The symbolic AI community is increasingly trying to embrace machine learning in neuro-symbolic architectures, yet is still struggling due to cultural barriers. To break the barrier, this rather opinionated personal memo attempts to explain and rectify the conventions in Statistics, Machine Learning, and Deep Learning from the viewpoint of outsiders. It provides a step-by-step protocol for designing a machine learning system that satisfies a minimum theoretical guarantee necessary for being taken seriously by the symbolic AI community, i.e., it discusses "in what condition we can stop worrying and accept statistical machine learning." Unlike most textbooks which are written for students trying to specialize in Stat/ML/DL and willing to accept jargons, this memo is written for experienced symbolic researchers that hear a lot of buzz but are still uncertain and skeptical. Information on Stat/ML/DL is currently too scattered or too noisy to invest in. This memo prioritizes compactness, citations to old papers (many in early 20th century), and concepts that resonate well with symbolic paradigms in order to offer time savings. It prioritizes general mathematical modeling and does not discuss any specific function approximator, such as neural networks (NNs), SVMs, decision trees, etc. Finally, it is open to corrections. Consider this memo as something similar to a blog post taking the form of a paper on Arxiv.
翻译:象征性的AI社区正日益试图将机器学习纳入神经 -- -- 共振结构中,但因文化障碍而仍在挣扎。为了打破障碍,这一颇具见解的个人备忘录试图从外部人士的角度解释和纠正统计、机修和深习方面的公约。它为设计一个符合象征性的AI社区认真对待所必须的最低理论保障的机器学习系统提供了一个渐进式协议,即它讨论的是 " 在什么条件下我们可以停止担忧和接受统计机学习。 " 与大多数试图在Stat/ML/DL中专门研究并愿意接受字典的学生所写的教科书不同,这一备忘录是为经验丰富的象征性研究人员编写的,他们听到大量的嗡嗡嗡声,但仍然不确定和怀疑。关于Stat/ML/DL的信息目前过于分散或过于吵闹,无法投资。这个备忘录优先考虑了紧凑、引用旧论文(20世纪早期的很多),以及与象征性的范式模式有共鸣的概念,以便节省时间。它优先考虑一般的数学模型,不讨论任何具体的功能模型,也不讨论任何亚罗琴的固定的图案,作为最后的图案。