May 2, 2025 05:45 PM
https://www.quantamagazine.org/ai-is-not...-20250430/
EXCERPT: In 1943, a pair of neuroscientists were trying to describe how the human nervous system works when they accidentally laid the foundation for artificial intelligence. In their mathematical framework for how systems of cells can encode and process information, Warren McCulloch and Walter Pitts argued that each brain cell, or neuron, could be thought of as a logic device: It either turns on or it doesn’t. A network of such “all-or-none” neurons, they wrote, can perform simple calculations through true or false statements.
“They were actually, in a sense, describing the very first artificial neural network,” said Tomaso Poggio of the Massachusetts Institute of Technology, who is one of the founders of computational neuroscience.
McCulloch and Pitts’ framework laid the groundwork for many of the neural networks that underlie the most powerful AI systems...
[...] But there is a problem: The initial McCulloch and Pitts framework is “complete rubbish,” said the science historian Matthew Cobb of the University of Manchester, who wrote the book The Idea of the Brain: The Past and Future of Neuroscience. “Nervous systems aren’t wired up like that at all.”
When you poke at even the most general comparison between biological and artificial intelligence — that both learn by processing information across layers of networked nodes — their similarities quickly crumble.
Artificial neural networks are “huge simplifications,” said Leo Kozachkov ... The vast cellular complex that is our nervous system generates our feelings, thoughts, consciousness and intelligence — everything that makes us who we are. Many processes seem to unfold instantaneously and simultaneously, orchestrated by an organ that evolution molded for hundreds of millions of years from pieces it found in the ancient oceans, culminating in an information storage and processing system that can ask existential questions about itself.
“[The brain] is the most complex piece of active matter in the known universe,” said Christof Koch, a neuroscientist at the Allen Institute for Brain Science in Seattle. “Brains have always been compared to the most advanced piece of machinery.”
But no piece of machinery — from telephone switchboard or radio tube to supercomputer or neural network — ever measured up.
The brain’s neuronal diversity and networked complexity is lost in artificial neural networks. But computational neuroscientists — experts on both brains and computers — say that’s OK. Although the two systems have diverged along separate evolutionary paths, computer scientists and neuroscientists still have much to learn by comparing them. Infusing biological strategies could improve the efficiency and effectiveness of artificial neural networks. The latter could, in turn, be a model to understand the human brain.
With AI, “we are in the process not of re-creating human biology,” said Thomas Naselaris (opens a new tab), a neuroscientist at the University of Minnesota, but “of discovering new routes to intelligence.” And in doing so, the hope is that we’ll understand more of our own... (MORE - missing details)
EXCERPT: In 1943, a pair of neuroscientists were trying to describe how the human nervous system works when they accidentally laid the foundation for artificial intelligence. In their mathematical framework for how systems of cells can encode and process information, Warren McCulloch and Walter Pitts argued that each brain cell, or neuron, could be thought of as a logic device: It either turns on or it doesn’t. A network of such “all-or-none” neurons, they wrote, can perform simple calculations through true or false statements.
“They were actually, in a sense, describing the very first artificial neural network,” said Tomaso Poggio of the Massachusetts Institute of Technology, who is one of the founders of computational neuroscience.
McCulloch and Pitts’ framework laid the groundwork for many of the neural networks that underlie the most powerful AI systems...
[...] But there is a problem: The initial McCulloch and Pitts framework is “complete rubbish,” said the science historian Matthew Cobb of the University of Manchester, who wrote the book The Idea of the Brain: The Past and Future of Neuroscience. “Nervous systems aren’t wired up like that at all.”
When you poke at even the most general comparison between biological and artificial intelligence — that both learn by processing information across layers of networked nodes — their similarities quickly crumble.
Artificial neural networks are “huge simplifications,” said Leo Kozachkov ... The vast cellular complex that is our nervous system generates our feelings, thoughts, consciousness and intelligence — everything that makes us who we are. Many processes seem to unfold instantaneously and simultaneously, orchestrated by an organ that evolution molded for hundreds of millions of years from pieces it found in the ancient oceans, culminating in an information storage and processing system that can ask existential questions about itself.
“[The brain] is the most complex piece of active matter in the known universe,” said Christof Koch, a neuroscientist at the Allen Institute for Brain Science in Seattle. “Brains have always been compared to the most advanced piece of machinery.”
But no piece of machinery — from telephone switchboard or radio tube to supercomputer or neural network — ever measured up.
The brain’s neuronal diversity and networked complexity is lost in artificial neural networks. But computational neuroscientists — experts on both brains and computers — say that’s OK. Although the two systems have diverged along separate evolutionary paths, computer scientists and neuroscientists still have much to learn by comparing them. Infusing biological strategies could improve the efficiency and effectiveness of artificial neural networks. The latter could, in turn, be a model to understand the human brain.
With AI, “we are in the process not of re-creating human biology,” said Thomas Naselaris (opens a new tab), a neuroscientist at the University of Minnesota, but “of discovering new routes to intelligence.” And in doing so, the hope is that we’ll understand more of our own... (MORE - missing details)
