深度學習領域專業詞匯

It’s a bit of an understatement to say that Deep Learning has recently become a hot topic. Within a decade alone, the field has made significant strides on problems once thought to be impossible, including facial recognition, generating text that mimics human writing, making art, and playing games involving strategy and intuition.

我T的有點輕描淡寫地說，深學，最近成為熱門話題。 僅在十年內，該領域就在人們認為不可能的問題上取得了長足的進步，包括面部識別，生成模仿人類文字的文字，制作藝術品以及玩涉及策略和直覺的游戲。

Given the buzz surrounding the (seemingly unreasonable) effectiveness of these algorithms, it’s easy to get lost in the extremes of speculation and skepticism towards Deep Learning. Instead, I’d like to focus on the following issues.

鑒于圍繞這些算法的有效性(看似不合理)的嗡嗡聲，很容易在對深度學習的猜測和懷疑的極端中迷失了方向。 相反，我想重點關注以下問題。

First, what is the role of human expertise in a world where Deep Learning is becoming more prevalent as a problem-solving tool? Second, where is the field of Deep Learning headed, and how can (human) expertise help move it forward?

首先，在深度學習日益成為解決問題的工具的世界中，人類專業知識的作用是什么？ 其次，深度學習領域的發展方向如何，(人類)專業知識如何幫助其向前發展？

As we will soon see, these two questions are intimately related. But first, let’s start with the basics.

我們將很快看到，這兩個問題密切相關。 但是首先，讓我們從基礎開始。

深度學習與人類學習 (Deep vs. human learning)

To get a better scope of the picture, it helps to get a common understanding of what we mean when we refer to Deep Learning.

為了更好地了解圖片，這有助于我們對深度學習的理解。

Simply put, Deep Learning refers to a broad range of algorithms, very loosely inspired by the human brain.

簡而言之，深度學習涉及廣泛的算法，受到人腦的啟發。

These algorithms take the form of networks, known as Deep Neural Networks (DNNs), which iteratively improve as they encounter new examples. It’s important to note that the kind of learning we refer to when we talk about Deep Learning is a very narrow subset of what we as humans might consider learning.

這些算法采用稱為深度神經網絡(DNN)的網絡形式，在遇到新示例時會不斷改進。 重要的是要注意，當我們談論深度學習時，我們所指的那種學習只是我們人類可能認為學習的一小部分。

Human learning often involves the ability to explain, generalize, and even teach concepts that we have come to understand, all of which encompass different forms and levels of learning. It turns out that a lot of the questions surrounding these variants of learning are still open problems in Deep Learning, ones where domain expertise may play a large role.

人類學習通常包含解釋，概括甚至教授我們已經理解的概念的能力，所有這些概念都包含不同形式和水平的學習。 事實證明，圍繞這些學習變體的許多問題仍然是深度學習中的開放問題，在這些領域中領域專業知識可能起著很大的作用。

可解釋性 (Explainability)

While Deep Learning algorithms outperform other machine learning methods at a wide variety of tasks, they are often notoriously difficult to interpret. Often, these networks are so complex that it’s difficult to know how they made their decisions. Like your pesky third-grade math teacher, we want these networks to “show their work”.

雖然深度學習算法在各種各樣的任務上勝過其他機器學習方法，但眾所周知，它們通常難以解釋。 通常，這些網絡是如此復雜，以至于很難知道它們是如何做出決定的。 就像您討厭的三年級數學老師一樣，我們希望這些網絡“展示他們的工作”。

This problem of understanding how and why a network arrives at its decisions is known as explainability. In machine learning literature, this term is often used in conjunction with concepts of interpretability, understandability, and trust. Explainability is important because it gives us a better reason to trust that these algorithms are doing what we want, and enables us to troubleshoot them when problems arise.

理解網絡如何以及為何做出決策的問題稱為可解釋性。 在機器學習文獻中，該術語通常與可解釋性，可理解性和信任度概念結合使用。 可解釋性很重要，因為它使我們有更好的理由相信這些算法可以實現我們想要的功能，并可以在出現問題時對它們進行故障排除。

It’s important to note that explainability isn’t a one-way street. In the vast majority of cases, you want to explain your decisions to someone. That’s where expertise comes in. Just as a doctor knows what information should be included in a medical report or diagram to reach a diagnosis, domain experts know what components are necessary to make sound decisions in their domains.

重要的是要注意，可解釋性不是一條單向的街道。 在大多數情況下，您想向某人解釋您的決定。 這就是專業知識的源泉。正如醫生知道應該在醫療報告或圖表中包含哪些信息以進行診斷一樣，領域專家也知道在他們的領域做出正確決策所必需的組件。

These experts are not simply peripheral consultants; they are necessary players in designing and implementing good problem-solving algorithms. Experts know what information is relevant and irrelevant to making a decision, and this domain expertise is incredibly valuable in constraining the kinds of algorithms that are worth considering. At the end of the day, it will be the experts who will be able to see if a decision is made correctly, and what steps need to be taken to fix it.

