人工智能大火，商界領袖人人都在談。新科技產品繞不開人工智能，頭條新聞也不斷記錄著相關討論。但對許多人來說，人工智能仍然是個謎。

為了幫助人們迅速了解，我們制作了一份指南解釋相關術語。簡單來說，人工智能處理的數據越多，就會越完善，對企業來說用處越大。

強化學習

這種人工智能技術類似于用食物訓練狗。軟件成功執行任務后即可學習經驗，失敗后也可以吸取教訓。強化學習和深度學習的融合帶來了巨大突破，比如說計算機已經在復雜的電子游戲和棋盤游戲中擊敗人類玩家。范例：Facebook的定向通知功能。

神經網絡

人工智能的興起可以追溯至數十年以前，當時開發的軟件主要模擬人腦學習過程。神經網絡內部是多層互聯節點用于計算，可以幫助計算機精確篩選數據，軟件也能從中學會識別最聰明的人類也可能忽略的模式。范例：百度搜索。

深度學習

結合神經網絡和機器學習就可以實現深度學習，這是一種強大的技術，可以處理海量音頻文件檔案等龐大的數據。人工智能最大的一些突破都是由此實現，比如在照片中識別雪豹等。范例：英偉達3D人工智能生成的人臉。

機器學習

老板發郵件問一份重要文件是否已經準備妥當，系統建議回復（“看起來沒問題”），又或者老板問中午能不能見面，系統建議回復（“到時見！”），這些都要感謝機器學習。這些對于算法幫計算機“學習”還是能算管中窺豹。該技術的最吸引人之處在于：企業無需耗費人力為處理每項具體任務編寫程序。范例：谷歌Gmail。

計算機視覺

計算機視覺設備幾乎能夠像人類一樣看到并理解周邊環境。比如可以自動解鎖iPhone的面部識別技術，或是能夠幫助自動駕駛汽車導航而不會撞到樹上的系統。這個問題看上去很容易解決，但實際上非常困難。范例：Waymo自動駕駛汽車。

自然語言處理

這種技術可以讓計算機理解人類對話和語言并作出回應。語音控制數字助理就是該技術的應用，可以接受語音指令，或是為聯網家用音箱提供支持。相關技術仍未完善，但進步很快。范例：Amazon Alexa數字助理服務。（財富中文網）

本文另一版本登載于《財富》雜志2019年6月刊，標題為《人工智能101》。

譯者：Charlie

審校：夏林

Artificial intelligence is having its moment. Business leaders can’t stop talking about it. New tech products invariably include it. And news headlines incessantly chronicle the buzz around it. But for many people, artificial intelligence remains a mystery.

To help, we’ve created a guide that explains some of the key terms associated with the technology, an increasingly useful tool for businesses that improves as it crunches more data.

Reinforcement Learning

This A.I. technique is like training a dog with treats. The software learns by successfully executing a task and, on the flip side, from failure. This fusion of reinforcement learning with deep learning has led to tremendous breakthroughs, like computers beating humans at complicated video and board games. Example: Facebook’s targeted notifications.

Neural Networks

A.I.’s rise can be traced to software developed decades ago that was intended to approximate how the human brain learns. Inside a neural network are layers of interconnected nodes where calculations take place that help computers sift though data in minute detail. By doing so, the software can learn to recognize patterns that even the most intelligent humans may overlook. Example: Baidu search.

Deep Learning

Mixing neural networks with machine learning makes for deep learning, a powerful technology that can crunch enormous amounts of data, like vast archives of audio clips. A.I.’s biggest breakthroughs—such as recognizing snow leopards in photos—can be traced to the technology. Example: Nvidia’s 3D A.I.-generated faces.

Machine Learning

You can thank machine learning for recommending how to respond to your boss when she emails asking whether an important document is in order (“Looks good to me”) or whether you can meet at noon (“Let’s do it!”). This is just a taste of how algorithms help computers “learn.” The chief attraction: Companies don’t need humans to program the technology for each specific task it handles. Example: Google Gmail.

Computer Vision

Devices using computer vision are able to see and understand their surroundings almost like a human. Think of facial-recognition technology that can automatically unlock your iPhone or the systems that help navigate self-driving cars without crashing them into trees. The problem seems easy to solve. But in reality, it’s very difficult. Example: Waymo’s autonomous vehicles.

Natural Language Processing

This technology makes it possible for computers to understand and react to human speech and language. Voice-controlled digital assistants, which take dictation or power Internet- connected home speakers, would be impossible without it. The technology is still imperfect, but it’s improving quickly. Example: Amazon Alexa digital assistant.

A version of this article appears in the June 2019 issue of Fortune with the headline “Artificial Intelligence 101.”