分类: 计算机科学 >> 计算机科学技术其他学科 提交时间: 2023-08-15
摘要: At present, the mainstream artificial intelligence generally adopts the technical path of attentionmechanism + deep learning + reinforcement learning. It has made great progress in the field ofAIGC (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 learningprocess that requires much trial and error is difficult to achieve. Therefore, in order to achieveArtificial General Intelligence(AGI) that can be applied to any field, we need to use both existingtechnologies and solve the defects of existing technologies, so as to further develop the technologicalwave of artificial intelligence. In this paper, we analyze the limitations of the technical route of largemodels, and by addressing these limitations, we propose solutions, thus solving the inherent defectsof large models. In this paper, we will reveal how to achieve true AGI step by step.
分类: 计算机科学 >> 计算机科学技术其他学科 提交时间: 2023-12-18
摘要: In this paper, we propose a new approach to building a artificial general intelligence with selfawareness, which includes: (1) a new method to implement attention mechanisms; (2) a way to givemachines self-demands; (3) how to form a value evaluation system compatible with the network; (4) away to create the world models; (5) how to realize a top-down, hierarchical thinking decision-makingchain; (6) a way to achieve general decision-making and response capabilities; (7) a way for a machineto 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 weanalyze why our scheme can solve the above problems and provide detailed steps for implementingour scheme. In chapter 6, we analyze the advantages and disadvantages of our scheme and proposefurther research directions. Finally, we propose our thoughts on the next step of AI development.