這些專家不僅僅是簡單的外圍顧問。 他們是設計和實施好的問題解決算法的必要參與者。 專家知道哪些信息與決策有關且無關，并且該領域的專業知識在限制值得考慮的算法種類方面具有不可估量的價值。 歸根結底，專家們將能夠查看決策是否正確以及需要采取哪些步驟來解決該問題。

Without experts, we cannot design good algorithms, period. It’s telling that when DeepMind decided to develop AlphaGo to defeat one of the reigning world champions at the game of Go, they regularly consulted with Go experts to design their algorithm?. This idea is so important it’s worth repeating again: domain expertise isn’t peripheral. It’s the whole damn picture.

沒有專家，我們就無法設計好的算法。 這說明DeepMind決定開發AlphaGo在Go游戲中擊敗一位統治的世界冠軍時，他們會定期與Go專家協商以設計算法?。 這個想法是如此重要，值得再次重復：領域專業知識不是外圍的。 這是整個該死的圖片。

概括 (Generalization)

Another key aspect of learning is the ability to generalize. In the Machine Learning community, generalization refers to the ability to learn beyond just the examples you’ve already seen. Any college undergrad knows how to brute-force memorize sections of the textbook, but how well do they really know the ideas they’ve crammed into their heads the night before? The test lies in the ability to answer questions that test similar, but slightly different information to the concepts they’ve studied.

學習的另一個關鍵方面是泛化能力。 在機器學習社區中，泛化指的是超越您已經看到的示例的學習能力。 任何一所大學的本科生都知道如何強行記住教科書的各個部分，但是他們到底有多了解前一天晚上塞入他們腦海的想法呢？ 測試在于回答與所研究概念相似但略有不同信息的問題的能力。

It’s no different for Deep Neural Networks (incidentally, there are a lot of similarities that can be drawn between DNNs and college undergrads, particularly in terms of decision-making efficacy, but I’m writing a Medium article and not a novel so I’ll leave it at that).

深度神經網絡沒有什么不同(順便說一下，DNN和大學本科生之間可以得出很多相似之處，特別是在決策效力方面，但是我寫的是中級文章，而不是小說，所以我我會留在那)。

There are two main kinds of generalization: generalization within distribution, and out-of-distribution. The first refers to examples where the examples you use to test your network are similar to the ones you used to train it. If I train a network on, say, thousands of pictures of apples, it should be able to identify new apple even if it hasn’t seen that specific image before. DNNs are actually fairly good at this.

歸納主要有兩種：分布內的歸納和分布外的歸納。 第一個參考示例，其中用于測試網絡的示例與用于訓練網絡的示例相似。 如果我在數千張蘋果圖片上訓練網絡，則即使以前沒有看到該特定圖片，它也應該能夠識別新蘋果。 DNN實際上對此非常擅長。

But while DNNs require tens of thousands of examples to identify an apple, humans and other intelligent animals can learn after just a handful of examples. This is known as few-shot learning (sometimes used in relation to zero-shot or one-shot learning), referring to the ability to learn or generalize over just a few examples.

但是，盡管DNN需要數以萬計的示例來識別一個蘋果，但人類和其他智能動物只需幾個示例就可以學習。 這被稱為少拍學習(有時用于零拍或單拍學習)，指的是僅通過幾個示例進行學習或概括的能力。

The second kind of generalization is out-of-distribution learning. Ever notice how your athletic friend seems to be good at not only running but also football and swimming? Or how the math whiz in your class is also great at physics and chemistry? That’s because those concepts, while distinct, are related. The ability to take one skill and apply it to another domain is known in Machine Learning circles as transfer learning.

第二種概括是分布外學習。 是否曾經注意到您的運動朋友似乎不僅擅長跑步，還擅長足球和游泳？ 或者您的班級中的數學天才在物理和化學方面也很出色？ 這是因為這些概念雖然不同，但卻是相關的。 掌握一項技能并將其應用于另一領域的能力在機器學習界稱為轉移學習。

Like few-shot learning, the idea of transfer learning centers around the idea that it shouldn’t take thousands of examples to learn concepts, especially if you’ve already learned a similar task before. Importantly, learning a new task shouldn’t require that you unlearn the old one.

