Artificial intelligence and machine learning technologies are being used more and more by businesses to streamline operations and enhance customer satisfaction. Artificial neural networks are used in deep learning, a sort of machine learning, to assist intelligent systems in data processing and analysis. Understanding artificial neural networks can help you comprehend how they work and how companies use them. In this article, we answer the question, “What are artificial neural networks?,” describe their operation, go over various kinds of ANNs, and identify five professions that employ them.
What Are Artificial Neural Networks?
Knowing the answer to ‘What are artificial neural networks?’ can help you understand their different business applications. Artificial neural networks are computational models that comprise several processing elements which receive inputs and deliver outputs based on pre-defined activation functions. Artificial neural networks function just like the neural networks of the human brain, and their structure is very similar to a biological neural network.
The human brain has networks of billions of neurons that can be highly complex, nonlinear and have trillions of synapses. Similarly, artificial neural networks comprise multiple layers of connected input units and output units called neurons. Much like the synapses in a human brain, all networks and connections in the artificial neural network can transmit a signal to other neurons. Every layer of a neural network is perceptron, but artificial neural networks can have multiple hidden layers. Artificial neural networks require high computational power and release a large amount of heat compared to biological neural networks.
How Does An Artificial Neural Network Function?
An artificial neuron behaves like a biological neuron by function and adds together the values of the inputs it receives. If the input is above its threshold, then it sends its signal to its output, which other neurons receive. By simulating the behaviour of biological systems, artificial neural networks or ANN are capable of machine learning and pattern recognition. An artificial neural network usually has three layers:
- Input layer: The input layer is the first layer that receives the raw information like numbers, texts or files.
- Hidden layer: The next is the hidden layer, which determines the activity of each hidden unit and performs mathematical computations on the input data and recognises the patterns. There can be a single or multiple hidden layers in an ANN.
- Output layer: The output layer shows the result that we get through different computations by the hidden layers.
Use of Artificial Neural Networks in Machine Learning
Artificial neural networks have several uses in machine learning, as they have a remarkable capacity to derive meaning from complex data, extract patterns and detect trends. Data or information is input in a network that consists of many interconnected processing computing elements. These elements work in unison to solve specific problems. ANNs have a novel structure as information processing systems.
Artificial neural networks can understand complex real-world problems. They can solve problems that are too complex for conventional computer systems and technologies. They also have organic learning and do not rely only on inputs. ANN systems are capable of non-linear data processing and have high fault tolerance that is capable of self-repair. All these properties make them suitable for deployment in machine learning tools and technologies.
Application of Artificial Neural Networks
Here are some common applications of artificial neural networks in the modern world:
Facial recognition
ANN is useful in facial recognition to create systems of surveillance. These systems match the human face and compare it with digital images. Artificial neural network systems can authenticate a human face and match it with the list of IDs present in its database. Many images are input into the database for training a neural network, and the model optimises the information for accurate recognition.
Social media
ANN has several applications in social media platforms as they can help study the behaviour of users, along with analysing the data shared through virtual conversations. Several factors, like a user’s favourite page, interaction or bookmarked choice, are inputs that help in training the ANN model. These models help social media platforms in analysing user data and predicting preferences.
Aerospace engineering
Aerospace engineering helps design and develop spacecraft and aircraft. As safety is of paramount importance in this industry, all systems and devices undergo rigorous testing. ANNs help ensure the accuracy of the aircraft during autopilot by securing the control and analysing the performance.
Stock market prediction
Stock markets can be very volatile, and it is nearly impossible to predict the upcoming changes in the stock market. The occurrence of bullish and bearish phases has become relatively more predictable by using multilayer perception or MLP, which comprises multiple layers of fully-connected nodes. Organisations input the stock’s past performance or annual returns while building ANN models that help improve the accuracy of forecasts.
Defence
Artificial neural networks can bolster the defence operations of technologically advanced countries and help develop active defence strategies. Their application includes air patrols, maritime patrols and controlling automated drones. ANNs can also help defence systems analyse attacks and identify object locations during missions.
Types Of Artificial Neural Networks
Here is a list of some common types of artificial neural networks:
Feed forward Neural Network
The feed forward neural network is one of the simplest types of ANN, where the data or the input travels in just one direction. They typically function using a front propagated wave through an activation function. A front propagated wave is when the data enters from the input node and exits using the output node. Feed forward neural networks usually have hidden layers in nodes. As they are easy to maintain, they are a popular choice for many scientists and engineers.
Recurrent Neural Network (RNN)
This type of network reuses the result of the inner layer to help in predicting the outcome of additional data. In recurrent neural networks, a neuron may remember the information from previous processes when it encounters similar data. In this case, when the system makes an incorrect prediction, the neuron uses it to improve future predictions. RNNs use backpropagation, which means that data has the ability to move back to change the node’s weight. RNNs power services like predictive text applications on your phone or computer.
Radial basis function neural network
The radial basis function uses the distance of a point concerning the centre. It has two layers, first where the radial basis function connects to the inner layer and the second layer, which performs a set of nonlinear functions which connect to the output. Common systems that use radial basis function neural networks include power networks and systems, particularly for restoration during outages.
5 Professions That Use Artificial Neural Networks
Here is a list of some popular career paths that use artificial neural networks:
1. Machine Learning Engineer
National average salary: ₹47,600 per month
Primary responsibility: Professionals who ideate, design, develop and improve artificial intelligence systems are machine learning engineers and experts. They usually create smart systems, resolve data-related issues, write algorithms and work to improve the performance of existing machine learning tools. These engineers also use data-driven machine learning solutions to solve complex business problems, generate forecasts and analyse different models.
2. Deep Learning Engineer
National average salary: ₹40,453 per month
Primary responsibility: Deep learning engineers and experts specialise in creating tools and software that replicate the functioning of the human brain. They typically deploy artificial intelligence tools and software to automate processes, develop predictive models and enable virtual assistants or chatbots to respond. They also create machine learning systems that can work independently using neural networks with minimal or zero human intervention.
3. Data Engineer
National average salary: ₹96,025 per month
Primary responsibility: A data engineer manages, cleanses, analyses and sorts data to create organised data systems and structures that fulfil specific goals. They typically design and develop specific data-based tools to build algorithms or products with a set purpose and function. Data engineers generally deploy artificial neural networks to create smart learning and modelling systems for data extraction and analysis.
4. Business Intelligence Developer
National average salary: ₹26,298 per month
Primary responsibility: Business intelligence developers create systems, tools and programs to derive business insights from data. They generally collaborate with more experienced data engineers and developers to conceptualise and implement business solutions that help businesses improve existing company software systems. They usually use artificial neural networks to create predictive analytical models to develop business strategies or organise unstructured big data.
5. Software Engineer
National average salary: ₹39,471 per month
Primary responsibility: Software engineers are responsible for designing, developing, testing, implementing and maintaining software systems. They usually create data pathways and information systems to facilitate the smooth functioning of software solutions. They may use artificial neural networks while writing algorithms, developing predictive models or sorting large quantities of raw data.