Here is an interesting article on a theory by Geoffrey Miller that the brain evolved as a sexual display, similar to a peacock’s tail.  His idea is that we evolved the brain to help us attract a mate.  He has some interesting experiments showing that men are more likely to make conspicuous purchases (such as fast cars) or donations when they’re in a romantic mood and that women are more likely to volunteer (but only in visible settings).  Apparently this means “that what women want in a partner is material support while men require self-sacrifice”.  It’s certainly interesting that these behaviors may have evolved to help us attract mates.  But making conspicuous purchases to attract a mate doesn’t mean the entire brain evolved for that purpose.

I just got back from RoboCup in Atlanta, GA (ok, I actually got back 3 weeks ago, but I’m just getting around to writing about it). RoboCup is basically the world cup of robot soccer. Our team is UT Austin Villa, and we compete in the legged league, where we play four on four soccer using Sony Aibo robots. All the teams have the same robots, so its mainly a programming competition. There are lots of difficult problems to solve, such as vision, localization, movement, behaviors, and communication. I’ve worked a lot on the localization, which means having the robot figure out where it is from the landmarks that it sees. I also worked a lot on the goalie, and then a little bit on many many other aspects of the team.

RoboCup was pretty crazy. We were in the ballroom of the Fox Theater, which was built in the 1920s. In the ballroom they had three soccer fields set up, and there was a side room where all the teams had their own tables. Every day we were there from 7 am (when it opened) to 10 pm (when they kicked us out). Everyone is there trying to fix things at the last minute and making adjustments after games. The team from Bowdoin College, Northern Bites, was especially relentless, you would always hear them having discussions about minute details of the robot soccer strategy and constantly trying to improve.

Our first game was against Carnegie Mellon, where my advisor went to school. So it was a big rivalry. Tekin and I got to the venue when it opened at 7 am to try to fix the many problems we saw the night before but we ended up losing to Carnegie Mellon 4-2 (video). We had some shots on goal but missed most of them wide of the post. We played a team from Japan, FC Twaves, in the afternoon. We were hoping to win this one, but they scored first. Then we had a problem where our robot got switched to the other team and it scored on our own goal. So we were down 2-0 to this team with a few minutes left in the half but luckily we managed to score three goals in about four and a half minutes and we won 4-2. The start of this video is right after their 2^nd goal and shows us (in blue) scoring three goals in four and a half minutes. This game was stressful, Tekin and I were both pretty concerned when they took a 2-0 lead, but thankfully we came back to win.

After playing pretty poorly in the two games on the first day, Tekin and I worked pretty hard that night and the next morning to try to fix things. We improved ball-grabbing, ball following, ball vision, shooting, and communication and strategy, among other things. The next day, the team looked the best I had seen it. If you compare the videos from the first two games and the videos from these three, you will see a significant difference. We beat the Baby Tigers from Osaka by a score of 5-0 and then beat SPQR by 5-0 as well. Baby Tigers has a ridiculous kick where they climb over the ball and shoot it out from behind (video). Our third game was against the German Team, who are one of the best teams, and won the German Open this year. They had won 3 of their 4 earlier games by 10-0 scores (after 10-0 the game is called). We managed to hold them to 3-0, which I was pretty excited about. My goalie looked really good during the game, making some sweet saves. A few people who worked on goalies from other teams came up to me afterward to tell me how good it looked, which was really cool. Here is the video from the first and second halves of the game against the German Team. UT is in red in the first half and blue in the second half.

Winning two of our three second round games meant that we made it to the quarterfinals, which is better than UT did last year. Considering there were only three of us at RoboCup (most teams had 5-12 people), I was pretty happy. We played Wright Eagle from China in the quarterfinal match. We were really slow in the first half because of some network issues so they went up 4-0. In the second half that was fixed and they only scored one goal. The entire second half consisted of the ball being right around and in front of our goal, and they were repeatedly taking shots and my goalie kept making nice saves. My goalie looked great, but I don’t think we ever had the ball on their half of the field. Here is the video from this game. Even though we lost in the quarterfinals, I was pretty happy with our performance.

Watching the championship games was pretty exciting. After talking to and hanging out with all the guys on the other teams all week I was definitely hoping some teams would win more than others. Plus all of the final four teams were really good so it was really entertaining to watch. Carnegie Mellon won 3^rd place over Wright Eagle in a game that went to overtime and took 8 penalty shots before CMU prevailed. The championship was between Northern Bites from Maine and NU Bots from Australia. Northern Bites won 5-1. Here is the video of the championship game.

