A new solution and concrete implementation steps for Artificial General Intelligence

TL;DR

This paper introduces a comprehensive approach to building artificial general intelligence (AGI) with self-awareness, detailing new methods for attention, self-demand, value systems, world models, hierarchical decision-making, and language-based experience acquisition.

Contribution

It presents a novel integrated framework for AGI development, including implementation steps and demonstrations, addressing limitations of current large language models.

Findings

Proposed a new attention mechanism method.

Outlined a step-by-step implementation roadmap.

Demonstrated a practical AGI system prototype.

Abstract

In this paper, we propose a new approach to building a artificial general intelligence with self awareness, which includes: (1) a new method to implement attention mechanisms; (2) a way to give machines self-demands; (3) how to form a value evaluation system compatible with the network; (4) a way to create the world models; (5) how to realize a top-down, hierarchical thinking decision-making chain; (6) a way to achieve general decision-making and response capabilities; (7) a way for a machine to directly obtain human experience through language. In the paper, we first analyze some of the shortcomings of current LLMs (Large Language Model) and propose ideas for improvement. Then we analyze why our scheme can solve the above problems and provide detailed steps for implementing our scheme. In chapter 4, we have presented a step-by-step mplementation roadmap. And in chapter 5, we have presented a specific implementation demonstration. In chapter 6, we analyze the advantages and disadvantages of our scheme and propose further research directions. In this article, we have put forward how to create genuine artificial general intelligence step by step. It can handle data of all modalities in a unified form and can directly understand the experience that humans already possess through language, thus avoiding the problem that reinforcement learning is required for every decision-making process.