California, nor were my back problems healed. In our cross country drive from Los Angeles to Boston, the two of us were faced with a rather uncomfortable situation. Several things we thought we heard from God, including the prayers of others, all agreed with each other, but they were all wrong. That's really not supposed to happen. At the end of that drive, we came to one undeniable conclusion:

We are not very good at hearing from God.

Maybe the reason is that there is no God, or maybe just no Christian God. Or maybe the Christian God exists, and the problem is with us. When we arrived in Boston, we set aside one month for God, each day praying, "God, if you're real, undeniably reveal yourself to us." Since we are very bad at hearing from God, if we are to have a relationship with God, the effort must be his. After one last month, we still experienced nothing. And at that point I came across one truth, written in the Bible, that forever shattered my faith.

If you're not familiar with the Old Testament, one of the most common themes is the warning against idolatry, the worship of false gods through graven images rather than the one, true God, the "I am". The Hebrew scriptures are littered with hundreds of these warnings, but the best story is from 1 Kings 18, where Elijah confronts the prophets if Baal. Here is an excerpt:

Elijah went before the people and said, "How long will you waver between two opinions? If the Lord is God, follow him; but if Baal is God, follow him." But the people said nothing. Then Elijah said to them, "I am the only one of the Lord's prophets left, but Baal has four hundred and fifty prophets. Get two bulls for us. Let them choose one for themselves, and let them cut it into pieces and put it on the wood but not set fire to it. I will prepare the other bull and put it on the wood but not set fire to it. Then you call on the name of your god, and I will call on the name of the Lord. The god who answers by fire—he is God."

Naturally the prophets of the false god Baal have no success, but Elijah has success:

At the time of sacrifice, the prophet Elijah stepped forward and prayed: "O Lord, God of Abraham, Isaac and Israel, let it be known today that you are God in Israel and that I am your servant and have done all these things at your command. Answer me, O Lord, answer me, so these people will know that you, O Lord, are God, and that you are turning their hearts back again." Then the fire of the Lord fell and burned up the sacrifice, the wood, the stones and the soil, and also licked up the water in the trench.

The basic argument is always the same. What separates idols from God was the success rate. You should worship God because God is real and will provide for you. A true God produces results and a false God does not. After all, what purpose does God have if he can't make an impact on you? When I remembered this, I could not escape its conclusion in my life:

God is my idol.

I could not deny the promises that hadn't come to pass, and those promises that had were easily explained by things other than God. At the very least, the Bible I believed in condemned my God as an idol, since it also did not produce results. I had peeked behind the curtain, expecting to see the magnificence of God, and instead encountered a mirror. All this time, I was the man behind the curtain. The things I thought I heard from God in prayers were just my own desires and fears. I am not good at being God.

To continue worshiping this God would be worshiping an idol, something the religion condemns as an obviously pointless and worthless activity, but it was the only God I had ever known. From that point on, I knew I could no longer be a Christian. I realized Christianity did not have practical applications in my life. But that's different from saying Christianity is ideologically false. Next week I'll explain how I concluded the core ideology of Christianity was flawed, and how I went from an outcast of Christianity to living in the freedom of heathenism.

Parallel Evolution

There is a biological concept called parallel evolution which refers to the phenomenon by which the same trait is evolves in the same manner in two different species. For example, all eyes came from the same base creature. But eventually marine and vertebrate animals both evolved the same set of eye advancements, despite lacking a common ancestor that had all those advancements. There are also cultural examples of parallel evolution for concepts like writing and the wheel. In each case, these concepts fit three basic criteria:

• Very useful when obtained
• Not completely obvious
• Little to no room for implementation differences

The key point is the last one. There's just one way to do a working wheel or for writing to work, so in that respect it's not surprising that the ancestors of the Mayans and the ancestors of Chinese came up with the similar concept of putting symbols on surfaces to record concepts. (And yes, the wheel is not a completely obvious concept, because it also requires the design of an axle.)

So I often wonder what an alien life form would be like, evolved totally independent of life on Earth. What concepts are there about life that really only have one good solution? For example, the structure of the carbon atom makes it highly likely that all life would be carbon based. It's theoretically possible to have silicon based life, because carbon and silicon have similar structures (both have four possible atomic links), although silicon is much heavier, but carbon is by far the most likely. Also, water is a fascinating molecule in liquid form, used for a variety of purposes in life, so it would be very surprising to see a life form that did not use water.

