Talk details

At the recent Vivatech conference, Yan LeCun, Chief AI Scientist at Meta, declared that Generative AI has only five years left to live. He argued that Large Language Models (LLMs) are not the future of artificial intelligence due to their limitations in four key areas: understanding the real world, maintaining persistent memory, reasoning, and planning.
Given this prognosis, let's take a comprehensive look at the budding field of Agentic AI and its future prospects. What exactly are AI Agents, and how do they operate? How do they compare to and interact with LLMs and functionalities such as function calling, chain-of-thought processing, assistants, tools, or actions?
In this talk, we'll delve into the unique features of Agentic AI, including perception, state estimation, goal setting, planning, and action selection & execution. We will define various levels of Agentic AI and form a map to help navigate this emerging landscape. By categorizing current agent-based or agent-related solutions with practical examples, we'll provide an overview of the current state of Agentic AI:

• LLM with Function Calling and Tools (OpenAI's GPT-4 or Gemini), these models will serve as a base reference to illustrate current capabilities and limitations in function calling, tool usage, and action execution.
• Agent Framework like AgentGPT, AutoGPT or CrewAI.
• LAM Framework like LaVague, designed for developing AI Web Agents and facilitating Web Automation.

Prepare to uncover how today's Large Language Models (LLMs) are being complemented —and perhaps even superseded— by Large Actions Models (LAMs) in the near future.