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: 2023-05-06
摘要: With a preliminary exploration of the capability boundaries of LLM(Language Large Model),we believe that the current mainstream artificial intelligence generally adopts the technical of attention mechanism + deep learning + reinforcement learning, which cannot be applied to those fields that are difficult to a lot of trial and error. So, to achieve AGI (Artificial General Intelligence) that works in any field, its better to change the way we do it. Therefore, we propose a set of machine learning solution different from deep learning + reinforcement learning. It adopts small samples and cumulative learning, and also realizes the attention mechanism similar to transformer, and also creates a fully connected knowledge network. In addition, it can realize interactive decision making with the environment without using lots of trial and error style learning. In addition, humans can preset different innate needs to it to achieve multi-objective balance, thus achieving far higher security than the current artificial intelligence. In this paper, we propose a set of new machine learning techniques which maybe guide humans realizes AGI