Constraints Make AI More Human. Who Would Have Guessed?

December 11, 2023

green-dino_thumb_thumb_thumbThis essay is the work of a dumb dinobaby. No smart software required.

AI developers could be one step closer at artificially recreating the human brain. Science Daily discusses a study from the University of Cambridge about, “AI System Self-Organizes To Develop Features of Brains Of Complex Organisms.” Neural systems are designed to organize, form connections, and balance an organism’s competing demands. They need energy and resources to grow an organism’s physical body, while they also optimize neural activity for information processing. This natural balance describes how animal brains have similar organizational solutions.

Brains are designed to solve and understand complex problems while exerting as little energy as possible. Biological systems usually evolve to maximize energy resources available to them.


“See how much better the output is when we constrain the smart software,” says the young keyboard operator. Thanks, MSFT Copilot. Good enough.

Scientists from the Medical Research Council Cognition and Brain Sciences Unit (MRC CBSU) at the University of Cambridge experimented with this concept when they made a simplified brain model and applied physical constraints. The model developed traits similar to human brains.

The scientists tested the model brain system by having it navigate a maze. Maze navigation was chosen because it requires various tasks to be completed. The different tasks activate different nodes in the model. Nodes are similar to brain neurons. The brain model needed to practice navigating the maze:

“Initially, the system does not know how to complete the task and makes mistakes. But when it is given feedback it gradually learns to get better at the task. It learns by changing the strength of the connections between its nodes, similar to how the strength of connections between brain cells changes as we learn. The system then repeats the task over and over again, until eventually it learns to perform it correctly.

With their system, however, the physical constraint meant that the further away two nodes were, the more difficult it was to build a connection between the two nodes in response to the feedback. In the human brain, connections that span a large physical distance are expensive to form and maintain.”

The physical constraints on the model forced its nodes to react and adapt similarly to a human brain. The implications for AI are that it could make algorithms process faster and more complex tasks as well as advance the evolution of “robot” brains.

Whitney Grace, December 11, 2023


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