All of life exists somewhere in the density spectrum between water (liquid) and carbon (solid). Some are closer to the water side, like a jellyfish. And some life forms are closer to solids, like trees. Land based animals ("fleshlings") are in the middle, and seem to offer a good mixture between the mobility of a jellyfish and a protective shell of a tree. So it seems likely that any sentient life form will be made of some kind of flesh.

So while we can guess about the physical form of another life form, deducing the nature of an alien brain is much harder. That's primarily because we don't know enough about the human brain to guess which features only have one simple solution. We've only encountered one kind of sentient life: humans ourselves. Would aliens need to sleep? Our research into sleep implies our brains use sleep to process memories, similar to defragmenting a hard disk. Important memories are stored, unimportant ones are discarded. But is our brain's implementation of memory the only way to solve this problem?

Well, computers solve the storage of data-- thoughts and memories-- through completely different means. Human thoughts are fluid. People can easily forget things that happened five seconds ago, or even remember the details incorrectly. Even photographic memory isn't perfect, and seems to decay with age. Short of hardware errors, however, a computer's memory management is perfect. All the data can be retrieved without error. The human brain seems to sacrifice accuracy for much greater storage capacity, access speed, and perhaps processing speed. It's possible there exists an alien life form that has found a solution to the memory problem that incorporates human memory capacity with computer memory accuracy, but I doubt it.

So this line of thought makes me think two things about the field of AI research. The first is that it's crucial to identify the portions of intelligence that only have one good solution and solve them first. For video game AI, these are problems like navigation and visualization. At this point everyone knows the navigation solution is A*, but there's still the question of how the mind identifies the potential way points A* requires. For general AI, the most fundamental problem is language processing, which is devilishly difficult and could take centuries to solve. But having a library of known solutions to basic AI problems will accelerate the ability to create good AI. This is similar to how an operating system abstracts away simple solution to problems a programmer doesn't want to worry about very much, such as processor scheduling and disk space access.

The second thought I have is this nagging feeling we may be designing AI on a fundamentally flawed hardware platform. The computer is excellent when what you want is ultimate precision. Designing AI on a computer involves writing sophisticated algorithms to artificially create the "randomness" that real life seems to incorporate. Perhaps one reason artificial intelligence is so hard is that sentient life requires a system of memories that trades precision for increased data capacity and faster access time the way a human brain does, and we'll never create satisfying AI until we start programming on that kind of a hardware platform. I don't know if that hardware platform is still based on transistors or if it's something more like DNA, but deep down I feel like designing AI on a computers is pushing a square peg into a round hole.

BrainWorks 1.0.1 Released

I've just released the latest update to BrainWorks implementing the fixes I talked about a few weeks ago. Basically there's a much more sophisticated algorithm for tracking and estimating how likely a bot is to shoot a weapon in a given situation. You can download them using the links on the right if you're interested in trying it out. Let me know what you think!

Related to this, I've been thinking about the question of when software is done. In some sense I still stand by my answer in The Attitude of the Knife, which is "when you say it is". The flip side is that as long as you still have ideas and commitment, there's always room for continual improvement. For a simple program meant to meet a specific purpose, such as reading your email, there comes a point where there isn't much more room for feature development. Artificial intelligence isn't like that though. To this day, researchers are still trying to figure out how different areas of the human brain work. And contrary to popular opinion, humans are not the end of evolution. The human brain itself continues to refine and advance itself through generations.

I believe the line dividing things that can be finished and things that cannot is the line of self reference. When the problem is best solved by something that can analyze and correct its own mistakes, a whole new field of issues apply. For an in depth explanation of why this is, I highly recommend the second of three books that influenced my mental framework for understanding the world. That book is Godel, Escher, Bach: The Eternal Golden Braid, and it is about the nature of intelligence. Very roughly paraphrased, the book talks about the mathematical theorem known as Godel's Incompleteness Theorem, which says that any system capable of describing itself can describe statements that are true but unprovable. Originally the theorem was discovered in attempts to work out some issues in Principia Mathematica, an attempt to derive all mathematical truths from first principles. However, the incompleteness theorem sheds unanticipated insights on the areas of philosophy and by extension the nature of thought and intelligence.

Viewed in the context of intelligence, you could conclude that there are things which an intelligent person would do, but there is no describable algorithm that could conclude what those things are. Perhaps this represents acts of creativity, intuition and insight. Or perhaps those things are describable, but other things are not.

Applied to Artificial Intelligence, it means that there are some aspects of AI that we cannot solve; we can only approximate. And there's always room for better approximations. This is the real that AI development can last forever. You're never really done. At least not until you say you are.