Monday, June 26, 2006
In thinking about this it seems to me that there are three broad categories of using new technology in a performance.
Technology for the audience only
This is some technology that is part of the performance that the characters do not react to. For example, there might be a video screen above the stage that shows what the characters are thinking. It helps tell the story.
This also includes technological ways in which the audience can affect the performance. For example, the way they shift in their seats can affect the lighting or how characters move. The characters are not aware of the presence.
Technology in the reality of the characters
This is technology in the world of the play. Characters interact with it. For example, there might be characters who have TV sets for heads, and the TV sets "speak." The characters treat this as normal, as they are familiar with this part of technology in their world, as we are with cell phones in ours.
Technology that the characters react to
The technology is a new part of the world of the play, and the characters react to the new technology appropriately: with fear, awe, etc.
Sunday, June 25, 2006
For example, just now, while I was reading A Companion To Cognitive Science's chapter on language processing, I noted something interesting about the tip-of-the-tongue phenomenon. When we are trying to think of a word and cannot, it is distinctly different from the state of knowing that there is not a word for something.
So you might not be able to remember that a pasta strainer is called a "colander." You might search for this word in memory, in vain, but you know that there is a word. How? If you can't think of the word, how can you be so sure that there is a word? How do you know it's in memory at all?
The project idea is to come up with an answer to these questions.
Some might say (in fact, I expect many of my students to say) "this isn't a project; it's just a problem." True, it is just a problem. But I like open-ended problems for my students. In doing this project they might find the problem has already been solved, or there are some theories out there, or whatever. The process of finding this stuff out is valuable to them. And as a bonus, I get an answer, or a start of an answer, to my question.
Friday, June 23, 2006
Peer review is a fundamental part of science. When a scientist wants to be published, the journal has anonymous scientific peers review the article to see if it should be rejected, modified or published. The article authors get to read these reviews. The journal "Nature," one the the top scientific journals, is doing a peer-review experiment in which after the normal review the paper is put online. Anyone can comment on the paper as long as they identify themselves. The folks at Nature will then evaluate the open reviews to see if they add anything. I can't wait to see the results!
Thursday, June 22, 2006
Monday, June 19, 2006
The next day was the pug competition, so I shelled out $40 for a round trip taxi ride and saw the cute little guys. See picture. Note the wrinkly head of a Char Pei on the left. I got a couple of names, and I'm following up on them now.
Check out this cute picture of a pekingese. Just a dome of fur with a face. Also note the ice pack he liked to sit on, because it was hot and he had a big fur coat on. Cute! To see more pictures from the show, see my new flickr account at
Friday, June 16, 2006
Please Hammer Don't Hurt 'Em!
On the other hand, you need to have a consistent research theme for long periods of your scientific career. You research theme should be some theory, some statement about the world that you endeavor to support through your research. In AI and cognitive science, this might involve the use of some methodology (e.g. Bayes nets, neural nets, case-based reasoning). You see the apparent conflict here.
Lately I've been interested in creating a cognitive architecture based on analogy (a cognitive architecture is a combination of a theory of how the mind works as well as a high-level programming language for cognitive modeling). In my efforts to do this, I've been going through all of the major things the mind can do and thinking of how they could be done analogically. Am I breaking the hammer rule? Yup.
The hammer rule is better used in applied/engineering settings, and is less appropriate for science, where parsimony is a major factor in theory design. If you're a computer scientist trying to find, perhaps, the most efficient way to do things, then the hammer rule must be taken to heart. But in science, we want the simplest explanation that accounts for the data. It is the responsibility of the scientist to push their ideas as far as they will go. If we can explain all of cognition with logic, or case-based reasoning, then that's great.
As my man Aaron Sloman says (1984) "It is sometimes a good strategy to adopt an extreme position and explore the ramifications, for instance choosing a particular language, or method, and acting as if it is best for everything. This can have two consequences. First, by striving to use only one approach one is forced to investigate ways in which that approach can be extended and applied to new problems. Secondly if the approach does have limitations we will be in a better position to know exactly what those limitations are and why they exist."
If you're a scientist who uses a different idea for every domain you approach, you are in danger of being scattered. This is a bad career move, even though you might be doing decent science.
My AI friend Kevin Murphy likes to say "are you working on solutions or are you working on problems?" (the correct answer, in his mind, is problems). The intention of this advice is to keep you from making solutions to problems that are not real. Again, I think this is a rather applied/engineering perspective. In engineering, if there is no real problem to solve, you're doing nothing important. In science, if your "solution" is a theory, however, then you're doing it right-- apply your theory as broadly as possible, look for new intellectual problems for it. It's good for your career. In fact, it drives me nuts when I look at faculty webpages and they say things like "My research interests are implicit memory and categorization," and say nothing about what their theory is. If you're defining your career with a problem, you're in trouble. If you're defining it as subject areas, you're even worse off.
