May/100
Paper summary: Interconnected musical networks: Toward a theoretical framework
Weinberg, G. (2005). Interconnected musical networks: Toward a theoretical framework. Computer Music Journal, 29(2):23–39
Weinberg (2005) discusses musical networks, the concept of performance as an interdependent art form. Electronics expands the range of possible interdependencies in musical performance: ‘Although acoustic-interdependent models provide an infrastructure for a variety of approaches for interconnections and interdependencies among players, they do not allow for actual manipulation and control of each other’s explicit musical voices. Only by constructing electronic (or mechanical) communication channels among players can participants take an active role in determining and influencing not only their own musical output but also that of their peers’ (Weinberg, 2005, p. 23).
May/100
Book Summary: “Musicophilia” by Oliver Sacks
Sacks, O. (2007). Musicophilia: Tales of Music and the Brain. Knopf.
Sacks explores the neurology of music and gives examples of patients who have various unusual responses to music. Told in a wonderful, person-focused, story-telling manner, Musicophilia expounds on music as the wonderful back door to our minds. One man gained a sudden appreciation of piano music after being struck by lightning. Some people get seizures from music or hear music in their seizures. Most people experience getting music stuck in their head, but some have more intense musical hallucinations.
Apr/100
ChucK: mind-bending programming language of the day
I've recently started teaching myself a new programming language: ChucK. You can read a paper by the authors that gives a quick outline of some of the salient features of the language:
Wang, G., Cook, P. R. (2003). ChucK: a concurrent, on-the-fly audio programming language. In Proceedings of International Computer Music Conference, pages 219–226
Mar/100
Robotic drumming with machine learning back end
This video is what I'd like to build: Jazari drumming robots controlled by Wiimotes and machine learning.
Of course, I'm in the business of building better and more algorithms for music and other purposes, but this is the kind of hardware platform that I'd be keen to demo algorithms on.
You can read more on the artist's website.
Peter
Jan/100
TED talk on evolving robots
Today I found this TED talk on evolving robots by Hod Lipson at Cornell.
Towards the end of the talk he makes the interesting point that in absence of a particular reward, a heterogeneous population of simulated robots ends up favouring those kinds of robots that can self-replicate.
Peter
Jan/100
The Unreasonable Effectiveness of Mathematics
Today I came across and insightful discussion on a dark-ish corner of the philosophy of science: why does math describe the universe so well?
The Unreasonable Effectiveness of Mathematics by Richard. W. Hamming (of Hamming distance and Hamming window fame). Originally this article appeared in The American Mathematical Monthly Volume 87 Number 2 February 1980.
It's a bit of a long read, but here are some highlights:
Jan/100
Can I Get Rid of This Parameter? (Statistical tests)
I'm working on a simulation that has a bunch of parameters. It's getting a bit complicated, so I'm on a witch-hunt for unimportant parameters. The suspect in question is 'channel length.' The channel length can take any non-negative integer value. The ultimate output of the simulation is a random variable, called the 'turn taking.' (More on the details of turn taking coming soon to a journal near you.)
So the question is: does the channel length affect the turn taking?
Dec/090
Robotic Kite Flying
I saw this post in the IEEE Spectrum blog about a robotic kite flying. The group that did it, Festo, has done some other cool project like flying robot penguins.
I guess it's always more impressive to demonstrate learning algorithms on big expensive hardware. But having big hardware is not essential for having great algorithms. I'm sure there are ways to demonstrate great algorithms using relatively primitive hardware.
Peter
Dec/091
Concurrent Hierarchical Reinforcement Learning & Lisp
Allan recently set me onto quite a good paper:
B. Marthi, S. Russell, D. Latham, and C. Guestrin, “Concurrent hierarchical reinforcement learning,” in Proceedings of the National Conference on Artificial Intelligence, vol. 20, 2005, p. 1652.
Marthi et al. describe an extension to Lisp that forms part of their concurrent hierarchical reinforcement learner. Their algorithm is trained to control simulated peasants in the Stratagus real-time strategy game domain. Their approach is similar to Parr and Russell's Hierarchical Abstract Machines (HAMs) in that a program defines a semi-Markov decision process which is then solved with a reinforcement learner and prior knowledge is included in the design of the program.
The paper made me interested in learning Lisp, although I have some hesitation because Lisp is (according to Wikipedia) the second oldest programming language still in use.
Peter
Oct/090
Paper: Recent Advances in Hierarchical Reinforcement Learning (2003)
I came across this article today in my search for work on hierarchical reinforcement learning:
Recent Advances in Hierarchical Reinforcement Learning by Barto and Mahadevan, 2003. While much of the paper is on Dietterich's MAXQ and Parr and Russell's HAM algorithms, there is a section on hierarchical memory that looks interesting and seems closer to my area of interest than the MAXQ algorithm.
I'm just beginning my exploration of the hierarchical memory concept, thanks to Barto and Mahadevan's paper. Please post if you have good references that are even more recent than 2003.
Peter