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Wilczek’s Multiverse
From physics to mind: where AI really came from
Despite grumbles in the scientific community, the Nobel Prize for Physics going to artificial intelligence pioneers was inspired
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Frank Anthony Wilczek, an American theoretical physicist and mathematician, was awarded the Nobel Prize in Physics in 2004 for his discovery of asymptotic freedom in the theory of the strong interaction.
Today, American theoretical physicist and Nobel laureate Frank Wilczek launches the first in a series of monthly columns written exclusively for the South China Morning Post. Here, he reflects on the significance of this year’s physics Nobel Prize and its implications for future work in artificial neural networks.
The 2024 Nobel Prize for Physics was awarded to John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”. Because neural nets lie beyond the conventional boundaries of physics, the award provoked some grumbling, both online and off, in the physics community. But I think it was an appropriate – indeed, an inspired – choice.
The historical roots of artificial neural nets trace back to the early 1940s, not long after the modern concept of what brains are became securely established. That is, the brains of humans and other animals are built from individual cells, neurons, that communicate with one another through electric pulses.
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