Artificial neural networks (ANNs) are a cornerstone of modern AI, enabling systems to learn and make decisions based on data. Inspired by the structure of the human brain, ANNs consist of interconnected nodes (neurons) organized into layers. Each neuron processes inputs, applies an activation function, and passes the output to subsequent layers. This architecture allows ANNs to approximate complex functions and solve problems across domains. ANNs are particularly effective in tasks like image recognition, speech processing, and natural language understanding. For instance, convolutional neural networks (CNNs) specialize in visual data, while recurrent neural networks (RNNs) handle sequential data like time series or text. By enabling machines to learn patterns and relationships, ANNs power many AI applications, from chatbots to self-driving cars.
What is the role of artificial neural networks in AI?

- The Definitive Guide to Building RAG Apps with LlamaIndex
- Evaluating Your RAG Applications: Methods and Metrics
- Advanced Techniques in Vector Database Management
- Master Video AI
- The Definitive Guide to Building RAG Apps with LangChain
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does the visual backbone (e.g., CNNs, ViTs) interact with language models in VLMs?
In Vision-Language Models (VLMs), the visual backbone, which typically consists of Convolutional Neural Networks (CNNs)
What is a multivariate time series, and how is it modeled?
A multivariate time series is a collection of data points recorded over time, where multiple variables or features are o
How Image to Text converter works using OCR technology?
An Image to Text converter using OCR (Optical Character Recognition) works by analyzing an image to identify and extract