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?
- Exploring Vector Database Use Cases
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Natural Language Processing (NLP) Basics
- Information Retrieval 101
- 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 LLM guardrails address systemic bias in training data?
Yes, LLM guardrails can help address systemic bias in training data by incorporating fairness and bias detection mechani
What is the significance of ACID compliance in benchmarks?
ACID compliance is crucial in database benchmarks as it ensures that transactions are processed reliably and adhere to c
How do you identify the optimal lag for a time series model?
Identifying the optimal lag involves analyzing how past values influence the current data. The autocorrelation function