To achieve the artificial general intelligence （AGI）, imitate the intelligence？ or imitate the brain？ This is the question! Most artificial intelligence （AI） approaches set the understanding of the intelligence ...To achieve the artificial general intelligence （AGI）, imitate the intelligence？ or imitate the brain？ This is the question! Most artificial intelligence （AI） approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the ＂new＂ approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.展开更多
文摘本文对生成式AI(Generative artificial intelligence,GenAI)的国内外发展现状进行了概述,重点分析了中美之间在算力、数据、算法、生态等方面存在的差距.为改变我国在生成式AI领域的落后现状,提出高能效算力建设、联邦数据、专业领域模型、基于TAO的联邦生态等应对策略,对大模型时代AI安全治理进行了论述,对通用人工智能(Artificial general intelligence,AGI)的未来发展进行了展望.
文摘自从OpenAI在2022年11月推出其生成式人工智能(AIGC,artificial intelligence generative content,也有人使用generative AI)产品——ChatGPT后,整个世界都为之颠覆.生成式人工智能主要有两个主流:大型语言模型(LLM,large language model)和扩散模型(diffusion model),新的应用和研究每天都在加速发表.在本文中,我们首先对大型语言模型表现出来的智能水平提出了一个严肃的问题:它是否真的拥有像普通人的智能能力一样的通用人工智能(AGI,artificial general intelligence)能力?在本文中,我首先提出了一个重要的假说:作为一个封闭的系统,通过一个大型的语言模型被设计成表示和存储人类的巨大知识和智能的能力和行为,并配备了最高的价值标准,即模型必须符合人类的价值,但大型语言模型内部结构和性质并没有显示其拥有通用人工智能能力.然而,作为一个开放的系统,一旦我们输入一些隐含人类知识和智能的格式化文本,我们就会突然发现,大型语言模型的输出显示出某些人类智能和行为的特征.其中格式化的输入文本被称为提示(prompt),提示的智能程度越高,模型的智能输出就越好.换句话说,大型语言模型拥有某种以prompt提示为条件的通用人工智能AGI能力.经济学研究和其他社会科学研究如政治、历史、语言学等包括了最复杂的社会形态和人类最深刻的思想,因此本文试图通过总结其他研究者最新的研究成果来探讨大语言模型的通用人工智能是事实还是错觉?以及大语言模型其他经济功能和效用,对于这个模型的类通用人工智能的能力,我们总结这些研究学者的最新研究成果,包括大语言模型的智商水平,生成式人工智能的产业经济学,生成式人工智能下的计算社会科学研究,大语言模型的商业决策制定,经济学和其他社会科学,以及虛拟生成式人工智能经济学家的范式研究等问题.
基金supported by the Natural Science Foundation of China(Nos.61425025 and 61390515)
文摘To achieve the artificial general intelligence （AGI）, imitate the intelligence？ or imitate the brain？ This is the question! Most artificial intelligence （AI） approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the ＂new＂ approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.
