纽约大学教授暨面子书首席人工智能科学家杨立昆：智能与学习密不可分。 你可以基本上只通过编程来创建智能机器的想法，对我来说不是可行的入门法。 [...] 许多计算机科学家怀疑深度学习的原因之一是，数学与深度学习有很大不同。 你用于深度学习的数学更多地与控制论学科（cybernetics）有关 - 它更接近应用于电气工程的数学，多过于应用在计算机科学里的数学。 在机器学习的过程里，没有什么是确切不变的。 计算机科学强迫性的关注细节 -- 譬如：每个索引都必须正确、你务必证明算法是正确的。（但机器学习没那么严苛） 机器学习是“草率的科学”。
杨立昆教授的提法，基本上与其他论者批评专家系统（Expert System）开发难度的看法相呼应：你不可能纯粹只倚靠脑力，用人手编译所有智能系统的指令。
[Quote] Chief AI Scientist at Facebook cum New York University Professor Yann Lecun: Intelligence is inseparable from learning. The idea somehow that you can create an intelligent machine by basically programming for me was a non-starter. [...] One reason why deep learning has been kind of look at with suspicion by a lot of computer scientists is because the math is very different. The math that you use for deep learning has more to do with cybernetics - the kind of math you do in electrical engineering than the kind of math you do in computer science. Nothing in machine learning is exact. Computer science is all about sort of obviously compulsive attention to details, every index has to be right and you can prove that an algorithm is correct. Machine Learning is the science of sloppiness. [/Quote]
The key is: You can't codify every rule of artificial intelligence into the machine via programming.
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