How are neural networks impacting today’s lives, and what do we expect in 2021?

Neural networks are defined as computing systems that are interconnected nodes and work much like neurons in the human brain. A neural network can be either a biological one that is made up of human biological neurons or an artificial neural network, aimed at solving artificial intelligence problems. Initially, this concept was designed to create a computational system that could solve problems like a human brain. With evolving technology advancements, researchers shifted their focus to using this approach to match specific tasks, leading to deviations from a strictly biological approach.

There are mainly three significant types of neural networks that form the basis for most pre-trained models in deep learning and are poised to become highly impactful in people’s lives. These are convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN).

Convolutional Neural Networks

A convolutional neural network (CNN) is mostly applied to assess visual imagery. It captures the spatial features from an image, which are the arrangement of pixels and the relationship between them in an image. CNNs are inspired by biological processes, where the connectivity pattern between neurons resembles the organization of the animal visual cortex. These networks are often used in image recognition systems and have lured more attention from researchers in the field of deep learning. CNNs are beneficial in autonomous vehicle development and incongruity detection in images and videos—for instance, cancer screening or automated quality assurance tasks.

When AlexNet achieved impressive outcomes in the ILSVRC-2012 image classification competition, many researchers started focusing on the improvement of the CNN architecture. This architecture is generally composed of convolution layers, subsampling layers, and fully connected layers. In the coming days, the interest in convolutional neural networks will surge as it is increasingly being used for smart surveillance and monitoring, social network photo tagging and image classification, robotics, drones, and autonomous technology.

Recurrent Neural Networks

This refers to a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. The recurrent neural networks (RNNs) are used extensively to refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse. RNNs are commonly used for ordinal or temporal problems, including language translation, NLP, speech recognition, and image captioning. These deep learning algorithms can also be used for robot control, time series prediction, speech synthesis, time series anomaly detection, rhythm learning, music composition, human action recognition, and prediction in medical care pathways, among others.

In contrast to traditional neural networks, all inputs to a recurrent neural network are not self-governed of each other, and the output for each element relies on the computations of its preceding elements.

Generative Adversarial Networks

As a class of machine learning frameworks, generative adversarial networks (GANs) refer to an approach to generative modeling using deep learning methods, such as CNNs. These networks are an effective way of training a generative model by framing issues like a supervised learning problem with two sub-models. First, the generator model that used to train to generate new examples, and second the discriminator model tries to classify examples as either real (from the domain) or fake (generated).

GANs will continue to gain attention as they have a myriad of real-world use cases such as art, music, video, and literature generation artificially. They can improve the quality of an image, render or colorize the image, generate faces, and can perform many more interesting tasks. The recent release of GPT-3, an autoregressive language model from Elon Musk’s OpenAI, has accelerated the improvement in GANs.