It was cool to see most of the teams get really into the games. TecRams from Mexico and Araibo from Japan both decided to cheer for Carnegie Mellon because CMU came out of the same first round group as them. During CMU’s games they would all be screaming and yelling. Araibo was probably the loudest team there, they were constantly screaming, yelling “GO GO GO” on offense and chanting “Goalie! Goalie!” on defense. I also was head referee for one of their games and it was hard to make calls over all their screaming. I have a great video of Araibo, TecRams, and CMU celebrating a goal during one of Carnegie Mellon’s games.

RoboCup was really cool and a lot of fun. It was a lot of work, not only at RoboCup, but all the work in the months leading up to RoboCup. But it was definitely a lot of fun. It was really cool to hang out with guys from other teams all over the world. One night we went out to dinner with guys from the German Team, Microsoft Hellhounds (Germany), NU Bots (Australia) and Northern Bites (Maine). Good times

Here are some various links to cool stuff. Northern Bites has a blog, which is very detailed and has some interesting stories. I have a set of photos I took at RoboCup along with some other photos I stole along the way. All the videos that we took of RoboCup are available at UT Austin Villa’s website. And here is a link to some photos on Time magazine’s website about RoboCup. Here are sets of random RoboCup photos and videos. Here is the full website with all of the results from the Legged League. Another really cool event at Robocup was the first ever game played with robots and humans, that video is available here.

The first ever Man-Machine Poker Championship was just completed at AAAI in Vancouver. Here is the New York Times article about it and here is the official tournament results page which has some interesting running commentary from some of the creators. The computer played two human players, Phil “The Unabomber” Laak and Ali Eslami, in four rounds of limit Texas Hold’Em Poker. They played duplicate matches, meaning the computer would play one human in one room, and in the other room would be playing the other human with the exact opposite sets of cards (i.e. the computer gets what the human in the other room got and vice versa). This was meant to remove some of the variability due to luck. Overall the humans won but the computer put up a good fight, getting a draw in the first round and a win in the second round. It looks like the humans were better at learning and figuring out how to adapt to the computer’s play than it was at adapting to the humans’ play.

I think this event is really cool. Poker is a much more difficult game to play than chess or checkers, which computers have already been very successful at. There’s no way to simply search to the end of the game to find the absolute best move because the opponent’s cards are hidden and unknown. In addition to all the probability and math involved in poker, there is the issue of trying to get a read on the opponent based on his actions, and trying to disguise your own hand with your actions, which adds a lot of complexity to the game. It is interesting that they competed in limit rather than no-limit Texas Hold’Em. Limit makes the action selection in the game much easier, as you simply have a discrete choice between a few actions such as fold, call, or raise. In a no-limit game, the player also has to choose the amount of money to bet, which can be any amount from the minimum allowed bet to all the money the player has. This addition of a continuously valued choice could make designing a no-limit computer player quite a bit harder, although the actual game of no-limit can sometimes be easier since you can bet a lot more, which can make bigger pots when you have good hands and make it easier to bluff people out.

I’ve thought about making my own computer poker player, and I actually have a couple different versions of code sitting around from my various attempts at it. It’s relatively easy for a computer to calculate exactly its probability of having the best hand as well as the probability of hitting a draw. You can calculate that a certain hand has say, a 75% chance of being the best hand right now. But this is something that professional poker players do fairly easily as well, but then they can also refine the probabilities much much more by getting a read on the player based on their facial tells as well as their bets. Trying to interpret this information is much harder for a computer. How do you tell a bluff from a bet? How do you predict the cards an opponent has based on the sequence of their actions?

It would be interesting to write a poker bot in a rigorous statistically valid way that treated each opponent action as an observation for a possible hypothesis and calculated probabilities of winning the hand. You could assign each action of the opponent to a hypothesis of the cards they have and have some probability that that action would have resulted from the opponent having those cards. That part could be specific to each opponent. Once you have a reliable idea of what the opponent’s cards are, you could calculate your probability of winning and compare it to pot odds and decide whether its worth it to be in. But even that wouldn’t account for trying to select your actions so they disguise your hand. So as you see, poker is a difficult and complex game. But a fun one. I may see about entering the computer tournament next year, this would be fun, but I have so many other interests as well.

Most people (or so it was believed) have three different types of cone cells in the eye to detect color.  Each type responds to a different set of wavelengths of light, and all the colors that we see are interpreted from the activations of the three different types of cone cells.  Now it turns out that many women (possibly more than 50%) have a fourth type of cone cell.  These women are able to detect a wider array of colors than other women or men with the standard 3 types of cone cells.  Pretty crazy.

Just a note that the researchers at the University of Alberta have now proved that their computer checkers player, Chinook, is unbeatable. The best anyone can hope to do against it is a draw. Checkers is now solved. I’m not sure how impressive it is that a computer has solved it, its requires the computer to be able to search through enough possible moves to find the correct move every time. It is cool that such a large and complex problem has been tackled, but I think it would be even cooler if the program had learned how to play on its own. Here are some opinions on the news from the New York Times: Is the Only Winning Move Not to Play?

