AI agents leverage a combination of technologies to perform tasks autonomously and intelligently. Machine learning, particularly deep learning, is at the core, enabling agents to recognize patterns, make decisions, and adapt to new data. Natural language processing (NLP) allows agents to understand and generate human language, which is essential for chatbots, virtual assistants, and customer service applications. Reinforcement learning is used for training agents in dynamic environments, such as robotics and gaming. Computer vision enables agents to process visual data for tasks like object recognition and navigation. Technologies like transformers, used in models such as GPT and BERT, have revolutionized NLP and multimodal AI capabilities. These technologies are often integrated with APIs, cloud computing, and edge devices to create scalable and efficient AI agents for various domains, including healthcare, finance, and customer support.
What AI technologies are used to power AI agents?
- Optimizing Your RAG Applications: Strategies and Methods
- Natural Language Processing (NLP) Advanced Guide
- Evaluating Your RAG Applications: Methods and Metrics
- Master Video AI
- Vector Database 101: Everything You Need to Know
- 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
Can I use Haystack for offline document search or batch processing?
Yes, you can use Haystack for offline document search and batch processing. Haystack is designed to facilitate search an
How do you benchmark database observability performance?
Benchmarking database observability performance involves measuring how effectively you can monitor and analyze database
What is the role of hashing in image search?
Hashing plays a significant role in image search by creating unique identifiers for images, allowing for fast and effici