Greater Spaces is written by Majken Overgaard and Vanessa Julia Carpenter who work to expand the narrative of what technology is and who creates it. They speak with Danish and international female role models within technology and between these interviews share what is most interesting to them, with a focus on diversity.
ING.dk is one of Denmark's oldest news agencies - first launched in 1892(!) and focuses solely on news in the engineering world - hence it's name, "Ingeniøren" - the engineer. It also covers science and technology.
Majken and Vanessa work with many brilliant people in tech, science and engineering, and were surprised that these people's stories were not showing up in places like Ingeniøren. They decided that the best way to change this was to interview all the people they knew who worked within tech in different ways and include them in the blog. Ingeniøren was excited about this and gave them a blog, and Greater Spaces was born.
Articles which interviewed people in diverse technical backgrounds asked the same series of questions to each interviewee to explore the many answers to the questions, for example "What do you find exciting about technology right now?
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Expanding the narrative of technology
Diversity and Inclusion
Interviews were just the beginning
As both Majken and Vanessa work in a variety of technological disciplines (check out Majken's work at https://www.catch.dk/ ) they also share their latest insights, thoughts and projects on this blog.
A selection of interviews:
SØS Gunver Ryberg — Machines are the essential tools for my creations (English) (Danish)
What’s happening in Detroit: Meet Carla Diana, designer of tangible future technologies. (English) (Danish)
Sarah Homewood: The future of technology tracking (English) (Danish)
The future is circular and Thürmer Tools is leading the way. (English) (Danish)
7. What do you think is something we should be paying attention to?
"Bias in machine learning. It’s something that’s easily forgotten and it’s really important whenever you use a computer tool, garbage in equals garbage out (GIGO). If you feed the data set with any level of statistical bias, then the learned results will also reflect bias."