Several core technologies are integral to AI development, with the most notable being machine learning (ML), natural language processing (NLP), and computer vision. Machine learning algorithms like regression, classification, and clustering form the backbone of most AI systems. These algorithms allow computers to learn from data, identify patterns, and make predictions without being explicitly programmed. Popular tools for ML include TensorFlow, PyTorch, and scikit-learn. Natural language processing (NLP) focuses on enabling machines to understand and generate human language. Technologies such as spaCy, NLTK, and transformers (e.g., BERT and GPT) are used to process text data and enable applications like sentiment analysis, chatbots, and machine translation. Computer vision, another prominent AI technology, allows machines to interpret visual information from images and videos. OpenCV, TensorFlow (for image recognition tasks), and YOLO (You Only Look Once) are commonly used libraries. Additionally, reinforcement learning (RL) is an emerging AI field that focuses on decision-making and optimizing actions in an environment to maximize rewards. Tools like OpenAI Gym and RLlib are designed to help in developing RL-based applications. AI also relies on supporting technologies such as cloud computing (for scalable resources) and big data technologies (for handling large datasets). These technologies collectively form the foundation of AI systems across industries like healthcare, finance, and autonomous vehicles.
What are the technologies used for AI?
- Master Video AI
- The Definitive Guide to Building RAG Apps with LangChain
- AI & Machine Learning
- Embedding 101
- Getting Started with Milvus
- 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
Why might I be hitting a rate limit or throttling error with Bedrock, and what can I do to prevent or handle this situation?
You might encounter rate limit or throttling errors with AWS Bedrock because the service enforces limits on the number o
What is hard negative mining and how does it improve embeddings?
Hard negative mining is a technique used in machine learning to improve the quality of embeddings—vector representations
How do approximate nearest neighbor settings (like search accuracy vs speed configurations) influence the end-to-end RAG latency and possibly the answer quality?
Approximate nearest neighbor (ANN) configurations directly impact RAG latency and answer quality by balancing the trade-