Atlas of AI summary

In the heart of the Nevada desert sits an unassuming town called Silver Peak. Silver Peak sits on the edge of a vast lithium deposit, a crucial component in the batteries that power our smartphones, laptops, and electric vehicles. This small mining community, with its evaporation ponds shimmering an eerie green, is just one of the many hidden places that form the backbone of the artificial intelligence industry. As you explore the true origins of AI, you discover a complex web of extraction, exploitation, and environmental destruction stretching across the globe.

From the rare earth mines of Inner Mongolia to the tin-rich islands of Indonesia, an intricate network of supply chains feeds the minerals and metals essential to modern computing. The human cost of this extraction is significant, from unregulated and dangerous working conditions faced by miners, to the displacement of local communities, to the destruction of fragile ecosystems. 

Imagine a world where your every move, word, and facial expression is quietly harvested and fed into massive databases. Welcome to another side of the AI revolution, where the tech industry's appetite for data knows no bounds.

As AI has exploded in recent years, so too has the scramble to amass the enormous training datasets needed to fuel machine learning models. But where does all this data come from? More often than not, it's scraped from the internet without the knowledge or consent of those concerned. Whether in the form of videos, images, or text this corpus of human expression is treated as little more than raw material to be mined and refined, stripped of its context and meaning. 

Imagine a room filled with five hundred human skulls, each carefully measured, labeled, and cataloged. This macabre collection, amassed by the 19th-century physician and natural historian Samuel Morton, was used to promote the pseudoscientific notion that intelligence and character could be determined by skull size and shape. Morton's work, which claimed that white people had the largest skulls and were therefore the most intelligent race, was hailed as objective science and used to justify slavery and racial segregation.

This story illustrates how classification systems can encode and perpetuate power imbalances and social inequalities. With the rise of machine learning, this phenomenon has taken on new urgency. As AI systems are trained on vast datasets and tasked with classifying everything from objects to human beings, we see the growth of a new politics of categorization.