……（弱）AI 项目。OpenCog 已经对现有特性发布了不少扎实的 demo 展示。而且 Goertzel 在他大量的著作和学术论文里绝不是在假大空地吹牛或扯淡，比如《Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference》（整整 336 页）。Hawkins 也凭借他的软件展示了非常有趣的进展，并且已经有了商业应用 https://www.numenta.com/grok_info.html……
…(narrow) AI projects. OpenCog has released a number of solid demonstrations of current features. And Goertzel isn't hand-waving or bullshitting in his numerous books and scientific papers, for example Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference (336 pages). Hawkins has demonstrated very interesting progress with his software and has a commercial application https://www.numenta.com/grok_info.html…
…rather than one of needing more knowledge, similar to not being able to find out what happens between planck unit time steps (if anything happens and if the question means anything). Ben Goertzel's book Probabilistic Logic Networks is another resource I'd recommend. Sorry if my branching reply is a bit big, but I'm constantly trying to clarify things for myself as well. ;)
……当你去掉所有对逻辑的引用，只考虑模糊集合时——它就变成了纯粹的模糊集合论，这在需要对部分集合从属关系（partial set membership）建模时很有用。我很喜欢读《Probabilistic Logic Networks》（https://www.amazon.com/Probabilistic-Logic-Networks-Comprehe...），因为它提供了一个可行的框架，让人能清醒理智地处理各种并不必然……的事物的不同真值。
…used when you take out all the references to logic and just think about fuzzy sets -- then it's just fuzzy set theory, which is useful when you need to model partial set membership. I enjoyed reading Probabilistic Logic Networks (https://www.amazon.com/Probabilistic-Logic-Networks-Comprehe...) as it introduces a possible framework to sanely deal with different kinds of truth values about different things which aren't necessarily…
……《Mind and Machine》- John F. Sowa；75. 《Inside Computer Understanding》- Roger C. Schank & Christopher K. Riesbeck；76. 《Integrating Rules and Connectionism for Robust Commonsense Reasoning》- Ron Sun；77. 《Probabilistic Logic Networks》- Ben Goertzel 等；78. 《The Subtlety of Sameness: A Theory and Computer Model of Analogy Making》- Robert M. French；79. 《Analogy Making as Perception》- Melanie Mitchell；80. 《Real World Reasoning》- ……
…Mind and Machine - John F. Sowa 75. Inside Computer Understanding - Roger C. Schank & Christopher K. Riesbeck 76. Integrating Rules and Connectionism for Robust Commonsense Reasoning - Ron Sun 77. Probabilistic Logic Networks - Ben Goertzel, et al. 78. The Subtlety of Sameness: A Theory and Computer Model of Analogy Making - Robert M. French 79. Analogy Making as Perception - Melanie Mitchell 80. Real World Reasoning -…
…some more specialized, some more general in nature — and connected together in a manner enabling them to (usually) synergetically assist rather than work against each other. http://wiki.opencog.org/w/Probabilistic_Logic_Networks > PLN is a novel conceptual, mathematical and computational approach to uncertain inference. In order to carry out effective reasoning in real-world circumstances, AI software must robustly handle…
……（弱）AI 项目。OpenCog 已经对现有特性发布了不少扎实的 demo 展示。而且 Goertzel 在他大量的著作和学术论文里绝不是在假大空地吹牛或扯淡，比如《Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference》（整整 336 页）。Hawkins 也凭借他的软件展示了非常有趣的进展，并且已经有了商业应用 https://www.numenta.com/grok_info.html……
…(narrow) AI projects. OpenCog has released a number of solid demonstrations of current features. And Goertzel isn't hand-waving or bullshitting in his numerous books and scientific papers, for example Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference (336 pages). Hawkins has demonstrated very interesting progress with his software and has a commercial application https://www.numenta.com/grok_info.html…
……而不是说需要更多知识的问题，这就好比我们无法探知在普朗克时间步长之间到底发生了什么（如果真有事情发生，或者这个问题本身有意义的话）。Ben Goertzel 的书《Probabilistic Logic Networks》是我推荐的另一个资源。不好意思我这回复扯得有点太散了，不过我自己也一直在努力把这些问题理清楚。;)
…rather than one of needing more knowledge, similar to not being able to find out what happens between planck unit time steps (if anything happens and if the question means anything). Ben Goertzel's book Probabilistic Logic Networks is another resource I'd recommend. Sorry if my branching reply is a bit big, but I'm constantly trying to clarify things for myself as well. ;)
……当你去掉所有对逻辑的引用，只考虑模糊集合时——它就变成了纯粹的模糊集合论，这在需要对部分集合从属关系（partial set membership）建模时很有用。我很喜欢读《Probabilistic Logic Networks》（https://www.amazon.com/Probabilistic-Logic-Networks-Comprehe...），因为它提供了一个可行的框架，让人能清醒理智地处理各种并不必然……的事物的不同真值。
…used when you take out all the references to logic and just think about fuzzy sets -- then it's just fuzzy set theory, which is useful when you need to model partial set membership. I enjoyed reading Probabilistic Logic Networks (https://www.amazon.com/Probabilistic-Logic-Networks-Comprehe...) as it introduces a possible framework to sanely deal with different kinds of truth values about different things which aren't necessarily…
……《Mind and Machine》- John F. Sowa；75. 《Inside Computer Understanding》- Roger C. Schank & Christopher K. Riesbeck；76. 《Integrating Rules and Connectionism for Robust Commonsense Reasoning》- Ron Sun；77. 《Probabilistic Logic Networks》- Ben Goertzel 等；78. 《The Subtlety of Sameness: A Theory and Computer Model of Analogy Making》- Robert M. French；79. 《Analogy Making as Perception》- Melanie Mitchell；80. 《Real World Reasoning》- ……
…Mind and Machine - John F. Sowa 75. Inside Computer Understanding - Roger C. Schank & Christopher K. Riesbeck 76. Integrating Rules and Connectionism for Robust Commonsense Reasoning - Ron Sun 77. Probabilistic Logic Networks - Ben Goertzel, et al. 78. The Subtlety of Sameness: A Theory and Computer Model of Analogy Making - Robert M. French 79. Analogy Making as Perception - Melanie Mitchell 80. Real World Reasoning -…
……有的更专一，有的更通用——并且以某种方式连接在一起，使它们（通常）能够协同增效、互相配合，而不是互相内耗。http://wiki.opencog.org/w/Probabilistic_Logic_Networks > PLN 是一种用于不确定性推理的全新概念、数学和计算方法。为了在真实世界环境下开展有效的推理，AI 软件必须鲁棒地处理……
…some more specialized, some more general in nature — and connected together in a manner enabling them to (usually) synergetically assist rather than work against each other. http://wiki.opencog.org/w/Probabilistic_Logic_Networks > PLN is a novel conceptual, mathematical and computational approach to uncertain inference. In order to carry out effective reasoning in real-world circumstances, AI software must robustly handle…