Artificial intelligence (AI) is a vast field of computer science that simulates human intelligence. Comprising many branches or subfields, AI has helped make significant advancements in areas such as disability inclusion, early cancer detection, weather forecasting, security and surveillance and others.

The role of AI in marketing and AI-based capabilities are ubiquitous in our daily lives. Think: Personalized recommendations on your favorite streaming service or self-driving cars.

Below are six main subfields that are critical to how AI works in business.

Machine learning: Machine learning (ML) is a branch of AI that uses statistical methods to classify or predict patterns in data. ML insights are used to make informed decisions in areas that impact growth such as marketing and business operations. ML can be supervised, unsupervised and reinforced. Supervised learning needs training data and is most commonly used in market research, predictive modeling and text parsing.

Neural networks: Neural networks or artificial neural networks (ANNs) are learning algorithms that depend on training data to learn. They are a subset of ML and structured to mimic how the human brain digests information and makes connections between different data points. Neural networks keep learning as they process more data, improving their accuracy over time.

Deep learning: Deep learning is a subfield of ANNs and refers to any neural network with three or more neuron layers. Deep learning algorithms are more powerful than regular neural networks because of their enhanced learning abilities in optimizing and refining results for accuracy. It enables several AI applications from smart assistants (Think: Siri and Alexa) to other areas such as healthcare, fraud detection and face recognition technologies.

Natural language processing: Natural language processing (NLP) enables a computer to understand language just as a human does. NLP models understand text or audio data within a frame of context and decipher homonyms, grammar, sentence structures as well as irregularities in a text. This enables businesses to use AI to process customer experience data, sentiment analysis and more. It is also what powers conversational AIs like ChatGPT.

Computer vision: Computer vision allows a system to derive meaningful information from digital images, graphs, videos and other visuals. It uses deep learning and convolutional neural networks (CNNs) to process millions of image data and create patterns. CNNs are used in many applications such as early cancer detection, surveillance, space exploration and developing visual effects for films.

Cognitive computing: Cognitive computing analyzes data similar to a human’s thought process to help us make decisions. It uses several branches of AI such as ML, NLP, neural networks, sentiment analysis and contextual knowledge to do so. It’s used for demand forecasting, optimizing customer journeys, designing web user interfaces, logistics and transportation, cyber security, healthcare, air traffic control and more.

Though we are seeing continuous advancements in AI, more research is needed to explore its full potential. As it becomes a more integral part of our lives, responsible AI that considers privacy, security, transparency, fairness of intellectual property, reliability and inclusion is critical.