Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2023-07-29
Abstract: At present, LLM(Large Language Model)adopts the technical path of "attention mechanism+deep learning"+"Reinforcement learning". In the field of AIGC (Artificial Intelligence Generated Content),significant progress has been made, sparking hope for realization of AGI (Artificial General Intelligence). However, in those fields that need to interact with the actual environment, such as elderly care, family nanny, agricultural production, vehicle driving and other fields, the cost of trial and error is very high, and the reinforcement learning process that requires a lot of trial and error is difficult to achieve. So, in order to achieve universal artificial intelligence that can be applied to any field, we need to not only utilize existing technology, but also solve the shortcomings of existing technology, thereby promoting the further development of the technological wave of artificial intelligence. In this paper, we analyze the limitations of the Technology roadmap of the LLM, and propose solutions to these limitations, thus solving the inherent defects of Large Model. In this article, we will reveal how to implement True AGI step by step.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Natural Language Understanding and Machine Translation Subjects: Library Science,Information Science >> Machine Translation submitted time 2023-04-18
Abstract:目前主流的人工智能,普遍采用注意力机制 + 深度学习+强化学习的技术道路。我们认为强化学习无法适用到那些难以大量试错的领域。所以,要想实现能适用于任何领域的通用人工智能,我们必须转变实现道路。所以,我们提出了一套不同于深度学习+强化学习的机器学习方案,它通过小样本、累积学习,同样实现了和 transformer 相似的注意力机制,也同样创建了全连接知识网络。并且,它不需要采用试错学习的方式,就可以实现和环境的互动决策。并且人类可以给它预置不同的先天需求,来实现多目标平衡,从而实现远高于目前人工智能的安全性。在本文中,我们提出了一套从0 到1 的新机器学习技术方案。
Peer Review Status:Awaiting Review