Here’s Everything You Need To Know About Artificial Neural Networks

What Are Artificial Neural Networks (ANNs)?

Artificial neural networks are machine learning algorithms that process data in a way inspired by the human brain. The network uses neurons, or interconnected computers, which mimic the layered structure of a human brain.

An artificial neural network creates an adaptive environment, allowing the network to learn from mistakes and improve continuously.

How Do Artificial Neural Networks Work?

Inspired by the human brain, an artificial neural network consists of artificial neurons working together to solve a problem. A typical artificial neural network consists of three components:

• Input Layer: This is where information from the outside world enters the artificial neural network, like pixels from an image or words from a sentence. Input nodes process the data, analyse or categorise it and pass it on to the next layer.
• Hidden Layers: These layers act as the network’s workhorses, performing calculations and transformations on the data using artificial neurons. Artificial neural networks can have multiple hidden layers, sometimes in the hundreds. Each hidden layer analyses the output from the previous layer, processes it further and passes it on to the next layer.
• Output Layer: Finally, the processed information reaches the output layer. Here, the network delivers its final result. The layer can have single or multiple nodes. For instance, if we have a binary classification problem, the output layer will have one output node, which will give the result as 1 or 0. However, if we have a multi-class classification problem, the output layer might consist of more than one output node. ANNs enhance their predictive analytics through corrective feedback loops, employing a method known as the backpropagation algorithm.

What Are The Applications Of Artificial Neural Networks?

The flexibility and computational capabilities of ANNs have resulted in a myriad of applications. While research is still on towards developing new applications, here are four of the most important ones:

• Computer Vision & Image Recognition: Computer vision allows computers to extract information and insights from images and videos. With neural networks, computers can distinguish and recognise images similar to humans. Some of the underlying applications include facial recognition, self-driving cars, and content moderation.
• Natural Language Processing: Natural language processing (NLP) allows ANNs to process natural, human-created text. NLP helps computers gather insights from text data and documents.
• Speech Recognition & Voice Assistants: ANNs are capable of analysing human speech despite variations in speech patterns, pitch, tone, language and accent. Virtual assistants and automatic transcription software use speech recognition.
• Recommendation Engines: ANNs can track user activity to develop personalised recommendations. They can analyse all user behaviour and discover new products or services that interest a specific user. Companies like Google, Meta, Amazon and others deploy ANNs to develop personalised recommendations for users.

What Is The Role Of ANNs In GenAI?

ANNs play a crucial role in GenAI, acting as the computational engines. All generative AI models, be they generative adversarial networks (GANs) or transformer-based algorithms like OpenAI’s GPT, use artificial neural networks to arrive at the correct answer based on the inputs they receive and iteratively improve themselves based on feedback.

Though artificial neural networks provide most of the computational muscle for GenAI, the two verticals have vastly different market opportunities, given the vast difference in the number of potential use cases that can be developed for both technologies.

For instance, while GenAI’s market size is estimated to be at around $1.3 Tn by 2032, ANNs are looking at a $1.4 Bn market opportunity by 2032.

What Does The Future Look Like For ANNs?

The next decade promises to be a transformational one for ANNs. Labs across the world are working on a new class of neural models, spiking neural networks (SNNs), which may be able to resemble human brain activity more closely and result in speed and efficiency boosts for ANNs. 

Work is also underway on developing neuromorphic hardware to leverage the power of ANNs by mimicking human brain activity as closely as possible.

During the next decade, we might also see the advent of explainable AI (XAI), which might give researchers a glimpse into the ‘black box’ of how AI works, increasing accountability and trust in the process and leading to more seamless human-AI interactions.