I saw this article on Scientific American’s website about how our expectations relate to the effects of placebos. They did both a study with a placebo painkiller and a study using a gambling game. Subjects who had higher dopamine activity (and thus higher expectations of reward) during the gambling game were the same subjects who received more of an effect from the placebo. The conclusion is that your expectations of rewards are closely linked to the effectiveness of the placebo.

This is pretty interesting for a number of reasons. One is that it shows that an important aspect of having placebos work is simply being convinced that they will work. Looking at it from a reinforcement learning perspective, the dopamine activity can be viewed as the prediction of the reward signal. And then somehow expecting a higher reward signal can reduce the pain? Is the pain signal just a negatively valued feedback? What is the relationship between the expectation of reward and the actual reward received? Can simply expecting more or less reward affect the actual amount of reward received?

Here is a Wired magazine interview with Brian Eno.  I’m a fan of Brian Eno because of his work with Talking Heads, and now he’s doing some really cool new stuff.  He’s been working on generative art and music, basically having a computer create new images or sounds in some unique way to create new art and music.  It’s a really cool idea, where the artist may set the parameters of the program, provide the starting points for it, but then the art and music created will be new even to the artist.

I finally got around to looking at some of this stuff on Jeff Hawkins and his Hierarchical Temporal Memory (HTM) that people have been sending me.  Hawkins is trying to build a biologically plausible model of learning and memory in the brain that will supposedly be general across different tasks (i.e. it works for vision processing, audio, motor stuff, etc).  What he has come up with is called an HTM, and it is a hierarchical network of nodes that use belief propagation.  Here is a video from 2003 where Hawkins lays out some of his ideas about the brain and his belief that intelligence is better defined as an ability to predict the future.  This video is from 2006 and is a presentation were Hawkins describes the theory and implementation of the HTM system.

I think the idea of the HTM system is very cool.  I really think we should be looking to the brain for ideas on good learning and memory systems and doing it hierarchically makes a lot of sense.  The system Hawkins shows seems to be like a sort of hierarchical structure of Self-Organizing Maps (SOM), which is a method for unsupervised learning and clustering.  His method connects these different nodes using belief propagation so they all agree on the same thing.  And he has some unspecified method for detecting temporal sequences.  But otherwise it seems a lot like a SOM, grouping similar sets of inputs together and being able to provide the closest matching set from memory.  The results he shows on a simple object recognition task look very much like something you would see from a SOM or neural network.  It would be cool to see some results where the temporal sequencing or the hierarchical structure come into play.

Even though I think the results of the work are a bit weak so far, I still think it is pretty cool.  I’m still trying to figure out what direction I want to go in for my research and this gives me some ideas.  I would definitely like to work on something motivated by or inspired by the brain.  And it should be general enough to be applicable to many different problems.  It will be interesting to see what kind of results Hawkins gets out of the HTM as it gets extended and applied to more interesting problems.

There was an interesting article by Andrian Kreye in the Edge this week on religion and reason.  Specifically it discusses some of the scientific research into faith that has been going on and has been mostly ignored by the “militant atheists” Harris, Dawkins, and Dennett.  Kreye discusses research by Scott Atran, who wondered what benefit religion had that made us pay such a cost in time and effort and lives to try to overpower rational explanations.  His conclusion is that religion must have had some evolutionary benefits such as the closer communities it can build.  Justin Barrett has done research showing that faith may be important developmentally, as small children have unwavering faith in their mother’s infallibility when they are young.

I thought the most interesting part of the article was its conclusion, which said:

“One advantage faith has over atheism is that it offers hope for an afterlife. Thus far, we have found only religious answers to assuage the fear of death. It always comes down to a choice between delusion and reality. Reality just may make you love your life so much more.”

This echoes something I have tried to say in previous blog posts on religion but I don’t think I was ever able to state it as clearly or eloquently as this.  It may be more difficult to accept the shortness of our lives than the afterlife promised by religions but I think understanding reality allows us to appreciate and live our lives that much better than living it for some supposed afterlife.

Yet another interesting article in the New York Times science section, this one on wasting time at work.  Some research shows that a lot of problem solving and thinking through problems actually goes on subconsciously.  A lot of times you are actually better off surfing the web or relaxing than trying to pound your head against some problem.  You just need to take some time away from the problem so your subconscious can figure it out.  So time spent surfing the web or napping or playing foosball can actually be productive.  If you are a “knowledge worker” then you’re really working subconsciously all the time and perhaps we shouldn’t worry as much about how much time we spent physically working.  Personally, I can’t count how many times I have had some frustrating problem that I can’t figure out, and then I go home to sleep and wake up in the morning with the solution in my head.  I think this research means that work and school should have more time for napping.