Editing Small Group Subproblems

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**Soon to be posted data on crcns.org of auditory brain cells.
**Soon to be posted data on crcns.org of auditory brain cells.
**Figure out how to predict brain cell output.
**Figure out how to predict brain cell output.
*Deep Belief Networks for Images/Sounds (Mike S)
**Train deep belief networks on images, project output to monitor to see what network is "thinking"
**Do same for sounds
**Use auto-encoders to recall memories based on new input
*Kinects (Jared, Praveen)
*Kinects (Jared, Praveen)
**Get another kinect and do awesome shit with it.
**Get another kinect and do awesome shit with it.
**Would be useful to prototype some Noisebrain stuff below.
*Noisebrain(Jared, Mike)
*Noisebrain(Jared, Mike)
**Deep Belief Networks for Images/Sounds (Mike S)
***Train deep belief networks on images, project output to monitor to see what network is "thinking"
***Do same for sounds
***Use auto-encoders to recall memories based on new input
**Install sensors all over Noisebridge.** (need to deal with some privacy concerns first)
**Install sensors all over Noisebridge.** (need to deal with some privacy concerns first)
***IR? PIR? Visual spectrum? Sonar? Decibel level?
***IR? PIR? Visual spectrum? Sonar? Decibel level? Audio seems like privacy landmine.
***Any logging of raw Stills, Video or Audio seems like privacy landmine.  That said, I suspect aggregating it in a mathy way should be OK with folks.
**We can train it to learn about what's going on in Noisebridge, for example:
**We can train it to learn about what's going on in Noisebridge, for example:
***How many people are in the space?
***How many people are in the space?
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***What parts of the space are being used? Main Hall? Church? Turing? Kitchen?
***What parts of the space are being used? Main Hall? Church? Turing? Kitchen?
***Where is there motion? Where are things out of place?
***Where is there motion? Where are things out of place?
**Perhaps Mike's interests in Deep Belief Networks and Auditory Brain Cells above can be applied here?
**Application ideas for the Noisebrain once working:
**Application ideas for the Noisebrain once working:
***Feed this information into [[Noise-Bot]] in addition to it's on-board information.
***Feed this information into [[Noise-Bot]] in addition to it's on-board information.
***Could provide additional context for Music Recommendation project below.
***Could provide additional context for Music Recommendation project below.
***Neat dashboard widgets for Noisebridge website/wiki showing what's going on.
***Neat widgets for Noisebridge website showing what's up.
***Hack the lighting in the various parts of the space to be auto controlled.
*Music Recommendation System (Jared)
*Music Recommendation System (Jared)
**Given different people in a space (who voluntarily "checkin"), and a jukebox, play music that appeals to their tastes.  Could also be used for personal playlist creation.
**Given different people in a space (who voluntarily "checkin"), and a jukebox, play music that appeals to their tastes.
**I would like to take a different approach to the recommendation algorithm from my undergrad approach, which was based around domain specific knowledge about relationships and influences about various musicians.  Instead I'd like to train models using features like time of date, location, weather, etc tied to track plays by individual users.  I do still want past play history to also influence the weightings in some way.
**<strike>Hard part is data gathering</strike> We are just use last.fm [http://www.last.fm/download#content_ipod scrobbler] & [http://ws.audioscrobbler.com/1.0/user/jareddunne/recenttracks.rss rss feeds].
**Capturing music play events isn't hard: We are just use last.fm [http://www.last.fm/download#content_ipod scrobbler] & [http://ws.audioscrobbler.com/1.0/user/jareddunne/recenttracks.rss rss feeds].
**Weather and Location feature collection might be some what time consuming but not unmanageable.  See 4square spying idea above.
**When a music play event (a 'scrobble') happens, we'd collect and log all the contextual information possible.
***When a song is scrobbled on iPhone or iTunes, we'd know about the event almost instantly allowing more contextual information.
***Songs played on normal iPods get scrobbled when it's synced with iTunes with scrobbler plugin.  This delay might make collecting some contextual features difficult after the fact.  Nonetheless the partial information is still useful.  


TODOs:
TODOs:
*Put together a github project
*Put together a github project
**Maybe we should just put things in the existing noisebridge-ml github repo as subfolders?
*Key is to have a bunch of projects going on at the same time so people can work on whatever they like at a given time.
*Key is to have a bunch of projects going on at the same time so people can work on whatever they like at a given time.
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