The Ancient World of Machines: Lessons from Archaeology for the Age of Machine Learning

Introduction

In recent years, the world has witnessed remarkable advancements in artificial intelligence (AI) and machine learning, sparking comparisons to major historical turning points like the Industrial Revolution. However, these comparisons also raise concerns about the impact of AI on society. To gain perspective, let’s delve into the past and explore lessons from archaeology that can inform our understanding of technology’s role in the Age of Machine Learning.

Historical Precedents

Concerns about technology’s impact on society are not new. Similar doubts accompanied previous technological breakthroughs, such as computers defeating human grandmasters in chess, the emergence of expert systems in engineering and medicine, and early explorations of AI in the 1950s. Even during the second industrial revolution in the early 20th century, there were concerns about how human spirit and social values could survive in the “Machine Age.”

Lewis Mumford’s Critique

One prominent critic of the Machine Age was Lewis Mumford. In his book “Technics and Civilization,” Mumford argued that while technology could bring about a better world, it could also betray human purpose, erode societies, and threaten the organic aspects of life. He believed humans should prioritize spontaneity, emotional values, and contact with the organic world. In his book “The City in History,” Mumford explored the impact of cities, which he saw as the greatest machines of the Industrial Age, on societies.

Cities as Machines

Mumford’s contemporary, archaeologist Vere Gordon Childe, proposed an “urban revolution” in the archaeological past. Childe argued that the earliest cities evolved alongside a revolutionary package of technologies, including writing and craft specialization, enabling humans to produce, communicate, and govern societies on an unprecedented scale.

Ancient cities incorporated a wealth of intelligent, social mechanics. Take Zeugma, a twin city strategically positioned on both sides of the River Euphrates to control access across the river. Its name, meaning “bridge” or “crossing,” подчеркивает роль города в соединении двух сторон этой пограничной реки и контроле над ней.

Another example is Córdoba, which underwent a transformation from a Roman to an Islamic city after the Umayyad conquest of Spain. The Umayyads created new suburbs based on Middle Eastern templates and constructed their headquarters near the bridge to control communication across the city.

Lessons from the Past

The parallels between ancient cities and modern AI offer valuable lessons for the Age of Machine Learning.

First, it’s not new for humans to use material technology to enhance cognition, communication, and control. Items like seal stamps, tally sticks, the abacus, clocks, pulleys, or rigging extended our capacities for gathering, retaining, and communicating information, analyzing data, and controlling phenomena.

Second, cities were the most complex cognitive technologies of the past. By exploring these parallels, we can use the past to put today’s AI into perspective.

Third, this experience can help us ask new questions of the ancient world. For example, how did ancient societies adapt to new technologies? How did they address the challenges and opportunities that arose?

Conclusion

The Age of Machine Learning presents both opportunities and challenges. By learning from the past, we can gain insights into how to harness the power of AI while mitigating its potential negative impacts.

The ancient world offers a rich tapestry of experiences and lessons that can inform our understanding of technology’s role in society. By exploring these parallels, we can create a future where AI enhances human capabilities without diminishing our humanity.