On May 10, Genevieve Bell of Intel gave a Department of Informatics talk at UC Irvine, titled
“Does Data Have a Secret Life: An Ethnographic Account of ‘Big Data’ and the Cloud”
For her remarkable achievements, I quote from the announcement for the lecture:
“BIO Dr. Genevieve Bell is an anthropologist with 15 years of experience driving innovation in the high tech industry. As the Director of Interaction and Experience Research in Intel Labs, Bell leads a team of social scientists, interaction designers, human factors engineers and computer scientists. This organization researches new computing experiences that are centered around people’s needs and desires. In 2010, Bell was named one of Fast Company’s inaugural “100 Most Creative People in Business.” In 2012 she was inducted into the Women In Technology International (WITI) hall of fame, and she was honored by the Anita Borg Institute as the 2013 Woman of Vision for Leadership. Her first book, “Divining the Digital Future: Mess and Mythology in Ubiquitous Computing,” was co-written with UC Irvine Professor Paul Dourish and released in April 2011.”
When first hired by Intel she was asked to cover what women needed in computing, and what the “rest of the world” besides the US needed. She didn’t like those categories and soon changed them.
Her division has 112 employees and 120 interns.
Their job is to bring the outside inside. It involves both sociology and technology.
I cannot possibly cover all of her thorough discussion. Either go to one of her talks elsewhere, and/or read the book cited above. I could only copy the topics that she was covering, as shown on her slides. They were:
Big data and the cloud.
What is it? Its old.
What’s new here?
Will everything produce data?
Will everyone produce data?
Telling Data’s stories and secrets
What does data want?
What is wanted from it?
Data keeps it real It is used real.
Data lives a good relationship.
Data doesn’t always have the best network.
Different forms flow differently.
Data has a country.
Data is feral.
Data has responsibilities.
Data keeps it messy.
Data likes to look good.
100% of members lied on one dating site.
Data doesn’t want to last forever.
There’s always new data.
Learning to ‘read’ an algorithm.
For example, the Ozone hole over the Antarctic, which was growing exponentially, was not considered or spotted in the NASA algorithm for seven years until pointed out by an independent observer. (“In 1984 British Antarctic Survey scientists, Joesph Farman , Brian Gardiner, and Jonathan Shanklin, discovered a recurring springtime Antarctic ozone hole.” See the informative site www.theozonehole.com )
Studying the new priests and alchemists
Regulating algorithms and data
Critiquing the new empiricism
Need theoretical tools
Does data equal truth?
What is it doing for us now?