Many AI scientists unconsciously assume a metaphysical position. It’s usually materialism

Understanding the Underlying Philosophical Assumptions in Artificial Intelligence Discourse

In the realm of artificial intelligence research, many scientists arrive at conclusions and beliefs that subtly hinge on underlying metaphysical assumptions. A common grounding assumption is materialism—the idea that everything, including human consciousness and intelligence, can ultimately be reduced to physical matter.

A notable example comes from recent remarks by Ilya Sutskever, a prominent figure in AI development. During a talk, he stated:

“How can I be so sure of that? The reason is that all of us have a brain. And the brain is a biological computer. That’s why. We have a brain. The brain is a biological computer. So why can’t the digital computer, a digital brain, do the same things? This is the core reasoning: because our brain is a biological computer, a digital counterpart could, in theory, replicate its functions.”

(Watch the full discussion here: [YouTube link])

This line of reasoning exemplifies a prevalent perspective within AI circles, one that equates human cognition with computational processes rooted in physical matter. While compelling, it’s crucial to recognize that such a stance reflects a metaphysical position—specifically, philosophical materialism.

Materialism posits that consciousness, thought, and experience emerge solely from physical processes, and often assumes that with enough computational power, machines could emulate human intelligence entirely. However, this viewpoint remains a hypothesis, not an empirically proven fact. Alternative philosophical viewpoints suggest that consciousness and mind may involve non-material aspects, which are not reducible to matter alone.

Interestingly, asserting the absence of any metaphysical stance often amounts to an unexamined commitment to materialism. Sometimes, researchers believe they are purely objective or scientific when, in fact, their perspective is influenced by implicit philosophical assumptions.

Being transparent about these foundational beliefs can enhance the clarity and rigor of AI research. Recognizing our philosophical biases doesn’t hinder progress—instead, it can deepen our inquiry, challenge assumptions, and potentially open new avenues for understanding intelligence, consciousness, and the future of artificial systems.