A new solution and concrete implementation steps for Artificial General
  Intelligence
Yongcong Chen Ting Zeng Jun Zhang
At present, the mainstream artificial intelligence generally adopts the
technical path of "attention mechanism + deep learning" + "reinforcement
learning". It has made great progress in the field of AIGC (Artificial
Intelligence Generated Content), setting off the technical wave of big models[
2][13 ]. But in areas that need to interact with the actual environment, such
as elderly care, home nanny, agricultural production, and vehicle driving,
trial and error are expensive and a reinforcement learning process that
requires much trial and error is difficult to achieve. Therefore, in order to
achieve Artificial General Intelligence(AGI) that can be applied to any field,
we need to use both existing technologies and solve the defects of existing
technologies, so as to further develop the technological wave of artificial
intelligence. In this paper, we analyze the limitations of the technical route
of large models, and by addressing these limitations, we propose solutions,
thus solving the inherent defects of large models. In this paper, we will
reveal how to achieve true AGI step by step.