Artificial Neural Networks

What are artificial neural networks?

Artificial neural networks (ANNs), as the name implies, mimic the human brain’s neural networks. Neural networks are comprised of node layers, in which an input layer, a hidden layer, activation function and an output layer exist. Each node can also be referred to as an artificial neuron. Each is composed of input data, weights, biases, and output. It is a data processing and output generation system that replicates the neural system to unravel non-linear relations in a large dataset. The data might come from sensory routes and might be in the form of text, pictures, or audio.

The term artificial neural networks, also often referred to simply as neural networks, is often used synonymously with deep learning. However, technically speaking, deep learning refers to training artificial neural networks. ANNs are a subset of machine learning (ML), which is itself a branch of artificial intelligence (AI) and computer science that entails collecting large amounts of data and using algorithms to help the machine learn like the human brain. 

The best way to understand how an ANN works is by understanding how a natural neural network inside the brain works and drawing a parallel between them. Neurons are the fundamental component of the human brain and are responsible for learning and retaining knowledge and information as we know it. You can consider them the processing unit in the brain. They take the sensory data as input, process it, and give the output data used by other neurons. The information is processed and passed until a decisive outcome is attained.

The more the machine “learns,” the more accurate it becomes. The phrase “artificial neural networks” was created in 1943 by Warren McCulloch and Walter Pitts. Artificial neural networks are a crucial aspect of the rapidly growing field of data science, where the processing of massive data sets allows computers to make classifications and predictions to develop business insights in data mining projects.

There are several product categories on G2’s website that use ANNs, including but not limited to Text Analysis software, Data Science and Machine Learning platforms, AI & Machine Learning Operationalization (MLOps) software, Image Recognition software, Voice Recognition software, and Artificial Neural Network software. In addition to platforms dedicated specifically to artificial neural networks, many solutions also incorporate artificial neural networks into the overall functionality of the tool. For example, medical transcription software converts words to text, and talent intelligence software helps HR professionals discover potential candidates during the recruitment process.

Artificial Neural Networks Software

Software that mention artificial neural networks as a feature or term.

Keras

Google Cloud Speech-to-Text

OpenCV

MATLAB

Types of artificial neural networks

There are many artificial neural networks, some of which are in a rudimentary research phase, and others are live in software products. The two main types are:

• Convolutional neural networks (CNNs): CNNs extract features directly from data, such as images, eliminating the need for manual feature extraction. Manual feature extraction would require the data scientist to go in and determine the various components and aspects of the data. With this technology, the neural network determines this by itself. None of the features are pre-trained; instead, they are learned by the network when it trains on the given set of images. This automated feature extraction characteristic makes deep learning models highly effective for object classification and other computer vision applications.

• Recurrent neural networks (RNNs): A recurrent neural network is an artificial neural network that uses sequential or time-series data. These deep learning algorithms are commonly used for ordinal or temporal problems, such as language translation, natural language processing (NLP), speech recognition, and image captioning.

Benefits of artificial neural networks

The explosive growth of big data has made AI in general and artificial neural networks in specific viable. Below are some of the key benefits:

• Allows businesses to stay agile and adapt to market changes: ML algorithms allow for the virtually limitless processing of data, which is useful when business decisions need to be made in response to market changes and predictions. An example of this could be better preparing global supply chains when certain geographic regions of business are more impacted by climate change.

• Improves logistics and business functioning: This technology can help logistics professionals predict consumer demand, assess stock levels, and make strategic inventory decisions.

• Offers robust user analysis for marketing and targeting: Algorithms can also help measure the success of marketing campaigns to create recommendations for optimization. In addition, mass analysis of consumer data can help develop more insightful target profiles.

• Assists with medical imaging and diagnosis: The field of bioinformatics uses data science and artificial neural networks to help with medical imaging and diagnosis as well as predict the risk for future diseases, such as cancer.

Artificial neural networks vs. natural language processing vs. machine learning vs. deep learning

Machine learning is sometimes used interchangeably with deep learning and is associated with neural networks and natural language processing. It is, however, important to highlight the key distinctions between these concepts. 

As mentioned above, machine learning is a branch of artificial intelligence and computer science. Natural language processing is a discipline within ML that focuses on helping AI learn the natural language of humans, both spoken and written. This field of ML is what helps run chatbots and assistants like Alexa and Siri and is largely built on artificial neural networks.

Neural networks are classes of ML algorithms modeled on the human brain. With neural networks, information moves through algorithms like electrical impulses through the brain. Finally, deep learning is a neural network with many layers, and each layer determines the “weight” of each link in the network.