How Brain-Inspired AI Mimics Childlike Reasoning

AI, akin to a wired brain, mirrors children's curiosity and learning.

By Sunil Sonkar 3 Min Read
3 Min Read
How Brain-Inspired AI Mimics Childlike Reasoning

In the ever-evolving world of technology, one concept stands out lately and it is artificial intelligence (AI). It is like a brain made of wires and chips, attempting to replicate the smarts of our most natural scientists—children.

Kids are curious creatures. They keep on exploring and testing their surroundings. They might come up with some funny explanations along the way, but they are learning, just like AI.

While AI, especially deep learning, is changing the game in technology—predicting weather, designing medicines and even diagnosing diseases—it has a big flaw. It can’t explain itself. It is like a puzzle box with no key and leaves us in the dark about how it reached its conclusions.


Imagine being told you have a serious illness but not understanding why or how the diagnosis was made. That is the problem we face with AI in critical areas like medicine.

Scientists at the University of Texas Southwestern Medical Center got smart ideas from our brains to solve this problem. They combined brain network principles with traditional AI methods to create a new approach.

This new AI, called “deep distilling,” works a bit like a curious child. It gathers information, sorts it into neat little bundles called “hubs,” and then translates those findings into simple explanations that even us humans can understand. It is like giving programmers a cheat sheet to understand what the AI is up to.

In areas like healthcare and scientific research, mistakes can be serious—or even deadly. We need AI we can trust, AI that can explain its reasoning.

Traditional deep learning AI learns by processing tons of data over and over until it gets things right. It is like cramming for a test without really understanding the material. Sure, it works sometimes, but it is not reliable when things get tricky.

That is where symbolic reasoning comes in. It is like giving the AI a set of rules to follow, like building blocks. It is more straightforward for us to understand, but it can be rigid and break when faced with something new.

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