Artificial intelligence is the intelligence demonstrated by machines as opposed to the natural intelligence displayed by animals and humans. In order to make artificially intelligent machines, there is a need to simulate the behaviour of the human brain.
This is possible with the help of neural networks, which allow computer programs to recognise patterns and solve common problems in the fields of AI, machine learning, and deep learning.
What are neural networks?
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are essentially a subset of machine learning. They form the heart of deep learning algorithms. According to IBM, their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Artificial neural networks (ANNs) are composed of a node layer, containing an input layer, one or more hidden layers, and an output layer. Each node, also called an artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network.
Neural networks rely on training data to learn and improve their accuracy over time. Once these learning algorithms are fine-tuned for accuracy, they become powerful tools in computer science and artificial intelligence. The most popular and well-known neural network is Google’s search algorithm.
How does a neural network work?
As explained in the AI model, there are several types of commonly used AI models and each individual node of a neural network is essentially its own linear regression model. It is thus composed of input data, weights, a bias or threshold, and an output. Once an input layer is determined, weights are assigned.
These weights play an important role in determining the importance of any given variable with larger ones contributing more significantly to the output compared to other inputs. The output is then passed through an activation function to determine the output.
What are the types of neural networks?
Neural networks can be classified into different types and are used for different purposes. While it is difficult to create a comprehensive list of types, the most common types of neural networks are the perceptron, feedforward neural networks, convolutional neural networks, and recurrent neural networks.
- Perceptron: This is the oldest and simplest form of a neural network. Created by Frank Rosenblatt in 1958, it has a single neuron and thus becomes the simplest form.
- Feedforward neural networks: Also called multi-layer perceptrons (MLPs), these are essentially the most commonly used form of neural networks. They are composed of an input layer, a hidden layer or layers, and an output layer. Data is usually fed into these models to train them, and they are the foundation for computer vision, natural language processing, and other neural networks.
- Convolutional neural networks (CNNs): These are similar to feedforward networks, but they are used for specialised applications such as image recognition, pattern recognition, and/or computer vision. The CNNs harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.
- Recurrent neural networks (RNNs): The recurrent neural networks can be easily identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.
How do neural networks differ from deep learning?
Deep learning and neural networks have been used interchangeably in conversation and are thus confused by the AI community. In simple terms, the word deep in deep learning refers to the depth of layers in a neural network.
A neural network, by default, is composed of more than three layers, including the input and output. Any neural network with such a structure can also be called a deep learning algorithm. “A neural network that only has two or three layers is just a basic neural network,” experts at IBM explain.
What are the advantages of neural networks?
There are multiple advantages offered by a neural network and they can be described as follows:
- The parallel processing ability means the network can perform more than one job at a time.
- The information is stored on an entire network and not just a database.
- Since neural networks support the ability to learn and model nonlinear, complex relationships, they help model the real-life relationships between input and output.
- With fault tolerance built-in, the corruption of one or more cells of an ANN does not stop the generation of output.
- Instead of destroying the network instantly, the neural networks degrade slowly over time
- There are no restrictions placed on the input variables
- Machine learning also leads to ANN learning from events and making decisions based on observations.
- ANNs also possess the ability to generalise and infer unseen relationships on unseen data and thus be able to predict the output of unseen data.
What are the disadvantages of neural networks?
The disadvantages of ANNs include:
- The lack of rules for determining the proper network structure means the appropriate artificial neural network architecture can only be found through trial and error and experience.
- Neural networks are extremely hardware-dependent since they require processors with parallel processing abilities.
- The network works with numerical information, therefore all problems must be translated into numerical values before they can be presented to the ANN.
- The lack of explanation behind probing solutions is one of the biggest disadvantages of ANNs. The inability to explain the why or how behind the solution generates a lack of trust in the network.
What are the common applications of neural networks?
Neural networks can be seen applied in our regular life and it has now successfully expanded beyond image recognition. Some of the common applications of neural networks include:
- Natural language processing, translation, and language generation
- Stock market prediction
- Delivery driver route planning and optimisation
- Drug discovery and development