What’s the distinction between synthetic neural networks and organic brains?

What is the master algorithm that allows humans to be so efficient at learning things? That is a question that has perplexed artificial intelligence scientists and researchers who, for the past decades, have tried to replicate the thinking and problem-solving capabilities of the human brain. The dream of creating thinking machines has spurred many innovations in the field of AI, and has most recently contributed to the rise of deep learning, AI algorithms that roughly mimic the learning functions of the brain.

But as some scientists argue, brute-force learning is not what gives humans and animals the ability to interact the world shortly after birth. The key is the structure and innate capabilities of the organic brain, an argument that is mostly dismissed in today’s AI community, which is dominated by artificial neural networks.

In a paper published in the peer-reviewed journal Nature, Anthony Zador, Professor of Neuroscience Cold Spring Harbor Laboratory, argues that it is a highly structured brain that allows animals to become very efficient learners. Titled “A critique of pure learning and what artificial neural networks can learn from animal brains,” Zador’s paper explains why scaling up the current data processing capabilities of AI algorithms will not help reach the intelligence of dogs, let alone humans. What we need, Zador explains, is not AI that learns everything from scratch, but algorithms that, like organic beings, have intrinsic capabilities that can be complemented with the learning experience.

Artificial vs natural learning

Throughout the history of artificial intelligence, scientists have used nature as a guide to developing technologies that can manifest smart behavior. Symbolic artificial intelligence and artificial neural networks have constituted the two main approaches to developing AI systems since the early days of the field’s history.

“Symbolic AI can be seen as the psychologist’s approach—it draws inspiration from the human cognitive processing, without attempting to crack open the black box—whereas ANNs, which use neuron-like elements, take their inspiration from neuroscience,” writes Zador.

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While symbolic systems, in which programmers explicitly define the rules of the system, dominated in the first few decades of AI history, today neural networks are the main highlight of most developments in artificial intelligence.

Artificial neural networks are inspired by their biological counterparts and try to emulate the learning behavior of organic brains. But as Zador explains, learning in ANNs is much different from what is happening in the brain.

“In ANNs, learning refers to the process of extracting structure—statistical regularities—from input data, and encoding that structure into the parameters of the network,” he writes.

For instance, when you develop a convolutional neural network, you start with a blank slate, an architecture of layers upon layers of artificial neurons connected with random weights. As you train the network on images and their associated labels, it will gradually tune its millions of parameters to be able to place each image in its rightful bucket. And the past few years have shown that the performance of neural networks increases with the addition of more layers, parameters, and data. (In reality, there are a lot of other intricacies involved too, such as tuning hyperparameters, but that would be the topic of another post.)

There are some similarities between artificial and biological neurons, such as the way ANNs manage to extract low- and high-level features from images.

Visualization of a neural network's features

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