Entries for January 2007
February Dorkbot - multimedia
The upcoming Dorkbot meeting took longer than usual to finalise, because our usual venue didn't work out, so we've been scrambling to find a plan B. It's all done now, we have a venue that I like the look of and I'm rather excited about the programme we have. Normally we have a movie night once a year, but this year we've upgraded somewhat, by finding two artists who combine their movies with live performances. Presenter details are after the cut:
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Knowing what to leave out
[this is really a continuation of last Thursday's post]
I remember going through a phase during my MSc of wanting to build neuron models that included every known detail. It ought in principle to be possible to model individual molecular interactions, in a non-deterministic way, and thereby effectively run a neuron in silico. My big idea was to test the theoretical understanding of how neurons work, by seeing if the model matched experimental observations, but I eventually came to realise some serious issues with this idea.
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The unfortunate need for specialisation
People love to bemoan the extent to which scientists specialise into disciplines and sub-disciplines. There is a real drawback to this—it makes it very easy to miss out on knowing about work that is directly relevant to one's interests but happened to fall into a different 'pigeonhole' to one's own topic—but I think it's important to understand why it happens and may even be unavoidable.
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Biological clocks as a model system
I've just read an impressive paper—Evolving Biological Clocks using Genetic Regulatory Networks—from ALife X. Above all, I was struck by the choice of model system—biological clocks—because it's something I wish I had thought up myself, and it's a very neat illustration of the kinds of phenomena I find most interesting in biology.
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How do we show that a model is appropriate?
In the background reading I'm doing to determine exactly what my new research project should be, certain questions keep popping up. Selfishly, this is a good thing, because anything that really seems to be unanswered by the literature is a potential starting point for interesting research, but for today I'm going to write about one that is worrying to me because I feel like it should have already been answered.
The issue is that for any given biological system, there are many possible ways to model that system. To a large extent, model selection has to depend on what the model is intended to achieve; missing out crucial details will invalidate any conclusions, but including more detail than necessary makes experimental data harder to interpret. We're also constrained by how much is known: for instance no-one can make gene regulation model that realistically accounts for the speed of gene transcription until someone finds a way to discover that speed, but this isn't really what I'm worried about. The question I'm interested in is how one can determine whether or not a given model captures all that is relevant about the process/system being modelled. This worries me most with respect to boolean network models of genetic regulatory networks, because there are many such models about, but they make some really big simplifications.
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Introducing Dorkbot
I do things other than work, from time to time. Mostly I don't write about them here because I want to keep this blog nice and focussed, but I think I will start regularly posting about one in particular—Dorkbot—because I think it's directly relevant. I am one of a loosely-organised group of "overlords" who organise the Seattle Dorkbot meetings and shows. Since I expect I'll start writing about this from time to time, I thought I should start with a post explaining what I'm talking about.
The Seattle Dorkbot website (which I maintain and needs updating... oops) gives a short blurb about what we are, which I'll reproduce here because it's a good start, and then I'll add my own words after the cut:
Welcome to dorkbot-sea, the Seattle-based spin-off of dorkbot-nyc, a monthly meeting of artists (sound/image/ movement/whatever), designers, engineers, students and other interested parties... who are involved in the creation of electronic art (in the broadest sense of the term.)
The purpose of dorkbot is to:
- Give artists / programmers / engineers an opportunity for informal peer review
- Establish a forum for the presentation of new artworks / technology / software / hardware
- Help build relationships and foster collaboration between people with various backgrounds and interests
- Give us all a chance to see the cool things that our neighbors are working on
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Keeping references organised
Because I'm starting on a somewhat new research track, I'm finding myself spending the majority of my work time reading. This brings me back to a question that I think about every time I'm in this sort of position, and never seem to have a satisfactory answer to: what is the best way to keep references organised?
The way I see it, there are two challenges involved in this: keeping track of what one has read, and keeping track of where one read it. The first part is a general problem with learning new things, and while I don't think there's any magical solution, I also don't think it's all that hard provided one pays attention and thinks through how each paper relates to what one already knows. It's really the second I'm interested in, because obviously it's impossible to cite work appropriately if, as I often find, one only remembers I read a paper about this once
without being able to pin down which paper it was.
Behind the cut I'll write a little about how I try to manage this, but I don't believe my own approach is optimal, so I'm very interested to hear from my 3 readers about how you do it.
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How do people respond to conference deadlines?
Together with Eduardo Izquierdo-Torres, I'm organisng a workshop (Evolution and Dynamics of Learning - note that we're still finalising the CFP and website) at this year's European Conference on Artificial Life. We're working out when to set the various deadlines (paper submission, return of reviews to us, camera-ready proofs), and it led me to wonder about something: do most researchers set out to write papers in response to a specific conference/workshop announcement, or do they write papers when they're ready and then look for a conference or workshop to submit them to? I know that a large proportion of my small readership are active scientists at one level or another; what do you do?
A new direction for my research
In December, I consulted with my advisor and we decided to shelve the research project I had been working on, because it wasn't getting anywhere, and I was getting increasingly nervous about the chances of graduating in a reasonable amount of time. One day I'd like to pick up the evolution-of-learning thread again, but it would either have to be using a different experimental paradigm or significantly more computing power, because experiments that take a week or more to run and then mostly have negative results are simply not practical. For the moment, this left me without a research project (which incidentally has a lot to do with why this site went quiet again) and that was obviously rather worrying in itself.
After making the decision to drop the old project, I took a couple of weeks off so I could put the frustration of being stuck with the old experiments behind me, and then started figuring out what to do next. I'm now well on the way to having a new project outline, and the past couple of weeks have seen me become progresively more optimistic as I put that together. Details are behind the cut; it's not yet as clearly defined as I'd like it to be, but I'm getting there.
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Saul Greenberg: Enhancing Creativity Through Toolkits
[note: I'm probably going to start writing rather more concise talk summaries, but more of them, because I need to be more thorough to satisfy course requirements in absentia]
Today I went to a CSE talk at the University of Washington: Saul Greenberg on Enhancing Creativity through Toolkits
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