lloyd's blog
256A Reading Response #8
This is a response to a definition in Ge Wang’s book Artful Design:
“Definition 8.14: The ‘pi’ shaped individual” (pg. 428)
“Definition 8.14: The ‘pi’ shaped individual” (pg. 428)
256A Reading Response #1
*Caution, contains severe nitpicking and strong yet under-argued thoughts*
“Artful design is conscious effort to elevate that natural process to a higher discipline” (pg. 52)
Does design need to be elevated in order to reach the sublime? Or should it’s “sublimeness” rather be revealed? Was it not always there? And should the majority of our work to experience it not be in adjusting, and readjusting, and readjusting our framing and perspective?
“Artful design is conscious effort to elevate that natural process to a higher discipline” (pg. 52)
Does design need to be elevated in order to reach the sublime? Or should it’s “sublimeness” rather be revealed? Was it not always there? And should the majority of our work to experience it not be in adjusting, and readjusting, and readjusting our framing and perspective?
256A Reading Response #7
This is a response to a principle in Ge Wang’s book Artful Design:
“Principle 7.7: A little anonymity can go a long way” (pg. 363)
“Principle 7.7: A little anonymity can go a long way” (pg. 363)
256A Reading Response #6
This is a response to a principle in Ge Wang’s book Artful Design:
“Principle 6.20 The Tofu Burger Principle” (pg. 341)
“Principle 6.20 The Tofu Burger Principle” (pg. 341)
256A Reading Response #5
This is a response to a principle in Ge Wang’s book Artful Design: “Principle 5.5 Have your machine learning – And the human in the loop!” (pg. 218)
Hey Robot! Share! Please?
Machine learning is generally structured around “tasks”, but never “tools”. There are countless papers and competitions about which algo or model can classify the emotion of a facial expression, but far fewer on what to do with that. While this feels like a classic case of “we were so preoccupied with if we could, we never stopped to ask if we should”, it gets at something a little deeper I think:
Machine learning is hard!
Hey Robot! Share! Please?
Machine learning is generally structured around “tasks”, but never “tools”. There are countless papers and competitions about which algo or model can classify the emotion of a facial expression, but far fewer on what to do with that. While this feels like a classic case of “we were so preoccupied with if we could, we never stopped to ask if we should”, it gets at something a little deeper I think:
Machine learning is hard!
256A Reading Response #4
“Now” and time in the computer music programming language Chuck: a response to chapter 4 of Ge Wang’s Artful Design.
Chuck navigates time in a very interesting way. Unlike other languages which might be event-based, like the bang system of Max/MSP, or durational, like Csound and many others, Chuck asks you to contemplate the trickiness of “now” and encourages a different way to think about time.
The big “woah” moment for me came when I realized that sound and time are one and the same in Chuck. Since it is strongly-timed, digital audio samples are the basis of time, which is kind of trippy to think about. Let’s say there is a glitch and the audio stops, you’ll lose time outside of Chuck and maybe get some silence, but Chuck never lost time because it’s audio wasn’t being calculated.