stormsewer

I mentioned on Twitter that Silently and Very Fast by Catherynne Valente (yuki_onna) is one of the best stories I ever read. Here are some more detailed thoughts: ( Read more... )

Understanding the financial mess we are in is hard. All these derivatives and sub-prime loans and whatnot are really hard to figure out. But this analogy (from here by way of here) is really good:

( Read more... )

So I went to another talk at the UT Energy Symposium, this one on "The Future Role of Natural Gas in the US," given by Marianne Kah, the chief economist at ConocoPhillips. My notes follow; the slides are available here. ( Read more... )

[Edited to correct errors, and add a link to the presentation.]

Last Thursday I went to a talk given by Ryne P. Raffaelle of the Rochester Institute of Technology, entitled "The Past, Present, and Future of Photovoltaics." Dr. Raffaelle is the former director of the National Center for Photovoltaics at the National Renewable Energy Lab, and the talk was given as part of the University of Texas Energy Symposium. The original presentation can be found here for the time being. The following is a write-up of my notes. ( Read more... )

So, they replaced 1% of the yeast genome with synthetic sequence. (The actual paper is available via paywall here (or via DOI).) The funny thing to me is that they removed repeat sequences from the original, saying they were destabilizing, and then added in a bunch more repeat sequences themselves. I suppose the synthetic repeats are much shorter than the natural repeats, and thus perhaps less likely to cause problems, but it still seems ironic.

So, the Hugo award winners were announced. Though I am late to the party in commenting on it, of course, I have to say I am pretty disappointed with the results. ( Feel my pain. )

"The prices of all important commodities except oil declined for 100 years until 2002, by an average of 70 percent. From 2002 until now, this entire decline was erased by a bigger price surge than occurred during World War II. Statistically, most commodities are now so far away from their former downward trend that it makes it very probable that the old trend has changed — that there is in fact a Paradigm Shift — perhaps the most important economic event since the Industrial Revolution." -Jeremy Grantham

I actually voted in the Hugo awards this year. I originally intended to write this post well before the voting deadline, in the unlikely hope of perhaps influencing the votes of others, but as I neglected to decide what my votes would be until just before said deadline, I'm posting this after said deadline. Sorry.

Anyway, the voting basically involves ranking your choices in order of preference, with the option of ranking "No Award," which means that you'd rather none of the lower ranked choices get an award; i.e., a vote for "if my baby can't have it, no one can." I'll list each of the works in reverse of the order I ranked them.

( Without further... )

The size of the human haploid genome (one of the two genome copies each person contains in each of their 10 trillion cells) is about 3 billion bases. Each base can be one of four, so the total possible number of DNA sequences of 3 billion base pairs is 4^{3,000,000,000}, which is about 1 x 10^{1,800,000,000}. You, snowflake, constitute one of those. Well, two, actually. (Not counting all the varied mutations I'm sure you've picked up on the way.)

While genetic algorithms are cool, this does point to a limitation as far as considering them as models of biological evolution- they're not going to do well with a 3-billion-value genome. The number of actual "genes" is something like 30,000, not counting the extra variation you get from alternative splicing, and the number of theoretically possible identities of each of those DNA stretches is... very large. Our genome is rather different from the genome of a workable genetic algorithm in other ways, too. It is not just a list of predetermined responses to a set of predetermined stimuli. It is instructions for coding an information processor. For example, those 3 billion base pairs contain instructions for building a brain containing 100 billion neurons. This kind of hierarchy (DNA bases -> genes -> cells -> organs -> organisms -> societies) is a hallmark of truly complex systems. If we think of each neuron as a bit (either on or off, 0 or 1), the number of possible brain states is 2^{100,000,000,000}, which is about 10^{30,000,000,000}. One of those brain states, snowflake, is exactly what you're thinking and feeling right now.

So, I've been toying with genetic algorithms a bit. Making a genetic algorithm basically involves making a list of responses to every situation the algorithm might encounter. Typically you start with a random pool of algorithms, compete them, mutate and mix and match the winners' genomes to make a new generation, and then repeat indefinitely. (Does this process sound familiar?) The idea of encoding a response for every possible situation works well for some problems, but not for others. For instance, it would never work for teaching a computer to play go. ( Why not? )