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The Brain’s Way of Solving Problems. @OpenLedger
Your brain is a prediction engine. When you touch fire as a child, your neurons wire together to remember pain. When you see a dog, your brain does not check a rulebook. Instead, millions of neurons fire in patterns that encode your past experiences of “dogness.”
This is intelligence: the ability to generalize from experience. Machines wanted to do the same. The first attempt was not to build neurons but to write rules. If fire = hot, do not touch. If dog = four legs, tail, barks, then classify as dog. This was called symbolic AI, a world of handcrafted logic.#OpenLedger
Illustration comparing brain-like and rule-based problem solving
It worked for narrow problems but collapsed the moment reality got messy. The brain thrives in messy situations, rules do not.
Learn more:
History of Artificial Intelligence (Wikipedia) $OPEN
The Dartmouth Conference: Where “AI” Was Born
In the summer of 1956, John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester gathered at Dartmouth College for the Dartmouth Summer Research Project on AI.
It was here that the term “Artificial Intelligence” was first coined. The proposal stated:
“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
This wasn’t a coding hackathon. It was a blueprint for a field, pointing to neural nets, search, symbolic reasoning, and language. The dream was set.
To learn more:
Dartmouth conference
From Rules to Learning: The Perceptron
In 1957, Frank Rosenblatt asked: what if machines could learn like neurons? He introduced the perceptron, the first mathematical model of a neuron.
The perceptron takes inputs, multiplies them by weights, adds a bias, and runs them through a step function:
f(x) = h(w ⋅ x + b)
Inputs (xi) = features, like pixel values
Weights (wi) = importance of each feature
Bias (b) = adjusts the decision boundary
Step function (h) = binary output (1 or 0)
This made the perceptron a linear classifier, able to draw a straight-line boundary between classes.
Rosenblatt also built hardware: the Mark I Perceptron (1960). It had a 20×20 grid of photocells acting like a retina, connected randomly to association units, with adjustable weights implemented by potentiometers. Motors updated these weights during learning.
It was able to classify simple patterns and created massive excitement. The New York Times even claimed it could one day walk, talk, and be conscious (
NYT Archive, 1958).