Paolo's coming up with ontology terms.
Michael's industrial meetings at Warwick.
Helping to gather mobile phone data using sensors in three different cars. Track people walking around whilst using mobile phones. (Weren't storing or decrypting anything).
Read about algorithms.
Worried data will be too simple, and not enough of it. Hard to decipher things from it.
As a result, project is different and more complicated than before.
Still interesting, but might be two projects worth... need to agree underlying theme and make it more science than engineering. Combining sensor data. Needs to formalise concept of needing context. Existing in subsets of world, so different sensors understand each others' data.
Fusion problem and communication problem are lots of work each. On top of that, in the end, they help each other do something.
Never be able to model reality exactly, so what do you need to model to do something sensible?
Maybe can make assumptions working in isolation if he can't work on everything at once.
People misrepresenting open data? If the power is in the hands of the people doing the visualisation.
When it becomes a huge part of every day life, have to deal with it differently. People being baffled by science. Assuming things are correct when they're published, but might not be. Citizens can't necessarily interpret raw data correctly. Just like all science at the moment; fallacies like creative and uncreative halves of brains. Things that are easy to understand get picked up and influence society, even if they're just wrong.
Being swamped with shitty data/interpretations? Like shitty movie reviews because anyone can make one with a webcam on YouTube that have no journalistic integrity. But Wikipedia works.. why? (People gave similar reasons that it wouldn't work when it started).
Might do MLPR.
Michael's industrial meetings at Warwick.
Helping to gather mobile phone data using sensors in three different cars. Track people walking around whilst using mobile phones. (Weren't storing or decrypting anything).
Read about algorithms.
Worried data will be too simple, and not enough of it. Hard to decipher things from it.
As a result, project is different and more complicated than before.
Still interesting, but might be two projects worth... need to agree underlying theme and make it more science than engineering. Combining sensor data. Needs to formalise concept of needing context. Existing in subsets of world, so different sensors understand each others' data.
Fusion problem and communication problem are lots of work each. On top of that, in the end, they help each other do something.
Never be able to model reality exactly, so what do you need to model to do something sensible?
Maybe can make assumptions working in isolation if he can't work on everything at once.
People misrepresenting open data? If the power is in the hands of the people doing the visualisation.
When it becomes a huge part of every day life, have to deal with it differently. People being baffled by science. Assuming things are correct when they're published, but might not be. Citizens can't necessarily interpret raw data correctly. Just like all science at the moment; fallacies like creative and uncreative halves of brains. Things that are easy to understand get picked up and influence society, even if they're just wrong.
Being swamped with shitty data/interpretations? Like shitty movie reviews because anyone can make one with a webcam on YouTube that have no journalistic integrity. But Wikipedia works.. why? (People gave similar reasons that it wouldn't work when it started).
Might do MLPR.
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