我们现在说的人工智能，很多时候指的是基于深度神经网络的机器学习（或者深度学习）方法。但实际上，人工智能是一个历史悠久和丰富内涵的学科。由于这两年机器学习取得了非常好的实际效果，其它研究方向似乎被大家遗忘了。最近这种情况有点变化，似乎其它方向也在更多的发出声音。比如，前两天看到的一个新闻，“美国国防部高级计划研究局（DARPA）于不久前对Gamalon注资720万美元”。这个Gamalon就是玩“Bayesian programming”的。
正好昨天看到两篇挺有意思的文章，都是聊人工智能领域的各个“部落”（原文是tribes）。我觉得用“门派”也挺合适。虽然同在人工智能这个“武林”，他们的关系也很微妙，既有竞争，也有合作，有时还会“badmouth each other”。一篇是“AI’s Factions Get Feisty. But Really, They’re All on the Same Team”[1]，第二篇是“The Many Tribes of Artificial Intelligence”[2]。特别是第二篇，还用来一张信息图形象的描述了他们之间的关系。
图片来自Intuition Machine, medium.com
这篇文章的作者非常“严肃”的给每个“部落”起了名字（当然也有的是公认的），还设计了“徽章”。我第一眼就看到了PAC Theorists那个。
下面我就搬运一下各个“部落”的说明。高亮的部分是Deep Learning，几个分支名字起的有点意思，内容也有亮点！
Symbolists - Folks who used symbolic rule-based systems to make inferences. Most of AI has revolved around this approach. The approaches that used Lisp and Prolog are in this group, as well as the SemanticWeb, RDF, and OWL. One of the most ambitious attempts at this is Doug Lenat’s Cyc that he started back in the 80’s, where he has attempted to encode in logic rules all that we understand about this world. The major flaw is the brittleness of this approach, one always seems to find edge cases where one’s rigid knowledge base doesn’t seem to apply. Reality just seems to have this kind of fuzziness and uncertainty that is inescapable. It is like playing an endless game of Whack-a-mole.
（简要翻译：符号主义者-用逻辑符号系统进行推理。主要问题是，人们总能找到一些逻辑规则的例外情况。看起来现实世界的逻辑并不是泾渭分明的，而存在一定程度的灰色地带，因此该方法遇到了瓶颈。）
Evolutionists - Folks who apply evolutionary processes like crossover and mutation to arrive at emergent intelligent behavior. This approach is typically known as Genetic Algorithms. We do see GA techniques used in replacement of a gradient descent approach in Deep Learning, so it’s not a approach that lives in isolation. Folks in this tribe also study cellular automata such as Conway’s Game of Life [CON] and Complex Adaptive Systems (CAS).
（简要翻译：进化算法主义者-用基因进化算法进行人工智能运算，引入随机突变，保留最好的部分，并淘汰效果较差的部分。在深度学习算法中，也可以使用基因进化算法来部分取代梯度下降算法去做优化，因此进化算法和深度学习并非水火不容。）
Bayesians - Folks who use probabilistic rules and their dependencies to make inferences. Probabilistic Graph Models (PGMs) are a generalization of this approach and the primary computational mechanism is the Monte-Carlo method for sampling distributions. The approach has some similarity with the Symbolist approach in that there is a way to arrive at an explanation of the results. One other advantage of this approach is that there is a measure of uncertainty that can be expressed in the results. Edward is one library that mixes this approach with Deep Learning.
（简要翻译：Bayes流- 依靠概率去做推理，使用诸如概率图模型[Probabilistic Graph Models]和蒙特卡洛算法之类的工具。与符号主义者相类似的是，Bayes流做人工智能方法也可以在逻辑上得到解释，而且还能量化不确定性。目前有结合Bayes方法和深度学习算法的库Edward。）
Kernel Conservatives - One of the most successful methods prior to the dominance of Deep Learning was SVM. Yann LeCun calls this glorified template matching. There is what is called a kernel trick that makes an otherwise non-linear separation problem into one that is linear. Practitioners in this field live in delight over the mathematical elegance of their approach. They believe the Deep Learners are nothing but alchemists conjuring up spells without the vaguest of understanding of the consequences.
（简要翻译：Kernel保守主义者-深度学习之前，SVM是最火的算法，当时使用Kernel Trick可以把非线性的问题映射到线性平面。Kernel保守主义者对于Kernel方法的优雅性大加赞许，并且认为搞深度学习的无非就是一帮自己也不懂自己搞出来的是什么东西的炼金术士。）
Tree Huggers - Folks who use tree-based models such as Random Forests and Gradient Boosted Decision Trees. These are essentially a tree of logic rules that slice up the domain recursively to build a classifier. This approach has actually been pretty effective in many Kaggle competitions. Microsoft has an approach that melds the tree based models with Deep Learning.
（简要翻译：抱树者- 这帮人使用基于树的模型，例如随机森林，决策树等等事实上基于树的模型在Kaggle中的许多问题里很有用。微软有一个模型，融合了树模型和深度学习。）
Connectionists - Folks who believe that intelligent behavior arises from simple mechanisms that are highly interconnected. The first manifestation of this were Perceptrons back in 1959. This approach died and resurrected a few times since then. The latest incarnation is Deep Learning.
