How The Human Brain Inspired Artificial Intelligence Deep Learning Networks

A diagram showing the similarity between biological and artificial neural networks (via

geeksforgeeks.org).

Artificial intelligence (AI) is a developing field in the sphere of computer science. It encompasses building machines based on human intelligence. Machine learning (ML) is a subset of AI, wherein computers learn from raw forms of data (images, tables, words) and make decisions or predictions based on them. An advantage of ML is that the computers don't need explicit communication of each specific task, as they can identify patterns on their own. Due to this, an ML algorithm’s accuracy is improved through experience.

For example, if not taught, a computer cannot tell the difference between an image of the number three and an image of pizza. In ML, datasets of images of pizza and datasets of images of three are fed into the system. After, the computer identifies distinguishing features (perhaps the pictures of pizza were more textured). The more data (images in this case) the computer is presented with, the higher its accuracy and efficiency will be in identifying which pictures are pizza and which are the number three. This process is akin to our own brains and how we learn new information, hence the fact that AI is deeply based on the human brain as a concept.

How the Brain Processes Information

To understand artificial neural networks that are utilized in ML, one must realize their similarity and reliance on biological neural pathways. These biological networks consist of neurons that communicate via electrochemical signals using the dendrites (input) and axon (output). These pathways are learned continuously through experience. As we age and gain more knowledge and experience, the neural connections in our brain change. This is reflected in how ML systems learn.

However, unlike ML networks, which are further discussed in the next part, our brain doesn’t require structured data. We, unlike AI, can process noisy and unfiltered data. Additionally, we are naturally very effective adapters and have the ability to self-learn.

Artificial Neural Networks

Artificial neural networks are called artificial because they simulate how neurons in the brain communicate. These networks are made up of nodes, an input layer, hidden layers between, and an output layer. Both biological and artificial systems require an input signal, which then passes through and gets transmitted along the axon, or layers in the artificial case, to the output. Each node is an artificial neuron that connects to the next via a linear regression model (basically a model that predicts the output value based on the input). Additionally, each node has a weight (how important it is to the output) and a threshold value. When a node’s output is higher than the threshold value, the node gets activated and sends its data to the next node, and so on.

An ML neural network consists of one hidden layer, making it quite simple. This neural network only processes data once using weighted connections (linear regressions) and non-linear regressions. The more hidden layers a network has, the greater the complexity concerning the raw data.

Now, neural networks are more extensively used in deep learning algorithms. These algorithms are a subset of ML but differ in the depth of layers in a neural network. Hence the name, deep learning features more layers. A neural network of more than three layers (including the input and output) can be considered a deep learning algorithm. These networks are typically feed-forward, meaning they flow from input to output. The network below features three hidden layers, meaning the data gets processed three times.

These networks can process large amounts of data and identify complex patterns. These models are at the forefront of most AI due to their efficiency. They power generative AI and self-driving cars, for example.

Future Applications

The different components of a deep neural network (via ibm.com).

Neural networks can be vital in new technology and their connection to our brains. Brain-computer interfaces are systems that connect our brain to external devices (for example, if a person thinks of saying “hello,” the computer will read those brain signals and output “hello”). These systems are based on neural networks. Studies show that integrating a higher level of neural networks in these interfaces can improve performance, therefore facilitating novel means of communication using purely your brain.

Furthermore, these neural systems can help researchers understand the human brain better by analyzing how ML systems respond to a certain stimulus. Because these systems were based on the brain, they can conclude that the human brain's response will not differ greatly from that of the AI.

Conclusion

Modern AI systems are based on biological neural networks. Our brains learn from experience, and ML systems mirror this process, though our brains are more capable of processing unstructured data. Artificial neural networks contain input, output, and hidden layers. The more hidden layers a network has, the more complex it is, being named deep learning systems as a result. These deep learning systems, which rely heavily on biological neural networks, are a growing field in AI and have serious future applications. Finally, artificial and biological systems are closely intertwined, and this article focuses on how a neural network works in AI and deep learning, specifically.