Understanding the Growth and Limitations of Artificial Neural Network (ANN)
The world is witnessing a phenomenal growth in technology-related fields, with Artificial Neural Network (ANN) at the forefront. A recent market report projects a significant expansion of the ANN market from 2021 to 2028. This article delves into understanding what Artificial Neural Networks are, their significance, limitations, and why they are making news.
Artificial Neural Network : An Overview
An Artificial Neural Network, often simply referred to as a Neural Network, is a key subset of machine learning. It supports computer scientists in executing complex tasks, including strategizing, making predictions, and recognizing trends.
A Neural Network is a computational model designed to mimic the structure and functions of the nerve cells in the human brain. It simulates how the brain processes and analyzes information. Unlike other machine learning algorithms that focus on number crunching or data organization, ANN learns from experience and repeated tasks executed by the users.
These networks are initially fed with massive volumes of data, typically through providing input and informing the network about the anticipated output. This process structures the ANN based on the input and output. Thus, the neural network’s structure is constantly influenced by the information that runs through it.
Applications of ANN: Case in Technology
Cutting-edge technology like facial recognition, integrated recently by many smartphone makers, demonstrates the practical application of Artificial Neural Networks. Rapid digitization is expected to boost the deployment of ANN platforms even further.
Predictive analytics is another common application of ANN. By recognizing trends from previous marketing campaigns, ANNs help marketers predict the outcome of an upcoming campaign. This technology, although available for a while, has become especially useful in the field of marketing due to the emergence of Big Data.
Cloud computing provides the massive computing resources needed for ANNs to handle extensive volumes of data. This has further facilitated the use of ANN in various fields.
Drivers of ANN Growth
The growth of Artificial Neural Networks is primarily driven by rapid digitization and its application in predictive analytics. Predicting consumer behavior and sales forecasting are expected to drive the ANN market during the forecast period.
Limitations of ANN
Despite their significant contributions to technology and machine learning, ANNs have a few limitations. One of the major technological challenges is the time required to train the networks. They often demand a high level of computational power even for complex tasks.
Secondly, while neural networks are capable of adjusting the responses, they do not provide access to the specific decision-making process. This lack of transparency in how decisions are made could present issues in some scenarios.
Overall, while there are challenges to be addressed, the benefits and potentials of Artificial Neural Networks are immense, indicating a promising future in the realm of technology.
