The Mother and Her Child: A Journey Through the History of Neural Networks

One warm summer afternoon, a mother gazes at her baby. He is going to be three soon. The boy looks up and beams at her with joy. Maybe it’s time, she ponders—time to introduce him to the world of letters. She picks up one of the colorful books that she had borrowed from the library. Her voice is patient as she traces each letter with her finger, guiding his small hands to follow. The boy looks up at her, feeling secure in her arms, his eyes full of trust as he faithfully follows her fingers on the page. To him, the letters are mere shapes—confusing, unintelligible. Many afternoons go by. Through repetition, through encouragement, through the warmth of love, recognition begins to form. The boy learns, makes mistakes, tries again, and slowly, the letters become words, and the words become meaning.

This simple bond between a mother and child and her earnestness to teach her little boy gave birth to the journey of neural networks—a tale of human ingenuity mimicking the way the human being learns.

Just like the mother who introduces the alphabet to her child, the earliest researchers sought to teach machines how to recognize patterns. In 1943, Warren McCulloch and Walter Pitts proposed a mathematical model of artificial neurons, inspired by the way biological neurons function in the human brain. They came up with the idea of a neuron, which was a simple binary threshold unit that would either "fire" (output 1) or remain inactive (output 0) based on a weighted sum of its inputs. This was the first step of the journey—tracing the letters, forming the foundation of understanding.

In the 1950s, Frank Rosenblatt built upon this foundation with the Perceptron, a rudimentary neural network capable of learning simple patterns. The perceptron learned how to distinguish between basic inputs, similar to a toddler identifying shapes. But a mother knows that recognizing letters is not enough for her child to grow—her child must learn to read, to think, to reason. Computer scientists realized that Perceptron was still far from true intelligence and had its limitations. It could not understand complex relationships, just as a three-year-old child cannot yet grasp the meaning behind all words.

As the child grows, he sometimes experiences frustration as he advances in his educational journey. There are times when he stumbles and falls. The frustration passes on to the teacher as the child struggles to read more complicated words, and the child is told that he would never understand books! As the limitations of the early neural networks came to light, funding and interest dwindled, and research in the field nearly stopped.

But a mother does not give up on her child, and some computer scientists did not give up either.

Finally, a time comes when the child begins learning from his mistakes and suddenly grasps the power of phonetics—understanding not just individual letters but how they connect to form words. Thanks to advances in computing and new mathematical techniques in the 1980s, Geoffrey Hinton and his colleagues developed the backpropagation algorithm, which gave neural networks the ability to improve. Backpropagation can be considered as a “backward propagation of errors," which iteratively minimizes the error between predicted and actual outcomes by propagating it backward from the output layer to the input layer by adjusting the weights of connections between neurons.

Neural networks were finally learning in a way that resembled human cognition!

With time and persistence, the child, now more confident, could tackle entire paragraphs, understand stories, and even predict what might happen next in a tale. The journey of the neural networks also moved towards a more sophisticated path. By the 2000s and 2010s, deep learning—a model with multiple layers of neurons—enabled computers to recognize images, understand speech, and even generate human-like text.

Now, in our present day, the child who once struggled to recognize letters has grown into an adult. He is capable of reading complex literature, of creating writing material, is up-to-date with the continuously changing world of technology, and is apprised of the political environment. His brain works at lightning speed, and he can easily surpass the intelligence of his first teacher. So have the neural networks grown! They began their journey by feebly attempting to mimic the human brain. But now they power technologies like ChatGPT, DeepSeek, AlphaGo, and autonomous systems that outperform human experts in various fields.

Many are scared of the future with AI and what could happen as it surpasses human intelligence. But just as a mother watches her child succeed with pride rather than fear, we must approach AI’s progress not as a threat but as a result of the creativity of the human brain.

The mother’s greatest achievement is seeing her child flourish and reach greater heights. And so, the story of neural networks is not just a tale of machines learning—it is a reflection of the child’s journey growing up. A testament to the power of patience, persistence, and the belief that with the right guidance, anything can learn and grow. But as we help it grow, we must ensure that it grows in a way that it retains the humility, regard, and respectfulness for its creator and for all humanity.