UGHHHHHHHHHHHHHHHHHHHHHH
Did I just watch pizza porn???
Named after George Bernard Shaw’s ingénue in Pygmalion, the ELIZA program operated within the Project MAC computer system at MIT and made natural language exchange between man and machine possible. ELIZA identified key words and phrases and responded with rote answers that simulated a conversation.
Here’s what happened when a young girl tried to reason with ELIZA in one of the first examples of The Turing Test.
(To hear the audio from this discussion, head over to Radiolab for their fantastic episode on the subject, “Talking to Machines”)
1966 / Cambridge, MA: Talking Cure
Client: Men are all alike.
ELIZA (a computer): In what way
Client: They’re always bugging us about something or other.
ELIZA: Can you think of a specific example
Client: Well, my boyfriend made me come here.
ELIZA: Your boyfriend made you come here
Client: He says I’m depressed much of the time.
ELIZA: I am sorry to hear you are depressed
Client: It’s true. I am unhappy.
ELIZA: Do you think coming here will help you not to be unhappy
Client: I need some help, that much seems certain.
ELIZA: What would it mean to you if you got some help
Client: Perhaps I could learn to get along with my mother.
ELIZA: Tell me more about your family
Client: My mother takes care of me.
ELIZA: Who else in your family takes care of you
Client: My father.
ELIZA: Your father
Client: You are like my father in some ways.
Last weekend I participated in the Sound Research Summit @ Eyebeam, a bazaar of sound-works in progress and experiments in listening curated by Jackson Moore. I thought the event was successful in juxtaposing a diversity of vocabularies through which we might approach sound, from Kyle Kessler directly manipulating a plate reverb (shown) to Ben Houge’s abstracted sonification of data from sensors at MIT. The result both embodied the spirit of research by challenging any particular framework as well as served as a collective performance in which the audible and conceptual boundaries between the works were continually renegotiated.
Particularly rewarding for me was the chance to work with Christine Sun Kim. Christine has been deaf from birth, but works with sound as her primary medium. She employs various strategies (such as transducers, piano wire, and feedback at the summit) to emphasize the tactile nature of sound and make it perceptible to her. However, I think that to suggest that she is simply transposing between senses is incorrect — it’s also the semantic and cultural components of sound, how and why we encode it, that comes across in what she does, and which for her I imagine is in many ways more readily approachable. Assisting her with her piece brought this into relief as we debated the qualities of sound it produced and I stumbled over my vocabulary.
More on my work for the summit next post.
Here’s yer 420!
watching the woodstock film makes me so fucking angry
because that generation came so close to actually changing something
in the world, but instead they went and became the people they hated most
Yep … Pretty much … Most of ‘em … ☯
Fortunately, not *all* of us got co-opted!
So, look out for my grandkids, you fückers!
Now it is such a bizarrely improbable coincidence that anything so mindbogglingly useful could have evolved purely by chance that some people have chosen to see it as the final and clinching evidence for the non-existence of God.
The argument goes something like this. ‘I refuse to prove that I exist,’ says God, ‘for proof denies faith and without faith I am nothing.’
‘But’, says Man, ‘the Babel fish is a dead giveaway isn’t it? It could not have evolved by chance. It proves you exist and so therefore, by your own arguments, you don’t. QED.’
‘Oh dear,’ says God, ‘I hadn’t thought of that’ and promptly vanishes in a puff of logic.
‘Oh that was easy,’ says Man, and for an encore goes on to prove that black is white and gets himself killed on the next zebra crossing.
The argument goes something like this. ‘I refuse to prove that I exist,’ says God, ‘for proof denies faith and without faith I am nothing.’
‘But’, says Man, ‘the Babel fish is a dead giveaway isn’t it? It could not have evolved by chance. It proves you exist and so therefore, by your own arguments, you don’t. QED.’
‘Oh dear,’ says God, ‘I hadn’t thought of that’ and promptly vanishes in a puff of logic.
‘Oh that was easy,’ says Man, and for an encore goes on to prove that black is white and gets himself killed on the next zebra crossing.
“
| — | Douglas Adams, The Hitchhiker’s Guide To The Galaxy (via hitchhikersguidequotes) |
Austin, TX. February 5th 2012-
Saturday evening, the Austin Police Department used an unnecessarily large police presence to intimidate Occupy Austin protesters who were legally and peaceably gathered at City Hall…
![rhythmanalysis:
CLUSTERING DATA IN PYTHON [code] Python (2.6), GPLPreviously, I demonstrated clustering approximate geographic points from OpenPaths to identify places of interest. Heres the code I used to accomplish that, which can be applied to any dataset, it doesnt have to be limited to two dimensions. The algorithm is known as agglomerative hierarchical clustering because it works by repeatedly grouping the closest two nodes together, starting with each data point as a node, and ending with the entire set in a single monster node. (“Closest” is defined by any distance metric; for many applications, it will be euclidean distance, but for geographic data, Im using the haversine distance formula.) Along the way, youve constructed a binary tree which represents the hierarchical relationships between the vectors in the set. The result is a kind of ad-hoc taxonomy, and is frequently used to hypothesize relatedness between images, documents, proteins, users, etc. (heres a nice diagram courtesy Razvan Musaloiu-E.) To use agglomerative clustering as a classifier, however, what we really want is just a flat set of clusters. It’s akin to choosing the branches of the tree that best represent the natural divisions in the data. Cut too close to the trunk and the clusters will be too general; cut by the leaves and youve got too much noise. With geographic information from OpenPaths, at least, we have a heuristic to decide what is appropriate — the platform is only going to be accurate to a quarter-mile or so, so we can look for the largest branches that do not exceed that limit. The code provides a method, get_pruned, that takes such a parameter and returns a resulting set of clusters. Clustering packages for python are out there, but I prefer touching the code and this is a dirt simple implementation that nonetheless should prove effective for most creative coding purposes.](http://24.media.tumblr.com/tumblr_lxlmjrBV7q1qk20gto1_500.png)
CLUSTERING DATA IN PYTHON
[code] Python (2.6), GPL
Previously, I demonstrated clustering approximate geographic points from OpenPaths to identify places of interest. Heres the code I used to accomplish that, which can be applied to any dataset, it doesnt have to be limited to two dimensions. The algorithm is known as agglomerative hierarchical clustering because it works by repeatedly grouping the closest two nodes together, starting with each data point as a node, and ending with the entire set in a single monster node. (“Closest” is defined by any distance metric; for many applications, it will be euclidean distance, but for geographic data, Im using the haversine distance formula.) Along the way, youve constructed a binary tree which represents the hierarchical relationships between the vectors in the set. The result is a kind of ad-hoc taxonomy, and is frequently used to hypothesize relatedness between images, documents, proteins, users, etc. (heres a nice diagram courtesy Razvan Musaloiu-E.)
To use agglomerative clustering as a classifier, however, what we really want is just a flat set of clusters. It’s akin to choosing the branches of the tree that best represent the natural divisions in the data. Cut too close to the trunk and the clusters will be too general; cut by the leaves and youve got too much noise. With geographic information from OpenPaths, at least, we have a heuristic to decide what is appropriate — the platform is only going to be accurate to a quarter-mile or so, so we can look for the largest branches that do not exceed that limit. The code provides a method, get_pruned, that takes such a parameter and returns a resulting set of clusters.
Clustering packages for python are out there, but I prefer touching the code and this is a dirt simple implementation that nonetheless should prove effective for most creative coding purposes.
