如何實踐AI深度學習的十大驚豔案例
10 Amazing Examples Of How Deep Learning AI Is Used In Practice?
數據觀|編譯：餘瑞琦校對：黃玉葉
You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans? And, an even scarier notion for some, why would we want machines to exhibit human-like behavior? Here, we look at 10 examples of how deep learning is used in practice that will help you visualize the potential.
你可能已經聽說過深度學習並認為它是駭人的數據科學裡的一個領域。怎麼可能讓機器像人類一樣學習呢?再者，對於某些人而言，更為駭人的是，我們為什麼要讓機器展現出類人的行為?這裡，請看深度學習在實際應用中的十大案例，以便將其潛能視覺化。
What is deep learning?
深度學習是什麼?
Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning. In machine learning, algorithms created by human programmers are responsible for parsing and learning from the data. They make decisions based on what they learn from the data. Deep learning learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as humans do. Deep learning machines don't require a human programmer to tell them what to do with the data. This is made possible by the extraordinary amount of data we collect and consume—data is the fuel for deep-learning models.
機器學習和深度學習都是人工智慧的分支，但深度學習是機器學習的進一步深化。在機器學習中，由人類程式設計師設計的算法負責分析、研究數據，然後根據數據分析和研究作出決策。深度學習通過一個人造的神經網絡來學習，這一人造神經網絡運轉起來與人類大腦非常相似，它可以讓機器在一個框架內像人一樣進行分析數據。深度學習的機器不需要人類程式設計師告訴他們要用數據做什麼，這得賴於我們收集並消耗了大量的數據——數據是深入學習模型的燃料。
10 ways deep learning is used in practice
深度學習的十大應用案例
1. Customer experience
用戶體驗
Machine learning is already used by many businesses to enhance the customer experience. Just a couple of examples include online self-service solutions and to create reliable workflows. There are already deep-learning models being used for chatbots, and as deep learning continues to mature, we can expect this to be an area deep learning will be used for many businesses.
機器學習已經被很多企業用來改善用戶體驗。部分案例諸如在線自助服務方案、定製靠譜的工作流程，部分聊天機器人等都已運用到深度學習模型。隨著深度學習發展日趨成熟，我們可以預期，未來這一領域將被更多企業用來改善用戶體驗。
2、 Translations
翻譯
Although automatic machine translation isn’t new, deep learning is helping enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images.
儘管自動機器翻譯並不新鮮，但深度學習正著力於使用神經網絡的堆疊網絡和圖像翻譯來增強文本的自動翻譯。
3、 Adding color to black-and-white images and videos
為黑白圖像、視頻著色
What used to be a very time-consuming process where humans had to add color to black-and-white images and videos by hand can now be automatically done with deep-learning models.
過去，人們手動為黑白圖像及視頻著色的過程往往曠日持久，如今，這一工作可以完全由深度學習模型自動完成。
4、 Language recognition
語言識別
Deep learning machines are beginning to differentiate dialects of a language. A machine decides that someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Once the dialect is determined, another AI will step in that specializes in that particular dialect. All of this happens without involvement from a human.
目前，深度學習機器開始致力於辨別不同的方言。機器確定某人說的是英語，然後利用AI學習辨別方言之間的差異。一旦確定是某種方言，另一個AI會繼續專研這種方言，而這所有的過程均不需要人類參與。
5、 Autonomous vehicles
自動駕駛汽車
There's not just one AI model at work as an autonomous vehicle drives down the street. Some deep-learning models specialize in streets signs while others are trained to recognize pedestrians. As a car navigates down the road, it can be informed by up to millions of individual AI models that allow the car to act.
自動駕駛汽車在街上行駛時，並不只有一個AI模型在起作用。一些深度學習模型專門研究街道標識，而另一些則訓練識別行人。當一輛自動駕駛的汽車在公路上行駛時，它將接收到成千上萬條人工智慧模型的信息來輔助其行駛。
6、 Computer vision
計算機視覺
Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems.
在圖片分類、目標檢測、圖片復原和分割方面，深度學習已經展現出超越人類的精確性——他們甚至能識別手寫的數字。深度學習藉助龐大的神經網絡，利用機器自動化人類視覺系統所執行的任務。
7、 Text generation
創作文本
The machines learn the punctuation, grammar and style of a piece of text and can use the model it developed to？automatically create entirely new text with the proper spelling, grammar and style of the example text. Everything from Shakespeare to Wikipedia entries have been created.
機器可以學習一段文本的標點、語法和風格，然後利用這個模式自動創作一篇全新的文章，這篇文章的拼寫和語法都是正確的且風格與樣本文章一致。從莎士比亞到維基百科，所有的文章都能由此創作。
8、 Image caption generation
生成圖片標題
Another impressive capability of deep learning is to identify an image and create a coherent caption with proper sentence structure for that image just like a human would write.
深度學習另一個能力也著實備受矚目——識別圖像，並創建一個符合語句結構的連貫標題，宛如人寫的一樣。
9、News aggregator based on sentiment
基於情感的新聞聚合器
When you want to filter out the negative coming to your world, advanced natural language processing and deep learning can help. News aggregators using this new technology can filter news based on sentiment, so you can create news streams that only cover the good news happening.
如果你想要過濾掉消極新聞，不讓它們進入你的世界，先進的自然語言處理程序和深度學習可以幫助你。使用這種新技術的新聞聚合器能夠基於用戶情感過濾新聞，因此你可以創建只報導正面消息的新聞流。
10、 Deep-learning robots
深度學習機器人
Deep-learning applications for robots are plentiful and powerful from an impressive deep-learning system that can teach？a robot just by observing the actions of a human completing a task to a？housekeeping robot that’s provided with input from several other AIs in order to take action. Just like how a human brain processes input from past experiences, current input from senses and any additional data that is provided, deep-learning models will help robots execute tasks based on the input of many different AI opinions.
機器人的深度學習應用程式豐富而強大，它來自一個令人印象深刻的深度學習系統。通過觀察人類完成任務的行為機器人就能學會家務，並通過幾個其他人工智慧的輸入來進行操作。就像人類大腦如何處理來自過去的經驗、當前的感官以及任何附加數據信息一樣，深度學習模型將幫助機器人執行基於多個不同人工智慧意見輸入的任務。
The growth of deep-learning models is expected to accelerate and create even more innovative applications in the next few years.
深度學習模型的增長被寄予厚望：在未來幾年裡將加速發展，創造更具創新性的應用程式。
註：《如何實踐深度學習AI的十大驚豔案例》來源於Forbes(點擊查看原文)。數據觀編譯/餘瑞琦，校稿/黃玉葉，轉載請註明譯者和來源。