Kaynak: KUTalks, Koç Üniversitesi'nin Youtube kanalı.
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June 08, 2012
June 04, 2012
Language visualization
This is a presentation on our ongoing language visualization project by Emre Unal. We thank the Alice project at CMU for giving us the platform for 3D visualization. This work is inspired by the work of Patrick Winston's Genesis Group at MIT and Bob Coyne's WordsEye project. We are also working on going from vision to language as demonstrated in this video.
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April 09, 2012
Probabilistic Programming
The probabilistic programming language Church brings together two of my favorite subjects: Scheme and Probability. I highly recommend this tutorial to graduate students interested in machine learning and statistical inference.
The tutorial explains probabilistic inference through programming starting from simple generative models with biased coins and dice leading up to hierarchical, non-parametric, recursive and nested models. Even at the undergraduate level, I have long thought probability and statistics should be taught in an integrated manner instead of their current almost independent treatment. One roadblock is that even the simplest statistical inference (e.g. three tosses of a coin with an unknown (uniformly distributed) weight results in H, H, T; what is the fourth toss?) requires some calculus at the undergraduate level. Using a programming language like Church may allow an instructor to introduce basic concepts without students getting confused about the details of integration.
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April 01, 2012
The wonderful xargs command
I finally found a way I like to run a whole bunch of commands N at a time on an N core machine (well maybe use N-1 to be polite):
1. Say you have a command rprun.pl that takes 4 arguments that you want to run with 1000 different argument combinations.
2. You write a script rprun-args.pl that generates all combinations you need. Say its output looks like:
10 185364 25 0.166
12 92682 25 0.166
18 65536 32 0.166
12 65536 25 0.7071
14 16384 25 0.166
...
3. Now you can use xargs to run these 24 at a time as follows:
rprun-args.pl | xargs -n4 -P24 rprun.pl > rprun.out
-n4 is to feed the arguments 4 at a time. So a typical command line will look like:
rprun.pl 14 16384 25 0.166
-P24 tells xargs to run through the list 24 at a time. If you run ps you will see 24 copies of rprun running together. As soon as the number drops to 23 another child is spawned.
Note that the command above combines the outputs of all runs (in the order they finish) in the same file, so make sure rprun.pl prints out its arguments as well as its result on its output.
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1. Say you have a command rprun.pl that takes 4 arguments that you want to run with 1000 different argument combinations.
2. You write a script rprun-args.pl that generates all combinations you need. Say its output looks like:
10 185364 25 0.166
12 92682 25 0.166
18 65536 32 0.166
12 65536 25 0.7071
14 16384 25 0.166
...
3. Now you can use xargs to run these 24 at a time as follows:
rprun-args.pl | xargs -n4 -P24 rprun.pl > rprun.out
-n4 is to feed the arguments 4 at a time. So a typical command line will look like:
rprun.pl 14 16384 25 0.166
-P24 tells xargs to run through the list 24 at a time. If you run ps you will see 24 copies of rprun running together. As soon as the number drops to 23 another child is spawned.
Note that the command above combines the outputs of all runs (in the order they finish) in the same file, so make sure rprun.pl prints out its arguments as well as its result on its output.
Full post...
March 10, 2012
On skill acquisition
A couple of months ago, I ran into this video by the Japanese coin magician Ponta the Smith. Its elegance awoke my long dormant interest in close-up sleight-of-hand magic which had started when I was a kid and had peaked in LA taking classes at the Magic Castle. I am especially fond of coin magic because its effects are so simple and direct. I started watching the masters and practicing again. My hands started being able to do things that they were not able to do a few days ago. It surprised me to remember how much fun it was to acquire a new physical skill, and that I had not done so in more than a decade!
Then my friend Alkan showed me a video of Terry Laughlin, a swim coach with a unique training style. He compares dolphins at 80% efficiency with the best olympic athletes at 8% and claims there is a lot to gain from reducing drag compared to adding power to the strokes. Ernest Maglischo's standard reference also has consistent advice on correct body alignment. While scanning Maglischo's book I was shocked to discover that it was not clear whether Newtonian or Bernoulli forces dominate the analysis of the swim stroke! (Hey physicists, when you take a break from looking for the Higgs boson maybe you can help out with this?) I have been swimming all my life and no matter how hard I tried I could not break my efficiency barrier at 17 strokes for a 25m pool. After watching a couple of Laughlin's videos I was able to do it in 13!
