Tag: Kate Crawford

Bookmarked Calculating Empires: A Genealogy of Technology and Power since 1500 (calculatingempires.net)

Explore how technical and social structures co-evolved over five centuries in this large-scale research visualization.

Calculating Empires is a large-scale research visualization exploring how technical and social structures co-evolved over five centuries. The aim is to view the contemporary period in a longer trajectory of ideas, devices, infrastructures, and systems of power. It traces technological patterns of colonialism, militarization, automation, and enclosure since 1500 to show how these forces still subjugate and how they might be unwound. By tracking these imperial pathways, Calculating Empires offers a means of seeing our technological present in a deeper historical context. And by investigating how past empires have calculated, we can see how they created the conditions of empire today.

Source: Calculating Empires by

This is an interesting visualisation capturing changes in technology over time. Useful to consider alongside Justin Smith’s book The Internet Is Not What You Think It Is?

Listened Atlas of AI with Kate Crawford from radicalai.podbean.com

What is the Atlas of AI? Why is it important? How is AI an industry of extraction? How is AI impacting the planet? What can be done? 

To answer these questions and more we welcome to the show Dr. Kate Crawford to discuss Kate’s new book Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

Kate Crawford speaks about her new book, Atlas of AI. In it, she attempts to capture the human side of artificial intelligence, whether it be the resources, the workforce, history, datasets or the escape to space. As she defines in a separate interview with WIRED:

AI is made from vast amounts of natural resources, fuel, and human labor. And it’s not intelligent in any kind of human intelligence way. It’s not able to discern things without extensive human training, and it has a completely different statistical logic for how meaning is made. Since the very beginning of AI back in 1956, we’ve made this terrible error, a sort of original sin of the field, to believe that minds are like computers and vice versa. We assume these things are an analog to human intelligence, and nothing could be further from the truth.

This builds upon her work a few years ago exploring the life cycle of an Amazon Echo.

In an extract published in The Atlantic, Crawford addresses concerns associated with emotion recognition:

This is the danger of automating emotion recognition. These tools can take us back to the phrenological past, when spurious claims were used to support existing systems of power. The decades of scientific controversy around inferring emotional states consistently from a person’s face underscores a central point: One-size-fits-all “detection” is not the right approach. Emotions are complicated, and they develop and change in relation to our cultures and histories—all the manifold contexts that live outside the AI frame.

For a different introduction, Zan Rowe leads dives into Crawford’s atlas of sound.