（1） AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. AI包羅永珍，既包括GOFAI（基於物理符號系統假設和有限合理性原理的人工智慧學派），又包括像深度學習這樣的連線結構.

ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data. ML是AI的分支，它通過訓練資料來研究學習演算法。

There are a whole swaths (not swatches) of techniques that have been developed over the years like Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN). 這些年來開發了一系列機器學習演算法，比如：線性迴歸、K-means、決策樹、隨機森林、主成分分析、支援向量機，人工神經網路。

Artificial Neural Networks is where the field of Deep Learning had its genesis from. 人工神經網路是深度學習領域的起源。

（2） Some ML practitioners who have had previous exposure to Neural Networks (ANN), after all it was invented in the early 60’s, would have the first impression that Deep Learning is nothing more than ANN with multiple layers. 人工神經網路是60年前發明的，所以一些接觸過人工神經網路的機器學習從業者最初會認為DL深度學習不過就是多層人工神經網路。 Furthermore, the success of DL is more due to the availability of more data and the availability of more powerful computational engines like Graphic Processing Units (GPU). 而且，DL的成功是因為：現在能夠獲取到更多的資料；更強大的計算引擎比如GPU. This of course is true, the emergence of DL is essentially due to these two advances, however the conclusion that DL is just a better algorithm than SVM or Decision Trees is akin to focusing only on the trees and not seeing the forest. 的確，DL的出現主要是因為這兩個進步，然而，DL只是比較SVM和決策樹哪個演算法更好。類似於只盯著樹木而不去看森林。

（3） To coin Andreesen who said “Software is eating the world”, “Deep Learning is eating ML”. 引用安德里森的話：“Software is eating the world”, “Deep Learning is eating ML”。 Two publications by practitioners of different machine learning fields have summarized it best as to why DL is taking over the world. 不同機器學習鄰域的從業者寫了兩本書，書中很好地解釋了為什麼DL深度學習正在接管這個世界。 Chris Manning an expert in NLP writes about the “Deep Learning Tsunami“: Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pundits are predicting that the final damage will be even worse.

NLP專家Chris Manning寫了一篇關於“深度學習海嘯”的文章：  多年以來，深層的學習浪潮在計算語言學領域中不斷湧現，但2015年似乎是海嘯襲擊主要自然語言處理（NLP）會議的一年。然而，一些權威人士預測，最終的損害將更嚴重。

Nicholas Paragios writes about the “Computer Vision Research: the Deep Depression“: It might be simply because deep learning on highly complex, hugely determined in terms of degrees of freedom graphs once endowed with massive amount of annotated data and unthinkable — until very recently — computing power can solve all computer vision problems. If this is the case, well it is simply a matter of time that industry (which seems to be already the case) takes over, research in computer vision becomes a marginal academic objective and the field follows the path of computer graphics (in terms of activity and volume of academic research). 

Nicholas Paragios寫的“計算機視覺研究：the deep depression”： 這可能僅僅因為深度學習的高度複雜，高度依賴於自由影象的度，一旦被賦予了大量的註釋資料和不可思議的直到現在才出現計算能力，所有的計算機視覺問題都可以被解決。如果是這樣的話，那就只是時間問題了，工業（似乎已經是這樣）接管了，計算機視覺的研究變成了一個邊緣的學術目標，這個領域遵循了計算機圖形學的道路（就學術研究的活動和數量而言）。

These two articles do highlight how the field of Deep Learning are fundamentally disruptive to conventional ML practices.  這兩篇文章確實強調了深度學習領域如何對傳統ML實踐的根本性破壞。

Certainly is should be equally disruptive in the business world. 當然在商業界也應該同樣具有破壞性。

 I am however stunned and perplexed that even Gartner fails to recognize the difference between ML and DL. Here is their August 2016 Hype Cycle and Deep Learning isn’t even mentioned on the slide: 然而，我甚至驚愕不已，甚至連加特納都沒有意識到ML和dl之間的區別。這是他們2016年8月的Hype Cycle ，幻燈片上甚至沒有提到深度學習：

（4） What a travesty!這是多麼悲劇啊！ Anyway, despite being ignored, DL continues to by hyped.  不管怎樣，儘管被忽視了，DL仍然被大肆宣傳。

The current DL hype tends to be that we have these commoditized machinery, that given enough data and enough training time, is able to learn on its own. 目前DL的炒作往往是 ，一旦我們有了這些商品化的機械， 如果有足夠的資料和足夠的訓練時間，深度學習就可以自己學習。 

This of course either an exaggeration of what the state-of-the-art is capable of or an over simplification of the actual practice of DL. 這當然也誇大了深度學習的能力，或者過分簡化DL的實際操作。 

DL has over the past few years given rise to a massive collection of ideas and techniques that were previously either unknown or known to be untenable. 在過去的幾年裡，DL產生了大量的思想和技術，這些想法和技術以前是未知的或已知但站不住腳的。

At first this collection of concepts, seems to be fragmented and disparate. However over time patterns and methodologies begin to emerge and we are frantically attempting to cover this space in “Design Patterns of Deep Learning“. 起初，這一系列概念似乎支離破碎，各不相同。然而，隨著時間的推移，模式和方法開始出現，我們瘋狂地試圖用“深度學習設計模式”覆蓋這個空間。

（5）

Deep Learning today goes beyond just multi-level perceptrons but instead is a collection of techniques and methods that are used to building composable differentiable architectures.  深度學習今天不僅僅是多層感知器，而是一個集技術與方法的集合，這個集合用於組合不同的結構。

These are extremely capable machine learning systems that we are only right now seeing just the tip of the iceberg.  他們都是非常有能力的機器學習系統，我們現在只看到冰山一角。

The key take away from this is that, Deep Learning may look like alchemy today, but we eventually will learn to practice it like chemistry. 關鍵在於，今天的深度學習可能看起來像鍊金術，但我們最終將學會像化學一樣去實踐它。

That is, we would have a more solid foundation so as to be able to build our learning machines with greater predictability of its capabilities. 也就是說，我們將有一個更堅實的基礎，以便能夠建立我們的學習機器具有更大的可預測性的能力。

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本文來自 haimianjie2012 的CSDN 部落格 ，