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Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation). Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas. Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. Besides transfer learning, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI. Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.

Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation). Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas. Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. Besides transfer learning, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI. Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.

历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类的所有认知。这些早期的项目未能逃脱非定量符号逻辑模型的限制，现在回过头看，这些项目大大低估了实现跨领域AI的难度。当下绝大多数AI研究人员主要研究易于处理的“狭义AI”应用(如医疗诊断或汽车导航)。许多研究人员预测，不同领域的“狭义AI”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合上文提到的大多数狭义功能，甚至在某种程度上在大多数或所有这些领域都超过人类。许多进展具有普遍的、跨领域的意义。一个著名的例子是，21世纪一零年代，DeepMind开发了一种“'''<font color=#ff8000>通用人工智能 Generalized Artificial Intelligence</font>'''” ，它可以自己学习许多不同的 Atari 游戏，后来又开发了这种系统的升级版，在序贯学习方面取得了成功。除了迁移学习，未来AGI 的突破可能包括开发能够进行决策理论元推理的反射架构，以及从整个非结构化的网页中整合一个全面的知识库。一些人认为，某种(目前尚未发现的)概念简单，但在数学上困难的“终极算法”可以导致 AGI。最后，一些“涌现”的方法着眼于尽可能地模拟人类智能，并相信如人工大脑或模拟儿童发展等拟人方案，有一天会达到一个临界点，通用智能在此涌现。

历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类的所有认知。这些早期的项目未能逃脱非定量符号逻辑模型的限制，现在回过头看，这些项目大大低估了实现跨领域AI的难度。当下绝大多数AI研究人员主要研究易于处理的“狭义AI”应用(如医疗诊断或汽车导航)。许多研究人员预测，不同领域的“狭义AI”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合上文提到的大多数狭义功能，甚至在某种程度上在大多数或所有这些领域都超过人类。许多进展具有普遍的、跨领域的意义。一个著名的例子是，21世纪一零年代，DeepMind开发了一种“'''<font color=#ff8000>通用人工智能 Generalized Artificial Intelligence</font>'''” ，它可以自己学习许多不同的 Atari 游戏，后来又开发了这种系统的升级版，在序贯学习方面取得了成功。除了迁移学习，未来AGI 的突破可能包括开发能够进行决策理论元推理的反射架构，以及从整个非结构化的网页中整合一个全面的知识库。一些人认为，某种(目前尚未发现的)概念简单，但在数学上困难的“终极算法”可以产生AGI。最后，一些“涌现”的方法着眼于尽可能地模拟人类智能，并相信如人工大脑或模拟儿童发展等拟人方案，有一天会达到一个临界点，通用智能在此涌现。