Summary
In the video, the difference between A.I. assistants and A.I. agents is explored, emphasizing that assistants are reactive while agents are proactive in achieving goals. A.I. assistants respond to user prompts to organize information and fetch content, primarily relying on launch language models. On the other hand, A.I. agents take initiative, break down tasks, and make decisions autonomously using external data and reasoning, without frequent user input. The discussion includes ways to enhance prompt responses by adapting underlying models through fine-tuning for specific tasks and highlights contrasting use cases in various industries such as customer service, finance, and network monitoring.
Introduction to A.I. Assistants and A.I. Agents
Exploring the difference between A.I. assistants and A.I. agents, where assistants are reactive while agents are proactive in achieving goals.
A.I. Assistants
A.I. assistants understand natural language, organize information, respond to queries, and rely on prompts from users to take action.
Launch Language Models (LLMs)
Most A.I. assistants are built on launch language models (LLMs) and use prompts from users to fetch information and generate content.
Improving Prompt Responses
Ways to enhance the quality of prompt responses, including adapting the underlying model for specific tasks through fine tuning.
A.I. Agents
A.I. agents take initiative, break down tasks, find optimal ways to achieve goals, and make decisions autonomously using external data, tools, and reasoning.
Comparison of A.I. Assistants and A.I. Agents
Contrasting the use cases of A.I. assistants and A.I. agents in customer service, automated trading, finance, and network monitoring based on their capabilities and dependencies on user input.
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