What is an artificial neural network? What are the applications? Are there any limitations? Read further to know more.
The report on the global Artificial Neural Network (ANN) Market was recently released. It is an information bank that provides thorough data about the market, from its inception to its expected growth trend.
According to the latest report, from 2021 to 2028, the ANN market will experience phenomenal growth.
An ANN’s concept is to define inputs and outputs, then feed pieces of input to computer programmes that function like neurons and make inferences or calculations.
What is an artificial neural network?
An Artificial Neural Network (ANN) is a type of machine learning model inspired by the structure and function of the human brain. It is a network of interconnected nodes, called artificial neurons, that are designed to process and analyze information.
The basic building block of an ANN is the artificial neuron, which receives input from other neurons and performs computations on those inputs to produce an output. These neurons are organized into layers, with the input layer receiving raw data, the hidden layers performing computations on that data, and the output layer providing the final result.
The connections between the neurons, called edges, have weights associated with them. These weights are adjusted during training, allowing the neural network to learn how to produce the correct output for a given input. This process is done through backpropagation.
The network uses the input, performs computations on it, and produces an output. But, the output is compared with the desired output and the error is calculated. This error is then propagated back through the network and the weights are adjusted accordingly. This process is repeated multiple times until the error is minimized.
There are many different types of neural networks, including feedforward neural networks, recurrent neural networks, and convolutional neural networks, each with its own unique properties and applications.
ANNs are widely used in various applications such as image recognition, natural language processing, speech recognition, and control systems. These models are very powerful and flexible, able to learn and generalize from examples and adapt to new situations.
What are the applications?
Artificial Neural Networks (ANNs) have a wide range of applications due to their ability to learn and generalize from examples and adapt to new situations. Some of the most common applications of ANNs include:
- Image recognition: Artificial Neural Networks can be trained to recognize images, such as identifying objects in photographs or videos. This technology is widely used in self-driving cars, security cameras, and medical imaging.
- Natural Language Processing (NLP): It can be used to understand and generate natural languages, such as in language translation, text summarization, and sentiment analysis.
- Speech recognition: ANNs can be used to recognize and transcribe speech, which is used in virtual assistants, voice-controlled devices, and automatic speech recognition systems.
- Recommender Systems: These are widely used in recommendation systems, to suggest products, videos, music, and more, based on users’ preferences, search history, and behaviour.
- Financial Forecasting: ANNs are used in finance to predict stock prices, currency values, and other financial data.
- Gaming: ANNs are also used in gaming to train computer players and also used in virtual reality applications.
- Medical Diagnosis: It can be used to analyze medical images and assist in the diagnosis of diseases.
- Robotics and Control Systems: These are used in robotics for tasks such as object recognition, motion planning, and control.
- Fraud Detection: ANNs are used to detect fraudulent transactions in areas such as credit card payments, insurance claims and tax filings.
These are just a few examples of the many applications of ANNs. Due to their flexibility and ability to learn, Artificial Neural Networks have the potential to revolutionize a wide range of industries and make our lives easier.
Also read Artificial Intelligence.
What are the Developments aiding the development of ANNs?
There have been several key developments that have helped to advance the field of Artificial Neural Networks (ANNs) in recent years:
- Big Data: The availability of large amounts of data has allowed researchers to train larger and more complex ANNs.
- Cloud Computing: Cloud computing services have made it possible to train ANNs on powerful remote servers with high computational power.
- GPUs: The use of Graphics Processing Units (GPUs) has greatly accelerated the training time of ANNs by providing parallel processing capabilities.
- AutoML: Automated Machine Learning (AutoML) techniques have made it easier to tune the hyperparameters of ANNs, which can improve their performance.
- Transfer learning: pre-trained models on massive datasets have been made available, this enables fine-tuning on specific datasets which can greatly improve the performance of the model.
- Better Activation functions: Research in activation functions has led to the development of new functions that allow ANNs to perform better on certain tasks, such as rectified linear units (ReLU) and softmax.
- Better Optimization Algorithms: Optimization Algorithms like Adam, Adagrad, RMSprop etc have greatly improved the performance of the network by reducing the computational time, faster convergence and providing better results.
- Multi-Modal Representations: Multi-modal representation learning, involves combining data from multiple sources, such as text, images, and audio, to improve the performance of ANNs.
All these developments have greatly improved the performance of ANNs and have made them a powerful tool for solving a wide range of problems. However, there is still much room for improvement and many open research questions in the field.
What are the limitations of artificial neural networks?
Artificial Neural Networks (ANNs) are powerful and flexible tools, but they do have some limitations. Some of the main limitations of ANNs include:
- Black Box Problem: ANNs are often considered to be a “black box” because it can be difficult to understand how they arrived at a particular decision or conclusion. This can make it difficult to trust the results or explain them to others.
- Overfitting: ANNs have the tendency to memorize the training data, and not generalize well to new data, a problem known as overfitting. This can lead to poor performance on unseen data. Regularization techniques and early stopping can be used to mitigate this issue
- Data Quality: The performance of ANNs heavily depends on the quality and quantity of data available for training. The presence of missing data, outliers or biased data can greatly affect the performance of the network.
- Computational Complexity: ANNs can be computationally intensive and require a lot of computational resources to train and run. This can be a limitation for some applications, particularly for those requiring real-time processing or for systems with limited computational resources.
- Limited interpretability: ANNs can be difficult to interpret, as it’s not always clear how the model arrived at a certain decision. This can make it difficult to understand the underlying relationships in the data or to identify specific factors that are causing the model to make a certain prediction.
- Limited to supervised learning: Most ANNs are limited to supervised learning, which means they need to have labelled data to learn from. This makes it difficult to use them in unsupervised or reinforcement learning scenarios
Despite these limitations, ANNs have proven to be a powerful tool for solving many complex problems and are widely used in various applications. Researchers and developers are constantly working to improve the capabilities and overcome the limitations of ANNs.
There are several areas of research that could help to further advance the field of Artificial Neural Networks (ANNs) and improve their capabilities:
- Explainable AI: Developing techniques that can provide more insight into the inner workings of ANNs and make them more interpretable to humans. This would make it easier to understand how the model arrived at a particular decision, making it more trustworthy.
- Unsupervised and Reinforcement Learning: Developing ANNs that can learn from unstructured and unlabelled data, as well as through trial-and-error interactions with the environment, as in reinforcement learning, would greatly expand the capabilities of ANNs to solve more complex and realistic problems
- Neuromorphic Computing: Developing hardware that is inspired by the structure and function of the human brain, and can more efficiently perform the computations required by ANNs. This could lead to more powerful and energy-efficient AI systems.
- Multi-modal Learning: Developing ANNs that can effectively combine and process data from multiple sources, such as text, images, and audio, which would enable ANNs to solve more complex and nuanced problems.
- Adversarial Learning: Developing techniques for training ANNs to better handle adversarial examples and improve their robustness to malicious attacks.
- Attention Mechanisms: Improving the ability of ANNs to focus on specific parts of the input, would enable them to perform better on tasks such as natural language processing and image recognition.
- Generative Models: Advancements in the generative models and their ability to generate new examples of data that look similar to the examples they were trained on, like Generative Adversarial Networks (GANs) and Variational Autoencoder (VAE).
Research in these areas could help to improve the performance and capabilities of ANNs and make them more useful for a wide range of applications.
It’s important to note that developments in Artificial Neural Networks are interdisciplinary and require collaboration across different fields such as computer science, mathematics, neuroscience, cognitive science, and many more.
Article written by: Remya