Continuing on a chain of skill-acquisition serendipities, I came across Moonwalking with Einstein by Joshua Foer. I should cover it more fully in a separate blog post. In addition to giving an excellent synopsis of our current understanding of memory, it introduced me to the work of Anders Ericsson on skill acquisition. Ericsson has achieved some recent fame thanks to his research showing that experts tend to require about ten thousand hours of training to achieve their word-class status. However what got my attention was the finding that when ordinary skill acquisition hits a plateau and improvement stops, that is rarely the sign of an innate limit, but rather the result of the skill becoming compiled and autonomous. The trick to going past your plateaus and improving further is to bring the activity back to consciousness in sessions of "deliberate practice" where you pay attention to your technique and get constant and immediate feedback on your performance. This is consistent with my swimming experience: Laughlin's videos made me pay attention to every stroke, in effect made me re-learn how to swim, and the 25m stroke count feedback pointed me in the right direction.
I am currently debating whether I should continue my self experimentation in the domain of Go, using techniques championed for chess by my friend Michael de la Maza, or improve my Bridge game by deliberate practice on card memory. This is just too much fun.
On a more serious note, all this shows how little we know about skill acquisition and education in general and how much room there might be for improvement. It seems to me the only way out of this conundrum is to allow experimentation in the educational domain with proper feedback and reward for innovative educators.
(*) Some of my favorite coin masters: David Roth, Michael Rubinstein, Jay Sankey, Gregory Wilson, David Stone, Giacomo Bertini, Kainoa Harbottle, Curtis Kam, Homer Liwag, Apollo Robins, Shoot Ogawa.
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Then my friend Alkan showed me a video of Terry Laughlin, a swim coach with a unique training style. He compares dolphins at 80% efficiency with the best olympic athletes at 8% and claims there is a lot to gain from reducing drag compared to adding power to the strokes. Ernest Maglischo's standard reference also has consistent advice on correct body alignment. While scanning Maglischo's book I was shocked to discover that it was not clear whether Newtonian or Bernoulli forces dominate the analysis of the swim stroke! (Hey physicists, when you take a break from looking for the Higgs boson maybe you can help out with this?) I have been swimming all my life and no matter how hard I tried I could not break my efficiency barrier at 17 strokes for a 25m pool. After watching a couple of Laughlin's videos I was able to do it in 13!
Continuing on a chain of skill-acquisition serendipities, I came across Moonwalking with Einstein by Joshua Foer. I should cover it more fully in a separate blog post. In addition to giving an excellent synopsis of our current understanding of memory, it introduced me to the work of Anders Ericsson on skill acquisition. Ericsson has achieved some recent fame thanks to his research showing that experts tend to require about ten thousand hours of training to achieve their word-class status. However what got my attention was the finding that when ordinary skill acquisition hits a plateau and improvement stops, that is rarely the sign of an innate limit, but rather the result of the skill becoming compiled and autonomous. The trick to going past your plateaus and improving further is to bring the activity back to consciousness in sessions of "deliberate practice" where you pay attention to your technique and get constant and immediate feedback on your performance. This is consistent with my swimming experience: Laughlin's videos made me pay attention to every stroke, in effect made me re-learn how to swim, and the 25m stroke count feedback pointed me in the right direction.
I am currently debating whether I should continue my self experimentation in the domain of Go, using techniques championed for chess by my friend Michael de la Maza, or improve my Bridge game by deliberate practice on card memory. This is just too much fun.
On a more serious note, all this shows how little we know about skill acquisition and education in general and how much room there might be for improvement. It seems to me the only way out of this conundrum is to allow experimentation in the educational domain with proper feedback and reward for innovative educators.
(*) Some of my favorite coin masters: David Roth, Michael Rubinstein, Jay Sankey, Gregory Wilson, David Stone, Giacomo Bertini, Kainoa Harbottle, Curtis Kam, Homer Liwag, Apollo Robins, Shoot Ogawa.
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