As an exercise, you can look at some of the faculty at Harvard's psych department
and see who does it right. Who makes a substantive statement about the world, and who just lists phenomena they like to look at?
And if you're unconvinced, ask this of yourself-- are the best and most famous scientists in history known for their areas of interest, the problems they tried to tackle, or their theories?
Sloman, A. (1984). Why we need many knowledge representation formalisms. In M. Bramer, (Ed.) Research and Development in Expert Systems, Proceedings BCS Expert Systems Conf. Cambridge University Press 1985. pp 163--183.
Thursday, June 15, 2006
Tuesday, June 13, 2006
I like to write very short stories. The problem is telling them. I say to someone, "hey, you want to hear a very short story?" Then I tell them something like this one:
"A woman was cutting meat. One of her arm bones asked to see the light of day. She cut herself open and bled all over the place. The nearby bones got excited and asked to see the world too."
The problem is everyone says something like "I don't get it." Seems that no matter how I deliver this story, people can't help but think it's supposed to be a joke. It might be that this genre of very short stories is not workable because of its superficial resemblence to jokes. People expect a punchline and a laugh, and when they don't get it, they are disappointed, and can't appreciate it on story terms.
I think it could work if I lied and prefaced it with "Let me tell you about this cool short film I saw. Here's what happened..." and then told the story. Then their expectations are completely different. Problem is, that would be lying.
Wednesday, June 07, 2006
I'm assuming we're talking about freestyle, unchoreographed dancing. A
huge constraint is, of course, the fact that I'm a human body working
in gravity. This eliminates a bunch of moves I might be able to do on
the moon or in freefall or if I was an octopus.
Since I'm keeping to a beat, the dance move I'm about to do must be
able to work with the previous move and still stay on beat. Sometimes
I just know I won't have time to execute this next move, so I try
In swing, I have to adjust my moves according to my partner's body.
For example, the amount of force I need to exert to hold her hand, or
to lift her arm for a spin, or to support her weight in a dip all
depends on my perceptions of her body's position, weight, and
Individuals vary in how they move. For instance, in a spin, I walk
around the woman a bit so that she does not have to spin as far.
Usually experienced dancers locomote a bit while they do this, but
inexperienced dancers do not. I have to remember when dancing with
beginners to adjust my movement to anticipate theirs. Of course, if
the dancer I'm with does not know a move, I have to decide if she is
good enough to figure out how to do the right thing based on context
(which is possible). But some moves require behaviour on the part of
the follow which breaks traditional swing rules, so they need to learn
how to react to the leading moves associated with it as an exception
to normal swing behaviour. I can't unleash that on an inexperienced
In all dance I strive to stay with the beat. The tempo has some
constraints on what moves I can and cannot do. Some beats are too fast
or too slow for certain moves. For example, many songs are too slow to
be able to do "the running man" to. In swing, some songs are so fast
that many swing moves would look crappy at best, and be dangerous at
worst. Sometimes if the move is good enough, you can do it anyway, and
just sacrifice, temporarily, staying with the beat. I'm loathe to do
this, ever, but sometimes it's worth it.
I do a lot of clubbing and swing dancing, so I know a lot of the
tracks that get played. This gives me a great advantage, because I
know the music well enough to be able to coordinate moves with bridges
and other dynamics of the track. In swing this is particularly
important. You want to dip so that she reaches the lowest point of the
dip on the last beat of the track. That's why I hate fade-outs on
swing songs. Also in swing, the music will speed up (rarely) or stop
completely. At a stop, it's good to freeze in some dramatic position
such as the one I'm in in this picture:
Learning to dance is a little like learning to drive stick. There's
way too much to concentrate on at the beginning, and you can't do
anything until some of the basics have become automatized. In hip hop
dancing, there are standard footwork patterns and then there are more
complicated moves. You must learn the footwork patterns well enough so
that you can think about the more complicated moves and plan for them.
In swing, there is the basic step, which you must get down cold to be
able to do much else. I'm often planning my swing moves a move or two
ahead. I can anticipate a part of a track coming up and plan how I can
get into a position to excecute just the right move when it hits. It's
hard-- it doesn't always work, and when it does it's thrilling for me
and my follow. I've been swing dancing for 11 years now, and I'm not
interested in really learning any new moves, and most of the moves I
do are so automatized that I can practically do it in my sleep. I can
hold a moderately deep conversation while doing a swing routine that
is so smooth that many people would think is choreographed. I'm not
trying to toot my own horn, but to demonstrate that even all the
contingencies described above can be automatized to a great extent.
When I talk about "moves" note that moves are organized hierarchically. Complex moves break down into simpler ones that can be executed on their own, or recombined into new complex moves.
Tuesday, June 06, 2006
You can play at http://www.turbulence.org/spotlight/thinking/chess.html
and watch the AI think as you play!
At the gallery, you can see snapshots of the thinking:
Above is an image from early in the game, below an image from near the endgame.