A new solution and concrete implementation steps for Artificial General Intelligence
On the map: World models → World models for language agents
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 8 of its 44 references that are in the map. First posted on arXiv 12 Aug 2023.
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8 of 44 references.
References · 8
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 982
- 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 684
- 2022Prompting GPT-3 To Be Reliablecited by 258
- 2023White-Box Transformers via Sparse Rate Reductioncited by 77
- 2023On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilotcited by 64
- 2023A Survey of Learning-based Automated Program Repaircited by 55
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