1. constructed subjects interrogating identities

2. topic shift reversing this week’s daily topics:
interrogating identities today;
composited representations on Thursday
but both are inter-related

3. sections
crashers should have received permission numbers in sections (or should still be seeking sections w/ room)

4. sections
this week:
drawing on readings about identity presentation on-line to develop fictional MySpace profiles
these will become characters in the narrative/drama (soap opera) we will be developing
next week: 2 composite photographs using photoshop for the character;
must use a specific technique discussed in Paul chapter 1 (more detail in posted assignment guidelines)

5. vblog social network narrative
lonelygirl15
Bree
4 months ago began posting videos, then video blogs to YouTube
cute, geeky, home-schooled, religious
boyfriend: Daniel (DanielBeast)
much speculation about whether she was real or part of a promotion
speculation based on aspects of her story

6. vblog social network narrative
excerpt of statement from “creators”:
“Right now, the biggest mystery of Lonelygirl15 is ‘who is she?’ We think this is an oversimplification. Lonelygirl15 is a reflection of everyone. She is no more real or fictitious than the portions of our personalities that we choose to show (or hide) when we interact with the people around us. Regardless, there are deeper mysteries buried within the plot, dialogue, and background of the Lonelygirl15 videos, and many of our tireless and dedicated fans have unearthed some of these. There are many more to come.

7. vblog social network narrative
lonelygirl15
cassieiswatching
unofficial spin-off?

8. vblog social network narrative
brookers
signed to development deal by Carson Daly
first YouTube “crossover” star (according to Variety)
in what ways can we judge her to be “authentic”?
how are we asking questions about this when we watch?

9. questioning thinking the “Turing test”:
an interrogation into whether machines can think
literally, an interrogation method to understand this question

10. questioning thinking
but it also raises more fundamental questions about the presentation of identity
how language and identity are connected -- or separate
how ‘thinking’ can create identification between human & machine
the problem: making ‘thinking’ unambiguous
the solution: the imitation game

11. imitation game round 1: man (A), woman (B), interrogator (C)
objectives of game for each player
mechanisms of game
strategies: lying, truth
is Turing’s point that the woman and man would be found to be different -- or not?

12. imitation game
round 2: machine takes the role of A (though B now becomes a man)
strategies: “providing answers that would naturally be given by a man.”
type of machine: digital computer
implication: the irrelevance of the body

13. imitation game
round 2: machine takes the role of A (though B now becomes a man)
what are other implications for understanding thinking and identity?
what does it say about the relationship between identity presentation and truth?

14. bots robots, chatbots, chatterbots, agents, intelligent agents
we’re aware of them most when they talk to us (as part of HCI)
but they’re more important when they talk to each other:
spiders, crawlers: programs in search engines, data mining, etc.

15. bots Eliza created by Joseph Weizenbaum (1966)
experiment in natural language communication; not trying to pass the Turing test
based on Rogerian (Carl Rogers) therapy (turns all your questions back on you); does not store responses in memory

16. bots Eliza online A.L.I.C.E. http://www-ai.ijs.si/eliza/eliza.html
Artificial Intelligence Foundation

17. bots Julia created by Michael Mauldin (Carnegie Mellon)
active in MUDs, MUCKs, and IRC in early 1990s
hockey, mapping, flirting

18. Julia architecture communications: TCP/IP connections to the server
protocol interpreter: decodes the various game related messages
world model: tracks the various rooms and objects, modeling the world as a directed graph
player memory: tracks the other players, human and robotic, and records up to 2000 bytes of their most recent utterances
[from michael mauldin, “chatterbots, tinymuds…” 1994]

19. Julia
chatterbot configuration [from michael mauldin, “chatterbots, tinymuds…”]

20. Julia conversation parses strings of characters typed to her (input);
matches input with set of outputs presented in random, non-repeating order
changes subjects; humorous replies
can remember players actions and interactions w/her

21. Julia
conversational network: pet domain [from mauldin]