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Publisher's Summary
When we talk about technology we always talk about tomorrow and the future - which makes it hard to figure out how to even get there. In Future Histories, public interest lawyer and digital specialist Lizzie O'Shea argues that we need to stop looking forward and start looking backwards. Weaving together histories of computing and progressive social movements with modern theories of the mind, society, and self, O'Shea constructs a "usable past" that can help us determine our digital future.
What, she asks, can the Paris Commune tell us about earlier experiments in sharing resources - like the internet - in common? How can Frantz Fanon's theories of anti-colonial self-determination help us build a digital world in which everyone can participate equally? Can debates over equal digital access be helped by American revolutionary Tom Paine's theories of democratic, economic redistribution? What can indigenous land struggles teach us about stewarding our digital climate? And, how is Elon Musk not a future visionary but a steampunk throwback to Victorian-era technological utopians?
In engaging, sparkling prose, O'Shea shows us how very human our understanding of technology is, and how when we draw on the resources of the past, we can see the potential for struggle, for liberation, for art, and poetry in our technological present.
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- Clare
- 08-14-19
Quite interesting
I was a little bored by the narration and couldn't agree with some of the pics behind the content, but it was insightful and interesting and worth the listen to.
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Overall5 out of 5 stars
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Performance5 out of 5 stars
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Story5 out of 5 stars
- Amazon Customer
- 05-27-19
Hard recommend on technology and capitalism!
Digital technologies such as large automated systems and machine learning are currently sitting at the tip of the spear of capitalism. This book takes us both through the understanding of what led us here, but also what to do about it.
This is a must read for anyone working in tech, as we must all understand the implications of our jobs (I work in machine learning)!