（简要翻译：联结主义者- 一群相信智能行为来源于大规模神经元互联的人。第一波是1959年的Perceptron，之后经过起起伏伏，最近一次复兴就是目前风口浪尖的深度学习。联结主义内部也不是铁板一块，而是分为几个宗派：）
The Canadian Conspirators - Hinton, LeCun, Bengio et al. End-to-end deep learning without manual feature engineering.
（加拿大派- Hinton，LeCun，Bengio等等，绝技是不需要手工做feature engineering的端到端学习）
Swiss Posse - Basically LSTM and that consciousness has been solved by two cooperating RNNs. This posse will have you lynched if you ever claim that you invented something before they did. GANs, the “coolest thing in the last 20 years” according to LeCun are also claimed to be invented by the posse.
（瑞士帮- LSTM的提出者以及宣称使用两个互相配合的RNN就能解决意识问题的帮派。任何敢宣称自己在他们之前就发明了什么东西的人都会被瑞士帮喷到死。比如，瑞士帮最近就号称其实是他们发明了GAN）
British AlphaGoist - Conjecture that AI = Deep Learning + Reinforcement Learning, despite LeCun’s claim that it is just the cherry on the cake. DeepMind is one of the major proponents in this area.
（英国狗娃- 搞出了AlphaGo的帮派，认准了AI就是深度学习加增强学习[ 虽然LeCun说增强学习不过是蛋糕上的樱桃点缀]。DeepMind是英国狗娃里面做得最出色的团队）
Predictive Learners - I’m using the term Yann LeCun conjured up to describe unsupervised learning. The cake of AI or the dark matter of AI. This is a major unsolved area of AI. I, however, tend to believe that the solution is in “Meta-Learning”.
（预测主义学者- 搞无监督学习的人，根据LeCun无监督学习是AI蛋糕中最大的部分，相当于宇宙中的暗物质，也是目前尚未解决的领域）
Compressionists - Cognition and learning are compression (Actually an idea that is shared by other tribes). The origins of Information theory derives from an argument about compression. This is a universal concept that it is more powerful than the all too often abused tool of aggregate statistics.
（简要翻译：压缩主义者-认为认知和学习的本质是信息压缩，和信息论的思想脉络一致。）
Complexity Theorists - Employ methods coming from physics, energy-based models, complexity theory, chaos theory and statistical mechanics. Swarm AI likely fits into this category. If there’s any group that has a chance at coming up with a good explanation why Deep Learning works, then it is likely this group.
（简要翻译：复杂系统理论家- 使用从物理学，能量模型，复杂系统理论，混沌理论和统计力学等学科继承来的方法。他们最得意的作品就是Swarm AI。另外他们是最有希望能够给深度学习给出理论解释的人。）
Biological Inspirationalists - Folks who create models that are closer to what neurons appear in biology. Examples are the Numenta folks and the Spike-and-Integrate folks like IBM’s TrueNorth chip.
（简要翻译：仿生主义者-喜欢搞仿生学的东东，做模仿真正生物神经元的模型，例如Numenta的那帮人，以及在IBM搞TrueNorth的团队。）
Connectomeist - Folks who believe that the interconnection of the brain (i.e. Connectome) is where intelligence comes from. There’s a project that is trying to replicate a virtual worm and there is some ambitious heavily funded research [HCP] that is trying to map the brain in this way.
（简要翻译：功能联结图谱论者- 认为大脑里的互相联结，即功能联结图谱，是智能的真正来源。这方面的项目包括人造蠕虫和获得大量资助的脑功能映射项目。）
Information Integration Theorists - Argue that consciou-ness emerges from some internal imagination of machines that mirrors the causality of reality. The motivation of this group is that if we are ever to understand consciousness then we have to at least start thinking about it! I, however, can’t see the relationship of learning and consciousness in their approach. It is possible that they aren’t related at all! That’s maybe why we need sleep.
（简要翻译：信息集成工程师- 认为机器意识来源于机器内部对真实世界中因果性的映射。这个团体认为我们必须首先认识“意识”的本质，才能做人工智能）
PAC Theorists - Are folks that don’t really want to discuss Artificial Intelligence, rather prefer just studying intelligence because at least they know it exists! Their whole idea is that adaptive systems perform computation expediently such that they are all probably approximately correct. In short, intelligence does not have the luxury of massive computation.
（简要翻译：PAC主义者- 这群人并不想真正讨论人工智能。他们的观点是，只要一个自适应系统能快速执行大几率近似正确的计算[probably approximately correct, PCA]就行。总而言之，智能根本不该基于大规模计算）
再说一点题外话，深度神经网络几个比较大的问题，比如“黑盒”问题，无监督学习，能耗的问题（和人类相比），有可能未来都要靠学习别的“门派”的“武功”来解决。
T.S.
参考：
1. CADE METZ，“AI’s Factions Get Feisty. But Really, They’re All on the Same Team”，wired.com
2. Carlos E. Perez, “The Many Tribes of Artificial Intelligence”，Medium.com
