While colorfully written, this article makes broad assumptions about the nature of consciousness and intelligence that science simply cannot confirm or deny.
Suimma Summarum, the author has no clue, experience or valuable insights - just ramblings of an ill-informed individual.
Firstly, conflating the origins of the universe with the potential of AGI is a false equivalence. Cosmology operates on vastly different scales and unknowns than computer science. Our lack of complete understanding about the Big Bang doesn't inherently mean non-material forces are at play in AI development.
Secondly, equating "life" with "volitional goals" is a narrow view. We don't have a scientific consensus on what constitutes consciousness, let alone whether it's replicable artificially. While current AI relies on human-provided data and goals, the possibility of self-learning systems evolving beyond their initial programming shouldn't be dismissed solely on a lack of "spirit."
The article falls into the "hard problem of consciousness" trap. Just because we experience subjective feelings doesn't mean those are necessary for intelligence as we may define it in the future.
To claim AGI is "impossible" is premature. It's more accurate to say current methods have limitations, but the field is young. Dismissing it based on non-material arguments stifles exploration into what complex computation might achieve.
Instead of framing this as "spirit vs. machine," a scientific approach would focus on:
Defining AGI rigorously: What capabilities would truly represent "general" intelligence, distinct from sophisticated programming?
Exploring emergent properties: Can complex systems develop traits not directly coded, analogous to how life arose from simpler chemistry?
Ethical considerations: Regardless of its source, an intelligence capable of self-preservation poses ethical questions worth serious consideration, as Sutskever suggests.
Dismissing AGI outright based on current limitations and spiritual arguments is as unscientific as claiming it's inevitable. A balanced, data-driven approach is needed.
