The Eye of the Master: Rethinking AI's Impact on Work, Economy, and Society

Artificial Intelligence (AI) is everywhere these days—from smartphones to self-driving cars. But what if our common understanding of AI is missing some crucial points? Matteo Pasquinelli makes that intriguing argument in his book, The Eye of the Master: A Social History of AI.

Matteo Pasquinelli, an Associate Professor at Università Ca' Foscari Venezia, brings a unique perspective to this discussion. Pasquinelli's research focuses on the intersection of philosophy of mind, political economy, and knowledge and cultural production automation. This multidisciplinary approach allows him to offer unique insights into the nature and impact of AI.

As I read through Pasquinelli's work, three big ideas jumped out at me, each offering a fresh framework for us to think about AI:

AI: Not Just a Brain in a Box

We imagine scientists trying to create a digital brain when we think about AI. But Pasquinelli suggests we should instead see AI as the latest chapter in a much older story: the history of automating work.

Think about it this way: In the 1800s, we invented machines for physical jobs like weaving cloth. Today, we're creating AI to handle mental tasks like writing emails or analyzing data. The same basic idea—using technology to do work that humans used to do—just applied to different jobs.

This view changes how we might think about AI's impacts. Instead of worrying about robots becoming conscious, maybe we should focus more on how AI changes the job market and the economy. It's less about sci-fi scenarios and more about real-world effects on our work and lives.

AI: Powered by All of Us

Here's a mind-bender: What if the "intelligence" in Artificial Intelligence isn't artificial but a reflection of our collective human knowledge?

Pasquinelli traces this idea back to the concept of "general intellect" (first put forward by Karl Marx). It's the idea that society's collective knowledge is a powerful force. Fast forward to today's AI, and we see this concept in action.

Take ChatGPT, for example. It seems smart, but it's not coming up with new knowledge. Instead, it's drawing on the vast amount of human-written text it was trained on - billions of words from books, articles, and websites. In a way, it's tapping into our collective knowledge and giving it back to us in new forms.

This perspective raises some interesting questions. If AI is built on our collective knowledge, who should benefit from it? How do we make sure the rewards are shared fairly?

AI: Master Pattern-Matcher, Not Master Thinker

We often imagine AI as a super-logical brain, but Pasquinelli argues that it's more like a super-powered pattern-matching machine.

Think about how you recognize a friend's face. You're not consciously thinking through a list of features—you just see the face and know who it is. AI works similarly, but with math. It's good at spotting patterns in data, whether patterns of pixels in images or patterns of words in text.

This is why AI can sometimes do things that seem intelligent or creative, like winning at complex games or generating artwork. It's not because it's thinking like we do but because it's incredibly good at recognizing and reproducing patterns.

Understanding this can help us think more clearly about AI's strengths and limitations. It's terrific at tasks that involve finding patterns in large amounts of data, but it might struggle with tasks that require fundamental understanding or reasoning.

Putting it all together

Pasquinelli's book offers a new lens for looking at AI. Instead of seeing it as a digital brain, we can view it as:

1) AI is the latest step in our long history of automating work.

2) AI is a technology that taps into our collective human knowledge.

3) Ai is a powerful tool for recognizing and reproducing patterns.

This view might change how we think about developing and using AI. It suggests we should focus more on:

• How AI is changing jobs and the economy.
• Ensuring the benefits of AI are shared equitably amongst society since it's built on our collective knowledge.
• Understanding what AI is good at (pattern recognition) and what it might not be suited for.

(Run, don't walk, and pick up Pasquinelli's book today) Amazon link here

Have any of these frameworks and ideas changed how you view AI? How might these ideas shape how we develop and use AI in the future? I’m excited to hear your thoughts.

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