Neural networks, also known as Artificial Neural Networks (ANNs) are a subset of Machine Learning (ML) that process data and identify patterns in a manner that is similar to the human brain. Emulating human cognitive thinking, neural networks, once refined with sufficient training data, can fulfil pattern recognition and problem solving at record speed.
Under the hood, neural networks are composed of a series of interconnected nodes, or neurons, that are organized in layers. These work together to process, analyze, and interpret large amounts of data. The different layers of neural networks primarily include:
- The input layer, which receives various forms of raw data, such as image pixels or written words.
- One or more hidden layers, which process the raw data through a network of nodes, or neurons. The nodes process the data and assign a weight to each input, which indicates its importance in the decision-making process. The network adjusts the weights based on the outcomes, which helps to improve accuracy over time.
- The output layer, which delivers the processed information.
Neural networks are closely related to deep learning platforms, but with a key distinction; while deep learning consists of more than three layers, neural networks consist up to three, thereby making them simpler than their deep learning counterpart.
Key Use Cases of Neural Networks
Organizations in many industries, from healthcare to financial services, are adopting and applying neural networks across a variety of use cases. Here are four common applications:
- Image, facial, and voice recognition: AI-based facial and image recognition uses a Convolutional Neural Network (CNN) that extracts features from images and classifies them into different categories. These models can learn more complex facial features such as shape or skin texture. Similarly, neural networks can be trained to recognize a human voice to identify the user. This technology powers some of the biometric identification features now common in smartphones and PCs.
- Natural Language Processing (NLP): Neural networks use NLP to interpret and understand human language. NLP tools can process unstructured data such as emails, text messages, social media, video, and audio – as well as the prompts used in GenAI platforms such as ChatGPT. NLP can analyze the intent or sentiment in the data and can respond in real time to a person’s comments or questions.
- Chatbots: Neural networks enable chatbots to engage and communicate with people in natural language. By mimicking the human brain and using pattern recognition and other algorithms, chatbots can conduct human-like conversations, learning and improving with each interaction. Chatbots have become prevalent on websites, serving as real-time assistants that can answer questions and provide information to visitors.
- Predictive analytics: Businesses use neural networks to predict future events, such as market trends, economic shifts, product demand, customer behavior, or potential threats. These types of predictive analytics tools help business leaders make more informed, data-driven decisions.
What Do Technology Buyers Need to Know about Neural Networks?
Technology buyers exploring the possibilities of neural networks should keep a few key considerations in mind:
- Integration and scalability: How well can neural networks be integrated into your existing systems? Will they be able to scale as your business grows?
- Data requirements: Do you have access to large volumes of high-quality, relevant data, which neural networks require to learn and produce accurate outputs?
- Skills: Do you have the right in-house talent to implement and manage neural network tools and platforms, or do you need to look externally for the right expertise?
- Ethical and privacy considerations: Are you putting processes and other guardrails in place to ensure regulatory compliance and build trust with customers?