像幾次學習一樣，轉移學習的思想圍繞這樣一個思想，即不應該使用成千上萬的示例來學習概念，特別是如果您之前已經學習過類似的任務。 重要的是，學習一項新任務不應要求您取消舊任務。

Again, human expertise is necessary to understand how to approach the design of these algorithms as well. When you talk to experts, they often tie distinct examples together, while also describing their differences: “Playing guitar is kind of like playing piano, except…” This is exactly the kind of thing that transfer learning seeks to mimic. Seeking expert opinions is the key to understanding if transfer learning is effective across the domains it seeks to transfer to.

同樣，必須有專門的人員才能理解如何進行這些算法的設計。 當您與專家交談時，他們通常將不同的例子聯系在一起，同時也描述了他們的差異：“彈吉他有點像彈鋼琴，除了……”這正是轉移學習試圖模仿的東西。 尋求專家意見是了解轉讓學習是否在其試圖轉移到的各個領域有效的關鍵。

元學習 (Meta-learning)

A deep (pardon the pun) question in the Deep Learning community is how to develop algorithms not only to learn but to learn how to learn. This idea of learning how to learn is known as meta-learning, a term just as confusing as it is mysterious.

深度學習社區中一個深層的問題是如何開發算法，不僅要學習，而且要學習如何學習。 這種學習如何學習的想法被稱為元學習，這個術語既神秘又令人困惑。

The key behind meta-learning is this: often what separates an expert from a simpleton is the way that they practice. The best violinists in the world practice for hours on end, but they do so smartly — that’s what makes them the best. If we can develop effective algorithms that can practice smarter, then we can make significant strides towards more-intelligent agents. It’s not hard to see where the value of expertise comes here too. Different domains require different approaches toward a variety of definitions of success. To practice well as a pianist may look very different from practicing well as a swimmer, for example.

元學習背后的關鍵是這樣的：通常，使專家與簡單者分開的是他們的實踐方式。 世界上最好的小提琴家連續練習了幾個小時，但他們做得很聰明-這就是使他們成為最好的小提琴家的原因。 如果我們能夠開發出可以實踐得更聰明的有效算法，那么我們就可以朝著更智能的代理邁進一大步。 不難看出專業知識的價值在這里也能體現出來。 不同的領域要求對成功的各種定義采取不同的方法。 例如，練好鋼琴家看起來與練好運動員完全不同。

What “smart learning” looks like may vary from discipline to discipline, ultimately requiring the informed design with human experts at the lead.

“智能學習”的外觀可能因學科而異，最終需要在人類專家的帶領下進行明智的設計。

期待 (Looking forward)

As impressive as the accomplishments of Deep Learning are, it’s hard to imagine that any of these networks would have achieved their level of prowess without the experts that were consulted to design and structure them. Human experts, not AI agents, facilitated the curation of datasets, the design of reward functions, and the deployment of these algorithms. On top of that, it’s up to human experts to translate the outputs of these algorithms to decisions and insights in the real world. For now, humans are the agents.

盡管深度學習取得了令人印象深刻的成就，但很難想象如果沒有經過咨詢的專家來設計和構造這些網絡，它們中的任何一個都將達到自己的水平。 人類專家而不是AI代理促進了數據集的管理，獎勵功能的設計以及這些算法的部署。 最重要的是，由人類專家將這些算法的輸出轉換為現實世界中的決策和見解。 目前，人類是主體。

In the future, as Deep Learning becomes more prevalent in industry applications, people outside the field will be faced with adapting their roles to accommodate these new and unfamiliar statistical tools. As domain experts, they can contribute meaningfully by considering the role they play in facilitating the ability to interpret, transfer, and teach these algorithms to learn and perform better within their domain of interest. They can interface with ML researchers and engineers by helping to narrow the search space of possible algorithms.

將來，隨著深度學習在行業應用中變得越來越普遍，該領域以外的人們將面臨著調整其角色以適應這些新的和不熟悉的統計工具的挑戰。 作為領域專家，他們可以通過考慮他們在促進解釋，轉移和教導這些算法的能力方面做出有意義的貢獻，以在自己感興趣的領域內學習和更好地發揮作用。 他們可以幫助縮小可能算法的搜索空間，從而與ML研究人員和工程師建立聯系。

I like to think of the role of expertise as constraining the set of possible algorithms from infinity to finite, and the role of ML researchers as narrowing from finite to a few. I leave it as an exercise to the reader to determine which of the two is harder.

我喜歡認為專業知識的作用是將可能的算法從無窮限制到有限，而ML研究人員的作用則從有限限制到少數。 我將其留給讀者練習，以確定兩者中哪個更難。

Human expertise isn’t dead, not by a long shot. In fact, in the age of Deep Learning, it may be more important than ever.

人類的專業知識不會死，也不會長遠。 實際上，在深度學習時代，它可能比以往任何時候都更加重要。

翻譯自: https://medium.com/swlh/human-domain-expertise-in-the-age-of-deep-learning-89b3381c5cba

深度學習領域專業詞匯

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