Smart machines

are giving storytellers and risk managers alike a helping hand.

智能機器給故事講述者和風險管理者提供幫助。

Burgeoning dataanalyzed by ever more intelligent machines are opening pathways to surprising applications and providing solutions to problems that have been out of reach.

In the film industry, machines 「watch」 movies and videos, charting their emotional intensity and giving content creators clues about to how to make stories more appealing.

And in banking, AI』s ability to detect anomalies among millions of transactions helps bank risk officers eliminate false positives that are a drain on productivity.

For a growing number of industries, AI is tilting the playing field—you』ll need to understand how before your competitors do.

由越來越智能的機器所分析的迅速增長的數據，正在為令人驚訝的應用程序打開通路，並為那些已經無法觸及的問題提供解決方案。

在電影行業，機器「觀看」電影和視頻，記錄他們的情感強度，並給內容創作者提供關於如何讓故事更有吸引力的線索。

在銀行業，人工智慧在數以百萬計的交易中發現異常的能力，可以幫助銀行風險官員消除導致生產力流失的誤報。

對於越來越多的行業來說，人工智慧正在向競爭對手傾斜——你需要在這方面做得很好。

burgeoning

英/"b?:d??ni?/

美/"b?:d??ni?/

adj. 增長迅速的；生機勃勃的

v. 成長（burgeon的ing形式）；迅速發展

Using artificial intelligence to engage audiences

利用人工智慧吸引觀眾

By Eric Chu, Deb Roy, and Jonathan Dunn

Machine-learning models can help screenwriters and directors fine-tune scripts and imagery. Company communicators should take note.

機器學習模型可以幫助編劇和導演調整劇本和圖像。公司的交流者應該注意。

Master storytellersare skilled at eliciting our emotions, but even the best sometimes miss the mark. Could machines, using artificial-intelligence (AI) capabilities, collaborate with writers to improve their stories?

講故事的人擅長於激發我們的情感，但即使是最好的，有時也會錯過這個標記。機器，使用人工智慧（AI）的能力，與作者合作來改進他們的故事嗎？

McKinsey and the Massachusetts Institute of Technology Media Lab recently studied that question, focusing on movies and videos.

We speculated that a story』s emotional arc—shifts in tension and emotion that shape a narrative as it progresses and develops—determines viewer engagement.To test our theory, we developed machine-learning models to 「watch」 small slices of video and estimate their emotional content. When the content of all the slices are considered together, the story』s emotional arc emerges. The models can evaluate audio and visual elements in isolation or together.

麥肯錫和麻省理工學院媒體實驗室最近研究了這個問題，主要關注電影和視頻。

我們推測，一個故事的情感弧線——緊張和情感的轉變，在它的發展和發展過程中形成敘事——決定了觀眾的參與。為了驗證我們的理論，我們開發了機器學習模型來「觀察」一小段視頻，並估計它們的情感內容。當所有片的內容被綜合考慮時，故事的情感弧線就出現了。這些模型可以單獨或一起評估音頻和可視元素。

Consider the opening sequence of the movieUp, which provides the backstory for Carl, the main character. The visual valence—or the extent to which an image elicits positive or negative emotions—alternates throughout the opening sequence (Exhibit 1). The valence plummets, for instance, when Carl returns home after his wife, Ellie, dies.

考慮一下電影的開頭部分，它為主要角色卡爾提供了背景故事。視覺上的價——或者是圖像引發正面或負面情緒的程度——在整個開放序列中交替出現（表1）。例如，當卡爾在他的妻子埃莉去世後回到家時，他的價格直線下降。

After analyzing data for thousands of videos, we classified stories into families based on their emotional arc.

Some families had stories with extremely positive endings, and these tended to generate the most comments on social media (Exhibit 2). This finding supports prior research showing that positive feelings generate the greatest audience engagement.

在分析了數千個視頻的數據後，我們根據他們的情感弧線將故事分為家庭。

有些家庭的故事有著非常積極的結局，這些故事往往會在社交媒體上產生最多的評論（展覽2），這一發現支持了先前的研究，表明積極的感覺會產生最大的觀眾參與。

Our results suggest that AI could play a supporting role in video creation.

As always, human storytellers would create a screenplay with clever plot twists and realistic dialogue.

AI would enhance their work by providing insights that increase a story』s emotional pull—for instance, identifying a musical score or visual image thathelps engender feelings of hope. This breakthrough technology could supercharge storytellers, and not just in the movie business. For example, AI insights could potentially improve the emotional pull of commercials or corporate communications.

我們的研究結果表明，人工智慧可以在視頻創作中發揮輔助作用。

像往常一樣，講故事的人會用巧妙的情節和現實的對話來創造一個劇本。

人工智慧將通過提供能增加故事情感吸引力的見解來增強他們的工作——例如，識別一個音樂樂譜或視覺圖像，幫助產生希望的感覺。這項突破性的技術可以給故事講述者帶來巨大的負擔，而不僅僅是電影行業。例如，人工智慧的洞察力可能會改善商業廣告或公司交流的情感吸引力。

About the authors

Eric Chuis a doctoral candidate at the Massachusetts Institute of Technology and conducts research at the Laboratory for Social Machines, part of MIT』s Media Lab, whereDeb Royis the director.Jonathan Dunnis a partner in McKinsey』s New York and Southern California offices.

The authors wish to thank Geoffrey Sands and MIT Media Lab』s Russell Stevens for their contributions to this article.

關於作者

Eric Chu是麻省理工學院的博士研究生，他在麻省理工學院媒體實驗室的社會機器實驗室進行研究，德布羅伊是該實驗室的主任。喬納森?鄧恩是麥肯錫紐約和南加州辦事處的合伙人。

作者想要感謝Geoffrey Sands和麻省理工學院媒體實驗室的Russell Stevens對本文的貢獻。

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