AI development is entering a transformative phase where AI agents can autonomously perform tasks by gathering real-time information from the internet. This capability enhances AI task automation, enabling actions such as monitoring website updates and making trading decisions based on live data. Using techniques like in-context learning and Retrieval-Augmented Generation (RAG), these agents can adapt to user interactions and retrieve more recent information. The latest evolution involves enabling AI to explore its environment, embodying a more human-like capability to make decisions and perform complex actions seamlessly.
AI development is shifting to an important new phase, with agents that can pull fresh information from the web, plan, and execute tasks autonomously.
The advance is complemented by Retrieval-Augmented Generation (RAG), allowing LLMs to pull in more recent information from external databases.
Exploration is the latest technique for improving LLMs with external knowledge, enabling AI agents to explore and engage with their environment.
By combining explorative abilities with reasoning and decision-making skills, agents can become much more human-like.
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